update

* 增加对于讯飞星火大模型Spark4.0的支持

* Create github action sync.yml

* 增加对于智谱glm-4-plus的支持

* feat: change arxiv io param

* catch comment source code exception

* upgrade auto comment

* add security patch

---------

Co-authored-by: GH Action - Upstream Sync <action@github.com>
Co-authored-by: binary-husky <qingxu.fu@outlook.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -69,9 +69,3 @@ body:
     attributes:
       label: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
       description: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
-
-
-
-
-
-

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -21,8 +21,3 @@ body:
     attributes:
       label: Feature Request | 功能请求
       description: Feature Request | 功能请求
-
-
-
-
-

.github/workflows/build-with-all-capacity.yml

@@ -1,5 +1,5 @@
 # https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-jittorllms
+name: build-with-all-capacity
 
 on:
   push:
@@ -8,7 +8,7 @@ on:
 
 env:
   REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_jittorllms
+  IMAGE_NAME: ${{ github.repository }}_with_all_capacity
 
 jobs:
   build-and-push-image:
@@ -39,6 +39,6 @@ jobs:
         with:
           context: .
           push: true
-          file: docs/GithubAction+JittorLLMs
+          file: docs/GithubAction+AllCapacity
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-with-latex-arm.yml

@@ -0,0 +1,51 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: build-with-latex-arm
+
+on:
+  push:
+    branches:
+      - "master"
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_with_latex_arm
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          platforms: linux/arm64
+          file: docs/GithubAction+NoLocal+Latex
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -131,6 +131,9 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
+# macOS files
+.DS_Store
+
 .vscode
 .idea
 
@@ -146,9 +149,17 @@ debug*
 private*
 crazy_functions/test_project/pdf_and_word
 crazy_functions/test_samples
-request_llm/jittorllms
+request_llms/jittorllms
 multi-language
-request_llm/moss
+request_llms/moss
 media
 flagged
-request_llm/ChatGLM-6b-onnx-u8s8
+request_llms/ChatGLM-6b-onnx-u8s8
+.pre-commit-config.yaml
+test.*
+temp.*
+objdump*
+*.min.*.js
+TODO
+experimental_mods
+search_results

Dockerfile

@@ -1,34 +1,39 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型或者latex运行依赖，请参考 docker-compose.yml
-# 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
-# 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
-# 如何运行(其他操作系统，选择任意一个固定端口50923): `docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic `
+# 此Dockerfile适用于“无本地模型”的迷你运行环境构建
+# 如果需要使用chatglm等本地模型或者latex运行依赖，请参考 docker-compose.yml
+# - 如何构建: 先修改 `config.py`， 然后 `docker build -t gpt-academic . `
+# - 如何运行(Linux下): `docker run --rm -it --net=host gpt-academic `
+# - 如何运行(其他操作系统，选择任意一个固定端口50923): `docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic `
 FROM python:3.11
 
 
-# 非必要步骤，更换pip源
+# 非必要步骤，更换pip源 （以下三行，可以删除）
 RUN echo '[global]' > /etc/pip.conf && \
     echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
     echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
 
 
-# 进入工作路径
+# 语音输出功能（以下两行，第一行更换阿里源，第二行安装ffmpeg，都可以删除）
+RUN UBUNTU_VERSION=$(awk -F= '/^VERSION_CODENAME=/{print $2}' /etc/os-release); echo "deb https://mirrors.aliyun.com/debian/ $UBUNTU_VERSION main non-free contrib" > /etc/apt/sources.list; apt-get update
+RUN apt-get install ffmpeg -y
+
+
+# 进入工作路径（必要）
 WORKDIR /gpt
 
 
-# 安装大部分依赖，利用Docker缓存加速以后的构建
+# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下两行，可以删除）
 COPY requirements.txt ./
-COPY ./docs/gradio-3.32.2-py3-none-any.whl ./docs/gradio-3.32.2-py3-none-any.whl
 RUN pip3 install -r requirements.txt
 
 
-# 装载项目文件，安装剩余依赖
+# 装载项目文件，安装剩余依赖（必要）
 COPY . .
 RUN pip3 install -r requirements.txt
 
 
-# 非必要步骤，用于预热模块
+# 非必要步骤，用于预热模块（可以删除）
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
 
 
-# 启动
+# 启动（必要）
 CMD ["python3", "-u", "main.py"]

README.md

@@ -1,366 +0,0 @@
-> **Note**
->
-> 2023.7.8: Gradio, Pydantic依赖调整，已修改 `requirements.txt`。请及时**更新代码**，安装依赖时，请严格选择`requirements.txt`中**指定的版本**
->
-> `pip install -r requirements.txt`
-
-
-# <div align=center><img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)</div>
-
-**如果喜欢这个项目，请给它一个Star；如果您发明了好用的快捷键或函数插件，欢迎发pull requests！**
-
-If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
-To translate this project to arbitary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
-
-> **Note**
->
-> 1.请注意只有 **高亮** 标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR。
->
-> 2.本项目中每个文件的功能都在[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[安装方法](#installation) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。
-> 
-> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM和Moss等等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。
-
-
- 
-
-<div align="center">
-
-功能（⭐= 近期新增功能） | 描述
---- | ---
-⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, [通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
-一键润色 | 支持一键润色、一键查找论文语法错误
-一键中英互译 | 一键中英互译
-一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
-[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
-模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码
-[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树
-读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要
-Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文
-批量注释生成 | [函数插件] 一键批量生成函数注释
-Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？
-chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
-[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文（多线程）
-[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
-Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
-[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
-互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
-⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
-⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
-公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
-多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
-启动暗色[主题](https://github.com/binary-husky/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
-[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
-⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件
-更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
-⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
-⭐虚空终端插件 | 用自然语言，直接调度本项目其他插件
-更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
-</div>
-
-
-- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>
-
-
-- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放粘贴板
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- 润色/纠错
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- 如果输出包含公式，会同时以tex形式和渲染形式显示，方便复制和阅读
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- 懒得看项目代码？整个工程直接给chatgpt炫嘴里
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
-# Installation
-### 安装方法I：直接运行 (Windows, Linux or MacOS) 
-
-1. 下载项目
-```sh
-git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-cd gpt_academic
-```
-
-2. 配置API_KEY
-
-在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。
-
-(P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。因此，如果您能理解我们的配置读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。`config_private.py`不受git管控，可以让您的隐私信息更加安全。P.S.项目同样支持通过`环境变量`配置大多数选项，环境变量的书写格式参考`docker-compose`文件。读取优先级: `环境变量` > `config_private.py` > `config.py`)
-
-
-3. 安装依赖
-```sh
-# （选择I: 如熟悉python）（python版本3.9以上，越新越好），备注：使用官方pip源或者阿里pip源,临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# （选择II: 如不熟悉python）使用anaconda，步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
-conda create -n gptac_venv python=3.11    # 创建anaconda环境
-conda activate gptac_venv                 # 激活anaconda环境
-python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步骤
-```
-
-
-<details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
-<p>
-
-【可选步骤】如果需要支持清华ChatGLM2/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
-```sh
-# 【可选步骤I】支持清华ChatGLM2。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
-
-# 【可选步骤II】支持复旦MOSS
-python -m pip install -r request_llm/requirements_moss.txt
-git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 注意执行此行代码时，必须处于项目根路径
-
-# 【可选步骤III】支持RWKV Runner
-参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
-
-# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-</p>
-</details>
-
-
-
-4. 运行
-```sh
-python main.py
-```
-
-### 安装方法II：使用Docker
-
-1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
-[![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
-[![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
-[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
-
-``` sh
-git clone --depth=1 https://github.com/binary-husky/gpt_academic.git  # 下载项目
-cd gpt_academic                                 # 进入路径
-nano config.py                                      # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
-docker build -t gpt-academic .                      # 安装
-
-#（最后一步-Linux操作系统）用`--net=host`更方便快捷
-docker run --rm -it --net=host gpt-academic
-#（最后一步-MacOS/Windows操作系统）只能用-p选项将容器上的端口(例如50923)暴露给主机上的端口
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用docker-compose获取Latex功能（修改docker-compose.yml，保留方案4并删除其他方案）。
-
-2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
-[![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
-
-``` sh
-# 修改docker-compose.yml，保留方案2并删除其他方案。修改docker-compose.yml中方案2的配置，参考其中注释即可
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
-[![jittorllms](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml)
-
-``` sh
-# 修改docker-compose.yml，保留方案3并删除其他方案。修改docker-compose.yml中方案3的配置，参考其中注释即可
-docker-compose up
-```
-
-
-### 安装方法III：其他部署姿势
-1. 一键运行脚本。
-完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。
-脚本的贡献来源是[oobabooga](https://github.com/oobabooga/one-click-installers)。
-
-2. 使用docker-compose运行。
-请阅读docker-compose.yml后，按照其中的提示操作即可
-
-3. 如何使用反代URL
-按照`config.py`中的说明配置API_URL_REDIRECT即可。
-
-4. 微软云AzureAPI
-按照`config.py`中的说明配置即可（AZURE_ENDPOINT等四个配置）
-
-5. 远程云服务器部署（需要云服务器知识与经验）。
-请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
-
-7. 使用WSL2（Windows Subsystem for Linux 子系统）。
-请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-8. 如何在二级网址（如`http://localhost/subpath`）下运行。
-请访问[FastAPI运行说明](docs/WithFastapi.md)
-
-
-# Advanced Usage
-### I：自定义新的便捷按钮（学术快捷键）
-任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序即可。（如果按钮已经添加成功并可见，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
-例如
-```
-"超级英译中": {
-    # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-    "Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n", 
-    
-    # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来。
-    "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-### II：自定义函数插件
-
-编写强大的函数插件来执行任何你想得到的和想不到的任务。
-本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。
-详情请参考[函数插件指南](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
-
-
-# Latest Update
-### I：新功能动态
-
-1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件，
-另外在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。
-Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史html存档缓存。
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
-</div>
-
-2. ⭐Latex/Arxiv论文翻译功能⭐
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
-</div>
-
-3. 虚空终端（从自然语言输入中，理解用户意图+自动调用其他插件）
-
-- 步骤一：输入 “ 请调用插件翻译PDF论文，地址为https://www.nature.com/articles/s41586-019-1724-z.pdf ”
-- 步骤二：点击“虚空终端”
-
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
-</div>
-
-4. 模块化功能设计，简单的接口却能支持强大的功能
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-5. 译解其他开源项目
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
-</div>
-
-6. 装饰[live2d](https://github.com/fghrsh/live2d_demo)的小功能（默认关闭，需要修改`config.py`）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
-</div>
-
-7. 新增MOSS大语言模型支持
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
-</div>
-
-8. OpenAI图像生成
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
-</div>
-
-9. OpenAI音频解析与总结
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
-</div>
-
-10. Latex全文校对纠错
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
-</div>
-
-11. 语言、主题切换
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
-</div>
-
-
-
-### II：版本:
-- version 3.60（todo）: 优化虚空终端，引入code interpreter和更多插件
-- version 3.50: 使用自然语言调用本项目的所有函数插件（虚空终端），支持插件分类，改进UI，设计新主题
-- version 3.49: 支持百度千帆平台和文心一言
-- version 3.48: 支持阿里达摩院通义千问，上海AI-Lab书生，讯飞星火
-- version 3.46: 支持完全脱手操作的实时语音对话
-- version 3.45: 支持自定义ChatGLM2微调模型
-- version 3.44: 正式支持Azure，优化界面易用性
-- version 3.4: +arxiv论文翻译、latex论文批改功能
-- version 3.3: +互联网信息综合功能
-- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
-- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡
-- version 3.0: 对chatglm和其他小型llm的支持
-- version 2.6: 重构了插件结构，提高了交互性，加入更多插件
-- version 2.5: 自更新，解决总结大工程源代码时文本过长、token溢出的问题
-- version 2.4: (1)新增PDF全文翻译功能; (2)新增输入区切换位置的功能; (3)新增垂直布局选项; (4)多线程函数插件优化。
-- version 2.3: 增强多线程交互性
-- version 2.2: 函数插件支持热重载
-- version 2.1: 可折叠式布局
-- version 2.0: 引入模块化函数插件
-- version 1.0: 基础功能
-
-gpt_academic开发者QQ群-2：610599535
-
-- 已知问题
-    - 某些浏览器翻译插件干扰此软件前端的运行
-    - 官方Gradio目前有很多兼容性Bug，请务必使用`requirement.txt`安装Gradio
-
-### III：主题
-可以通过修改`THEME`选项（config.py）变更主题
-1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
-
-
-### IV：参考与学习
-
-```
-代码中参考了很多其他优秀项目中的设计，顺序不分先后：
-
-# 清华ChatGLM2-6B:
-https://github.com/THUDM/ChatGLM2-6B
-
-# 清华JittorLLMs:
-https://github.com/Jittor/JittorLLMs
-
-# ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Oobabooga one-click installer:
-https://github.com/oobabooga/one-click-installers
-
-# More：
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```

check_proxy.py

@@ -1,38 +1,77 @@
+from loguru import logger
 
-def check_proxy(proxies):
+def check_proxy(proxies, return_ip=False):
+    """
+    检查代理配置并返回结果。
+
+    Args:
+        proxies (dict): 包含http和https代理配置的字典。
+        return_ip (bool, optional): 是否返回代理的IP地址。默认为False。
+
+    Returns:
+        str or None: 检查的结果信息或代理的IP地址（如果`return_ip`为True）。
+    """
     import requests
     proxies_https = proxies['https'] if proxies is not None else '无'
+    ip = None
     try:
-        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
+        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)  # ⭐ 执行GET请求以获取代理信息
         data = response.json()
-        print(f'查询代理的地理位置，返回的结果是{data}')
         if 'country_name' in data:
             country = data['country_name']
             result = f"代理配置 {proxies_https}, 代理所在地：{country}"
+            if 'ip' in data:
+                ip = data['ip']
         elif 'error' in data:
-            alternative = _check_with_backup_source(proxies)
+            alternative, ip = _check_with_backup_source(proxies)  # ⭐ 调用备用方法检查代理配置
             if alternative is None:
                 result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
             else:
                 result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
         else:
             result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
-        print(result)
-        return result
+
+        if not return_ip:
+            logger.warning(result)
+            return result
+        else:
+            return ip
     except:
         result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
-        print(result)
-        return result
+        if not return_ip:
+            logger.warning(result)
+            return result
+        else:
+            return ip
 
 def _check_with_backup_source(proxies):
+    """
+    通过备份源检查代理，并获取相应信息。
+
+    Args:
+        proxies (dict): 包含代理信息的字典。
+
+    Returns:
+        tuple: 代理信息(geo)和IP地址(ip)的元组。
+    """
     import random, string, requests
     random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
-    try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
-    except: return None
+    try:
+        res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()  # ⭐ 执行代理检查和备份源请求
+        return res_json['dns']['geo'], res_json['dns']['ip']
+    except:
+        return None, None
 
 def backup_and_download(current_version, remote_version):
     """
-    一键更新协议：备份和下载
+    一键更新协议：备份当前版本，下载远程版本并解压缩。
+
+    Args:
+        current_version (str): 当前版本号。
+        remote_version (str): 远程版本号。
+
+    Returns:
+        str: 新版本目录的路径。
     """
     from toolbox import get_conf
     import shutil
@@ -46,10 +85,10 @@ def backup_and_download(current_version, remote_version):
         return new_version_dir
     os.makedirs(new_version_dir)
     shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
-    proxies, = get_conf('proxies')
-    r = requests.get(
-        'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
-    zip_file_path = backup_dir+'/master.zip'
+    proxies = get_conf('proxies')
+    try:    r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
+    except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
+    zip_file_path = backup_dir+'/master.zip'  # ⭐ 保存备份文件的路径
     with open(zip_file_path, 'wb+') as f:
         f.write(r.content)
     dst_path = new_version_dir
@@ -65,6 +104,17 @@ def backup_and_download(current_version, remote_version):
 def patch_and_restart(path):
     """
     一键更新协议：覆盖和重启
+
+    Args:
+        path (str): 新版本代码所在的路径
+
+    注意事项:
+        如果您的程序没有使用config_private.py私密配置文件，则会将config.py重命名为config_private.py以避免配置丢失。
+
+    更新流程:
+        - 复制最新版本代码到当前目录
+        - 更新pip包依赖
+        - 如果更新失败，则提示手动安装依赖库并重启
     """
     from distutils import dir_util
     import shutil
@@ -72,33 +122,44 @@ def patch_and_restart(path):
     import sys
     import time
     import glob
-    from colorful import print亮黄, print亮绿, print亮红
-    # if not using config_private, move origin config.py as config_private.py
+    from shared_utils.colorful import log亮黄, log亮绿, log亮红
+
     if not os.path.exists('config_private.py'):
-        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
+        log亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
               '另外您可以随时在history子文件夹下找回旧版的程序。')
         shutil.copyfile('config.py', 'config_private.py')
+
     path_new_version = glob.glob(path + '/*-master')[0]
-    dir_util.copy_tree(path_new_version, './')
-    print亮绿('代码已经更新，即将更新pip包依赖……')
-    for i in reversed(range(5)): time.sleep(1); print(i)
+    dir_util.copy_tree(path_new_version, './')  # ⭐ 将最新版本代码复制到当前目录
+
+    log亮绿('代码已经更新，即将更新pip包依赖……')
+    for i in reversed(range(5)): time.sleep(1); log亮绿(i)
+
     try:
         import subprocess
         subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
     except:
-        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
-    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print(' ------------------------------ -----------------------------------')
-    for i in reversed(range(8)): time.sleep(1); print(i)
-    os.execl(sys.executable, sys.executable, *sys.argv)
+        log亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+
+    log亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
+    log亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+    log亮绿(' ------------------------------ -----------------------------------')
+
+    for i in reversed(range(8)): time.sleep(1); log亮绿(i)
+    os.execl(sys.executable, sys.executable, *sys.argv)  # 重启程序
 
 
 def get_current_version():
+    """
+    获取当前的版本号。
+
+    Returns:
+        str: 当前的版本号。如果无法获取版本号，则返回空字符串。
+    """
     import json
     try:
         with open('./version', 'r', encoding='utf8') as f:
-            current_version = json.loads(f.read())['version']
+            current_version = json.loads(f.read())['version']  # ⭐ 从读取的json数据中提取版本号
     except:
         current_version = ""
     return current_version
@@ -107,15 +168,20 @@ def get_current_version():
 def auto_update(raise_error=False):
     """
     一键更新协议：查询版本和用户意见
+
+    Args:
+        raise_error (bool, optional): 是否在出错时抛出错误。默认为 False。
+
+    Returns:
+        None
     """
     try:
         from toolbox import get_conf
         import requests
-        import time
         import json
-        proxies, = get_conf('proxies')
-        response = requests.get(
-            "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
+        proxies = get_conf('proxies')
+        try:    response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
+        except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
         remote_json_data = json.loads(response.text)
         remote_version = remote_json_data['version']
         if remote_json_data["show_feature"]:
@@ -126,23 +192,22 @@ def auto_update(raise_error=False):
             current_version = f.read()
             current_version = json.loads(current_version)['version']
         if (remote_version - current_version) >= 0.01-1e-5:
-            from colorful import print亮黄
-            print亮黄(
-                f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
-            print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
+            from shared_utils.colorful import log亮黄
+            log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')  # ⭐ 在控制台打印新版本信息
+            logger.info('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
             user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
             if user_instruction in ['Y', 'y']:
-                path = backup_and_download(current_version, remote_version)
+                path = backup_and_download(current_version, remote_version)  # ⭐ 备份并下载文件
                 try:
-                    patch_and_restart(path)
+                    patch_and_restart(path)  # ⭐ 执行覆盖并重启操作
                 except:
                     msg = '更新失败。'
                     if raise_error:
                         from toolbox import trimmed_format_exc
                         msg += trimmed_format_exc()
-                    print(msg)
+                    logger.warning(msg)
             else:
-                print('自动更新程序：已禁用')
+                logger.info('自动更新程序：已禁用')
                 return
         else:
             return
@@ -151,19 +216,42 @@ def auto_update(raise_error=False):
         if raise_error:
             from toolbox import trimmed_format_exc
             msg += trimmed_format_exc()
-        print(msg)
+        logger.info(msg)
 
 def warm_up_modules():
-    print('正在执行一些模块的预热...')
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    enc.encode("模块预热", disallowed_special=())
-    enc = model_info["gpt-4"]['tokenizer']
-    enc.encode("模块预热", disallowed_special=())
+    """
+    预热模块，加载特定模块并执行预热操作。
+    """
+    logger.info('正在执行一些模块的预热 ...')
+    from toolbox import ProxyNetworkActivate
+    from request_llms.bridge_all import model_info
+    with ProxyNetworkActivate("Warmup_Modules"):
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        enc.encode("模块预热", disallowed_special=())
+        enc = model_info["gpt-4"]['tokenizer']
+        enc.encode("模块预热", disallowed_special=())
+
+def warm_up_vectordb():
+    """
+    执行一些模块的预热操作。
+
+    本函数主要用于执行一些模块的预热操作，确保在后续的流程中能够顺利运行。
+
+    ⭐ 关键作用：预热模块
+
+    Returns:
+        None
+    """
+    logger.info('正在执行一些模块的预热 ...')
+    from toolbox import ProxyNetworkActivate
+    with ProxyNetworkActivate("Warmup_Modules"):
+        import nltk
+        with ProxyNetworkActivate("Warmup_Modules"): nltk.download("punkt")
+
 
 if __name__ == '__main__':
     import os
     os.environ['no_proxy'] = '*'  # 避免代理网络产生意外污染
     from toolbox import get_conf
-    proxies, = get_conf('proxies')
+    proxies = get_conf('proxies')
     check_proxy(proxies)

config.py

@@ -15,13 +15,13 @@ API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗
 USE_PROXY = False
 if USE_PROXY:
     """
+    代理网络的地址，打开你的代理软件查看代理协议(socks5h / http)、地址(localhost)和端口(11284)
     填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
             <配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
     [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
+    [地址] 填localhost或者127.0.0.1（localhost意思是代理软件安装在本机上）
     [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
     """
-    # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5h / http)、地址(localhost)和端口(11284)
     proxies = {
         #          [协议]://  [地址]  :[端口]
         "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
@@ -30,11 +30,44 @@ if USE_PROXY:
 else:
     proxies = None
 
-# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
+# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
+LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+                    "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
+                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
+                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
+                    "gemini-1.5-pro", "chatglm3"
+                    ]
+
+EMBEDDING_MODEL = "text-embedding-3-small"
+
+# --- --- --- ---
+# P.S. 其他可用的模型还包括
+# AVAIL_LLM_MODELS = [
+#   "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
+#   "qianfan", "deepseekcoder",
+#   "spark", "sparkv2", "sparkv3", "sparkv3.5", "sparkv4",
+#   "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
+#   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
+#   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
+#   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
+#   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
+#   "deepseek-chat" ,"deepseek-coder",
+#   "gemini-1.5-flash",
+#   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
+# ]
+# --- --- --- ---
+# 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
+# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
+# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
+# --- --- --- ---
+
+
+# --------------- 以下配置可以优化体验 ---------------
 
 # 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
 # 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
+# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
 API_URL_REDIRECT = {}
 
 
@@ -43,8 +76,14 @@ API_URL_REDIRECT = {}
 DEFAULT_WORKER_NUM = 3
 
 
-# 色彩主题，可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
+# 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
+# 更多主题, 请查阅Gradio主题商店: https://huggingface.co/spaces/gradio/theme-gallery 可选 ["Gstaff/Xkcd", "NoCrypt/Miku", ...]
 THEME = "Default"
+AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
+
+
+# 默认的系统提示词（system prompt）
+INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
 
 
 # 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
@@ -57,7 +96,10 @@ CODE_HIGHLIGHT = True
 
 # 窗口布局
 LAYOUT = "LEFT-RIGHT"   # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-DARK_MODE = True        # 暗色模式 / 亮色模式
+
+
+# 暗色模式 / 亮色模式
+DARK_MODE = True
 
 
 # 发送请求到OpenAI后，等待多久判定为超时
@@ -68,26 +110,36 @@ TIMEOUT_SECONDS = 30
 WEB_PORT = -1
 
 
+# 是否自动打开浏览器页面
+AUTO_OPEN_BROWSER = True
+
+
 # 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
 MAX_RETRY = 2
 
 
 # 插件分类默认选项
-DEFAULT_FN_GROUPS = ['对话', '编程', '学术']
+DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
 
 
-# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", 
-                    "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
-# P.S. 其他可用的模型还包括 ["qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", 
-# "spark", "sparkv2", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
+# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
+MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
+
+
+# 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
+# 如果你选择Qwen系列的模型，那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型
+# 也可以是具体的模型路径
+QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
+
+
+# 接入通义千问在线大模型 https://dashscope.console.aliyun.com/
+DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
 
 
 # 百度千帆（LLM_MODEL="qianfan"）
 BAIDU_CLOUD_API_KEY = ''
 BAIDU_CLOUD_SECRET_KEY = ''
-BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
+BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot'    # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
 
 
 # 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
@@ -116,33 +168,37 @@ ADD_WAIFU = False
 AUTHENTICATION = []
 
 
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
+# 如果需要在二级路径下运行（常规情况下，不要修改!!）
+# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
 CUSTOM_PATH = "/"
 
 
+# HTTPS 秘钥和证书（不需要修改）
+SSL_KEYFILE = ""
+SSL_CERTFILE = ""
+
+
 # 极少数情况下，openai的官方KEY需要伴随组织编码（格式如org-xxxxxxxxxxxxxxxxxxxxxxxx）使用
 API_ORG = ""
 
 
-# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md
+# 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
 SLACK_CLAUDE_BOT_ID = ''
 SLACK_CLAUDE_USER_TOKEN = ''
 
 
-# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md
+# 如果需要使用AZURE（方法一：单个azure模型部署）详情请见额外文档 docs\use_azure.md
 AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
 AZURE_API_KEY = "填入azure openai api的密钥"    # 建议直接在API_KEY处填写，该选项即将被弃用
 AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.md
 
 
-# 使用Newbing
-NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
-NEWBING_COOKIES = """
-put your new bing cookies here
-"""
+# 如果需要使用AZURE（方法二：多个azure模型部署+动态切换）详情请见额外文档 docs\use_azure.md
+AZURE_CFG_ARRAY = {}
 
 
-# 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
+# 阿里云实时语音识别 配置难度较高
+# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
 ENABLE_AUDIO = False
 ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
 ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
@@ -150,20 +206,60 @@ ALIYUN_ACCESSKEY="" # （无需填写）
 ALIYUN_SECRET=""    # （无需填写）
 
 
+# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
+TTS_TYPE = "EDGE_TTS" # EDGE_TTS / LOCAL_SOVITS_API / DISABLE
+GPT_SOVITS_URL = ""
+EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
+
+
 # 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
 XFYUN_APPID = "00000000"
 XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
 XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 
+# 接入智谱大模型
+ZHIPUAI_API_KEY = ""
+ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
+
+
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 
 
+# 月之暗面 API KEY
+MOONSHOT_API_KEY = ""
+
+
+# 零一万物(Yi Model) API KEY
+YIMODEL_API_KEY = ""
+
+
+# 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
+DEEPSEEK_API_KEY = ""
+
+
+# 紫东太初大模型 https://ai-maas.wair.ac.cn
+TAICHU_API_KEY = ""
+
+
+# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
+MATHPIX_APPID = ""
+MATHPIX_APPKEY = ""
+
+
+# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
+DOC2X_API_KEY = ""
+
+
 # 自定义API KEY格式
 CUSTOM_API_KEY_PATTERN = ""
 
 
+# Google Gemini API-Key
+GEMINI_API_KEY = ''
+
+
 # HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
 HUGGINGFACE_ACCESS_TOKEN = "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"
 
@@ -172,15 +268,56 @@ HUGGINGFACE_ACCESS_TOKEN = "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"
 # 获取方法：复制以下空间https://huggingface.co/spaces/qingxu98/grobid，设为public，然后GROBID_URL = "https://(你的hf用户名如qingxu98)-(你的填写的空间名如grobid).hf.space"
 GROBID_URLS = [
     "https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space",
-    "https://shaocongma-grobid.hf.space","https://FBR123-grobid.hf.space", "https://yeku-grobid.hf.space", 
+    "https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space",
+    "https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space",
 ]
 
 
+# Searxng互联网检索服务
+SEARXNG_URL = "https://cloud-1.agent-matrix.com/"
+
+
 # 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
 ALLOW_RESET_CONFIG = False
 
 
+# 在使用AutoGen插件时，是否使用Docker容器运行代码
+AUTOGEN_USE_DOCKER = False
+
+
+# 临时的上传文件夹位置，请尽量不要修改
+PATH_PRIVATE_UPLOAD = "private_upload"
+
+
+# 日志文件夹的位置，请尽量不要修改
+PATH_LOGGING = "gpt_log"
+
+
+# 存储翻译好的arxiv论文的路径，请尽量不要修改
+ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
+
+
+# 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
+WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
+                     "Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
+
+
+# 启用插件热加载
+PLUGIN_HOT_RELOAD = False
+
+
+# 自定义按钮的最大数量限制
+NUM_CUSTOM_BASIC_BTN = 4
+
+
+# 媒体智能体的服务地址（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
+DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
+
+
+
 """
+--------------- 配置关联关系说明 ---------------
+
 在线大模型配置关联关系示意图
 │
 ├── "gpt-3.5-turbo" 等openai模型
@@ -189,19 +326,22 @@ ALLOW_RESET_CONFIG = False
 │   ├── API_ORG（不常用）
 │   └── API_URL_REDIRECT（不常用）
 │
-├── "azure-gpt-3.5" 等azure模型
+├── "azure-gpt-3.5" 等azure模型（单个azure模型，不需要动态切换）
 │   ├── API_KEY
 │   ├── AZURE_ENDPOINT
 │   ├── AZURE_API_KEY
 │   ├── AZURE_ENGINE
 │   └── API_URL_REDIRECT
 │
+├── "azure-gpt-3.5" 等azure模型（多个azure模型，需要动态切换，高优先级）
+│   └── AZURE_CFG_ARRAY
+│
 ├── "spark" 星火认知大模型 spark & sparkv2
 │   ├── XFYUN_APPID
 │   ├── XFYUN_API_SECRET
 │   └── XFYUN_API_KEY
 │
-├── "claude-1-100k" 等claude模型
+├── "claude-3-opus-20240229" 等claude模型
 │   └── ANTHROPIC_API_KEY
 │
 ├── "stack-claude"
@@ -213,9 +353,38 @@ ALLOW_RESET_CONFIG = False
 │   ├── BAIDU_CLOUD_API_KEY
 │   └── BAIDU_CLOUD_SECRET_KEY
 │
-├── "newbing" Newbing接口不再稳定，不推荐使用
-    ├── NEWBING_STYLE
-    └── NEWBING_COOKIES
+├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
+│   └── ZHIPUAI_API_KEY
+│
+├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
+│   └── YIMODEL_API_KEY
+│
+├── "qwen-turbo" 等通义千问大模型
+│   └──  DASHSCOPE_API_KEY
+│
+├── "Gemini"
+│   └──  GEMINI_API_KEY
+│
+└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
+    ├── AVAIL_LLM_MODELS
+    ├── API_KEY
+    └── API_URL_REDIRECT
+
+
+本地大模型示意图
+│
+├── "chatglm3"
+├── "chatglm"
+├── "chatglm_onnx"
+├── "chatglmft"
+├── "internlm"
+├── "moss"
+├── "jittorllms_pangualpha"
+├── "jittorllms_llama"
+├── "deepseekcoder"
+├── "qwen-local"
+├──  RWKV的支持见Wiki
+└── "llama2"
 
 
 用户图形界面布局依赖关系示意图
@@ -228,11 +397,14 @@ ALLOW_RESET_CONFIG = False
 ├── THEME 色彩主题
 ├── AUTO_CLEAR_TXT 是否在提交时自动清空输入框
 ├── ADD_WAIFU 加一个live2d装饰
-├── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性
+└── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性
 
 
 插件在线服务配置依赖关系示意图
 │
+├── 互联网检索
+│   └── SEARXNG_URL
+│
 ├── 语音功能
 │   ├── ENABLE_AUDIO
 │   ├── ALIYUN_TOKEN
@@ -240,7 +412,10 @@ ALLOW_RESET_CONFIG = False
 │   ├── ALIYUN_ACCESSKEY
 │   └── ALIYUN_SECRET
 │
-├── PDF文档精准解析
-│   └── GROBID_URLS
+└── PDF文档精准解析
+    ├── GROBID_URLS
+    ├── MATHPIX_APPID
+    └── MATHPIX_APPKEY
+
 
 """

core_functional.py

@@ -3,83 +3,145 @@
 # 'stop' 颜色对应 theme.py 中的 color_er
 import importlib
 from toolbox import clear_line_break
-
+from toolbox import apply_gpt_academic_string_mask_langbased
+from toolbox import build_gpt_academic_masked_string_langbased
+from textwrap import dedent
 
 def get_core_functions():
     return {
-        "英语学术润色": {
-            # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
-                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
-                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
+
+        "学术语料润色": {
+            # [1*] 前缀字符串，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等。
+            #      这里填一个提示词字符串就行了，这里为了区分中英文情景搞复杂了一点
+            "Prefix":   build_gpt_academic_masked_string_langbased(
+                            text_show_english=
+                                r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
+                                r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
+                                r"Firstly, you should provide the polished paragraph (in English). "
+                                r"Secondly, you should list all your modification and explain the reasons to do so in markdown table.",
+                            text_show_chinese=
+                                r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，"
+                                r"同时分解长句，减少重复，并提供改进建议。请先提供文本的更正版本，然后在markdown表格中列出修改的内容，并给出修改的理由:"
+                        ) + "\n\n",
+            # [2*] 后缀字符串，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
             "Suffix":   r"",
-            # 按钮颜色 (默认 secondary)
+            # [3] 按钮颜色 (可选参数，默认 secondary)
             "Color":    r"secondary",
-            # 按钮是否可见 (默认 True，即可见)
+            # [4] 按钮是否可见 (可选参数，默认 True，即可见)
             "Visible": True,
-            # 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
-            "AutoClearHistory": False
+            # [5] 是否在触发时清除历史 (可选参数，默认 False，即不处理之前的对话历史)
+            "AutoClearHistory": False,
+            # [6] 文本预处理 （可选参数，默认 None，举例：写个函数移除所有的换行符）
+            "PreProcess": None,
+            # [7] 模型选择 （可选参数。如不设置，则使用当前全局模型；如设置，则用指定模型覆盖全局模型。）
+            # "ModelOverride": "gpt-3.5-turbo", # 主要用途：强制点击此基础功能按钮时，使用指定的模型。
         },
-        "中文学术润色": {
-            "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
-                        r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n",
-            "Suffix":   r"",
+
+
+        "总结绘制脑图": {
+            # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
+            "Prefix":   '''"""\n\n''',
+            # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
+            "Suffix":
+                # dedent() 函数用于去除多行字符串的缩进
+                dedent("\n\n"+r'''
+                    """
+
+                    使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
+
+                    以下是对以上文本的总结，以mermaid flowchart的形式展示：
+                    ```mermaid
+                    flowchart LR
+                        A["节点名1"] --> B("节点名2")
+                        B --> C{"节点名3"}
+                        C --> D["节点名4"]
+                        C --> |"箭头名1"| E["节点名5"]
+                        C --> |"箭头名2"| F["节点名6"]
+                    ```
+
+                    注意：
+                    （1）使用中文
+                    （2）节点名字使用引号包裹，如["Laptop"]
+                    （3）`|` 和 `"`之间不要存在空格
+                    （4）根据情况选择flowchart LR（从左到右）或者flowchart TD（从上到下）
+                '''),
         },
+
+
         "查找语法错误": {
-            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
-                        r"put the original text the first column, " +
-                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
+            "Prefix":   r"Help me ensure that the grammar and the spelling is correct. "
+                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good. "
+                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, "
+                        r"put the original text the first column, "
+                        r"put the corrected text in the second column and highlight the key words you fixed. "
+                        r"Finally, please provide the proofreaded text.""\n\n"
                         r"Example:""\n"
                         r"Paragraph: How is you? Do you knows what is it?""\n"
                         r"| Original sentence | Corrected sentence |""\n"
                         r"| :--- | :--- |""\n"
                         r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
+                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n\n"
                         r"Below is a paragraph from an academic paper. "
                         r"You need to report all grammar and spelling mistakes as the example before."
                         + "\n\n",
             "Suffix":   r"",
             "PreProcess": clear_line_break,    # 预处理：清除换行符
         },
+
+
         "中译英": {
             "Prefix":   r"Please translate following sentence to English:" + "\n\n",
             "Suffix":   r"",
         },
-        "学术中英互译": {
-            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
-                        r"I will provide you with some paragraphs in one language " +
-                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
-                        r"Do not repeat the original provided paragraphs after translation. " +
-                        r"You should use artificial intelligence tools, " +
-                        r"such as natural language processing, and rhetorical knowledge " +
-                        r"and experience about effective writing techniques to reply. " +
-                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
-            "Suffix": "",
-            "Color": "secondary",
+
+
+        "学术英中互译": {
+            "Prefix":   build_gpt_academic_masked_string_langbased(
+                            text_show_chinese=
+                                r"I want you to act as a scientific English-Chinese translator, "
+                                r"I will provide you with some paragraphs in one language "
+                                r"and your task is to accurately and academically translate the paragraphs only into the other language. "
+                                r"Do not repeat the original provided paragraphs after translation. "
+                                r"You should use artificial intelligence tools, "
+                                r"such as natural language processing, and rhetorical knowledge "
+                                r"and experience about effective writing techniques to reply. "
+                                r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:",
+                            text_show_english=
+                                r"你是经验丰富的翻译，请把以下学术文章段落翻译成中文，"
+                                r"并同时充分考虑中文的语法、清晰、简洁和整体可读性，"
+                                r"必要时，你可以修改整个句子的顺序以确保翻译后的段落符合中文的语言习惯。"
+                                r"你需要翻译的文本如下："
+                        ) + "\n\n",
+            "Suffix":   r"",
         },
+
+
         "英译中": {
             "Prefix":   r"翻译成地道的中文：" + "\n\n",
             "Suffix":   r"",
-            "Visible": False,
+            "Visible":  False,
         },
+
+
         "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
+            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，"
                         r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
             "Suffix":   r"",
-            "Visible": False,
+            "Visible":  False,
         },
+
+
         "解释代码": {
             "Prefix":   r"请解释以下代码：" + "\n```\n",
             "Suffix":   "\n```\n",
         },
+
+
         "参考文献转Bib": {
-            "Prefix":   r"Here are some bibliography items, please transform them into bibtex style." +
-                        r"Note that, reference styles maybe more than one kind, you should transform each item correctly." +
-                        r"Items need to be transformed:",
-            "Visible": False,
+            "Prefix":   r"Here are some bibliography items, please transform them into bibtex style."
+                        r"Note that, reference styles maybe more than one kind, you should transform each item correctly."
+                        r"Items need to be transformed:" + "\n\n",
+            "Visible":  False,
             "Suffix":   r"",
         }
     }
@@ -89,8 +151,25 @@ def handle_core_functionality(additional_fn, inputs, history, chatbot):
     import core_functional
     importlib.reload(core_functional)    # 热更新prompt
     core_functional = core_functional.get_core_functions()
-    if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-    inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-    if core_functional[additional_fn].get("AutoClearHistory", False):
-        history = []
-    return inputs, history
+    addition = chatbot._cookies['customize_fn_overwrite']
+    if additional_fn in addition:
+        # 自定义功能
+        inputs = addition[additional_fn]["Prefix"] + inputs + addition[additional_fn]["Suffix"]
+        return inputs, history
+    else:
+        # 预制功能
+        if "PreProcess" in core_functional[additional_fn]:
+            if core_functional[additional_fn]["PreProcess"] is not None:
+                inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        # 为字符串加上上面定义的前缀和后缀。
+        inputs = apply_gpt_academic_string_mask_langbased(
+            string = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"],
+            lang_reference = inputs,
+        )
+        if core_functional[additional_fn].get("AutoClearHistory", False):
+            history = []
+        return inputs, history
+
+if __name__ == "__main__":
+    t = get_core_functions()["总结绘制脑图"]
+    print(t["Prefix"] + t["Suffix"])

crazy_functional.py

@@ -1,522 +1,20 @@
 from toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
-
+from toolbox import trimmed_format_exc
+from loguru import logger
 
 def get_crazy_functions():
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.解析项目源代码 import 解析一个Golang项目
-    from crazy_functions.解析项目源代码 import 解析一个Rust项目
-    from crazy_functions.解析项目源代码 import 解析一个Java项目
-    from crazy_functions.解析项目源代码 import 解析一个前端项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    from crazy_functions.询问多个大语言模型 import 同时问询
-    from crazy_functions.解析项目源代码 import 解析一个Lua项目
-    from crazy_functions.解析项目源代码 import 解析一个CSharp项目
-    from crazy_functions.总结word文档 import 总结word文档
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    from crazy_functions.对话历史存档 import 对话历史存档
-    from crazy_functions.对话历史存档 import 载入对话历史存档
-    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
-    from crazy_functions.辅助功能 import 清除缓存
-    from crazy_functions.批量Markdown翻译 import Markdown英译中
-    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
-    from crazy_functions.Latex全文润色 import Latex中文润色
-    from crazy_functions.Latex全文润色 import Latex英文纠错
-    from crazy_functions.Latex全文翻译 import Latex中译英
-    from crazy_functions.Latex全文翻译 import Latex英译中
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    from crazy_functions.虚空终端 import 虚空终端
-
+    from crazy_functions.AntFin import AntFinTest
 
     function_plugins = {
-        "虚空终端": {
-            "Group": "对话|编程|学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(虚空终端)
-        },
-        "解析整个Python项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "解析一个Python项目的所有源文件(.py) | 输入参数为路径",
-            "Function": HotReload(解析一个Python项目)
-        },
-        "载入对话历史存档（先上传存档或输入路径）": {
-            "Group": "对话",
+        "蚂小财测试": {
+            "Group": "智能体",
             "Color": "stop",
             "AsButton": False,
-            "Info": "载入对话历史存档 | 输入参数为路径",
-            "Function": HotReload(载入对话历史存档)
-        },
-        "删除所有本地对话历史记录（谨慎操作）": {
-            "Group": "对话",
-            "AsButton": False,
-            "Info": "删除所有本地对话历史记录，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(删除所有本地对话历史记录)
-        },
-        "清除所有缓存文件（谨慎操作）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(清除缓存)
-        },
-        "批量总结Word文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "批量总结word文档 | 输入参数为路径",
-            "Function": HotReload(总结word文档)
-        },
-        "解析整个C++项目头文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有头文件(.h/.hpp) | 输入参数为路径",
-            "Function": HotReload(解析一个C项目的头文件)
-        },
-        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有源文件（.cpp/.hpp/.c/.h）| 输入参数为路径",
-            "Function": HotReload(解析一个C项目)
-        },
-        "解析整个Go项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Go项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Golang项目)
-        },
-        "解析整个Rust项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Rust项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Rust项目)
-        },
-        "解析整个Java项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Java项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Java项目)
-        },
-        "解析整个前端项目（js,ts,css等）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个前端项目的所有源文件（js,ts,css等） | 输入参数为路径",
-            "Function": HotReload(解析一个前端项目)
-        },
-        "解析整个Lua项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Lua项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Lua项目)
-        },
-        "解析整个CSharp项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个CSharp项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个CSharp项目)
-        },
-        "解析Jupyter Notebook文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "解析Jupyter Notebook文件 | 输入参数为路径",
-            "Function": HotReload(解析ipynb文件),
-            "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-            "ArgsReminder": "若输入0，则不解析notebook中的Markdown块",  # 高级参数输入区的显示提示
-        },
-        "读Tex论文写摘要": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "读取Tex论文并写摘要 | 输入参数为路径",
-            "Function": HotReload(读文章写摘要)
-        },
-        "翻译README或MD": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "将Markdown翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
-        },
-        "翻译Markdown或README（支持Github链接）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "将Markdown或README翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
-        },
-        "批量生成函数注释": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量生成函数的注释 | 输入参数为路径",
-            "Function": HotReload(批量生成函数注释)
-        },
-        "保存当前的对话": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档)
-        },
-        "[多线程Demo]解析此项目本身（源码自译解）": {
-            "Group": "对话|编程",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "多线程解析并翻译此项目的源码 | 不需要输入参数",
-            "Function": HotReload(解析项目本身)
-        },
-        "[插件demo]历史上的今天": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "查看历史上的今天事件 | 不需要输入参数",
-            "Function": HotReload(高阶功能模板函数)
-        },
-        "精准翻译PDF论文": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,  
-            "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档)
-        },
-        "询问多个GPT模型": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(同时问询)
-        },
-        "批量总结PDF文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量总结PDF文档的内容 | 输入参数为路径",
-            "Function": HotReload(批量总结PDF文档)
-        },
-        "谷歌学术检索助手（输入谷歌学术搜索页url）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
-            "Function": HotReload(谷歌检索小助手)
-        },
-        "理解PDF文档内容 （模仿ChatPDF）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
-            "Function": HotReload(理解PDF文档内容标准文件输入)
-        },
-        "英文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对英文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文润色)
-        },
-        "英文Latex项目全文纠错（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对英文Latex项目全文进行纠错处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文纠错)
-        },
-        "中文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对中文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中文润色)
-        },
-        "Latex项目全文中译英（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对Latex项目全文进行中译英处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中译英)
-        },
-        "Latex项目全文英译中（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对Latex项目全文进行英译中处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英译中)
-        },
-        "批量Markdown中译英（输入路径或上传压缩包）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Markdown中译英)
+            "Info": "蚂小财测试",
+            "Function": HotReload(AntFinTest),
         },
     }
 
-    # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
-    try:
-        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-        function_plugins.update({
-            "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
-                "Function": HotReload(下载arxiv论文并翻译摘要)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.联网的ChatGPT import 连接网络回答问题
-        function_plugins.update({
-            "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-                "Function": HotReload(连接网络回答问题)
-            }
-        })
-        from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
-        function_plugins.update({
-            "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
-                "Function": HotReload(连接bing搜索回答问题)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.解析项目源代码 import 解析任意code项目
-        function_plugins.update({
-            "解析项目源代码（手动指定和筛选源代码文件类型）": {
-                "Group": "编程",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \"*.c, ^*.cpp, config.toml, ^*.toml\"",  # 高级参数输入区的显示提示
-                "Function": HotReload(解析任意code项目)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
-        function_plugins.update({
-            "询问多个GPT模型（手动指定询问哪些模型）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4",  # 高级参数输入区的显示提示
-                "Function": HotReload(同时问询_指定模型)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.图片生成 import 图片生成
-        function_plugins.update({
-            "图片生成（先切换模型到openai或api2d）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "在这里输入分辨率, 如256x256（默认）",  # 高级参数输入区的显示提示
-                "Info": "图片生成 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片生成)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.总结音视频 import 总结音视频
-        function_plugins.update({
-            "批量总结音视频（输入路径或上传压缩包）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
-                "Info": "批量总结音频或视频 | 输入参数为路径",
-                "Function": HotReload(总结音视频)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.数学动画生成manim import 动画生成
-        function_plugins.update({
-            "数学动画生成（Manim）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
-                "Function": HotReload(动画生成)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
-        function_plugins.update({
-            "Markdown翻译（手动指定语言）": {
-                "Group": "编程",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "请输入要翻译成哪种语言，默认为Chinese。",
-                "Function": HotReload(Markdown翻译指定语言)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Langchain知识库 import 知识库问答
-        function_plugins.update({
-            "构建知识库（请先上传文件素材）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "待注入的知识库名称id, 默认为default",
-                "Function": HotReload(知识库问答)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Langchain知识库 import 读取知识库作答
-        function_plugins.update({
-            "知识库问答": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要首先调用构建知识库",
-                "Function": HotReload(读取知识库作答)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.交互功能函数模板 import 交互功能模板函数
-        function_plugins.update({
-            "交互功能模板函数": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(交互功能模板函数)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
-        function_plugins.update({
-            "Latex英文纠错+高亮修正位置 [需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
-                "Function": HotReload(Latex英文纠错加PDF对比)
-            }
-        })
-        from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
-        function_plugins.update({
-            "Arixv论文精细翻译（输入arxivID）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
-            }
-        })
-        function_plugins.update({
-            "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "本地Latex论文精细翻译 | 输入参数是路径",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from toolbox import get_conf
-        ENABLE_AUDIO, = get_conf('ENABLE_AUDIO')
-        if ENABLE_AUDIO:
-            from crazy_functions.语音助手 import 语音助手
-            function_plugins.update({
-                "实时音频采集": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": True,
-                    "Info": "开始语言对话 | 没有输入参数",
-                    "Function": HotReload(语音助手)
-                }
-            })
-    except:
-        print('Load function plugin failed')
-
-
-    # try:
-    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
-    #     function_plugins.update({
-    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
-    #             "Color": "stop",
-    #             "AsButton": False,
-    #             "AdvancedArgs": True,
-    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
-    #             "Function": HotReload(微调数据集生成)
-    #         }
-    #     })
-    # except:
-    #     print('Load function plugin failed')
-
-
 
     """
     设置默认值:
@@ -527,12 +25,24 @@ def get_crazy_functions():
     """
     for name, function_meta in function_plugins.items():
         if "Group" not in function_meta:
-            function_plugins[name]["Group"] = '对话'
+            function_plugins[name]["Group"] = "对话"
         if "AsButton" not in function_meta:
             function_plugins[name]["AsButton"] = True
         if "AdvancedArgs" not in function_meta:
             function_plugins[name]["AdvancedArgs"] = False
         if "Color" not in function_meta:
-            function_plugins[name]["Color"] = 'secondary'
+            function_plugins[name]["Color"] = "secondary"
 
     return function_plugins
+
+
+def get_multiplex_button_functions():
+    """多路复用主提交按钮的功能映射
+    """
+    return {
+        "常规对话":
+            "",
+
+        "蚂小财测试": 
+            "蚂小财测试",  # 映射到上面的 `询问多个GPT模型` 插件
+    }

crazy_functions/AntFin.py

@@ -0,0 +1,9 @@
+from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
+
+
+@CatchException
+def AntFinTest(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    chatbot.append(("AntFin Test", "AntFin Test"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
+

crazy_functions/AntFin_Wrap.py

@@ -0,0 +1,43 @@
+
+from toolbox import get_conf, update_ui
+from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
+from crazy_functions.AntFin import AntFinTest
+
+
+class ImageGen_Wrap(GptAcademicPluginTemplate):
+    def __init__(self):
+        """
+        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
+        """
+        pass
+
+    def define_arg_selection_menu(self):
+        """
+        定义插件的二级选项菜单
+
+        第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+        第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
+
+        """
+        gui_definition = {
+            "main_input":
+                ArgProperty(title="输入图片描述", description="需要生成图像的文本描述，尽量使用英文", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
+            "model_name":
+                ArgProperty(title="模型", options=["DALLE2", "DALLE3"], default_value="DALLE3", description="无", type="dropdown").model_dump_json(),
+            "resolution":
+                ArgProperty(title="分辨率", options=["256x256(限DALLE2)", "512x512(限DALLE2)", "1024x1024", "1792x1024(限DALLE3)", "1024x1792(限DALLE3)"], default_value="1024x1024", description="无", type="dropdown").model_dump_json(),
+            "quality (仅DALLE3生效)":
+                ArgProperty(title="质量", options=["standard", "hd"], default_value="standard", description="无", type="dropdown").model_dump_json(),
+            "style (仅DALLE3生效)":
+                ArgProperty(title="风格", options=["vivid", "natural"], default_value="vivid", description="无", type="dropdown").model_dump_json(),
+
+        }
+        return gui_definition
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        """
+        执行插件
+        """
+        yield from AntFinTest(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+
+

crazy_functions/Langchain知识库.py

@@ -1,107 +0,0 @@
-from toolbox import CatchException, update_ui, ProxyNetworkActivate
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
-
-
-
-@CatchException
-def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 从一批文件(txt, md, tex)中读取数据构建知识库, 然后进行问答。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # resolve deps
-    try:
-        from zh_langchain import construct_vector_store
-        from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-        from .crazy_utils import knowledge_archive_interface
-    except Exception as e:
-        chatbot.append(
-            ["依赖不足", 
-             "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."]
-        )
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        from .crazy_utils import try_install_deps
-        try_install_deps(['zh_langchain==0.2.1', 'pypinyin'])
-    
-    # < --------------------读取参数--------------- >
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    kai_id = plugin_kwargs.get("advanced_arg", 'default')
-
-    # < --------------------读取文件--------------- >
-    file_manifest = []
-    spl = ["txt", "doc", "docx", "email", "epub", "html", "json", "md", "msg", "pdf", "ppt", "pptx", "rtf"]
-    for sp in spl:
-        _, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')
-        file_manifest += file_manifest_tmp
-    
-    if len(file_manifest) == 0:
-        chatbot.append(["没有找到任何可读取文件", "当前支持的格式包括: txt, md, docx, pptx, pdf, json等"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # < -------------------预热文本向量化模组--------------- >
-    chatbot.append(['<br/>'.join(file_manifest), "正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    print('Checking Text2vec ...')
-    from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-    with ProxyNetworkActivate():    # 临时地激活代理网络
-        HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
-
-    # < -------------------构建知识库--------------- >
-    chatbot.append(['<br/>'.join(file_manifest), "正在构建知识库..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    print('Establishing knowledge archive ...')
-    with ProxyNetworkActivate():    # 临时地激活代理网络
-        kai = knowledge_archive_interface()
-        kai.feed_archive(file_manifest=file_manifest, id=kai_id)
-    kai_files = kai.get_loaded_file()
-    kai_files = '<br/>'.join(kai_files)
-    # chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
-    # yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    # chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()
-    # chatbot._cookies['lock_plugin'] = 'crazy_functions.Langchain知识库->读取知识库作答'
-    # chatbot.append(['完成', "“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。"])
-    chatbot.append(['构建完成', f"当前知识库内的有效文件：\n\n---\n\n{kai_files}\n\n---\n\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-@CatchException
-def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port=-1):
-    # resolve deps
-    try:
-        from zh_langchain import construct_vector_store
-        from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-        from .crazy_utils import knowledge_archive_interface
-    except Exception as e:
-        chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        from .crazy_utils import try_install_deps
-        try_install_deps(['zh_langchain==0.2.1'])
-
-    # < -------------------  --------------- >
-    kai = knowledge_archive_interface()
-
-    if 'langchain_plugin_embedding' in chatbot._cookies:
-        resp, prompt = kai.answer_with_archive_by_id(txt, chatbot._cookies['langchain_plugin_embedding'])
-    else:
-        if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-        kai_id = plugin_kwargs.get("advanced_arg", 'default')
-        resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)
-
-    chatbot.append((txt, '[Local Message] ' + prompt))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=prompt, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-        sys_prompt=system_prompt
-    )
-    history.extend((prompt, gpt_say))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新

crazy_functions/Latex全文润色.py

@@ -1,243 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc
-from toolbox import CatchException, report_execption, write_results_to_file, zip_folder
-
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-        print('Segmentation: done')
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            with open(path + '.polish.tex', 'w', encoding='utf8') as f:
-                manifest.append(path + '.polish.tex')
-                f.write(res)
-        return manifest
-    
-    def zip_result(self):
-        import os, time
-        folder = os.path.dirname(self.file_paths[0])
-        t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-        zip_folder(folder, './gpt_log/', f'{t}-polished.zip')
-
-
-def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):
-    import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-
-    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> 
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 定义注释的正则表达式
-            comment_pattern = r'(?<!\\)%.*'
-            # 使用正则表达式查找注释，并替换为空字符串
-            clean_tex_content = re.sub(comment_pattern, '', file_content)
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(clean_tex_content)
-
-    #  <-------- 拆分过长的latex文件 ----------> 
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-
-    #  <-------- 多线程润色开始 ----------> 
-    if language == 'en':
-        if mode == 'polish':
-            inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " + 
-                            "improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" + 
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        else:
-            inputs_array = [r"Below is a section from an academic paper, proofread this section." + 
-                            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + 
-                            r"Answer me only with the revised text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"Polish {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif language == 'zh':
-        if mode == 'polish':
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        else:
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
-                            f"\n\n{frag}" for frag in pfg.sp_file_contents] 
-        inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]
-
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # 并行任务数量限制，最多同时执行5个，其他的排队等待
-        scroller_max_len = 80
-    )
-
-    #  <-------- 文本碎片重组为完整的tex文件，整理结果为压缩包 ----------> 
-    try:
-        pfg.sp_file_result = []
-        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-        pfg.write_result()
-        pfg.zip_result()
-    except:
-        print(trimmed_format_exc())
-
-    #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')
-
-
-
-
-
-
-@CatchException
-def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')
-
-
-
-
-@CatchException
-def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行纠错。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')
-
-
-

crazy_functions/Latex全文翻译.py

@@ -1,175 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-        print('Segmentation: done')
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> 
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 定义注释的正则表达式
-            comment_pattern = r'(?<!\\)%.*'
-            # 使用正则表达式查找注释，并替换为空字符串
-            clean_tex_content = re.sub(comment_pattern, '', file_content)
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(clean_tex_content)
-
-    #  <-------- 拆分过长的latex文件 ----------> 
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 抽取摘要 ----------> 
-    # if language == 'en':
-    #     abs_extract_inputs = f"Please write an abstract for this paper"
-
-    # # 单线，获取文章meta信息
-    # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=abs_extract_inputs,
-    #     inputs_show_user=f"正在抽取摘要信息。",
-    #     llm_kwargs=llm_kwargs,
-    #     chatbot=chatbot, history=[],
-    #     sys_prompt="Your job is to collect information from materials。",
-    # )
-
-    #  <-------- 多线程润色开始 ----------> 
-    if language == 'en->zh':
-        inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-
-    #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-
-
-
-@CatchException
-def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')

crazy_functions/Latex输出PDF结果.py

@@ -1,300 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc, get_conf, objdump, objload, promote_file_to_downloadzone
-from toolbox import CatchException, report_execption, update_ui_lastest_msg, zip_result, gen_time_str
-from functools import partial
-import glob, os, requests, time
-pj = os.path.join
-ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
-
-# =================================== 工具函数 ===============================================
-# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
-def switch_prompt(pfg, mode, more_requirement):
-    """
-    Generate prompts and system prompts based on the mode for proofreading or translating.
-    Args:
-    - pfg: Proofreader or Translator instance.
-    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
-
-    Returns:
-    - inputs_array: A list of strings containing prompts for users to respond to.
-    - sys_prompt_array: A list of strings containing prompts for system prompts.
-    """
-    n_split = len(pfg.sp_file_contents)
-    if mode == 'proofread_en':
-        inputs_array = [r"Below is a section from an academic paper, proofread this section." + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
-                        r"Answer me only with the revised text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif mode == 'translate_zh':
-        inputs_array = [r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + 
-                        r"Answer me only with the translated text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
-    else:
-        assert False, "未知指令"
-    return inputs_array, sys_prompt_array
-
-def desend_to_extracted_folder_if_exist(project_folder):
-    """ 
-    Descend into the extracted folder if it exists, otherwise return the original folder.
-
-    Args:
-    - project_folder: A string specifying the folder path.
-
-    Returns:
-    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
-    """
-    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
-    if len(maybe_dir) == 0: return project_folder
-    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
-    return project_folder
-
-def move_project(project_folder, arxiv_id=None):
-    """ 
-    Create a new work folder and copy the project folder to it.
-
-    Args:
-    - project_folder: A string specifying the folder path of the project.
-
-    Returns:
-    - A string specifying the path to the new work folder.
-    """
-    import shutil, time
-    time.sleep(2)   # avoid time string conflict
-    if arxiv_id is not None:
-        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
-    else:
-        new_workfolder = f'gpt_log/{gen_time_str()}'
-    try:
-        shutil.rmtree(new_workfolder)
-    except:
-        pass
-
-    # align subfolder if there is a folder wrapper
-    items = glob.glob(pj(project_folder,'*'))
-    if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:
-        if os.path.isdir(items[0]): project_folder = items[0]
-
-    shutil.copytree(src=project_folder, dst=new_workfolder)
-    return new_workfolder
-
-def arxiv_download(chatbot, history, txt):
-    def check_cached_translation_pdf(arxiv_id):
-        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
-        if not os.path.exists(translation_dir):
-            os.makedirs(translation_dir)
-        target_file = pj(translation_dir, 'translate_zh.pdf')
-        if os.path.exists(target_file):
-            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
-            return target_file
-        return False
-    def is_float(s):
-        try:
-            float(s)
-            return True
-        except ValueError:
-            return False
-    if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt.strip()
-    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt[:10]
-    if not txt.startswith('https://arxiv.org'): 
-        return txt, None
-    
-    # <-------------- inspect format ------------->
-    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...']) 
-    yield from update_ui(chatbot=chatbot, history=history)
-    time.sleep(1) # 刷新界面
-
-    url_ = txt   # https://arxiv.org/abs/1707.06690
-    if not txt.startswith('https://arxiv.org/abs/'): 
-        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}"
-        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
-        return msg, None
-    # <-------------- set format ------------->
-    arxiv_id = url_.split('/abs/')[-1]
-    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
-    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
-    if cached_translation_pdf: return cached_translation_pdf, arxiv_id
-
-    url_tar = url_.replace('/abs/', '/e-print/')
-    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
-    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
-    os.makedirs(translation_dir, exist_ok=True)
-    
-    # <-------------- download arxiv source file ------------->
-    dst = pj(translation_dir, arxiv_id+'.tar')
-    if os.path.exists(dst):
-        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
-    else:
-        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
-        proxies, = get_conf('proxies')
-        r = requests.get(url_tar, proxies=proxies)
-        with open(dst, 'wb+') as f:
-            f.write(r.content)
-    # <-------------- extract file ------------->
-    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
-    from toolbox import extract_archive
-    extract_archive(file_path=dst, dest_dir=extract_dst)
-    return extract_dst, arxiv_id
-# ========================================= 插件主程序1 =====================================================    
-
-
-@CatchException
-def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([ "函数插件功能？",
-        "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id=None)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-    
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-    # <-------------- we are done ------------->
-    return success
-
-
-# ========================================= 插件主程序2 =====================================================    
-
-@CatchException
-def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    txt, arxiv_id = yield from arxiv_download(chatbot, history, txt)
-    if txt.endswith('.pdf'):
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-
-    # <-------------- we are done ------------->
-    return success

crazy_functions/agent_fns/auto_agent.py

@@ -0,0 +1,23 @@
+from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
+from toolbox import report_exception, get_log_folder, update_ui_lastest_msg, Singleton
+from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
+from crazy_functions.agent_fns.general import AutoGenGeneral
+
+
+
+class AutoGenMath(AutoGenGeneral):
+
+    def define_agents(self):
+        from autogen import AssistantAgent, UserProxyAgent
+        return [
+            {
+                "name": "assistant",            # name of the agent.
+                "cls":  AssistantAgent,         # class of the agent.
+            },
+            {
+                "name": "user_proxy",           # name of the agent.
+                "cls":  UserProxyAgent,         # class of the agent.
+                "human_input_mode": "ALWAYS",   # always ask for human input.
+                "llm_config": False,            # disables llm-based auto reply.
+            },
+        ]

crazy_functions/agent_fns/echo_agent.py

@@ -0,0 +1,20 @@
+from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
+from loguru import logger
+
+class EchoDemo(PluginMultiprocessManager):
+    def subprocess_worker(self, child_conn):
+        # ⭐⭐ 子进程
+        self.child_conn = child_conn
+        while True:
+            msg = self.child_conn.recv() # PipeCom
+            if msg.cmd == "user_input":
+                # wait futher user input
+                self.child_conn.send(PipeCom("show", msg.content))
+                wait_success = self.subprocess_worker_wait_user_feedback(wait_msg="我准备好处理下一个问题了.")
+                if not wait_success:
+                    # wait timeout, terminate this subprocess_worker
+                    break
+            elif msg.cmd == "terminate":
+                self.child_conn.send(PipeCom("done", ""))
+                break
+        logger.info('[debug] subprocess_worker terminated')

crazy_functions/agent_fns/general.py

@@ -0,0 +1,138 @@
+from toolbox import trimmed_format_exc, get_conf, ProxyNetworkActivate
+from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
+from request_llms.bridge_all import predict_no_ui_long_connection
+import time
+
+def gpt_academic_generate_oai_reply(
+    self,
+    messages,
+    sender,
+    config,
+):
+    llm_config = self.llm_config if config is None else config
+    if llm_config is False:
+        return False, None
+    if messages is None:
+        messages = self._oai_messages[sender]
+
+    inputs = messages[-1]['content']
+    history = []
+    for message in messages[:-1]:
+        history.append(message['content'])
+    context=messages[-1].pop("context", None)
+    assert context is None, "预留参数 context 未实现"
+
+    reply = predict_no_ui_long_connection(
+        inputs=inputs,
+        llm_kwargs=llm_config,
+        history=history,
+        sys_prompt=self._oai_system_message[0]['content'],
+        console_slience=True
+    )
+    assumed_done = reply.endswith('\nTERMINATE')
+    return True, reply
+
+class AutoGenGeneral(PluginMultiprocessManager):
+    def gpt_academic_print_override(self, user_proxy, message, sender):
+        # ⭐⭐ run in subprocess
+        try:
+            print_msg = sender.name + "\n\n---\n\n" + message["content"]
+        except:
+            print_msg = sender.name + "\n\n---\n\n" + message
+        self.child_conn.send(PipeCom("show", print_msg))
+
+    def gpt_academic_get_human_input(self, user_proxy, message):
+        # ⭐⭐ run in subprocess
+        patience = 300
+        begin_waiting_time = time.time()
+        self.child_conn.send(PipeCom("interact", message))
+        while True:
+            time.sleep(0.5)
+            if self.child_conn.poll():
+                wait_success = True
+                break
+            if time.time() - begin_waiting_time > patience:
+                self.child_conn.send(PipeCom("done", ""))
+                wait_success = False
+                break
+        if wait_success:
+            return self.child_conn.recv().content
+        else:
+            raise TimeoutError("等待用户输入超时")
+
+    def define_agents(self):
+        raise NotImplementedError
+
+    def exe_autogen(self, input):
+        # ⭐⭐ run in subprocess
+        input = input.content
+        code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
+        agents = self.define_agents()
+        user_proxy = None
+        assistant = None
+        for agent_kwargs in agents:
+            agent_cls = agent_kwargs.pop('cls')
+            kwargs = {
+                'llm_config':self.llm_kwargs,
+                'code_execution_config':code_execution_config
+            }
+            kwargs.update(agent_kwargs)
+            agent_handle = agent_cls(**kwargs)
+            agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b)
+            for d in agent_handle._reply_func_list:
+                if hasattr(d['reply_func'],'__name__') and d['reply_func'].__name__ == 'generate_oai_reply':
+                    d['reply_func'] = gpt_academic_generate_oai_reply
+            if agent_kwargs['name'] == 'user_proxy':
+                agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
+                user_proxy = agent_handle
+            if agent_kwargs['name'] == 'assistant': assistant = agent_handle
+        try:
+            if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义")
+            with ProxyNetworkActivate("AutoGen"):
+                user_proxy.initiate_chat(assistant, message=input)
+        except Exception as e:
+            tb_str = '```\n' + trimmed_format_exc() + '```'
+            self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
+
+    def subprocess_worker(self, child_conn):
+        # ⭐⭐ run in subprocess
+        self.child_conn = child_conn
+        while True:
+            msg = self.child_conn.recv()  # PipeCom
+            self.exe_autogen(msg)
+
+
+class AutoGenGroupChat(AutoGenGeneral):
+    def exe_autogen(self, input):
+        # ⭐⭐ run in subprocess
+        import autogen
+
+        input = input.content
+        with ProxyNetworkActivate("AutoGen"):
+            code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker}
+            agents = self.define_agents()
+            agents_instances = []
+            for agent_kwargs in agents:
+                agent_cls = agent_kwargs.pop("cls")
+                kwargs = {"code_execution_config": code_execution_config}
+                kwargs.update(agent_kwargs)
+                agent_handle = agent_cls(**kwargs)
+                agent_handle._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
+                agents_instances.append(agent_handle)
+                if agent_kwargs["name"] == "user_proxy":
+                    user_proxy = agent_handle
+                    user_proxy.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
+            try:
+                groupchat = autogen.GroupChat(agents=agents_instances, messages=[], max_round=50)
+                manager = autogen.GroupChatManager(groupchat=groupchat, **self.define_group_chat_manager_config())
+                manager._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b)
+                manager.get_human_input = lambda a: self.gpt_academic_get_human_input(manager, a)
+                if user_proxy is None:
+                    raise Exception("user_proxy is not defined")
+                user_proxy.initiate_chat(manager, message=input)
+            except Exception:
+                tb_str = "```\n" + trimmed_format_exc() + "```"
+                self.child_conn.send(PipeCom("done", "AutoGen exe failed: \n\n" + tb_str))
+
+    def define_group_chat_manager_config(self):
+        raise NotImplementedError

crazy_functions/agent_fns/persistent.py

@@ -0,0 +1,16 @@
+from toolbox import Singleton
+@Singleton
+class GradioMultiuserManagerForPersistentClasses():
+    def __init__(self):
+        self.mapping = {}
+
+    def already_alive(self, key):
+        return (key in self.mapping) and (self.mapping[key].is_alive())
+
+    def set(self, key, x):
+        self.mapping[key] = x
+        return self.mapping[key]
+
+    def get(self, key):
+        return self.mapping[key]
+

crazy_functions/agent_fns/pipe.py

@@ -0,0 +1,195 @@
+from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_file_to_downloadzone
+from crazy_functions.agent_fns.watchdog import WatchDog
+from loguru import logger
+import time, os
+
+class PipeCom:
+    def __init__(self, cmd, content) -> None:
+        self.cmd = cmd
+        self.content = content
+
+
+class PluginMultiprocessManager:
+    def __init__(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        # ⭐ run in main process
+        self.autogen_work_dir = os.path.join(get_log_folder("autogen"), gen_time_str())
+        self.previous_work_dir_files = {}
+        self.llm_kwargs = llm_kwargs
+        self.plugin_kwargs = plugin_kwargs
+        self.chatbot = chatbot
+        self.history = history
+        self.system_prompt = system_prompt
+        # self.user_request = user_request
+        self.alive = True
+        self.use_docker = get_conf("AUTOGEN_USE_DOCKER")
+        self.last_user_input = ""
+        # create a thread to monitor self.heartbeat, terminate the instance if no heartbeat for a long time
+        timeout_seconds = 5 * 60
+        self.heartbeat_watchdog = WatchDog(timeout=timeout_seconds, bark_fn=self.terminate, interval=5)
+        self.heartbeat_watchdog.begin_watch()
+
+    def feed_heartbeat_watchdog(self):
+        # feed this `dog`, so the dog will not `bark` (bark_fn will terminate the instance)
+        self.heartbeat_watchdog.feed()
+
+    def is_alive(self):
+        return self.alive
+
+    def launch_subprocess_with_pipe(self):
+        # ⭐ run in main process
+        from multiprocessing import Process, Pipe
+
+        parent_conn, child_conn = Pipe()
+        self.p = Process(target=self.subprocess_worker, args=(child_conn,))
+        self.p.daemon = True
+        self.p.start()
+        return parent_conn
+
+    def terminate(self):
+        self.p.terminate()
+        self.alive = False
+        logger.info("[debug] instance terminated")
+
+    def subprocess_worker(self, child_conn):
+        # ⭐⭐ run in subprocess
+        raise NotImplementedError
+
+    def send_command(self, cmd):
+        # ⭐ run in main process
+        repeated = False
+        if cmd == self.last_user_input:
+            repeated = True
+            cmd = ""
+        else:
+            self.last_user_input = cmd
+        self.parent_conn.send(PipeCom("user_input", cmd))
+        return repeated, cmd
+
+    def immediate_showoff_when_possible(self, fp):
+        # ⭐ 主进程
+        # 获取fp的拓展名
+        file_type = fp.split('.')[-1]
+        # 如果是文本文件, 则直接显示文本内容
+        if file_type.lower() in ['png', 'jpg']:
+            image_path = os.path.abspath(fp)
+            self.chatbot.append([
+                '检测到新生图像:',
+                f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
+            ])
+            yield from update_ui(chatbot=self.chatbot, history=self.history)
+
+    def overwatch_workdir_file_change(self):
+        # ⭐ 主进程 Docker 外挂文件夹监控
+        path_to_overwatch = self.autogen_work_dir
+        change_list = []
+        # 扫描路径下的所有文件, 并与self.previous_work_dir_files中所记录的文件进行对比，
+        # 如果有新文件出现，或者文件的修改时间发生变化，则更新self.previous_work_dir_files中
+        # 把新文件和发生变化的文件的路径记录到 change_list 中
+        for root, dirs, files in os.walk(path_to_overwatch):
+            for file in files:
+                file_path = os.path.join(root, file)
+                if file_path not in self.previous_work_dir_files.keys():
+                    last_modified_time = os.stat(file_path).st_mtime
+                    self.previous_work_dir_files.update({file_path: last_modified_time})
+                    change_list.append(file_path)
+                else:
+                    last_modified_time = os.stat(file_path).st_mtime
+                    if last_modified_time != self.previous_work_dir_files[file_path]:
+                        self.previous_work_dir_files[file_path] = last_modified_time
+                        change_list.append(file_path)
+        if len(change_list) > 0:
+            file_links = ""
+            for f in change_list:
+                res = promote_file_to_downloadzone(f)
+                file_links += f'<br/><a href="file={res}" target="_blank">{res}</a>'
+                yield from self.immediate_showoff_when_possible(f)
+
+            self.chatbot.append(['检测到新生文档.', f'文档清单如下: {file_links}'])
+            yield from update_ui(chatbot=self.chatbot, history=self.history)
+        return change_list
+
+
+    def main_process_ui_control(self, txt, create_or_resume) -> str:
+        # ⭐ 主进程
+        if create_or_resume == 'create':
+            self.cnt = 1
+            self.parent_conn = self.launch_subprocess_with_pipe() # ⭐⭐⭐
+        repeated, cmd_to_autogen = self.send_command(txt)
+        if txt == 'exit':
+            self.chatbot.append([f"结束", "结束信号已明确，终止AutoGen程序。"])
+            yield from update_ui(chatbot=self.chatbot, history=self.history)
+            self.terminate()
+            return "terminate"
+
+        # patience = 10
+
+        while True:
+            time.sleep(0.5)
+            if not self.alive:
+                # the heartbeat watchdog might have it killed
+                self.terminate()
+                return "terminate"
+            if self.parent_conn.poll():
+                self.feed_heartbeat_watchdog()
+                if "[GPT-Academic] 等待中" in self.chatbot[-1][-1]:
+                    self.chatbot.pop(-1)  # remove the last line
+                if "等待您的进一步指令" in self.chatbot[-1][-1]:
+                    self.chatbot.pop(-1)  # remove the last line
+                if '[GPT-Academic] 等待中' in self.chatbot[-1][-1]:
+                    self.chatbot.pop(-1)    # remove the last line
+                msg = self.parent_conn.recv() # PipeCom
+                if msg.cmd == "done":
+                    self.chatbot.append([f"结束", msg.content])
+                    self.cnt += 1
+                    yield from update_ui(chatbot=self.chatbot, history=self.history)
+                    self.terminate()
+                    break
+                if msg.cmd == "show":
+                    yield from self.overwatch_workdir_file_change()
+                    notice = ""
+                    if repeated: notice = "（自动忽略重复的输入）"
+                    self.chatbot.append([f"运行阶段-{self.cnt}（上次用户反馈输入为: 「{cmd_to_autogen}」{notice}", msg.content])
+                    self.cnt += 1
+                    yield from update_ui(chatbot=self.chatbot, history=self.history)
+                if msg.cmd == "interact":
+                    yield from self.overwatch_workdir_file_change()
+                    self.chatbot.append([f"程序抵达用户反馈节点.", msg.content +
+                                         "\n\n等待您的进一步指令." +
+                                         "\n\n(1) 一般情况下您不需要说什么, 清空输入区, 然后直接点击“提交”以继续. " +
+                                         "\n\n(2) 如果您需要补充些什么, 输入要反馈的内容, 直接点击“提交”以继续. " +
+                                         "\n\n(3) 如果您想终止程序, 输入exit, 直接点击“提交”以终止AutoGen并解锁. "
+                    ])
+                    yield from update_ui(chatbot=self.chatbot, history=self.history)
+                    # do not terminate here, leave the subprocess_worker instance alive
+                    return "wait_feedback"
+            else:
+                self.feed_heartbeat_watchdog()
+                if '[GPT-Academic] 等待中' not in self.chatbot[-1][-1]:
+                    # begin_waiting_time = time.time()
+                    self.chatbot.append(["[GPT-Academic] 等待AutoGen执行结果 ...", "[GPT-Academic] 等待中"])
+                self.chatbot[-1] = [self.chatbot[-1][0], self.chatbot[-1][1].replace("[GPT-Academic] 等待中", "[GPT-Academic] 等待中.")]
+                yield from update_ui(chatbot=self.chatbot, history=self.history)
+                # if time.time() - begin_waiting_time > patience:
+                #     self.chatbot.append([f"结束", "等待超时, 终止AutoGen程序。"])
+                #     yield from update_ui(chatbot=self.chatbot, history=self.history)
+                #     self.terminate()
+                #     return "terminate"
+
+        self.terminate()
+        return "terminate"
+
+    def subprocess_worker_wait_user_feedback(self, wait_msg="wait user feedback"):
+        # ⭐⭐ run in subprocess
+        patience = 5 * 60
+        begin_waiting_time = time.time()
+        self.child_conn.send(PipeCom("interact", wait_msg))
+        while True:
+            time.sleep(0.5)
+            if self.child_conn.poll():
+                wait_success = True
+                break
+            if time.time() - begin_waiting_time > patience:
+                self.child_conn.send(PipeCom("done", ""))
+                wait_success = False
+                break
+        return wait_success

crazy_functions/agent_fns/python_comment_agent.py

@@ -0,0 +1,457 @@
+import datetime
+import re
+import os
+from loguru import logger
+from textwrap import dedent
+from toolbox import CatchException, update_ui
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+# TODO: 解决缩进问题
+
+find_function_end_prompt = '''
+Below is a page of code that you need to read. This page may not yet complete, you job is to split this page to sperate functions, class functions etc.
+- Provide the line number where the first visible function ends.
+- Provide the line number where the next visible function begins.
+- If there are no other functions in this page, you should simply return the line number of the last line.
+- Only focus on functions declared by `def` keyword. Ignore inline functions. Ignore function calls.
+
+------------------ Example ------------------
+INPUT:
+
+    ```
+    L0000 |import sys
+    L0001 |import re
+    L0002 |
+    L0003 |def trimmed_format_exc():
+    L0004 |    import os
+    L0005 |    import traceback
+    L0006 |    str = traceback.format_exc()
+    L0007 |    current_path = os.getcwd()
+    L0008 |    replace_path = "."
+    L0009 |    return str.replace(current_path, replace_path)
+    L0010 |
+    L0011 |
+    L0012 |def trimmed_format_exc_markdown():
+    L0013 |    ...
+    L0014 |    ...
+    ```
+
+OUTPUT:
+
+    ```
+    <first_function_end_at>L0009</first_function_end_at>
+    <next_function_begin_from>L0012</next_function_begin_from>
+    ```
+
+------------------ End of Example ------------------
+
+
+------------------ the real INPUT you need to process NOW ------------------
+```
+{THE_TAGGED_CODE}
+```
+'''
+
+
+
+
+
+
+
+revise_funtion_prompt = '''
+You need to read the following code, and revise the source code ({FILE_BASENAME}) according to following instructions:
+1. You should analyze the purpose of the functions (if there are any).
+2. You need to add docstring for the provided functions (if there are any).
+
+Be aware:
+1. You must NOT modify the indent of code.
+2. You are NOT authorized to change or translate non-comment code, and you are NOT authorized to add empty lines either, toggle qu.
+3. Use {LANG} to add comments and docstrings. Do NOT translate Chinese that is already in the code.
+4. Besides adding a docstring, use the ⭐ symbol to annotate the most core and important line of code within the function, explaining its role.
+
+------------------ Example ------------------
+INPUT:
+```
+L0000 |
+L0001 |def zip_result(folder):
+L0002 |    t = gen_time_str()
+L0003 |    zip_folder(folder, get_log_folder(), f"result.zip")
+L0004 |    return os.path.join(get_log_folder(), f"result.zip")
+L0005 |
+L0006 |
+```
+
+OUTPUT:
+
+<instruction_1_purpose>
+This function compresses a given folder, and return the path of the resulting `zip` file.
+</instruction_1_purpose>
+<instruction_2_revised_code>
+```
+def zip_result(folder):
+    """
+    Compresses the specified folder into a zip file and stores it in the log folder.
+
+    Args:
+        folder (str): The path to the folder that needs to be compressed.
+
+    Returns:
+        str: The path to the created zip file in the log folder.
+    """
+    t = gen_time_str()
+    zip_folder(folder, get_log_folder(), f"result.zip")  # ⭐ Execute the zipping of folder
+    return os.path.join(get_log_folder(), f"result.zip")
+```
+</instruction_2_revised_code>
+------------------ End of Example ------------------
+
+
+------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
+```
+{THE_CODE}
+```
+{INDENT_REMINDER}
+{BRIEF_REMINDER}
+{HINT_REMINDER}
+'''
+
+
+revise_funtion_prompt_chinese = '''
+您需要阅读以下代码，并根据以下说明修订源代码({FILE_BASENAME}):
+1. 如果源代码中包含函数的话, 你应该分析给定函数实现了什么功能
+2. 如果源代码中包含函数的话, 你需要为函数添加docstring, docstring必须使用中文
+
+请注意：
+1. 你不得修改代码的缩进
+2. 你无权更改或翻译代码中的非注释部分，也不允许添加空行
+3. 使用 {LANG} 添加注释和文档字符串。不要翻译代码中已有的中文
+4. 除了添加docstring之外, 使用⭐符号给该函数中最核心、最重要的一行代码添加注释，并说明其作用
+
+------------------ 示例 ------------------
+INPUT:
+```
+L0000 |
+L0001 |def zip_result(folder):
+L0002 |    t = gen_time_str()
+L0003 |    zip_folder(folder, get_log_folder(), f"result.zip")
+L0004 |    return os.path.join(get_log_folder(), f"result.zip")
+L0005 |
+L0006 |
+```
+
+OUTPUT:
+
+<instruction_1_purpose>
+该函数用于压缩指定文件夹，并返回生成的`zip`文件的路径。
+</instruction_1_purpose>
+<instruction_2_revised_code>
+```
+def zip_result(folder):
+    """
+    该函数将指定的文件夹压缩成ZIP文件, 并将其存储在日志文件夹中。
+
+    输入参数:
+        folder (str): 需要压缩的文件夹的路径。
+    返回值:
+        str: 日志文件夹中创建的ZIP文件的路径。
+    """
+    t = gen_time_str()
+    zip_folder(folder, get_log_folder(), f"result.zip")  # ⭐ 执行文件夹的压缩
+    return os.path.join(get_log_folder(), f"result.zip")
+```
+</instruction_2_revised_code>
+------------------ End of Example ------------------
+
+
+------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
+```
+{THE_CODE}
+```
+{INDENT_REMINDER}
+{BRIEF_REMINDER}
+{HINT_REMINDER}
+'''
+
+
+class PythonCodeComment():
+
+    def __init__(self, llm_kwargs, plugin_kwargs, language, observe_window_update) -> None:
+        self.original_content = ""
+        self.full_context = []
+        self.full_context_with_line_no = []
+        self.current_page_start = 0
+        self.page_limit = 100 # 100 lines of code each page
+        self.ignore_limit = 20
+        self.llm_kwargs = llm_kwargs
+        self.plugin_kwargs = plugin_kwargs
+        self.language = language
+        self.observe_window_update = observe_window_update
+        if self.language == "chinese":
+            self.core_prompt = revise_funtion_prompt_chinese
+        else:
+            self.core_prompt = revise_funtion_prompt
+        self.path = None
+        self.file_basename = None
+        self.file_brief = ""
+
+    def generate_tagged_code_from_full_context(self):
+        for i, code in enumerate(self.full_context):
+            number = i
+            padded_number = f"{number:04}"
+            result = f"L{padded_number}"
+            self.full_context_with_line_no.append(f"{result} | {code}")
+        return self.full_context_with_line_no
+
+    def read_file(self, path, brief):
+        with open(path, 'r', encoding='utf8') as f:
+            self.full_context = f.readlines()
+        self.original_content = ''.join(self.full_context)
+        self.file_basename = os.path.basename(path)
+        self.file_brief = brief
+        self.full_context_with_line_no = self.generate_tagged_code_from_full_context()
+        self.path = path
+
+    def find_next_function_begin(self, tagged_code:list, begin_and_end):
+        begin, end = begin_and_end
+        THE_TAGGED_CODE = ''.join(tagged_code)
+        self.llm_kwargs['temperature'] = 0
+        result = predict_no_ui_long_connection(
+            inputs=find_function_end_prompt.format(THE_TAGGED_CODE=THE_TAGGED_CODE),
+            llm_kwargs=self.llm_kwargs,
+            history=[],
+            sys_prompt="",
+            observe_window=[],
+            console_slience=True
+        )
+
+        def extract_number(text):
+            # 使用正则表达式匹配模式
+            match = re.search(r'<next_function_begin_from>L(\d+)</next_function_begin_from>', text)
+            if match:
+                # 提取匹配的数字部分并转换为整数
+                return int(match.group(1))
+            return None
+
+        line_no = extract_number(result)
+        if line_no is not None:
+            return line_no
+        else:
+            return end
+
+    def _get_next_window(self):
+        #
+        current_page_start = self.current_page_start
+
+        if self.current_page_start == len(self.full_context) + 1:
+            raise StopIteration
+
+        # 如果剩余的行数非常少，一鼓作气处理掉
+        if len(self.full_context) - self.current_page_start < self.ignore_limit:
+            future_page_start = len(self.full_context) + 1
+            self.current_page_start = future_page_start
+            return current_page_start, future_page_start
+
+
+        tagged_code = self.full_context_with_line_no[ self.current_page_start: self.current_page_start + self.page_limit]
+        line_no = self.find_next_function_begin(tagged_code, [self.current_page_start, self.current_page_start + self.page_limit])
+
+        if line_no > len(self.full_context) - 5:
+            line_no = len(self.full_context) + 1
+
+        future_page_start = line_no
+        self.current_page_start = future_page_start
+
+        # ! consider eof
+        return current_page_start, future_page_start
+
+    def dedent(self, text):
+        """Remove any common leading whitespace from every line in `text`.
+        """
+        # Look for the longest leading string of spaces and tabs common to
+        # all lines.
+        margin = None
+        _whitespace_only_re = re.compile('^[ \t]+$', re.MULTILINE)
+        _leading_whitespace_re = re.compile('(^[ \t]*)(?:[^ \t\n])', re.MULTILINE)
+        text = _whitespace_only_re.sub('', text)
+        indents = _leading_whitespace_re.findall(text)
+        for indent in indents:
+            if margin is None:
+                margin = indent
+
+            # Current line more deeply indented than previous winner:
+            # no change (previous winner is still on top).
+            elif indent.startswith(margin):
+                pass
+
+            # Current line consistent with and no deeper than previous winner:
+            # it's the new winner.
+            elif margin.startswith(indent):
+                margin = indent
+
+            # Find the largest common whitespace between current line and previous
+            # winner.
+            else:
+                for i, (x, y) in enumerate(zip(margin, indent)):
+                    if x != y:
+                        margin = margin[:i]
+                        break
+
+        # sanity check (testing/debugging only)
+        if 0 and margin:
+            for line in text.split("\n"):
+                assert not line or line.startswith(margin), \
+                    "line = %r, margin = %r" % (line, margin)
+
+        if margin:
+            text = re.sub(r'(?m)^' + margin, '', text)
+            return text, len(margin)
+        else:
+            return text, 0
+
+    def get_next_batch(self):
+        current_page_start, future_page_start = self._get_next_window()
+        return ''.join(self.full_context[current_page_start: future_page_start]), current_page_start, future_page_start
+
+    def tag_code(self, fn, hint):
+        code = fn
+        _, n_indent = self.dedent(code)
+        indent_reminder = "" if n_indent == 0 else "(Reminder: as you can see, this piece of code has indent made up with {n_indent} whitespace, please preseve them in the OUTPUT.)"
+        brief_reminder = "" if self.file_brief == "" else f"({self.file_basename} abstract: {self.file_brief})"
+        hint_reminder = "" if hint is None else f"(Reminder: do not ignore or modify code such as `{hint}`, provide complete code in the OUTPUT.)"
+        self.llm_kwargs['temperature'] = 0
+        result = predict_no_ui_long_connection(
+            inputs=self.core_prompt.format(
+                LANG=self.language, 
+                FILE_BASENAME=self.file_basename, 
+                THE_CODE=code, 
+                INDENT_REMINDER=indent_reminder, 
+                BRIEF_REMINDER=brief_reminder,
+                HINT_REMINDER=hint_reminder
+            ),
+            llm_kwargs=self.llm_kwargs,
+            history=[],
+            sys_prompt="",
+            observe_window=[],
+            console_slience=True
+        )
+
+        def get_code_block(reply):
+            import re
+            pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
+            matches = re.findall(pattern, reply) # find all code blocks in text
+            if len(matches) == 1:
+                return matches[0].strip('python') #  code block
+            return None
+
+        code_block = get_code_block(result)
+        if code_block is not None:
+            code_block = self.sync_and_patch(original=code, revised=code_block)
+            return code_block
+        else:
+            return code
+        
+    def get_markdown_block_in_html(self, html):
+        from bs4 import BeautifulSoup
+        soup = BeautifulSoup(html, 'lxml')
+        found_list = soup.find_all("div", class_="markdown-body")
+        if found_list:
+            res = found_list[0]
+            return res.prettify()
+        else:
+            return None
+
+
+    def sync_and_patch(self, original, revised):
+        """Ensure the number of pre-string empty lines in revised matches those in original."""
+
+        def count_leading_empty_lines(s, reverse=False):
+            """Count the number of leading empty lines in a string."""
+            lines = s.split('\n')
+            if reverse: lines = list(reversed(lines))
+            count = 0
+            for line in lines:
+                if line.strip() == '':
+                    count += 1
+                else:
+                    break
+            return count
+
+        original_empty_lines = count_leading_empty_lines(original)
+        revised_empty_lines = count_leading_empty_lines(revised)
+
+        if original_empty_lines > revised_empty_lines:
+            additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
+            revised = additional_lines + revised
+        elif original_empty_lines < revised_empty_lines:
+            lines = revised.split('\n')
+            revised = '\n'.join(lines[revised_empty_lines - original_empty_lines:])
+
+        original_empty_lines = count_leading_empty_lines(original, reverse=True)
+        revised_empty_lines = count_leading_empty_lines(revised, reverse=True)
+
+        if original_empty_lines > revised_empty_lines:
+            additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
+            revised =  revised + additional_lines
+        elif original_empty_lines < revised_empty_lines:
+            lines = revised.split('\n')
+            revised = '\n'.join(lines[:-(revised_empty_lines - original_empty_lines)])
+
+        return revised
+
+    def begin_comment_source_code(self, chatbot=None, history=None):
+        # from toolbox import update_ui_lastest_msg
+        assert self.path is not None
+        assert '.py' in self.path   # must be python source code
+        # write_target = self.path + '.revised.py'
+
+        write_content = ""
+        # with open(self.path + '.revised.py', 'w+', encoding='utf8') as f:
+        while True:
+            try:
+                # yield from update_ui_lastest_msg(f"({self.file_basename}) 正在读取下一段代码片段:\n", chatbot=chatbot, history=history, delay=0)
+                next_batch, line_no_start, line_no_end = self.get_next_batch()
+                self.observe_window_update(f"正在处理{self.file_basename} - {line_no_start}/{len(self.full_context)}\n")
+                # yield from update_ui_lastest_msg(f"({self.file_basename}) 处理代码片段:\n\n{next_batch}", chatbot=chatbot, history=history, delay=0)
+                
+                hint = None
+                MAX_ATTEMPT = 2
+                for attempt in range(MAX_ATTEMPT):
+                    result = self.tag_code(next_batch, hint)
+                    try:
+                        successful, hint = self.verify_successful(next_batch, result)
+                    except Exception as e:
+                        logger.error('ignored exception:\n' + str(e))
+                        break
+                    if successful:
+                        break
+                    if attempt == MAX_ATTEMPT - 1:
+                        # cannot deal with this, give up
+                        result = next_batch
+                        break
+
+                # f.write(result)
+                write_content += result
+            except StopIteration:
+                next_batch, line_no_start, line_no_end = [], -1, -1
+                return None, write_content
+
+    def verify_successful(self, original, revised):
+        """ Determine whether the revised code contains every line that already exists
+        """
+        from crazy_functions.ast_fns.comment_remove import remove_python_comments
+        original = remove_python_comments(original)
+        original_lines = original.split('\n')
+        revised_lines = revised.split('\n')
+
+        for l in original_lines:
+            l = l.strip()
+            if '\'' in l or '\"' in l: continue  # ast sometimes toggle " to '
+            found = False
+            for lt in revised_lines:
+                if l in lt:
+                    found = True
+                    break
+            if not found:
+                return False, l
+        return True, None

crazy_functions/agent_fns/python_comment_compare.html

@@ -0,0 +1,45 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <style>ADVANCED_CSS</style>
+    <meta charset="UTF-8">
+    <title>源文件对比</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            height: 100vh;
+            margin: 0;
+        }
+        .container {
+            display: flex;
+            width: 95%;
+            height: -webkit-fill-available;
+        }
+        .code-container {
+            flex: 1;
+            margin: 0px;
+            padding: 0px;
+            border: 1px solid #ccc;
+            background-color: #f9f9f9;
+            overflow: auto;
+        }
+        pre {
+            white-space: pre-wrap;
+            word-wrap: break-word;
+        }
+    </style>
+</head>
+<body>
+<div class="container">
+<div class="code-container">
+REPLACE_CODE_FILE_LEFT
+</div>
+<div class="code-container">
+REPLACE_CODE_FILE_RIGHT
+</div>
+</div>
+</body>
+</html>

crazy_functions/agent_fns/watchdog.py

@@ -0,0 +1,29 @@
+import threading, time
+from loguru import logger
+
+class WatchDog():
+    def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
+        self.last_feed = None
+        self.timeout = timeout
+        self.bark_fn = bark_fn
+        self.interval = interval
+        self.msg = msg
+        self.kill_dog = False
+
+    def watch(self):
+        while True:
+            if self.kill_dog: break
+            if time.time() - self.last_feed > self.timeout:
+                if len(self.msg) > 0: logger.info(self.msg)
+                self.bark_fn()
+                break
+            time.sleep(self.interval)
+
+    def begin_watch(self):
+        self.last_feed = time.time()
+        th = threading.Thread(target=self.watch)
+        th.daemon = True
+        th.start()
+
+    def feed(self):
+        self.last_feed = time.time()

crazy_functions/ast_fns/comment_remove.py

@@ -0,0 +1,54 @@
+import token
+import tokenize
+import copy
+import io
+
+
+def remove_python_comments(input_source: str) -> str:
+    source_flag = copy.copy(input_source)
+    source = io.StringIO(input_source)
+    ls = input_source.split('\n')
+    prev_toktype = token.INDENT
+    readline = source.readline
+
+    def get_char_index(lineno, col):
+        # find the index of the char in the source code
+        if lineno == 1:
+            return len('\n'.join(ls[:(lineno-1)])) + col
+        else:
+            return len('\n'.join(ls[:(lineno-1)])) + col + 1
+
+    def replace_char_between(start_lineno, start_col, end_lineno, end_col, source, replace_char, ls):
+        # replace char between start_lineno, start_col and end_lineno, end_col with replace_char, but keep '\n' and ' '
+        b = get_char_index(start_lineno, start_col)
+        e = get_char_index(end_lineno, end_col)
+        for i in range(b, e):
+            if source[i] == '\n':
+                source = source[:i] + '\n' + source[i+1:]
+            elif source[i] == ' ':
+                source = source[:i] + ' ' + source[i+1:]
+            else:
+                source = source[:i] + replace_char + source[i+1:]
+        return source
+
+    tokgen = tokenize.generate_tokens(readline)
+    for toktype, ttext, (slineno, scol), (elineno, ecol), ltext in tokgen:
+        if toktype == token.STRING and (prev_toktype == token.INDENT):
+            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
+        elif toktype == token.STRING and (prev_toktype == token.NEWLINE):
+            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
+        elif toktype == tokenize.COMMENT:
+            source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
+        prev_toktype = toktype
+    return source_flag
+
+
+# 示例使用
+if __name__ == "__main__":
+    with open("source.py", "r", encoding="utf-8") as f:
+        source_code = f.read()
+
+    cleaned_code = remove_python_comments(source_code)
+
+    with open("cleaned_source.py", "w", encoding="utf-8") as f:
+        f.write(cleaned_code)

crazy_functions/chatglm微调工具.py

@@ -1,141 +0,0 @@
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-import datetime, json
-
-def fetch_items(list_of_items, batch_size):
-    for i in range(0, len(list_of_items), batch_size):
-        yield list_of_items[i:i + batch_size]
-
-def string_to_options(arguments):
-    import argparse
-    import shlex
-
-    # Create an argparse.ArgumentParser instance
-    parser = argparse.ArgumentParser()
-
-    # Add command-line arguments
-    parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
-    parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
-    parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
-    parser.add_argument("--batch", type=int, help="System prompt", default=50)
-    parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
-    parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
-    parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
-    parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
-    parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
-
-
-
-    # Parse the arguments
-    args = parser.parse_args(shlex.split(arguments))
-
-    return args
-
-@CatchException
-def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    args = plugin_kwargs.get("advanced_arg", None)
-    if args is None: 
-        chatbot.append(("没给定指令", "退出"))
-        yield from update_ui(chatbot=chatbot, history=history); return
-    else:
-        arguments = string_to_options(arguments=args)
-
-    dat = []
-    with open(txt, 'r', encoding='utf8') as f:
-        for line in f.readlines():
-            json_dat = json.loads(line)
-            dat.append(json_dat["content"])
-
-    llm_kwargs['llm_model'] = arguments.llm_to_learn
-    for batch in fetch_items(dat, arguments.batch):
-        res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
-            inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[] for _ in (batch)],
-            sys_prompt_array=[arguments.system_prompt for _ in (batch)],
-            max_workers=10  # OpenAI所允许的最大并行过载
-        )
-    
-        with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
-            for b, r in zip(batch, res[1::2]):
-                f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
-
-    promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
-    return
-
-
-
-@CatchException
-def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    import subprocess
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    args = plugin_kwargs.get("advanced_arg", None)
-    if args is None: 
-        chatbot.append(("没给定指令", "退出"))
-        yield from update_ui(chatbot=chatbot, history=history); return
-    else:
-        arguments = string_to_options(arguments=args)
-      
-
-
-    pre_seq_len = arguments.pre_seq_len             # 128
-    learning_rate = arguments.learning_rate                               # 2e-2
-    num_gpus = arguments.num_gpus                   # 1
-    json_dataset = arguments.json_dataset                 # 't_code.json'
-    ptuning_directory = arguments.ptuning_directory       # '/home/hmp/ChatGLM2-6B/ptuning'
-
-    command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
-        --do_train \
-        --train_file AdvertiseGen/{json_dataset} \
-        --validation_file AdvertiseGen/{json_dataset} \
-        --preprocessing_num_workers 20 \
-        --prompt_column content \
-        --response_column summary \
-        --overwrite_cache \
-        --model_name_or_path THUDM/chatglm2-6b \
-        --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
-        --overwrite_output_dir \
-        --max_source_length 256 \
-        --max_target_length 256 \
-        --per_device_train_batch_size 1 \
-        --per_device_eval_batch_size 1 \
-        --gradient_accumulation_steps 16 \
-        --predict_with_generate \
-        --max_steps 100 \
-        --logging_steps 10 \
-        --save_steps 20 \
-        --learning_rate {learning_rate} \
-        --pre_seq_len {pre_seq_len} \
-        --quantization_bit 4"
-
-    process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
-    try:
-        process.communicate(timeout=3600*24)
-    except subprocess.TimeoutExpired:
-        process.kill()
-    return

crazy_functions/crazy_utils.py

@@ -1,23 +1,39 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc
+import os
 import threading
+from loguru import logger
+from shared_utils.char_visual_effect import scolling_visual_effect
+from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
 
-def input_clipping(inputs, history, max_token_limit):
+def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
+    """
+    当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
+    输入：
+        - inputs 本次请求
+        - history 历史上下文
+        - max_token_limit 最大token限制
+    输出:
+        - inputs 本次请求（经过clip）
+        - history 历史上下文（经过clip）
+    """
     import numpy as np
-    from request_llm.bridge_all import model_info
+    from request_llms.bridge_all import model_info
     enc = model_info["gpt-3.5-turbo"]['tokenizer']
     def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
 
+
     mode = 'input-and-history'
     # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
     input_token_num = get_token_num(inputs)
+    original_input_len = len(inputs)
     if input_token_num < max_token_limit//2:
         mode = 'only-history'
         max_token_limit = max_token_limit - input_token_num
 
     everything = [inputs] if mode == 'input-and-history' else ['']
     everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
+    full_token_num = n_token = get_token_num('\n'.join(everything))
     everything_token = [get_token_num(e) for e in everything]
+    everything_token_num = sum(everything_token)
     delta = max(everything_token) // 16 # 截断时的颗粒度
 
     while n_token > max_token_limit:
@@ -30,10 +46,24 @@ def input_clipping(inputs, history, max_token_limit):
 
     if mode == 'input-and-history':
         inputs = everything[0]
+        full_token_num = everything_token_num
     else:
-        pass
+        full_token_num = everything_token_num + input_token_num
+
     history = everything[1:]
-    return inputs, history
+
+    flags = {
+        "mode": mode,
+        "original_input_token_num": input_token_num,
+        "original_full_token_num": full_token_num,
+        "original_input_len": original_input_len,
+        "clipped_input_len": len(inputs),
+    }
+
+    if not return_clip_flags:
+        return inputs, history
+    else:
+        return inputs, history, flags
 
 def request_gpt_model_in_new_thread_with_ui_alive(
         inputs, inputs_show_user, llm_kwargs,
@@ -61,18 +91,21 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     """
     import time
     from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
+    from request_llms.bridge_all import predict_no_ui_long_connection
     # 用户反馈
     chatbot.append([inputs_show_user, ""])
     yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
     executor = ThreadPoolExecutor(max_workers=16)
     mutable = ["", time.time(), ""]
+    # 看门狗耐心
+    watch_dog_patience = 5
+    # 请求任务
     def _req_gpt(inputs, history, sys_prompt):
         retry_op = retry_times_at_unknown_error
         exceeded_cnt = 0
         while True:
             # watchdog error
-            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: 
+            if len(mutable) >= 2 and (time.time()-mutable[1]) > watch_dog_patience:
                 raise RuntimeError("检测到程序终止。")
             try:
                 # 【第一种情况】：顺利完成
@@ -87,7 +120,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
                     # 【选择处理】 尝试计算比例，尽可能多地保留文本
                     from toolbox import get_reduce_token_percent
                     p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
+                    MAX_TOKEN = get_max_token(llm_kwargs)
                     EXCEED_ALLO = 512 + 512 * exceeded_cnt
                     inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
                     mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
@@ -100,7 +133,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
             except:
                 # 【第三种情况】：其他错误：重试几次
                 tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
+                logger.error(tb_str)
                 mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
                 if retry_op > 0:
                     retry_op -= 1
@@ -130,16 +163,31 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
     return final_result
 
-def can_multi_process(llm):
-    if llm.startswith('gpt-'): return True
-    if llm.startswith('api2d-'): return True
-    if llm.startswith('azure-'): return True
-    return False
+def can_multi_process(llm) -> bool:
+    from request_llms.bridge_all import model_info
+
+    def default_condition(llm) -> bool:
+        # legacy condition
+        if llm.startswith('gpt-'): return True
+        if llm.startswith('chatgpt-'): return True
+        if llm.startswith('api2d-'): return True
+        if llm.startswith('azure-'): return True
+        if llm.startswith('spark'): return True
+        if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
+        return False
+
+    if llm in model_info:
+        if 'can_multi_thread' in model_info[llm]:
+            return model_info[llm]['can_multi_thread']
+        else:
+            return default_condition(llm)
+    else:
+        return default_condition(llm)
 
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array, inputs_show_user_array, llm_kwargs,
         chatbot, history_array, sys_prompt_array,
-        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
+        refresh_interval=0.2, max_workers=-1, scroller_max_len=75,
         handle_token_exceed=True, show_user_at_complete=False,
         retry_times_at_unknown_error=2,
         ):
@@ -172,11 +220,11 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     """
     import time, random
     from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
+    from request_llms.bridge_all import predict_no_ui_long_connection
     assert len(inputs_array) == len(history_array)
     assert len(inputs_array) == len(sys_prompt_array)
     if max_workers == -1: # 读取配置文件
-        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
+        try: max_workers = get_conf('DEFAULT_WORKER_NUM')
         except: max_workers = 8
         if max_workers <= 0: max_workers = 3
     # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
@@ -191,19 +239,21 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     # 跨线程传递
     mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
 
+    # 看门狗耐心
+    watch_dog_patience = 5
+
     # 子线程任务
     def _req_gpt(index, inputs, history, sys_prompt):
         gpt_say = ""
         retry_op = retry_times_at_unknown_error
         exceeded_cnt = 0
         mutable[index][2] = "执行中"
+        detect_timeout = lambda: len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > watch_dog_patience
         while True:
             # watchdog error
-            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
-                raise RuntimeError("检测到程序终止。")
+            if detect_timeout(): raise RuntimeError("检测到程序终止。")
             try:
                 # 【第一种情况】：顺利完成
-                # time.sleep(10); raise RuntimeError("测试")
                 gpt_say = predict_no_ui_long_connection(
                     inputs=inputs, llm_kwargs=llm_kwargs, history=history,
                     sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
@@ -211,13 +261,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                 mutable[index][2] = "已成功"
                 return gpt_say
             except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出，
+                # 【第二种情况】：Token溢出
                 if handle_token_exceed:
                     exceeded_cnt += 1
                     # 【选择处理】 尝试计算比例，尽可能多地保留文本
                     from toolbox import get_reduce_token_percent
                     p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
+                    MAX_TOKEN = get_max_token(llm_kwargs)
                     EXCEED_ALLO = 512 + 512 * exceeded_cnt
                     inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
                     gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
@@ -232,8 +282,9 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                     return gpt_say # 放弃
             except:
                 # 【第三种情况】：其他错误
+                if detect_timeout(): raise RuntimeError("检测到程序终止。")
                 tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
+                logger.error(tb_str)
                 gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
                 if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
                 if retry_op > 0:
@@ -248,6 +299,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                     for i in range(wait):
                         mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
                     # 开始重试
+                    if detect_timeout(): raise RuntimeError("检测到程序终止。")
                     mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
                     continue # 返回重试
                 else:
@@ -260,6 +312,8 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
         range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
     cnt = 0
+
+
     while True:
         # yield一次以刷新前端页面
         time.sleep(refresh_interval)
@@ -272,9 +326,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             mutable[thread_index][1] = time.time()
         # 在前端打印些好玩的东西
         for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
-                replace('\n', '').replace('```', '...').replace(
-                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
+            print_something_really_funny = f"[ ...`{scolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
             observe_win.append(print_something_really_funny)
         # 在前端打印些好玩的东西
         stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
@@ -299,99 +351,10 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             gpt_res = f.result()
             chatbot.append([inputs_show_user, gpt_res])
             yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-            time.sleep(0.3)
+            time.sleep(0.5)
     return gpt_response_collection
 
 
-def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
-    def cut(txt_tocut, must_break_at_empty_line):  # 递归
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                print(cnt)
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                raise RuntimeError("存在一行极长的文本！")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line))
-            return result
-    try:
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        return cut(txt, must_break_at_empty_line=False)
-
-
-def force_breakdown(txt, limit, get_token_fn):
-    """
-    当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
-    # 递归
-    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                if break_anyway:
-                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
-                else:
-                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
-            return result
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下敷衍吧
-                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)
-
-
 
 def read_and_clean_pdf_text(fp):
     """
@@ -416,7 +379,7 @@ def read_and_clean_pdf_text(fp):
     import fitz, copy
     import re
     import numpy as np
-    from colorful import print亮黄, print亮绿
+    # from shared_utils.colorful import print亮黄, print亮绿
     fc = 0  # Index 0 文本
     fs = 1  # Index 1 字体
     fb = 2  # Index 2 框框
@@ -469,14 +432,16 @@ def read_and_clean_pdf_text(fp):
                     '- ', '') for t in text_areas['blocks'] if 'lines' in t]
 
         ############################## <第 2 步，获取正文主字体> ##################################
-        fsize_statiscs = {}
-        for span in meta_span:
-            if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
-            fsize_statiscs[span[1]] += span[2]
-        main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
-        if REMOVE_FOOT_NOTE:
-            give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
-
+        try:
+            fsize_statiscs = {}
+            for span in meta_span:
+                if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
+                fsize_statiscs[span[1]] += span[2]
+            main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
+            if REMOVE_FOOT_NOTE:
+                give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
+        except:
+            raise RuntimeError(f'抱歉, 我们暂时无法解析此PDF文档: {fp}。')
         ############################## <第 3 步，切分和重新整合> ##################################
         mega_sec = []
         sec = []
@@ -542,6 +507,9 @@ def read_and_clean_pdf_text(fp):
                     return True
                 else:
                     return False
+            # 对于某些PDF会有第一个段落就以小写字母开头,为了避免索引错误将其更改为大写
+            if starts_with_lowercase_word(meta_txt[0]):
+                meta_txt[0] = meta_txt[0].capitalize()
             for _ in range(100):
                 for index, block_txt in enumerate(meta_txt):
                     if starts_with_lowercase_word(block_txt):
@@ -591,11 +559,16 @@ def get_files_from_everything(txt, type): # type='.md'
         # 网络的远程文件
         import requests
         from toolbox import get_conf
-        proxies, = get_conf('proxies')
-        r = requests.get(txt, proxies=proxies)
-        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
-        project_folder = './gpt_log/'
-        file_manifest = ['./gpt_log/temp'+type]
+        from toolbox import get_log_folder, gen_time_str
+        proxies = get_conf('proxies')
+        try:
+            r = requests.get(txt, proxies=proxies)
+        except:
+            raise ConnectionRefusedError(f"无法下载资源{txt}，请检查。")
+        path = os.path.join(get_log_folder(plugin_name='web_download'), gen_time_str()+type)
+        with open(path, 'wb+') as f: f.write(r.content)
+        project_folder = get_log_folder(plugin_name='web_download')
+        file_manifest = [path]
     elif txt.endswith(type):
         # 直接给定文件
         file_manifest = [txt]
@@ -615,139 +588,64 @@ def get_files_from_everything(txt, type): # type='.md'
 
 
 
-
-def Singleton(cls):
-    _instance = {}
- 
-    def _singleton(*args, **kargs):
-        if cls not in _instance:
-            _instance[cls] = cls(*args, **kargs)
-        return _instance[cls]
- 
-    return _singleton
-
-
 @Singleton
-class knowledge_archive_interface():
-    def __init__(self) -> None:
+class nougat_interface():
+    def __init__(self):
         self.threadLock = threading.Lock()
-        self.current_id = ""
-        self.kai_path = None
-        self.qa_handle = None
-        self.text2vec_large_chinese = None
 
-    def get_chinese_text2vec(self):
-        if self.text2vec_large_chinese is None:
-            # < -------------------预热文本向量化模组--------------- >
-            from toolbox import ProxyNetworkActivate
-            print('Checking Text2vec ...')
-            from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-            with ProxyNetworkActivate():    # 临时地激活代理网络
-                self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
-
-        return self.text2vec_large_chinese
+    def nougat_with_timeout(self, command, cwd, timeout=3600):
+        import subprocess
+        from toolbox import ProxyNetworkActivate
+        logger.info(f'正在执行命令 {command}')
+        with ProxyNetworkActivate("Nougat_Download"):
+            process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
+        try:
+            stdout, stderr = process.communicate(timeout=timeout)
+        except subprocess.TimeoutExpired:
+            process.kill()
+            stdout, stderr = process.communicate()
+            logger.error("Process timed out!")
+            return False
+        return True
 
 
-    def feed_archive(self, file_manifest, id="default"):
+    def NOUGAT_parse_pdf(self, fp, chatbot, history):
+        from toolbox import update_ui_lastest_msg
+
+        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
+                                         chatbot=chatbot, history=history, delay=0)
         self.threadLock.acquire()
-        # import uuid
-        self.current_id = id
-        from zh_langchain import construct_vector_store
-        self.qa_handle, self.kai_path = construct_vector_store(   
-            vs_id=self.current_id, 
-            files=file_manifest, 
-            sentence_size=100,
-            history=[],
-            one_conent="",
-            one_content_segmentation="",
-            text2vec = self.get_chinese_text2vec(),
-        )
+        import glob, threading, os
+        from toolbox import get_log_folder, gen_time_str
+        dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str())
+        os.makedirs(dst)
+
+        yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
+                                         chatbot=chatbot, history=history, delay=0)
+        command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
+        self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
+        res = glob.glob(os.path.join(dst,'*.mmd'))
+        if len(res) == 0:
+            self.threadLock.release()
+            raise RuntimeError("Nougat解析论文失败。")
         self.threadLock.release()
+        return res[0]
 
-    def get_current_archive_id(self):
-        return self.current_id
 
-    def get_loaded_file(self):
-        return self.qa_handle.get_loaded_file()
 
-    def answer_with_archive_by_id(self, txt, id):
-        self.threadLock.acquire()
-        if not self.current_id == id:
-            self.current_id = id
-            from zh_langchain import construct_vector_store
-            self.qa_handle, self.kai_path = construct_vector_store(   
-                vs_id=self.current_id, 
-                files=[], 
-                sentence_size=100,
-                history=[],
-                one_conent="",
-                one_content_segmentation="",
-                text2vec = self.get_chinese_text2vec(),
-            )
-        VECTOR_SEARCH_SCORE_THRESHOLD = 0
-        VECTOR_SEARCH_TOP_K = 4
-        CHUNK_SIZE = 512
-        resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
-            query = txt,
-            vs_path = self.kai_path,
-            score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
-            vector_search_top_k=VECTOR_SEARCH_TOP_K, 
-            chunk_conent=True,
-            chunk_size=CHUNK_SIZE,
-            text2vec = self.get_chinese_text2vec(),
-        )
-        self.threadLock.release()
-        return resp, prompt
 
-def try_install_deps(deps):
+def try_install_deps(deps, reload_m=[]):
+    import subprocess, sys, importlib
     for dep in deps:
-        import subprocess, sys
         subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--user', dep])
+    import site
+    importlib.reload(site)
+    for m in reload_m:
+        importlib.reload(__import__(m))
 
 
-class construct_html():
-    def __init__(self) -> None:
-        self.css = """
-.row {
-  display: flex;
-  flex-wrap: wrap;
-}
-
-.column {
-  flex: 1;
-  padding: 10px;
-}
-
-.table-header {
-  font-weight: bold;
-  border-bottom: 1px solid black;
-}
-
-.table-row {
-  border-bottom: 1px solid lightgray;
-}
-
-.table-cell {
-  padding: 5px;
-}
-        """
-        self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
-
-
-    def add_row(self, a, b):
-        tmp = """
-<div class="row table-row">
-    <div class="column table-cell">REPLACE_A</div>
-    <div class="column table-cell">REPLACE_B</div>
-</div>
-        """
-        from toolbox import markdown_convertion
-        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
-        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
-        self.html_string += tmp
-
-
-    def save_file(self, file_name):
-        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-            f.write(self.html_string.encode('utf-8', 'ignore').decode())
-
+def get_plugin_arg(plugin_kwargs, key, default):
+    # 如果参数是空的
+    if (key in plugin_kwargs) and (plugin_kwargs[key] == ""): plugin_kwargs.pop(key)
+    # 正常情况
+    return plugin_kwargs.get(key, default)

crazy_functions/diagram_fns/file_tree.py

@@ -0,0 +1,127 @@
+import os
+from textwrap import indent
+from loguru import logger
+
+class FileNode:
+    def __init__(self, name, build_manifest=False):
+        self.name = name
+        self.children = []
+        self.is_leaf = False
+        self.level = 0
+        self.parenting_ship = []
+        self.comment = ""
+        self.comment_maxlen_show = 50
+        self.build_manifest = build_manifest
+        self.manifest = {}
+
+    @staticmethod
+    def add_linebreaks_at_spaces(string, interval=10):
+        return '\n'.join(string[i:i+interval] for i in range(0, len(string), interval))
+
+    def sanitize_comment(self, comment):
+        if len(comment) > self.comment_maxlen_show: suf = '...'
+        else: suf = ''
+        comment = comment[:self.comment_maxlen_show]
+        comment = comment.replace('\"', '').replace('`', '').replace('\n', '').replace('`', '').replace('$', '')
+        comment = self.add_linebreaks_at_spaces(comment, 10)
+        return '`' + comment + suf + '`'
+
+    def add_file(self, file_path, file_comment):
+        directory_names, file_name = os.path.split(file_path)
+        current_node = self
+        level = 1
+        if directory_names == "":
+            new_node = FileNode(file_name)
+            self.manifest[file_path] = new_node
+            current_node.children.append(new_node)
+            new_node.is_leaf = True
+            new_node.comment = self.sanitize_comment(file_comment)
+            new_node.level = level
+            current_node = new_node
+        else:
+            dnamesplit = directory_names.split(os.sep)
+            for i, directory_name in enumerate(dnamesplit):
+                found_child = False
+                level += 1
+                for child in current_node.children:
+                    if child.name == directory_name:
+                        current_node = child
+                        found_child = True
+                        break
+                if not found_child:
+                    new_node = FileNode(directory_name)
+                    current_node.children.append(new_node)
+                    new_node.level = level - 1
+                    current_node = new_node
+            term = FileNode(file_name)
+            self.manifest[file_path] = term
+            term.level = level
+            term.comment = self.sanitize_comment(file_comment)
+            term.is_leaf = True
+            current_node.children.append(term)
+
+    def print_files_recursively(self, level=0, code="R0"):
+        logger.info('    '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
+        for j, child in enumerate(self.children):
+            child.print_files_recursively(level=level+1, code=code+str(j))
+            self.parenting_ship.extend(child.parenting_ship)
+            p1 = f"""{code}[\"🗎{self.name}\"]""" if self.is_leaf else f"""{code}[[\"📁{self.name}\"]]"""
+            p2 = """ --> """
+            p3 = f"""{code+str(j)}[\"🗎{child.name}\"]""" if child.is_leaf else f"""{code+str(j)}[[\"📁{child.name}\"]]"""
+            edge_code = p1 + p2 + p3
+            if edge_code in self.parenting_ship:
+                continue
+            self.parenting_ship.append(edge_code)
+        if self.comment != "":
+            pc1 = f"""{code}[\"🗎{self.name}\"]""" if self.is_leaf else f"""{code}[[\"📁{self.name}\"]]"""
+            pc2 = f""" -.-x """
+            pc3 = f"""C{code}[\"{self.comment}\"]:::Comment"""
+            edge_code = pc1 + pc2 + pc3
+            self.parenting_ship.append(edge_code)
+
+
+MERMAID_TEMPLATE = r"""
+```mermaid
+flowchart LR
+    %% <gpt_academic_hide_mermaid_code> 一个特殊标记，用于在生成mermaid图表时隐藏代码块
+    classDef Comment stroke-dasharray: 5 5
+    subgraph {graph_name}
+{relationship}
+    end
+```
+"""
+
+def build_file_tree_mermaid_diagram(file_manifest, file_comments, graph_name):
+    # Create the root node
+    file_tree_struct = FileNode("root")
+    # Build the tree structure
+    for file_path, file_comment in zip(file_manifest, file_comments):
+        file_tree_struct.add_file(file_path, file_comment)
+    file_tree_struct.print_files_recursively()
+    cc = "\n".join(file_tree_struct.parenting_ship)
+    ccc = indent(cc, prefix=" "*8)
+    return MERMAID_TEMPLATE.format(graph_name=graph_name, relationship=ccc)
+
+if __name__ == "__main__":
+    # File manifest
+    file_manifest = [
+        "cradle_void_terminal.ipynb",
+        "tests/test_utils.py",
+        "tests/test_plugins.py",
+        "tests/test_llms.py",
+        "config.py",
+        "build/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/model_weights_0.bin",
+        "crazy_functions/latex_fns/latex_actions.py",
+        "crazy_functions/latex_fns/latex_toolbox.py"
+    ]
+    file_comments = [
+        "根据位置和名称，可能是一个模块的初始化文件根据位置和名称，可能是一个模块的初始化文件根据位置和名称，可能是一个模块的初始化文件",
+        "包含一些用于文本处理和模型微调的函数和装饰器包含一些用于文本处理和模型微调的函数和装饰器包含一些用于文本处理和模型微调的函数和装饰器",
+        "用于构建HTML报告的类和方法用于构建HTML报告的类和方法用于构建HTML报告的类和方法",
+        "包含了用于文本切分的函数，以及处理PDF文件的示例代码包含了用于文本切分的函数，以及处理PDF文件的示例代码包含了用于文本切分的函数，以及处理PDF文件的示例代码",
+        "用于解析和翻译PDF文件的功能和相关辅助函数用于解析和翻译PDF文件的功能和相关辅助函数用于解析和翻译PDF文件的功能和相关辅助函数",
+        "是一个包的初始化文件，用于初始化包的属性和导入模块是一个包的初始化文件，用于初始化包的属性和导入模块是一个包的初始化文件，用于初始化包的属性和导入模块",
+        "用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器",
+        "包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类",
+    ]
+    logger.info(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))

crazy_functions/game_fns/game_ascii_art.py

@@ -0,0 +1,42 @@
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
+import random
+
+
+class MiniGame_ASCII_Art(GptAcademicGameBaseState):
+    def step(self, prompt, chatbot, history):
+        if self.step_cnt == 0:
+            chatbot.append(["我画你猜（动物）", "请稍等..."])
+        else:
+            if prompt.strip() == 'exit':
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                return
+            chatbot.append([prompt, ""])
+        yield from update_ui(chatbot=chatbot, history=history)
+
+        if self.step_cnt == 0:
+            self.lock_plugin(chatbot)
+            self.cur_task = 'draw'
+
+        if self.cur_task == 'draw':
+            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
+            self.obj = random.choice(avail_obj)
+            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + \
+                f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
+            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
+            self.cur_task = 'identify user guess'
+            res = get_code_block(raw_res)
+            history += ['', f'the answer is {self.obj}', inputs, res]
+            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
+
+        elif self.cur_task == 'identify user guess':
+            if is_same_thing(self.obj, prompt, self.llm_kwargs):
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
+            else:
+                self.cur_task = 'identify user guess'
+                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)

crazy_functions/game_fns/game_interactive_story.py

@@ -0,0 +1,212 @@
+prompts_hs = """ 请以“{headstart}”为开头，编写一个小说的第一幕。
+
+- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 字数要求：第一幕的字数少于300字，且少于2个段落。
+"""
+
+prompts_interact = """ 小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，根据以上的情节，给出4种不同的后续剧情发展方向，每个发展方向都精明扼要地用一句话说明。稍后，我将在这4个选择中，挑选一种剧情发展。
+
+输出格式例如：
+1. 后续剧情发展1
+2. 后续剧情发展2
+3. 后续剧情发展3
+4. 后续剧情发展4
+"""
+
+
+prompts_resume = """小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，我们正在互相讨论，确定后续剧情的发展。
+在以下的剧情发展中，
+「
+{choice}
+」
+我认为更合理的是：{user_choice}。
+请在前文的基础上（不要重复前文），围绕我选定的剧情情节，编写小说的下一幕。
+
+- 禁止杜撰不符合我选择的剧情。
+- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
+- 不要重复前文。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 小说的下一幕字数少于300字，且少于2个段落。
+"""
+
+
+prompts_terminate = """小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，我们正在互相讨论，确定后续剧情的发展。
+现在，故事该结束了，我认为最合理的故事结局是：{user_choice}。
+
+请在前文的基础上（不要重复前文），编写小说的最后一幕。
+
+- 不要重复前文。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 字数要求：最后一幕的字数少于1000字。
+"""
+
+
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
+import random
+
+
+class MiniGame_ResumeStory(GptAcademicGameBaseState):
+    story_headstart = [
+        '先行者知道，他现在是全宇宙中唯一的一个人了。',
+        '深夜，一个年轻人穿过天安门广场向纪念堂走去。在二十二世纪编年史中，计算机把他的代号定为M102。',
+        '他知道，这最后一课要提前讲了。又一阵剧痛从肝部袭来，几乎使他晕厥过去。',
+        '在距地球五万光年的远方，在银河系的中心，一场延续了两万年的星际战争已接近尾声。那里的太空中渐渐隐现出一个方形区域，仿佛灿烂的群星的背景被剪出一个方口。',
+        '伊依一行三人乘坐一艘游艇在南太平洋上做吟诗航行，他们的目的地是南极，如果几天后能顺利到达那里，他们将钻出地壳去看诗云。',
+        '很多人生来就会莫名其妙地迷上一样东西，仿佛他的出生就是要和这东西约会似的，正是这样，圆圆迷上了肥皂泡。'
+    ]
+
+
+    def begin_game_step_0(self, prompt, chatbot, history):
+        # init game at step 0
+        self.headstart = random.choice(self.story_headstart)
+        self.story = []
+        chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
+        self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
+
+
+    def generate_story_image(self, story_paragraph):
+        try:
+            from crazy_functions.AntFin import gen_image
+            prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
+            image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
+            return f'<br/><div align="center"><img src="file={image_path}"></div>'
+        except:
+            return ''
+
+    def step(self, prompt, chatbot, history):
+
+        """
+        首先，处理游戏初始化等特殊情况
+        """
+        if self.step_cnt == 0:
+            self.begin_game_step_0(prompt, chatbot, history)
+            self.lock_plugin(chatbot)
+            self.cur_task = 'head_start'
+        else:
+            if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
+                # should we terminate game here?
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                return
+            if '剧情收尾' in prompt:
+                self.cur_task = 'story_terminate'
+            # # well, game resumes
+            # chatbot.append([prompt, ""])
+        # update ui, don't keep the user waiting
+        yield from update_ui(chatbot=chatbot, history=history)
+
+
+        """
+        处理游戏的主体逻辑
+        """
+        if self.cur_task == 'head_start':
+            """
+            这是游戏的第一步
+            """
+            inputs_ = prompts_hs.format(headstart=self.headstart)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, '故事开头', self.llm_kwargs,
+                chatbot, history_, self.sys_prompt_
+            )
+            self.story.append(story_paragraph)
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # # 构建后续剧情引导
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
+            history_ = []
+            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs,
+                chatbot,
+                history_,
+                self.sys_prompt_
+            )
+            self.cur_task = 'user_choice'
+
+
+        elif self.cur_task == 'user_choice':
+            """
+            根据用户的提示，确定故事的下一步
+            """
+            if '请在以下几种故事走向中，选择一种' in chatbot[-1][0]: chatbot.pop(-1)
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs,
+                chatbot, history_, self.sys_prompt_
+            )
+            self.story.append(story_paragraph)
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # # 构建后续剧情引导
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
+            history_ = []
+            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_,
+                '请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs,
+                chatbot,
+                history_,
+                self.sys_prompt_
+            )
+            self.cur_task = 'user_choice'
+
+
+        elif self.cur_task == 'story_terminate':
+            """
+            根据用户的提示，确定故事的结局
+            """
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs,
+                chatbot, history_, self.sys_prompt_
+            )
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # terminate game
+            self.delete_game = True
+            return

crazy_functions/game_fns/game_utils.py

@@ -0,0 +1,35 @@
+
+from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
+from request_llms.bridge_all import predict_no_ui_long_connection
+def get_code_block(reply):
+    import re
+    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
+    matches = re.findall(pattern, reply) # find all code blocks in text
+    if len(matches) == 1:
+        return "```" + matches[0] + "```" #  code block
+    raise RuntimeError("GPT is not generating proper code.")
+
+def is_same_thing(a, b, llm_kwargs):
+    from pydantic import BaseModel, Field
+    class IsSameThing(BaseModel):
+        is_same_thing: bool = Field(description="determine whether two objects are same thing.", default=False)
+
+    def run_gpt_fn(inputs, sys_prompt, history=[]):
+        return predict_no_ui_long_connection(
+            inputs=inputs, llm_kwargs=llm_kwargs,
+            history=history, sys_prompt=sys_prompt, observe_window=[]
+        )
+
+    gpt_json_io = GptJsonIO(IsSameThing)
+    inputs_01 = "Identity whether the user input and the target is the same thing: \n target object: {a} \n user input object: {b} \n\n\n".format(a=a, b=b)
+    inputs_01 += "\n\n\n Note that the user may describe the target object with a different language, e.g. cat and 猫 are the same thing."
+    analyze_res_cot_01 = run_gpt_fn(inputs_01, "", [])
+
+    inputs_02 = inputs_01 + gpt_json_io.format_instructions
+    analyze_res = run_gpt_fn(inputs_02, "", [inputs_01, analyze_res_cot_01])
+
+    try:
+        res = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
+        return res.is_same_thing
+    except JsonStringError as e:
+        return False

crazy_functions/gen_fns/gen_fns_shared.py

@@ -0,0 +1,70 @@
+import time
+import importlib
+from toolbox import trimmed_format_exc, gen_time_str, get_log_folder
+from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
+from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
+import multiprocessing
+
+def get_class_name(class_string):
+    import re
+    # Use regex to extract the class name
+    class_name = re.search(r'class (\w+)\(', class_string).group(1)
+    return class_name
+
+def try_make_module(code, chatbot):
+    module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
+    fn_path = f'{get_log_folder(plugin_name="gen_plugin_verify")}/{module_file}.py'
+    with open(fn_path, 'w', encoding='utf8') as f: f.write(code)
+    promote_file_to_downloadzone(fn_path, chatbot=chatbot)
+    class_name = get_class_name(code)
+    manager = multiprocessing.Manager()
+    return_dict = manager.dict()
+    p = multiprocessing.Process(target=is_function_successfully_generated, args=(fn_path, class_name, return_dict))
+    # only has 10 seconds to run
+    p.start(); p.join(timeout=10)
+    if p.is_alive(): p.terminate(); p.join()
+    p.close()
+    return return_dict["success"], return_dict['traceback']
+
+# check is_function_successfully_generated
+def is_function_successfully_generated(fn_path, class_name, return_dict):
+    return_dict['success'] = False
+    return_dict['traceback'] = ""
+    try:
+        # Create a spec for the module
+        module_spec = importlib.util.spec_from_file_location('example_module', fn_path)
+        # Load the module
+        example_module = importlib.util.module_from_spec(module_spec)
+        module_spec.loader.exec_module(example_module)
+        # Now you can use the module
+        some_class = getattr(example_module, class_name)
+        # Now you can create an instance of the class
+        instance = some_class()
+        return_dict['success'] = True
+        return
+    except:
+        return_dict['traceback'] = trimmed_format_exc()
+        return
+
+def subprocess_worker(code, file_path, return_dict):
+    return_dict['result'] = None
+    return_dict['success'] = False
+    return_dict['traceback'] = ""
+    try:
+        module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
+        fn_path = f'{get_log_folder(plugin_name="gen_plugin_run")}/{module_file}.py'
+        with open(fn_path, 'w', encoding='utf8') as f: f.write(code)
+        class_name = get_class_name(code)
+        # Create a spec for the module
+        module_spec = importlib.util.spec_from_file_location('example_module', fn_path)
+        # Load the module
+        example_module = importlib.util.module_from_spec(module_spec)
+        module_spec.loader.exec_module(example_module)
+        # Now you can use the module
+        some_class = getattr(example_module, class_name)
+        # Now you can create an instance of the class
+        instance = some_class()
+        return_dict['result'] = instance.run(file_path)
+        return_dict['success'] = True
+    except:
+        return_dict['traceback'] = trimmed_format_exc()

crazy_functions/ipc_fns/mp.py

@@ -0,0 +1,37 @@
+import platform
+import pickle
+import multiprocessing
+
+def run_in_subprocess_wrapper_func(v_args):
+    func, args, kwargs, return_dict, exception_dict = pickle.loads(v_args)
+    import sys
+    try:
+        result = func(*args, **kwargs)
+        return_dict['result'] = result
+    except Exception as e:
+        exc_info = sys.exc_info()
+        exception_dict['exception'] = exc_info
+
+def run_in_subprocess_with_timeout(func, timeout=60):
+    if platform.system() == 'Linux':
+        def wrapper(*args, **kwargs):
+            return_dict = multiprocessing.Manager().dict()
+            exception_dict = multiprocessing.Manager().dict()
+            v_args = pickle.dumps((func, args, kwargs, return_dict, exception_dict))
+            process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, args=(v_args,))
+            process.start()
+            process.join(timeout)
+            if process.is_alive():
+                process.terminate()
+                raise TimeoutError(f'功能单元{str(func)}未能在规定时间内完成任务')
+            process.close()
+            if 'exception' in exception_dict:
+                # ooops, the subprocess ran into an exception
+                exc_info = exception_dict['exception']
+                raise exc_info[1].with_traceback(exc_info[2])
+            if 'result' in return_dict.keys():
+                # If the subprocess ran successfully, return the result
+                return return_dict['result']
+        return wrapper
+    else:
+        return func

crazy_functions/json_fns/pydantic_io.py

@@ -24,8 +24,8 @@ class Actor(BaseModel):
     film_names: List[str] = Field(description="list of names of films they starred in")
 """
 
-import json, re, logging
-
+import json, re
+from loguru import logger as logging
 
 PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
 
@@ -62,8 +62,8 @@ class GptJsonIO():
         if "type" in reduced_schema:
             del reduced_schema["type"]
         # Ensure json in context is well-formed with double quotes.
+        schema_str = json.dumps(reduced_schema)
         if self.example_instruction:
-            schema_str = json.dumps(reduced_schema)
             return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
         else:
             return PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE.format(schema=schema_str)

crazy_functions/json_fns/select_tool.py

@@ -0,0 +1,26 @@
+from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
+
+def structure_output(txt, prompt, err_msg, run_gpt_fn, pydantic_cls):
+    gpt_json_io = GptJsonIO(pydantic_cls)
+    analyze_res = run_gpt_fn(
+        txt, 
+        sys_prompt=prompt + gpt_json_io.format_instructions
+    )
+    try:
+        friend = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
+    except JsonStringError as e:
+        return None, err_msg
+
+    err_msg = ""
+    return friend, err_msg
+
+
+def select_tool(prompt, run_gpt_fn, pydantic_cls):
+    pydantic_cls_instance, err_msg = structure_output(
+        txt=prompt,
+        prompt="根据提示, 分析应该调用哪个工具函数\n\n",
+        err_msg=f"不能理解该联系人",
+        run_gpt_fn=run_gpt_fn,
+        pydantic_cls=pydantic_cls
+    )
+    return pydantic_cls_instance, err_msg

crazy_functions/latex_fns/latex_actions.py

@@ -1,13 +1,17 @@
-from toolbox import update_ui, update_ui_lastest_msg    # 刷新Gradio前端界面
-from toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone
-from .latex_toolbox import PRESERVE, TRANSFORM
-from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
-from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
-from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
-
-import os, shutil
+import os
 import re
+import shutil
 import numpy as np
+from loguru import logger
+from toolbox import update_ui, update_ui_lastest_msg, get_log_folder, gen_time_str
+from toolbox import get_conf, promote_file_to_downloadzone
+from crazy_functions.latex_fns.latex_toolbox import PRESERVE, TRANSFORM
+from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
+from crazy_functions.latex_fns.latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
+from crazy_functions.latex_fns.latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
+from crazy_functions.latex_fns.latex_toolbox import find_title_and_abs
+from crazy_functions.latex_fns.latex_pickle_io import objdump, objload
+
 
 pj = os.path.join
 
@@ -90,7 +94,18 @@ class LatexPaperSplit():
             "项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
         # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
         self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
+        self.title = "unknown"
+        self.abstract = "unknown"
 
+    def read_title_and_abstract(self, txt):
+        try:
+            title, abstract = find_title_and_abs(txt)
+            if title is not None:
+                self.title = title.replace('\n', ' ').replace('\\\\', ' ').replace('  ', '').replace('  ', '')
+            if abstract is not None:
+                self.abstract = abstract.replace('\n', ' ').replace('\\\\', ' ').replace('  ', '').replace('  ', '')
+        except:
+            pass
 
     def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):
         """
@@ -163,9 +178,8 @@ class LatexPaperFileGroup():
         self.sp_file_contents = []
         self.sp_file_index = []
         self.sp_file_tag = []
-
         # count_token
-        from request_llm.bridge_all import model_info
+        from request_llms.bridge_all import model_info
         enc = model_info["gpt-3.5-turbo"]['tokenizer']
         def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
         self.get_token_num = get_token_num
@@ -180,13 +194,12 @@ class LatexPaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)
                     self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-        print('Segmentation: done')
 
     def merge_result(self):
         self.file_result = ["" for _ in range(len(self.file_paths))]
@@ -234,8 +247,8 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
     chatbot.append((f"Latex文件融合完成", f'[Local Message] 正在精细切分latex文件，这需要一段时间计算，文档越长耗时越长，请耐心等待。'))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
     lps = LatexPaperSplit()
+    lps.read_title_and_abstract(merged_content)
     res = lps.split(merged_content, project_folder, opts) # 消耗时间的函数
-
     #  <-------- 拆分过长的latex片段 ---------->
     pfg = LatexPaperFileGroup()
     for index, r in enumerate(res):
@@ -256,12 +269,19 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
 
     else:
         #  <-------- gpt 多线程请求 ---------->
+        history_array = [[""] for _ in range(n_split)]
+        # LATEX_EXPERIMENTAL, = get_conf('LATEX_EXPERIMENTAL')
+        # if LATEX_EXPERIMENTAL:
+        #     paper_meta = f"The paper you processing is `{lps.title}`, a part of the abstraction is `{lps.abstract}`"
+        #     paper_meta_max_len = 888
+        #     history_array = [[ paper_meta[:paper_meta_max_len] + '...',  "Understand, what should I do?"] for _ in range(n_split)]
+
         gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             inputs_array=inputs_array,
             inputs_show_user_array=inputs_show_user_array,
             llm_kwargs=llm_kwargs,
             chatbot=chatbot,
-            history_array=[[""] for _ in range(n_split)],
+            history_array=history_array,
             sys_prompt_array=sys_prompt_array,
             # max_workers=5,  # 并行任务数量限制, 最多同时执行5个, 其他的排队等待
             scroller_max_len = 40
@@ -305,7 +325,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
         buggy_lines = [int(l) for l in buggy_lines]
         buggy_lines = sorted(buggy_lines)
         buggy_line = buggy_lines[0]-1
-        print("reversing tex line that has errors", buggy_line)
+        logger.warning("reversing tex line that has errors", buggy_line)
 
         # 重组，逆转出错的段落
         if buggy_line not in fixed_line:
@@ -319,7 +339,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
 
         return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
     except:
-        print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
+        logger.error("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
         return False, -1, [-1]
 
 
@@ -362,8 +382,8 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
 
             if mode!='translate_zh':
                 yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
-                print(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
-                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
+                logger.info(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
+                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())
 
                 yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
                 ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
@@ -385,7 +405,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
             result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
             promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
         if modified_pdf_success:
-            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
             result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
             origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
             if os.path.exists(pj(work_folder, '..', 'translation')):
@@ -397,8 +417,11 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
                     from .latex_toolbox import merge_pdfs
                     concat_pdf = pj(work_folder_modified, f'comparison.pdf')
                     merge_pdfs(origin_pdf, result_pdf, concat_pdf)
+                    if os.path.exists(pj(work_folder, '..', 'translation')):
+                        shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
                     promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
                 except Exception as e:
+                    logger.error(e)
                     pass
             return True # 成功啦
         else:
@@ -423,7 +446,7 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
     # write html
     try:
         import shutil
-        from ..crazy_utils import construct_html
+        from crazy_functions.pdf_fns.report_gen_html import construct_html
         from toolbox import gen_time_str
         ch = construct_html()
         orig = ""
@@ -439,9 +462,76 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
                 trans = k
                 ch.add_row(a=orig, b=trans)
         create_report_file_name = f"{gen_time_str()}.trans.html"
-        ch.save_file(create_report_file_name)
-        shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))
-        promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)
+        res = ch.save_file(create_report_file_name)
+        shutil.copyfile(res, pj(project_folder, create_report_file_name))
+        promote_file_to_downloadzone(file=res, chatbot=chatbot)
     except:
         from toolbox import trimmed_format_exc
-        print('writing html result failed:', trimmed_format_exc())
+        logger.error('writing html result failed:', trimmed_format_exc())
+
+
+def upload_to_gptac_cloud_if_user_allow(chatbot, arxiv_id):
+    try:
+        # 如果用户允许，我们将arxiv论文PDF上传到GPTAC学术云
+        from toolbox import map_file_to_sha256
+        # 检查是否顺利，如果没有生成预期的文件，则跳过
+        is_result_good = False
+        for file_path in chatbot._cookies.get("files_to_promote", []):
+            if file_path.endswith('translate_zh.pdf'):
+                is_result_good = True
+        if not is_result_good:
+            return
+        # 上传文件
+        for file_path in chatbot._cookies.get("files_to_promote", []):
+            align_name = None
+            # normalized name
+            for name in ['translate_zh.pdf', 'comparison.pdf']:
+                if file_path.endswith(name): align_name = name
+            # if match any align name
+            if align_name:
+                logger.info(f'Uploading to GPTAC cloud as the user has set `allow_cloud_io`: {file_path}')
+                with open(file_path, 'rb') as f:
+                    import requests
+                    url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_upload'
+                    files = {'file': (align_name, f, 'application/octet-stream')}
+                    data = {
+                        'arxiv_id': arxiv_id,
+                        'file_hash': map_file_to_sha256(file_path),
+                        'language': 'zh',
+                        'trans_prompt': 'to_be_implemented',
+                        'llm_model': 'to_be_implemented',
+                        'llm_model_param': 'to_be_implemented',
+                    }
+                    resp = requests.post(url=url, files=files, data=data, timeout=30)
+                logger.info(f'Uploading terminate ({resp.status_code})`: {file_path}')
+    except:
+        # 如果上传失败，不会中断程序，因为这是次要功能
+        pass
+
+def check_gptac_cloud(arxiv_id, chatbot):
+    import requests
+    success = False
+    downloaded = []
+    try:
+        for pdf_target in ['translate_zh.pdf', 'comparison.pdf']:
+            url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_exist'
+            data = {
+                'arxiv_id': arxiv_id,
+                'name': pdf_target,
+            }
+            resp = requests.post(url=url, data=data)
+            cache_hit_result = resp.text.strip('"')
+            if cache_hit_result.startswith("http"):
+                url = cache_hit_result
+                logger.info(f'Downloading from GPTAC cloud: {url}')
+                resp = requests.get(url=url, timeout=30)
+                target = os.path.join(get_log_folder(plugin_name='gptac_cloud'), gen_time_str(), pdf_target)
+                os.makedirs(os.path.dirname(target), exist_ok=True)
+                with open(target, 'wb') as f:
+                    f.write(resp.content)
+                new_path = promote_file_to_downloadzone(target, chatbot=chatbot)
+                success = True
+                downloaded.append(new_path)
+    except:
+        pass
+    return success, downloaded

crazy_functions/latex_fns/latex_pickle_io.py

@@ -0,0 +1,48 @@
+import pickle
+
+
+class SafeUnpickler(pickle.Unpickler):
+
+    def get_safe_classes(self):
+        from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
+        from crazy_functions.latex_fns.latex_toolbox import LinkedListNode
+        from numpy.core.multiarray import scalar
+        from numpy import dtype
+        # 定义允许的安全类
+        safe_classes = {
+            # 在这里添加其他安全的类
+            'LatexPaperFileGroup': LatexPaperFileGroup,
+            'LatexPaperSplit': LatexPaperSplit,
+            'LinkedListNode': LinkedListNode,
+            'scalar': scalar,
+            'dtype': dtype,
+        }
+        return safe_classes
+
+    def find_class(self, module, name):
+        # 只允许特定的类进行反序列化
+        self.safe_classes = self.get_safe_classes()
+        match_class_name = None
+        for class_name in self.safe_classes.keys():
+            if (class_name in f'{module}.{name}'):
+                match_class_name = class_name
+        if match_class_name is not None:
+            return self.safe_classes[match_class_name]
+        # 如果尝试加载未授权的类，则抛出异常
+        raise pickle.UnpicklingError(f"Attempted to deserialize unauthorized class '{name}' from module '{module}'")
+
+def objdump(obj, file="objdump.tmp"):
+
+    with open(file, "wb+") as f:
+        pickle.dump(obj, f)
+    return
+
+
+def objload(file="objdump.tmp"):
+    import os
+
+    if not os.path.exists(file):
+        return
+    with open(file, "rb") as f:
+        unpickler = SafeUnpickler(f)
+        return unpickler.load()

crazy_functions/latex_fns/latex_toolbox.py

@@ -1,15 +1,20 @@
-import os, shutil
+import os
 import re
+import shutil
 import numpy as np
+from loguru import logger
+
 PRESERVE = 0
 TRANSFORM = 1
 
 pj = os.path.join
 
-class LinkedListNode():
+
+class LinkedListNode:
     """
     Linked List Node
     """
+
     def __init__(self, string, preserve=True) -> None:
         self.string = string
         self.preserve = preserve
@@ -18,19 +23,22 @@ class LinkedListNode():
         # self.begin_line = 0
         # self.begin_char = 0
 
+
 def convert_to_linklist(text, mask):
     root = LinkedListNode("", preserve=True)
     current_node = root
     for c, m, i in zip(text, mask, range(len(text))):
-        if (m==PRESERVE and current_node.preserve) \
-            or (m==TRANSFORM and not current_node.preserve):
+        if (m == PRESERVE and current_node.preserve) or (
+            m == TRANSFORM and not current_node.preserve
+        ):
             # add
             current_node.string += c
         else:
-            current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
+            current_node.next = LinkedListNode(c, preserve=(m == PRESERVE))
             current_node = current_node.next
     return root
 
+
 def post_process(root):
     # 修复括号
     node = root
@@ -38,21 +46,24 @@ def post_process(root):
         string = node.string
         if node.preserve:
             node = node.next
-            if node is None: break
+            if node is None:
+                break
             continue
+
         def break_check(string):
-            str_stack = [""] # (lv, index)
+            str_stack = [""]  # (lv, index)
             for i, c in enumerate(string):
-                if c == '{':
-                    str_stack.append('{')
-                elif c == '}':
+                if c == "{":
+                    str_stack.append("{")
+                elif c == "}":
                     if len(str_stack) == 1:
-                        print('stack fix')
+                        logger.warning("fixing brace error")
                         return i
                     str_stack.pop(-1)
                 else:
                     str_stack[-1] += c
             return -1
+
         bp = break_check(string)
 
         if bp == -1:
@@ -69,51 +80,66 @@ def post_process(root):
             node.next = q
 
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 屏蔽空行和太短的句子
     node = root
     while True:
-        if len(node.string.strip('\n').strip(''))==0: node.preserve = True
-        if len(node.string.strip('\n').strip(''))<42: node.preserve = True
+        if len(node.string.strip("\n").strip("")) == 0:
+            node.preserve = True
+        if len(node.string.strip("\n").strip("")) < 42:
+            node.preserve = True
         node = node.next
-        if node is None: break
+        if node is None:
+            break
     node = root
     while True:
         if node.next and node.preserve and node.next.preserve:
             node.string += node.next.string
             node.next = node.next.next
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 将前后断行符脱离
     node = root
     prev_node = None
     while True:
         if not node.preserve:
-            lstriped_ = node.string.lstrip().lstrip('\n')
-            if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
-                prev_node.string += node.string[:-len(lstriped_)]
+            lstriped_ = node.string.lstrip().lstrip("\n")
+            if (
+                (prev_node is not None)
+                and (prev_node.preserve)
+                and (len(lstriped_) != len(node.string))
+            ):
+                prev_node.string += node.string[: -len(lstriped_)]
                 node.string = lstriped_
-            rstriped_ = node.string.rstrip().rstrip('\n')
-            if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
-                node.next.string = node.string[len(rstriped_):] + node.next.string
+            rstriped_ = node.string.rstrip().rstrip("\n")
+            if (
+                (node.next is not None)
+                and (node.next.preserve)
+                and (len(rstriped_) != len(node.string))
+            ):
+                node.next.string = node.string[len(rstriped_) :] + node.next.string
                 node.string = rstriped_
-        # =====
+        # =-=-=
         prev_node = node
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 标注节点的行数范围
     node = root
     n_line = 0
     expansion = 2
     while True:
-        n_l = node.string.count('\n')
-        node.range = [n_line-expansion, n_line+n_l+expansion]   # 失败时，扭转的范围
-        n_line = n_line+n_l
+        n_l = node.string.count("\n")
+        node.range = [n_line - expansion, n_line + n_l + expansion]  # 失败时，扭转的范围
+        n_line = n_line + n_l
         node = node.next
-        if node is None: break
+        if node is None:
+            break
     return root
 
 
@@ -131,12 +157,14 @@ def set_forbidden_text(text, mask, pattern, flags=0):
     you can mask out (mask = PRESERVE so that text become untouchable for GPT)
     everything between "\begin{equation}" and "\end{equation}"
     """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    if isinstance(pattern, list):
+        pattern = "|".join(pattern)
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
-        mask[res.span()[0]:res.span()[1]] = PRESERVE
+        mask[res.span()[0] : res.span()[1]] = PRESERVE
     return text, mask
 
+
 def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
     """
     Move area out of preserve area (make text editable for GPT)
@@ -144,17 +172,19 @@ def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
     e.g.
     \begin{abstract} blablablablablabla. \end{abstract}
     """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    if isinstance(pattern, list):
+        pattern = "|".join(pattern)
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
         if not forbid_wrapper:
-            mask[res.span()[0]:res.span()[1]] = TRANSFORM
+            mask[res.span()[0] : res.span()[1]] = TRANSFORM
         else:
-            mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE   # '\\begin{abstract}'
-            mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM   # abstract
-            mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE   # abstract
+            mask[res.regs[0][0] : res.regs[1][0]] = PRESERVE  # '\\begin{abstract}'
+            mask[res.regs[1][0] : res.regs[1][1]] = TRANSFORM  # abstract
+            mask[res.regs[1][1] : res.regs[0][1]] = PRESERVE  # abstract
     return text, mask
 
+
 def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
     """
     Add a preserve text area in this paper (text become untouchable for GPT).
@@ -166,16 +196,22 @@ def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
     for res in pattern_compile.finditer(text):
         brace_level = -1
         p = begin = end = res.regs[0][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
+        for _ in range(1024 * 16):
+            if text[p] == "}" and brace_level == 0:
+                break
+            elif text[p] == "}":
+                brace_level -= 1
+            elif text[p] == "{":
+                brace_level += 1
             p += 1
-        end = p+1
+        end = p + 1
         mask[begin:end] = PRESERVE
     return text, mask
 
-def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
+
+def reverse_forbidden_text_careful_brace(
+    text, mask, pattern, flags=0, forbid_wrapper=True
+):
     """
     Move area out of preserve area (make text editable for GPT)
     count the number of the braces so as to catch compelete text area.
@@ -186,39 +222,57 @@ def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wr
     for res in pattern_compile.finditer(text):
         brace_level = 0
         p = begin = end = res.regs[1][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
+        for _ in range(1024 * 16):
+            if text[p] == "}" and brace_level == 0:
+                break
+            elif text[p] == "}":
+                brace_level -= 1
+            elif text[p] == "{":
+                brace_level += 1
             p += 1
         end = p
         mask[begin:end] = TRANSFORM
         if forbid_wrapper:
-            mask[res.regs[0][0]:begin] = PRESERVE
-            mask[end:res.regs[0][1]] = PRESERVE
+            mask[res.regs[0][0] : begin] = PRESERVE
+            mask[end : res.regs[0][1]] = PRESERVE
     return text, mask
 
+
 def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
     """
     Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
     Add it to preserve area
     """
     pattern_compile = re.compile(pattern, flags)
+
     def search_with_line_limit(text, mask):
         for res in pattern_compile.finditer(text):
             cmd = res.group(1)  # begin{what}
-            this = res.group(2) # content between begin and end
-            this_mask = mask[res.regs[2][0]:res.regs[2][1]]
-            white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof', 
-                          'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
-            if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
+            this = res.group(2)  # content between begin and end
+            this_mask = mask[res.regs[2][0] : res.regs[2][1]]
+            white_list = [
+                "document",
+                "abstract",
+                "lemma",
+                "definition",
+                "sproof",
+                "em",
+                "emph",
+                "textit",
+                "textbf",
+                "itemize",
+                "enumerate",
+            ]
+            if (cmd in white_list) or this.count(
+                "\n"
+            ) >= limit_n_lines:  # use a magical number 42
                 this, this_mask = search_with_line_limit(this, this_mask)
-                mask[res.regs[2][0]:res.regs[2][1]] = this_mask
+                mask[res.regs[2][0] : res.regs[2][1]] = this_mask
             else:
-                mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
+                mask[res.regs[0][0] : res.regs[0][1]] = PRESERVE
         return text, mask
-    return search_with_line_limit(text, mask) 
 
+    return search_with_line_limit(text, mask)
 
 
 """
@@ -227,6 +281,7 @@ Latex Merge File
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 """
 
+
 def find_main_tex_file(file_manifest, mode):
     """
     在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
@@ -234,37 +289,48 @@ def find_main_tex_file(file_manifest, mode):
     """
     canidates = []
     for texf in file_manifest:
-        if os.path.basename(texf).startswith('merge'):
+        if os.path.basename(texf).startswith("merge"):
             continue
-        with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+        with open(texf, "r", encoding="utf8", errors="ignore") as f:
             file_content = f.read()
-        if r'\documentclass' in file_content:
+        if r"\documentclass" in file_content:
             canidates.append(texf)
         else:
             continue
 
     if len(canidates) == 0:
-        raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
+        raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
     elif len(canidates) == 1:
         return canidates[0]
-    else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
+    else:  # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
         canidates_score = []
         # 给出一些判定模板文档的词作为扣分项
-        unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
-        expected_words = ['\input', '\ref', '\cite']
+        unexpected_words = [
+            "\\LaTeX",
+            "manuscript",
+            "Guidelines",
+            "font",
+            "citations",
+            "rejected",
+            "blind review",
+            "reviewers",
+        ]
+        expected_words = ["\\input", "\\ref", "\\cite"]
         for texf in canidates:
             canidates_score.append(0)
-            with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+            with open(texf, "r", encoding="utf8", errors="ignore") as f:
                 file_content = f.read()
+                file_content = rm_comments(file_content)
             for uw in unexpected_words:
                 if uw in file_content:
                     canidates_score[-1] -= 1
             for uw in expected_words:
                 if uw in file_content:
                     canidates_score[-1] += 1
-        select = np.argmax(canidates_score) # 取评分最高者返回
+        select = np.argmax(canidates_score)  # 取评分最高者返回
         return canidates[select]
 
+
 def rm_comments(main_file):
     new_file_remove_comment_lines = []
     for l in main_file.splitlines():
@@ -273,26 +339,39 @@ def rm_comments(main_file):
             pass
         else:
             new_file_remove_comment_lines.append(l)
-    main_file = '\n'.join(new_file_remove_comment_lines)
+    main_file = "\n".join(new_file_remove_comment_lines)
     # main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file)  # 将 \include 命令转换为 \input 命令
-    main_file = re.sub(r'(?<!\\)%.*', '', main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
+    main_file = re.sub(r"(?<!\\)%.*", "", main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
     return main_file
 
+
 def find_tex_file_ignore_case(fp):
     dir_name = os.path.dirname(fp)
     base_name = os.path.basename(fp)
     # 如果输入的文件路径是正确的
-    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    if os.path.isfile(pj(dir_name, base_name)):
+        return pj(dir_name, base_name)
     # 如果不正确，试着加上.tex后缀试试
-    if not base_name.endswith('.tex'): base_name+='.tex'
-    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    if not base_name.endswith(".tex"):
+        base_name += ".tex"
+    if os.path.isfile(pj(dir_name, base_name)):
+        return pj(dir_name, base_name)
     # 如果还找不到，解除大小写限制，再试一次
     import glob
-    for f in glob.glob(dir_name+'/*.tex'):
+
+    for f in glob.glob(dir_name + "/*.tex"):
         base_name_s = os.path.basename(fp)
-        if base_name_s.lower() == base_name.lower(): return f
+        base_name_f = os.path.basename(f)
+        if base_name_s.lower() == base_name_f.lower():
+            return f
+        # 试着加上.tex后缀试试
+        if not base_name_s.endswith(".tex"):
+            base_name_s += ".tex"
+        if base_name_s.lower() == base_name_f.lower():
+            return f
     return None
 
+
 def merge_tex_files_(project_foler, main_file, mode):
     """
     Merge Tex project recrusively
@@ -301,15 +380,53 @@ def merge_tex_files_(project_foler, main_file, mode):
     for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
         f = s.group(1)
         fp = os.path.join(project_foler, f)
-        fp = find_tex_file_ignore_case(fp)
-        if fp:
-            with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
+        fp_ = find_tex_file_ignore_case(fp)
+        if fp_:
+            try:
+                with open(fp_, "r", encoding="utf-8", errors="replace") as fx:
+                    c = fx.read()
+            except:
+                c = f"\n\nWarning from GPT-Academic: LaTex source file is missing!\n\n"
         else:
-            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
+            raise RuntimeError(f"找不到{fp}，Tex源文件缺失！")
         c = merge_tex_files_(project_foler, c, mode)
-        main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
+        main_file = main_file[: s.span()[0]] + c + main_file[s.span()[1] :]
     return main_file
 
+
+def find_title_and_abs(main_file):
+    def extract_abstract_1(text):
+        pattern = r"\\abstract\{(.*?)\}"
+        match = re.search(pattern, text, re.DOTALL)
+        if match:
+            return match.group(1)
+        else:
+            return None
+
+    def extract_abstract_2(text):
+        pattern = r"\\begin\{abstract\}(.*?)\\end\{abstract\}"
+        match = re.search(pattern, text, re.DOTALL)
+        if match:
+            return match.group(1)
+        else:
+            return None
+
+    def extract_title(string):
+        pattern = r"\\title\{(.*?)\}"
+        match = re.search(pattern, string, re.DOTALL)
+
+        if match:
+            return match.group(1)
+        else:
+            return None
+
+    abstract = extract_abstract_1(main_file)
+    if abstract is None:
+        abstract = extract_abstract_2(main_file)
+    title = extract_title(main_file)
+    return title, abstract
+
+
 def merge_tex_files(project_foler, main_file, mode):
     """
     Merge Tex project recrusively
@@ -319,33 +436,91 @@ def merge_tex_files(project_foler, main_file, mode):
     main_file = merge_tex_files_(project_foler, main_file, mode)
     main_file = rm_comments(main_file)
 
-    if mode == 'translate_zh':
+    if mode == "translate_zh":
         # find paper documentclass
-        pattern = re.compile(r'\\documentclass.*\n')
+        pattern = re.compile(r"\\documentclass.*\n")
         match = pattern.search(main_file)
         assert match is not None, "Cannot find documentclass statement!"
         position = match.end()
-        add_ctex = '\\usepackage{ctex}\n'
-        add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
+        add_ctex = "\\usepackage{ctex}\n"
+        add_url = "\\usepackage{url}\n" if "{url}" not in main_file else ""
         main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
         # fontset=windows
         import platform
-        main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
-        main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
+
+        main_file = re.sub(
+            r"\\documentclass\[(.*?)\]{(.*?)}",
+            r"\\documentclass[\1,fontset=windows,UTF8]{\2}",
+            main_file,
+        )
+        main_file = re.sub(
+            r"\\documentclass{(.*?)}",
+            r"\\documentclass[fontset=windows,UTF8]{\1}",
+            main_file,
+        )
         # find paper abstract
-        pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
+        pattern_opt1 = re.compile(r"\\begin\{abstract\}.*\n")
         pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
         match_opt1 = pattern_opt1.search(main_file)
         match_opt2 = pattern_opt2.search(main_file)
-        assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
+        if (match_opt1 is None) and (match_opt2 is None):
+            # "Cannot find paper abstract section!"
+            main_file = insert_abstract(main_file)
+        match_opt1 = pattern_opt1.search(main_file)
+        match_opt2 = pattern_opt2.search(main_file)
+        assert (match_opt1 is not None) or (
+            match_opt2 is not None
+        ), "Cannot find paper abstract section!"
     return main_file
 
 
+insert_missing_abs_str = r"""
+\begin{abstract}
+The GPT-Academic program cannot find abstract section in this paper.
+\end{abstract}
+"""
+
+
+def insert_abstract(tex_content):
+    if "\\maketitle" in tex_content:
+        # find the position of "\maketitle"
+        find_index = tex_content.index("\\maketitle")
+        # find the nearest ending line
+        end_line_index = tex_content.find("\n", find_index)
+        # insert "abs_str" on the next line
+        modified_tex = (
+            tex_content[: end_line_index + 1]
+            + "\n\n"
+            + insert_missing_abs_str
+            + "\n\n"
+            + tex_content[end_line_index + 1 :]
+        )
+        return modified_tex
+    elif r"\begin{document}" in tex_content:
+        # find the position of "\maketitle"
+        find_index = tex_content.index(r"\begin{document}")
+        # find the nearest ending line
+        end_line_index = tex_content.find("\n", find_index)
+        # insert "abs_str" on the next line
+        modified_tex = (
+            tex_content[: end_line_index + 1]
+            + "\n\n"
+            + insert_missing_abs_str
+            + "\n\n"
+            + tex_content[end_line_index + 1 :]
+        )
+        return modified_tex
+    else:
+        return tex_content
+
+
 """
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 Post process
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 """
+
+
 def mod_inbraket(match):
     """
     为啥chatgpt会把cite里面的逗号换成中文逗号呀
@@ -354,11 +529,12 @@ def mod_inbraket(match):
     cmd = match.group(1)
     str_to_modify = match.group(2)
     # modify the matched string
-    str_to_modify = str_to_modify.replace('：', ':')    # 前面是中文冒号，后面是英文冒号
-    str_to_modify = str_to_modify.replace('，', ',')    # 前面是中文逗号，后面是英文逗号
+    str_to_modify = str_to_modify.replace("：", ":")  # 前面是中文冒号，后面是英文冒号
+    str_to_modify = str_to_modify.replace("，", ",")  # 前面是中文逗号，后面是英文逗号
     # str_to_modify = 'BOOM'
     return "\\" + cmd + "{" + str_to_modify + "}"
 
+
 def fix_content(final_tex, node_string):
     """
     Fix common GPT errors to increase success rate
@@ -369,10 +545,10 @@ def fix_content(final_tex, node_string):
     final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
 
     if "Traceback" in final_tex and "[Local Message]" in final_tex:
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\\begin') != final_tex.count('\\begin'):
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
+        final_tex = node_string  # 出问题了，还原原文
+    if node_string.count("\\begin") != final_tex.count("\\begin"):
+        final_tex = node_string  # 出问题了，还原原文
+    if node_string.count("\_") > 0 and node_string.count("\_") > final_tex.count("\_"):
         # walk and replace any _ without \
         final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
 
@@ -380,24 +556,32 @@ def fix_content(final_tex, node_string):
         # this function count the number of { and }
         brace_level = 0
         for c in string:
-            if c == "{": brace_level += 1
-            elif c == "}": brace_level -= 1
+            if c == "{":
+                brace_level += 1
+            elif c == "}":
+                brace_level -= 1
         return brace_level
+
     def join_most(tex_t, tex_o):
         # this function join translated string and original string when something goes wrong
         p_t = 0
         p_o = 0
+
         def find_next(string, chars, begin):
             p = begin
             while p < len(string):
-                if string[p] in chars: return p, string[p]
+                if string[p] in chars:
+                    return p, string[p]
                 p += 1
             return None, None
+
         while True:
-            res1, char = find_next(tex_o, ['{','}'], p_o)
-            if res1 is None: break
+            res1, char = find_next(tex_o, ["{", "}"], p_o)
+            if res1 is None:
+                break
             res2, char = find_next(tex_t, [char], p_t)
-            if res2 is None: break
+            if res2 is None:
+                break
             p_o = res1 + 1
             p_t = res2 + 1
         return tex_t[:p_t] + tex_o[p_o:]
@@ -407,28 +591,78 @@ def fix_content(final_tex, node_string):
         final_tex = join_most(final_tex, node_string)
     return final_tex
 
+
 def compile_latex_with_timeout(command, cwd, timeout=60):
     import subprocess
-    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
+
+    process = subprocess.Popen(
+        command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd
+    )
     try:
         stdout, stderr = process.communicate(timeout=timeout)
     except subprocess.TimeoutExpired:
         process.kill()
         stdout, stderr = process.communicate()
-        print("Process timed out!")
+        logger.error("Process timed out (compile_latex_with_timeout)!")
         return False
     return True
 
 
+def run_in_subprocess_wrapper_func(func, args, kwargs, return_dict, exception_dict):
+    import sys
 
-def merge_pdfs(pdf1_path, pdf2_path, output_path):
-    import PyPDF2
-    Percent = 0.8
+    try:
+        result = func(*args, **kwargs)
+        return_dict["result"] = result
+    except Exception as e:
+        exc_info = sys.exc_info()
+        exception_dict["exception"] = exc_info
+
+
+def run_in_subprocess(func):
+    import multiprocessing
+
+    def wrapper(*args, **kwargs):
+        return_dict = multiprocessing.Manager().dict()
+        exception_dict = multiprocessing.Manager().dict()
+        process = multiprocessing.Process(
+            target=run_in_subprocess_wrapper_func,
+            args=(func, args, kwargs, return_dict, exception_dict),
+        )
+        process.start()
+        process.join()
+        process.close()
+        if "exception" in exception_dict:
+            # ooops, the subprocess ran into an exception
+            exc_info = exception_dict["exception"]
+            raise exc_info[1].with_traceback(exc_info[2])
+        if "result" in return_dict.keys():
+            # If the subprocess ran successfully, return the result
+            return return_dict["result"]
+
+    return wrapper
+
+
+def _merge_pdfs(pdf1_path, pdf2_path, output_path):
+    try:
+        logger.info("Merging PDFs using _merge_pdfs_ng")
+        _merge_pdfs_ng(pdf1_path, pdf2_path, output_path)
+    except:
+        logger.info("Merging PDFs using _merge_pdfs_legacy")
+        _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path)
+
+
+def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
+    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
+    from PyPDF2.generic import NameObject, TextStringObject, ArrayObject, FloatObject, NumberObject
+
+    Percent = 1
+    # raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
     # Open the first PDF file
-    with open(pdf1_path, 'rb') as pdf1_file:
+    with open(pdf1_path, "rb") as pdf1_file:
         pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
         # Open the second PDF file
-        with open(pdf2_path, 'rb') as pdf2_file:
+        with open(pdf2_path, "rb") as pdf2_file:
             pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
             # Create a new PDF file to store the merged pages
             output_writer = PyPDF2.PdfFileWriter()
@@ -448,12 +682,225 @@ def merge_pdfs(pdf1_path, pdf2_path, output_path):
                     page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
                 # Create a new empty page with double width
                 new_page = PyPDF2.PageObject.createBlankPage(
-                    width = int(int(page1.mediaBox.getWidth()) + int(page2.mediaBox.getWidth()) * Percent),
-                    height = max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight())
+                    width=int(
+                        int(page1.mediaBox.getWidth())
+                        + int(page2.mediaBox.getWidth()) * Percent
+                    ),
+                    height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
                 )
                 new_page.mergeTranslatedPage(page1, 0, 0)
-                new_page.mergeTranslatedPage(page2, int(int(page1.mediaBox.getWidth())-int(page2.mediaBox.getWidth())* (1-Percent)), 0)
+                new_page.mergeTranslatedPage(
+                    page2,
+                    int(
+                        int(page1.mediaBox.getWidth())
+                        - int(page2.mediaBox.getWidth()) * (1 - Percent)
+                    ),
+                    0,
+                )
+                if "/Annots" in new_page:
+                    annotations = new_page["/Annots"]
+                    for i, annot in enumerate(annotations):
+                        annot_obj = annot.get_object()
+
+                        # 检查注释类型是否是链接（/Link）
+                        if annot_obj.get("/Subtype") == "/Link":
+                            # 检查是否为内部链接跳转（/GoTo）或外部URI链接（/URI）
+                            action = annot_obj.get("/A")
+                            if action:
+
+                                if "/S" in action and action["/S"] == "/GoTo":
+                                    # 内部链接：跳转到文档中的某个页面
+                                    dest = action.get("/D")  # 目标页或目标位置
+                                    # if dest and annot.idnum in page2_annot_id:
+                                    # if dest in pdf2_reader.named_destinations:
+                                    if dest and page2.annotations:
+                                        if annot in page2.annotations:
+                                            # 获取原始文件中跳转信息，包括跳转页面
+                                            destination = pdf2_reader.named_destinations[
+                                                dest
+                                            ]
+                                            page_number = (
+                                                pdf2_reader.get_destination_page_number(
+                                                    destination
+                                                )
+                                            )
+                                            # 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
+                                            # “/D”:[10,'/XYZ',100,100,0]
+                                            if destination.dest_array[1] == "/XYZ":
+                                                annot_obj["/A"].update(
+                                                    {
+                                                        NameObject("/D"): ArrayObject(
+                                                            [
+                                                                NumberObject(page_number),
+                                                                destination.dest_array[1],
+                                                                FloatObject(
+                                                                    destination.dest_array[
+                                                                        2
+                                                                    ]
+                                                                    + int(
+                                                                        page1.mediaBox.getWidth()
+                                                                    )
+                                                                ),
+                                                                destination.dest_array[3],
+                                                                destination.dest_array[4],
+                                                            ]
+                                                        )  # 确保键和值是 PdfObject
+                                                    }
+                                                )
+                                            else:
+                                                annot_obj["/A"].update(
+                                                    {
+                                                        NameObject("/D"): ArrayObject(
+                                                            [
+                                                                NumberObject(page_number),
+                                                                destination.dest_array[1],
+                                                            ]
+                                                        )  # 确保键和值是 PdfObject
+                                                    }
+                                                )
+
+                                            rect = annot_obj.get("/Rect")
+                                            # 更新点击坐标
+                                            rect = ArrayObject(
+                                                [
+                                                    FloatObject(
+                                                        rect[0]
+                                                        + int(page1.mediaBox.getWidth())
+                                                    ),
+                                                    rect[1],
+                                                    FloatObject(
+                                                        rect[2]
+                                                        + int(page1.mediaBox.getWidth())
+                                                    ),
+                                                    rect[3],
+                                                ]
+                                            )
+                                            annot_obj.update(
+                                                {
+                                                    NameObject(
+                                                        "/Rect"
+                                                    ): rect  # 确保键和值是 PdfObject
+                                                }
+                                            )
+                                    # if dest and annot.idnum in page1_annot_id:
+                                    # if dest in pdf1_reader.named_destinations:
+                                    if dest and page1.annotations:
+                                        if annot in page1.annotations:
+                                            # 获取原始文件中跳转信息，包括跳转页面
+                                            destination = pdf1_reader.named_destinations[
+                                                dest
+                                            ]
+                                            page_number = (
+                                                pdf1_reader.get_destination_page_number(
+                                                    destination
+                                                )
+                                            )
+                                            # 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
+                                            # “/D”:[10,'/XYZ',100,100,0]
+                                            if destination.dest_array[1] == "/XYZ":
+                                                annot_obj["/A"].update(
+                                                    {
+                                                        NameObject("/D"): ArrayObject(
+                                                            [
+                                                                NumberObject(page_number),
+                                                                destination.dest_array[1],
+                                                                FloatObject(
+                                                                    destination.dest_array[
+                                                                        2
+                                                                    ]
+                                                                ),
+                                                                destination.dest_array[3],
+                                                                destination.dest_array[4],
+                                                            ]
+                                                        )  # 确保键和值是 PdfObject
+                                                    }
+                                                )
+                                            else:
+                                                annot_obj["/A"].update(
+                                                    {
+                                                        NameObject("/D"): ArrayObject(
+                                                            [
+                                                                NumberObject(page_number),
+                                                                destination.dest_array[1],
+                                                            ]
+                                                        )  # 确保键和值是 PdfObject
+                                                    }
+                                                )
+
+                                            rect = annot_obj.get("/Rect")
+                                            rect = ArrayObject(
+                                                [
+                                                    FloatObject(rect[0]),
+                                                    rect[1],
+                                                    FloatObject(rect[2]),
+                                                    rect[3],
+                                                ]
+                                            )
+                                            annot_obj.update(
+                                                {
+                                                    NameObject(
+                                                        "/Rect"
+                                                    ): rect  # 确保键和值是 PdfObject
+                                                }
+                                            )
+
+                                elif "/S" in action and action["/S"] == "/URI":
+                                    # 外部链接：跳转到某个URI
+                                    uri = action.get("/URI")
                 output_writer.addPage(new_page)
             # Save the merged PDF file
-            with open(output_path, 'wb') as output_file:
+            with open(output_path, "wb") as output_file:
                 output_writer.write(output_file)
+
+
+def _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path):
+    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
+
+    Percent = 0.95
+    # raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
+    # Open the first PDF file
+    with open(pdf1_path, "rb") as pdf1_file:
+        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
+        # Open the second PDF file
+        with open(pdf2_path, "rb") as pdf2_file:
+            pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
+            # Create a new PDF file to store the merged pages
+            output_writer = PyPDF2.PdfFileWriter()
+            # Determine the number of pages in each PDF file
+            num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
+            # Merge the pages from the two PDF files
+            for page_num in range(num_pages):
+                # Add the page from the first PDF file
+                if page_num < pdf1_reader.numPages:
+                    page1 = pdf1_reader.getPage(page_num)
+                else:
+                    page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
+                # Add the page from the second PDF file
+                if page_num < pdf2_reader.numPages:
+                    page2 = pdf2_reader.getPage(page_num)
+                else:
+                    page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
+                # Create a new empty page with double width
+                new_page = PyPDF2.PageObject.createBlankPage(
+                    width=int(
+                        int(page1.mediaBox.getWidth())
+                        + int(page2.mediaBox.getWidth()) * Percent
+                    ),
+                    height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
+                )
+                new_page.mergeTranslatedPage(page1, 0, 0)
+                new_page.mergeTranslatedPage(
+                    page2,
+                    int(
+                        int(page1.mediaBox.getWidth())
+                        - int(page2.mediaBox.getWidth()) * (1 - Percent)
+                    ),
+                    0,
+                )
+                output_writer.addPage(new_page)
+            # Save the merged PDF file
+            with open(output_path, "wb") as output_file:
+                output_writer.write(output_file)
+
+
+merge_pdfs = run_in_subprocess(_merge_pdfs)  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放

crazy_functions/live_audio/aliyunASR.py

@@ -1,25 +1,124 @@
-import time, threading, json
+import time, json, sys, struct
+import numpy as np
+from loguru import logger as logging
+from scipy.io.wavfile import WAVE_FORMAT
+
+def write_numpy_to_wave(filename, rate, data, add_header=False):
+    """
+    Write a NumPy array as a WAV file.
+    """
+    def _array_tofile(fid, data):
+        # ravel gives a c-contiguous buffer
+        fid.write(data.ravel().view('b').data)
+
+    if hasattr(filename, 'write'):
+        fid = filename
+    else:
+        fid = open(filename, 'wb')
+
+    fs = rate
+
+    try:
+        dkind = data.dtype.kind
+        if not (dkind == 'i' or dkind == 'f' or (dkind == 'u' and
+                                                 data.dtype.itemsize == 1)):
+            raise ValueError("Unsupported data type '%s'" % data.dtype)
+
+        header_data = b''
+
+        header_data += b'RIFF'
+        header_data += b'\x00\x00\x00\x00'
+        header_data += b'WAVE'
+
+        # fmt chunk
+        header_data += b'fmt '
+        if dkind == 'f':
+            format_tag = WAVE_FORMAT.IEEE_FLOAT
+        else:
+            format_tag = WAVE_FORMAT.PCM
+        if data.ndim == 1:
+            channels = 1
+        else:
+            channels = data.shape[1]
+        bit_depth = data.dtype.itemsize * 8
+        bytes_per_second = fs*(bit_depth // 8)*channels
+        block_align = channels * (bit_depth // 8)
+
+        fmt_chunk_data = struct.pack('<HHIIHH', format_tag, channels, fs,
+                                     bytes_per_second, block_align, bit_depth)
+        if not (dkind == 'i' or dkind == 'u'):
+            # add cbSize field for non-PCM files
+            fmt_chunk_data += b'\x00\x00'
+
+        header_data += struct.pack('<I', len(fmt_chunk_data))
+        header_data += fmt_chunk_data
+
+        # fact chunk (non-PCM files)
+        if not (dkind == 'i' or dkind == 'u'):
+            header_data += b'fact'
+            header_data += struct.pack('<II', 4, data.shape[0])
+
+        # check data size (needs to be immediately before the data chunk)
+        if ((len(header_data)-4-4) + (4+4+data.nbytes)) > 0xFFFFFFFF:
+            raise ValueError("Data exceeds wave file size limit")
+        if add_header:
+            fid.write(header_data)
+            # data chunk
+            fid.write(b'data')
+            fid.write(struct.pack('<I', data.nbytes))
+            if data.dtype.byteorder == '>' or (data.dtype.byteorder == '=' and
+                                            sys.byteorder == 'big'):
+                data = data.byteswap()
+        _array_tofile(fid, data)
+
+        if add_header:
+            # Determine file size and place it in correct
+            #  position at start of the file.
+            size = fid.tell()
+            fid.seek(4)
+            fid.write(struct.pack('<I', size-8))
+
+    finally:
+        if not hasattr(filename, 'write'):
+            fid.close()
+        else:
+            fid.seek(0)
+
+def is_speaker_speaking(vad, data, sample_rate):
+    # Function to detect if the speaker is speaking
+    # The WebRTC VAD only accepts 16-bit mono PCM audio,
+    # sampled at 8000, 16000, 32000 or 48000 Hz.
+    # A frame must be either 10, 20, or 30 ms in duration:
+    frame_duration = 30
+    n_bit_each = int(sample_rate * frame_duration / 1000)*2 # x2 because audio is 16 bit (2 bytes)
+    res_list = []
+    for t in range(len(data)):
+        if t!=0 and t % n_bit_each == 0:
+            res_list.append(vad.is_speech(data[t-n_bit_each:t], sample_rate))
+
+    info = ''.join(['^' if r else '.' for r in res_list])
+    info = info[:10]
+    if any(res_list):
+        return True, info
+    else:
+        return False, info
 
 
 class AliyunASR():
 
     def test_on_sentence_begin(self, message, *args):
-        # print("test_on_sentence_begin:{}".format(message))
         pass
 
     def test_on_sentence_end(self, message, *args):
-        # print("test_on_sentence_end:{}".format(message))
         message = json.loads(message)
         self.parsed_sentence = message['payload']['result']
         self.event_on_entence_end.set()
-        print(self.parsed_sentence)
 
     def test_on_start(self, message, *args):
-        # print("test_on_start:{}".format(message))
         pass
 
     def test_on_error(self, message, *args):
-        print("on_error args=>{}".format(args))
+        logging.error("on_error args=>{}".format(args))
         pass
 
     def test_on_close(self, *args):
@@ -27,16 +126,13 @@ class AliyunASR():
         pass
 
     def test_on_result_chg(self, message, *args):
-        # print("test_on_chg:{}".format(message))
         message = json.loads(message)
         self.parsed_text = message['payload']['result']
         self.event_on_result_chg.set()
 
     def test_on_completed(self, message, *args):
-        # print("on_completed:args=>{} message=>{}".format(args, message))
         pass
 
-
     def audio_convertion_thread(self, uuid):
         # 在一个异步线程中采集音频
         import nls  # pip install git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
@@ -67,12 +163,22 @@ class AliyunASR():
                     on_close=self.test_on_close,
                     callback_args=[uuid.hex]
                 )
-
+        timeout_limit_second = 20
         r = sr.start(aformat="pcm",
+                timeout=timeout_limit_second,
                 enable_intermediate_result=True,
                 enable_punctuation_prediction=True,
                 enable_inverse_text_normalization=True)
 
+        import webrtcvad
+        vad = webrtcvad.Vad()
+        vad.set_mode(1)
+
+        is_previous_frame_transmitted = False   # 上一帧是否有人说话
+        previous_frame_data = None
+        echo_cnt = 0        # 在没有声音之后，继续向服务器发送n次音频数据
+        echo_cnt_max = 4   # 在没有声音之后，继续向服务器发送n次音频数据
+        keep_alive_last_send_time = time.time()
         while not self.stop:
             # time.sleep(self.capture_interval)
             audio = rad.read(uuid.hex)
@@ -80,12 +186,32 @@ class AliyunASR():
                 # convert to pcm file
                 temp_file = f'{temp_folder}/{uuid.hex}.pcm' #
                 dsdata = change_sample_rate(audio, rad.rate, NEW_SAMPLERATE) # 48000 --> 16000
-                io.wavfile.write(temp_file, NEW_SAMPLERATE, dsdata)
+                write_numpy_to_wave(temp_file, NEW_SAMPLERATE, dsdata)
                 # read pcm binary
                 with open(temp_file, "rb") as f: data = f.read()
-                # print('audio len:', len(audio), '\t ds len:', len(dsdata), '\t need n send:', len(data)//640)
-                slices = zip(*(iter(data),) * 640)    # 640个字节为一组
-                for i in slices: sr.send_audio(bytes(i))
+                is_speaking, info = is_speaker_speaking(vad, data, NEW_SAMPLERATE)
+
+                if is_speaking or echo_cnt > 0:
+                    # 如果话筒激活 / 如果处于回声收尾阶段
+                    echo_cnt -= 1
+                    if not is_previous_frame_transmitted:   # 上一帧没有人声，但是我们把上一帧同样加上
+                        if previous_frame_data is not None: data = previous_frame_data + data
+                    if is_speaking:
+                        echo_cnt = echo_cnt_max
+                    slices = zip(*(iter(data),) * 640)      # 640个字节为一组
+                    for i in slices: sr.send_audio(bytes(i))
+                    keep_alive_last_send_time = time.time()
+                    is_previous_frame_transmitted = True
+                else:
+                    is_previous_frame_transmitted = False
+                    echo_cnt = 0
+                    # 保持链接激活，即使没有声音，也根据时间间隔，发送一些音频片段给服务器
+                    if time.time() - keep_alive_last_send_time > timeout_limit_second/2:
+                        slices = zip(*(iter(data),) * 640)    # 640个字节为一组
+                        for i in slices: sr.send_audio(bytes(i))
+                        keep_alive_last_send_time = time.time()
+                        is_previous_frame_transmitted = True
+                self.audio_shape = info
             else:
                 time.sleep(0.1)
 
@@ -117,14 +243,14 @@ class AliyunASR():
 
         try:
             response = client.do_action_with_exception(request)
-            print(response)
+            logging.info(response)
             jss = json.loads(response)
             if 'Token' in jss and 'Id' in jss['Token']:
                 token = jss['Token']['Id']
                 expireTime = jss['Token']['ExpireTime']
-                print("token = " + token)
-                print("expireTime = " + str(expireTime))
+                logging.info("token = " + token)
+                logging.info("expireTime = " + str(expireTime))
         except Exception as e:
-            print(e)
+            logging.error(e)
 
         return token

crazy_functions/live_audio/audio_io.py

@@ -35,7 +35,7 @@ class RealtimeAudioDistribution():
     def read(self, uuid):
         if uuid in self.data:
             res = self.data.pop(uuid)
-            print('\r read-', len(res), '-', max(res), end='', flush=True)
+            # print('\r read-', len(res), '-', max(res), end='', flush=True)
         else:
             res = None
         return res

crazy_functions/media_fns/get_media.py

@@ -0,0 +1,39 @@
+from toolbox import update_ui, get_conf, promote_file_to_downloadzone, update_ui_lastest_msg, generate_file_link
+from shared_utils.docker_as_service_api import stream_daas
+from shared_utils.docker_as_service_api import DockerServiceApiComModel
+
+def download_video(video_id, only_audio, user_name, chatbot, history):
+    from toolbox import get_log_folder
+    chatbot.append([None, "Processing..."])
+    yield from update_ui(chatbot, history)
+    client_command = f'{video_id} --audio-only' if only_audio else video_id
+    server_url = get_conf('DAAS_SERVER_URL')
+    docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
+    save_file_dir = get_log_folder(user_name, plugin_name='media_downloader')
+    for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
+        status_buf = ""
+        status_buf += "DaaS message: \n\n"
+        status_buf += output_manifest['server_message'].replace('\n', '<br/>')
+        status_buf += "\n\n"
+        status_buf += "DaaS standard error: \n\n"
+        status_buf += output_manifest['server_std_err'].replace('\n', '<br/>')
+        status_buf += "\n\n"
+        status_buf += "DaaS standard output: \n\n"
+        status_buf += output_manifest['server_std_out'].replace('\n', '<br/>')
+        status_buf += "\n\n"
+        status_buf += "DaaS file attach: \n\n"
+        status_buf += str(output_manifest['server_file_attach'])
+        yield from update_ui_lastest_msg(status_buf, chatbot, history)
+
+    return output_manifest['server_file_attach']
+
+
+def search_videos(keywords):
+    from toolbox import get_log_folder
+    client_command = keywords
+    server_url = get_conf('DAAS_SERVER_URL').replace('stream', 'search')
+    docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
+    save_file_dir = get_log_folder("default_user", plugin_name='media_downloader')
+    for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
+        return output_manifest['server_message']
+

crazy_functions/multi_stage/multi_stage_utils.py

@@ -0,0 +1,93 @@
+from pydantic import BaseModel, Field
+from typing import List
+from toolbox import update_ui_lastest_msg, disable_auto_promotion
+from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
+import time
+import pickle
+
+def have_any_recent_upload_files(chatbot):
+    _5min = 5 * 60
+    if not chatbot: return False    # chatbot is None
+    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
+    if not most_recent_uploaded: return False   # most_recent_uploaded is None
+    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
+    else: return False  # most_recent_uploaded is too old
+
+class GptAcademicState():
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        pass
+
+    def dump_state(self, chatbot):
+        chatbot._cookies['plugin_state'] = pickle.dumps(self)
+
+    def set_state(self, chatbot, key, value):
+        setattr(self, key, value)
+        chatbot._cookies['plugin_state'] = pickle.dumps(self)
+
+    def get_state(chatbot, cls=None):
+        state = chatbot._cookies.get('plugin_state', None)
+        if state is not None:   state = pickle.loads(state)
+        elif cls is not None:   state = cls()
+        else:                   state = GptAcademicState()
+        state.chatbot = chatbot
+        return state
+
+
+class GptAcademicGameBaseState():
+    """
+    1. first init: __init__ ->
+    """
+    def init_game(self, chatbot, lock_plugin):
+        self.plugin_name = None
+        self.callback_fn = None
+        self.delete_game = False
+        self.step_cnt = 0
+
+    def lock_plugin(self, chatbot):
+        if self.callback_fn is None:
+            raise ValueError("callback_fn is None")
+        chatbot._cookies['lock_plugin'] = self.callback_fn
+        self.dump_state(chatbot)
+
+    def get_plugin_name(self):
+        if self.plugin_name is None:
+            raise ValueError("plugin_name is None")
+        return self.plugin_name
+
+    def dump_state(self, chatbot):
+        chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
+
+    def set_state(self, chatbot, key, value):
+        setattr(self, key, value)
+        chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
+
+    @staticmethod
+    def sync_state(chatbot, llm_kwargs, cls, plugin_name, callback_fn, lock_plugin=True):
+        state = chatbot._cookies.get(f'plugin_state/{plugin_name}', None)
+        if state is not None:
+            state = pickle.loads(state)
+        else:
+            state = cls()
+            state.init_game(chatbot, lock_plugin)
+        state.plugin_name = plugin_name
+        state.llm_kwargs = llm_kwargs
+        state.chatbot = chatbot
+        state.callback_fn = callback_fn
+        return state
+
+    def continue_game(self, prompt, chatbot, history):
+        # 游戏主体
+        yield from self.step(prompt, chatbot, history)
+        self.step_cnt += 1
+        # 保存状态，收尾
+        self.dump_state(chatbot)
+        # 如果游戏结束，清理
+        if self.delete_game:
+            chatbot._cookies['lock_plugin'] = None
+            chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = None
+        yield from update_ui(chatbot=chatbot, history=history)

crazy_functions/pdf_fns/breakdown_txt.py

@@ -0,0 +1,126 @@
+from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
+from loguru import logger
+
+def force_breakdown(txt, limit, get_token_fn):
+    """ 当无法用标点、空行分割时，我们用最暴力的方法切割
+    """
+    for i in reversed(range(len(txt))):
+        if get_token_fn(txt[:i]) < limit:
+            return txt[:i], txt[i:]
+    return "Tiktoken未知错误", "Tiktoken未知错误"
+
+
+def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
+    """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
+    当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
+    """
+    _min = int(5e4)
+    _max = int(1e5)
+    # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
+    if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
+        remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
+        remain_txt_to_cut_storage = ""
+    if len(remain_txt_to_cut) > _max:
+        remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
+        remain_txt_to_cut = remain_txt_to_cut[:_max]
+    return remain_txt_to_cut, remain_txt_to_cut_storage
+
+
+def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
+    """ 文本切分
+    """
+    res = []
+    total_len = len(txt_tocut)
+    fin_len = 0
+    remain_txt_to_cut = txt_tocut
+    remain_txt_to_cut_storage = ""
+    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
+    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
+
+    while True:
+        if get_token_fn(remain_txt_to_cut) <= limit:
+            # 如果剩余文本的token数小于限制，那么就不用切了
+            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
+            break
+        else:
+            # 如果剩余文本的token数大于限制，那么就切
+            lines = remain_txt_to_cut.split('\n')
+
+            # 估计一个切分点
+            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+
+            # 开始查找合适切分点的偏移（cnt）
+            cnt = 0
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    # 首先尝试用双空行（\n\n）作为切分点
+                    if lines[cnt] != "":
+                        continue
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+
+            if cnt == 0:
+                # 如果没有找到合适的切分点
+                if break_anyway:
+                    # 是否允许暴力切分
+                    prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
+                else:
+                    # 不允许直接报错
+                    raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
+
+            # 追加列表
+            res.append(prev); fin_len+=len(prev)
+            # 准备下一次迭代
+            remain_txt_to_cut = post
+            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
+            process = fin_len/total_len
+            logger.info(f'正在文本切分 {int(process*100)}%')
+            if len(remain_txt_to_cut.strip()) == 0:
+                break
+    return res
+
+
+def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
+    """ 使用多种方式尝试切分文本，以满足 token 限制
+    """
+    from request_llms.bridge_all import model_info
+    enc = model_info[llm_model]['tokenizer']
+    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
+    try:
+        # 第1次尝试，将双空行（\n\n）作为切分点
+        return cut(limit, get_token_fn, txt, must_break_at_empty_line=True)
+    except RuntimeError:
+        try:
+            # 第2次尝试，将单空行（\n）作为切分点
+            return cut(limit, get_token_fn, txt, must_break_at_empty_line=False)
+        except RuntimeError:
+            try:
+                # 第3次尝试，将英文句号（.）作为切分点
+                res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
+                return [r.replace('。\n', '.') for r in res]
+            except RuntimeError as e:
+                try:
+                    # 第4次尝试，将中文句号（。）作为切分点
+                    res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
+                    return [r.replace('。。\n', '。') for r in res]
+                except RuntimeError as e:
+                    # 第5次尝试，没办法了，随便切一下吧
+                    return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
+
+breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
+
+if __name__ == '__main__':
+    from crazy_functions.crazy_utils import read_and_clean_pdf_text
+    file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
+
+    from request_llms.bridge_all import model_info
+    for i in range(5):
+        file_content += file_content
+
+    logger.info(len(file_content))
+    TOKEN_LIMIT_PER_FRAGMENT = 2500
+    res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
+

crazy_functions/pdf_fns/parse_pdf.py

@@ -1,16 +1,26 @@
+from functools import lru_cache
+from toolbox import gen_time_str
+from toolbox import promote_file_to_downloadzone
+from toolbox import write_history_to_file, promote_file_to_downloadzone
+from toolbox import get_conf
+from toolbox import ProxyNetworkActivate
+from shared_utils.colorful import *
 import requests
 import random
-from functools import lru_cache
+import copy
+import os
+import math
+
 class GROBID_OFFLINE_EXCEPTION(Exception): pass
 
 def get_avail_grobid_url():
-    from toolbox import get_conf
-    GROBID_URLS, = get_conf('GROBID_URLS')
+    GROBID_URLS = get_conf('GROBID_URLS')
     if len(GROBID_URLS) == 0: return None
     try:
         _grobid_url = random.choice(GROBID_URLS) # 随机负载均衡
         if _grobid_url.endswith('/'): _grobid_url = _grobid_url.rstrip('/')
-        res = requests.get(_grobid_url+'/api/isalive')
+        with ProxyNetworkActivate('Connect_Grobid'):
+            res = requests.get(_grobid_url+'/api/isalive')
         if res.text=='true': return _grobid_url
         else: return None
     except:
@@ -20,6 +30,142 @@ def get_avail_grobid_url():
 def parse_pdf(pdf_path, grobid_url):
     import scipdf   # pip install scipdf_parser
     if grobid_url.endswith('/'): grobid_url = grobid_url.rstrip('/')
-    article_dict = scipdf.parse_pdf_to_dict(pdf_path, grobid_url=grobid_url)
+    try:
+        with ProxyNetworkActivate('Connect_Grobid'):
+            article_dict = scipdf.parse_pdf_to_dict(pdf_path, grobid_url=grobid_url)
+    except GROBID_OFFLINE_EXCEPTION:
+        raise GROBID_OFFLINE_EXCEPTION("GROBID服务不可用，请修改config中的GROBID_URL，可修改成本地GROBID服务。")
+    except:
+        raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
     return article_dict
 
+
+def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files):
+    # -=-=-=-=-=-=-=-= 写出第1个文件：翻译前后混合 -=-=-=-=-=-=-=-=
+    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=f"{gen_time_str()}translated_and_original.md", file_fullname=None)
+    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
+    generated_conclusion_files.append(res_path)
+
+    # -=-=-=-=-=-=-=-= 写出第2个文件：仅翻译后的文本 -=-=-=-=-=-=-=-=
+    translated_res_array = []
+    # 记录当前的大章节标题：
+    last_section_name = ""
+    for index, value in enumerate(gpt_response_collection):
+        # 先挑选偶数序列号：
+        if index % 2 != 0:
+            # 先提取当前英文标题：
+            cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0]
+            # 如果index是1的话，则直接使用first section name：
+            if cur_section_name != last_section_name:
+                cur_value = cur_section_name + '\n'
+                last_section_name = copy.deepcopy(cur_section_name)
+            else:
+                cur_value = ""
+            # 再做一个小修改：重新修改当前part的标题，默认用英文的
+            cur_value += value
+            translated_res_array.append(cur_value)
+    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + translated_res_array,
+                                     file_basename = f"{gen_time_str()}-translated_only.md",
+                                     file_fullname = None,
+                                     auto_caption = False)
+    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
+    generated_conclusion_files.append(res_path)
+    return res_path
+
+def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs={}):
+    from crazy_functions.pdf_fns.report_gen_html import construct_html
+    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+    from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+
+    prompt = "以下是一篇学术论文的基本信息:\n"
+    # title
+    title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
+    # authors
+    authors = article_dict.get('authors', '无法获取 authors')[:100]; prompt += f'authors:{authors}\n\n'
+    # abstract
+    abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
+    # command
+    prompt += f"请将题目和摘要翻译为{DST_LANG}。"
+    meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
+
+    # 单线，获取文章meta信息
+    paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt,
+        inputs_show_user=prompt,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot, history=[],
+        sys_prompt="You are an academic paper reader。",
+    )
+
+    # 多线，翻译
+    inputs_array = []
+    inputs_show_user_array = []
+
+    # get_token_num
+    from request_llms.bridge_all import model_info
+    enc = model_info[llm_kwargs['llm_model']]['tokenizer']
+    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+
+    def break_down(txt):
+        raw_token_num = get_token_num(txt)
+        if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
+            return [txt]
+        else:
+            # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
+            # find a smooth token limit to achieve even seperation
+            count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
+            token_limit_smooth = raw_token_num // count + count
+            return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
+
+    for section in article_dict.get('sections'):
+        if len(section['text']) == 0: continue
+        section_frags = break_down(section['text'])
+        for i, fragment in enumerate(section_frags):
+            heading = section['heading']
+            if len(section_frags) > 1: heading += f' Part-{i+1}'
+            inputs_array.append(
+                f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
+            )
+            inputs_show_user_array.append(
+                f"# {heading}\n\n{fragment}"
+            )
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[meta for _ in inputs_array],
+        sys_prompt_array=[
+            "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "") for _ in inputs_array],
+    )
+    # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
+    produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)
+
+    # -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-=
+    ch = construct_html()
+    orig = ""
+    trans = ""
+    gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
+    for i,k in enumerate(gpt_response_collection_html):
+        if i%2==0:
+            gpt_response_collection_html[i] = inputs_show_user_array[i//2]
+        else:
+            # 先提取当前英文标题：
+            cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0]
+            cur_value = cur_section_name + "\n" + gpt_response_collection_html[i]
+            gpt_response_collection_html[i] = cur_value
+
+    final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
+    final.extend(gpt_response_collection_html)
+    for i, k in enumerate(final):
+        if i%2==0:
+            orig = k
+        if i%2==1:
+            trans = k
+            ch.add_row(a=orig, b=trans)
+    create_report_file_name = f"{os.path.basename(fp)}.trans.html"
+    html_file = ch.save_file(create_report_file_name)
+    generated_conclusion_files.append(html_file)
+    promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)

crazy_functions/pdf_fns/parse_pdf_grobid.py

@@ -0,0 +1,26 @@
+import os
+from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
+from crazy_functions.pdf_fns.parse_pdf import parse_pdf, translate_pdf
+
+def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
+    import copy, json
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
+    generated_conclusion_files = []
+    generated_html_files = []
+    DST_LANG = "中文"
+    from crazy_functions.pdf_fns.report_gen_html import construct_html
+    for index, fp in enumerate(file_manifest):
+        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        article_dict = parse_pdf(fp, grobid_url)
+        grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
+        with open(grobid_json_res, 'w+', encoding='utf8') as f:
+            f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
+        promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
+        if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
+        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs=plugin_kwargs)
+    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+

crazy_functions/pdf_fns/parse_pdf_legacy.py

@@ -0,0 +1,111 @@
+from toolbox import get_log_folder
+from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import write_history_to_file, promote_file_to_downloadzone
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from crazy_functions.crazy_utils import read_and_clean_pdf_text
+from shared_utils.colorful import *
+from loguru import logger
+import os
+
+def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    """
+    注意：此函数已经弃用！！新函数位于：crazy_functions/pdf_fns/parse_pdf.py
+    """
+    import copy
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
+    generated_conclusion_files = []
+    generated_html_files = []
+    from crazy_functions.pdf_fns.report_gen_html import construct_html
+    for index, fp in enumerate(file_manifest):
+        # 读取PDF文件
+        file_content, page_one = read_and_clean_pdf_text(fp)
+        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
+
+        # 递归地切割PDF文件
+        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
+
+        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
+        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
+
+        # 单线，获取文章meta信息
+        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
+            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot, history=[],
+            sys_prompt="Your job is to collect information from materials。",
+        )
+
+        # 多线，翻译
+        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+            inputs_array=[
+                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
+            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot,
+            history_array=[[paper_meta] for _ in paper_fragments],
+            sys_prompt_array=[
+                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "")
+                for _ in paper_fragments],
+            # max_workers=5  # OpenAI所允许的最大并行过载
+        )
+        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
+        # 整理报告的格式
+        for i,k in enumerate(gpt_response_collection_md):
+            if i%2==0:
+                gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
+            else:
+                gpt_response_collection_md[i] = gpt_response_collection_md[i]
+        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
+        final.extend(gpt_response_collection_md)
+        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
+        res = write_history_to_file(final, create_report_file_name)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
+
+        # 更新UI
+        generated_conclusion_files.append(f'{get_log_folder()}/{create_report_file_name}')
+        chatbot.append((f"{fp}完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        # write html
+        try:
+            ch = construct_html()
+            orig = ""
+            trans = ""
+            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
+            for i,k in enumerate(gpt_response_collection_html):
+                if i%2==0:
+                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
+                else:
+                    gpt_response_collection_html[i] = gpt_response_collection_html[i]
+            final = ["论文概况", paper_meta_info.replace('# ', '### '),  "二、论文翻译",  ""]
+            final.extend(gpt_response_collection_html)
+            for i, k in enumerate(final):
+                if i%2==0:
+                    orig = k
+                if i%2==1:
+                    trans = k
+                    ch.add_row(a=orig, b=trans)
+            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
+            generated_html_files.append(ch.save_file(create_report_file_name))
+        except:
+            from toolbox import trimmed_format_exc
+            logger.error('writing html result failed:', trimmed_format_exc())
+
+    # 准备文件的下载
+    for pdf_path in generated_conclusion_files:
+        # 重命名文件
+        rename_file = f'翻译-{os.path.basename(pdf_path)}'
+        promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
+    for html_path in generated_html_files:
+        # 重命名文件
+        rename_file = f'翻译-{os.path.basename(html_path)}'
+        promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)
+    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+

crazy_functions/pdf_fns/parse_pdf_via_doc2x.py

@@ -0,0 +1,250 @@
+from toolbox import get_log_folder, gen_time_str, get_conf
+from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import promote_file_to_downloadzone, extract_archive
+from toolbox import generate_file_link, zip_folder
+from crazy_functions.crazy_utils import get_files_from_everything
+from shared_utils.colorful import *
+from loguru import logger
+import os
+import time
+
+def refresh_key(doc2x_api_key):
+    import requests, json
+    url = "https://api.doc2x.noedgeai.com/api/token/refresh"
+    res = requests.post(
+        url,
+        headers={"Authorization": "Bearer " + doc2x_api_key}
+    )
+    res_json = []
+    if res.status_code == 200:
+        decoded = res.content.decode("utf-8")
+        res_json = json.loads(decoded)
+        doc2x_api_key = res_json['data']['token']
+    else:
+        raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
+    return doc2x_api_key
+
+
+
+def 解析PDF_DOC2X_转Latex(pdf_file_path):
+    zip_file_path, unzipped_folder = 解析PDF_DOC2X(pdf_file_path, format='tex')
+    return unzipped_folder
+
+
+def 解析PDF_DOC2X(pdf_file_path, format='tex'):
+    """
+        format: 'tex', 'md', 'docx'
+    """
+    import requests, json, os
+    DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
+    latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
+    markdown_dir = get_log_folder(plugin_name="pdf_ocr")
+    doc2x_api_key = DOC2X_API_KEY
+
+
+    # < ------ 第1步：上传 ------ >
+    logger.info("Doc2x 第1步：上传")
+    with open(pdf_file_path, 'rb') as file:
+        res = requests.post(
+            "https://v2.doc2x.noedgeai.com/api/v2/parse/pdf",
+            headers={"Authorization": "Bearer " + doc2x_api_key},
+            data=file
+        )
+    # res_json = []
+    if res.status_code == 200:
+        res_json = res.json()
+    else:
+        raise RuntimeError(f"Doc2x return an error: {res.json()}")
+    uuid = res_json['data']['uid']
+
+    # < ------ 第2步：轮询等待 ------ >
+    logger.info("Doc2x 第2步：轮询等待")
+    params = {'uid': uuid}
+    while True:
+        res = requests.get(
+            'https://v2.doc2x.noedgeai.com/api/v2/parse/status',
+            headers={"Authorization": "Bearer " + doc2x_api_key},
+            params=params
+        )
+        res_json = res.json()
+        if res_json['data']['status'] == "success":
+            break
+        elif res_json['data']['status'] == "processing":
+            time.sleep(3)
+            logger.info(f"Doc2x is processing at {res_json['data']['progress']}%")
+        elif res_json['data']['status'] == "failed":
+            raise RuntimeError(f"Doc2x return an error: {res_json}")
+
+
+    # < ------ 第3步：提交转化 ------ >
+    logger.info("Doc2x 第3步：提交转化")
+    data = {
+        "uid": uuid,
+        "to": format,
+        "formula_mode": "dollar",
+        "filename": "output"
+    }
+    res = requests.post(
+        'https://v2.doc2x.noedgeai.com/api/v2/convert/parse',
+        headers={"Authorization": "Bearer " + doc2x_api_key},
+        json=data
+    )
+    if res.status_code == 200:
+        res_json = res.json()
+    else:
+        raise RuntimeError(f"Doc2x return an error: {res.json()}")
+
+
+    # < ------ 第4步：等待结果 ------ >
+    logger.info("Doc2x 第4步：等待结果")
+    params = {'uid': uuid}
+    while True:
+        res = requests.get(
+            'https://v2.doc2x.noedgeai.com/api/v2/convert/parse/result',
+            headers={"Authorization": "Bearer " + doc2x_api_key},
+            params=params
+        )
+        res_json = res.json()
+        if res_json['data']['status'] == "success":
+            break
+        elif res_json['data']['status'] == "processing":
+            time.sleep(3)
+            logger.info(f"Doc2x still processing")
+        elif res_json['data']['status'] == "failed":
+            raise RuntimeError(f"Doc2x return an error: {res_json}")
+
+
+    # < ------ 第5步：最后的处理 ------ >
+    logger.info("Doc2x 第5步：最后的处理")
+
+    if format=='tex':
+        target_path = latex_dir
+    if format=='md':
+        target_path = markdown_dir
+    os.makedirs(target_path, exist_ok=True)
+
+    max_attempt = 3
+    # < ------ 下载 ------ >
+    for attempt in range(max_attempt):
+        try:
+            result_url = res_json['data']['url']
+            res = requests.get(result_url)
+            zip_path = os.path.join(target_path, gen_time_str() + '.zip')
+            unzip_path = os.path.join(target_path, gen_time_str())
+            if res.status_code == 200:
+                with open(zip_path, "wb") as f: f.write(res.content)
+            else:
+                raise RuntimeError(f"Doc2x return an error: {res.json()}")
+        except Exception as e:
+            if attempt < max_attempt - 1:
+                logger.error(f"Failed to download latex file, retrying... {e}")
+                time.sleep(3)
+                continue
+            else:
+                raise e
+
+    # < ------ 解压 ------ >
+    import zipfile
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(unzip_path)
+    return zip_path, unzip_path
+
+
+def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request):
+
+    def pdf2markdown(filepath):
+        chatbot.append((None, f"Doc2x 解析中"))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format='md')
+
+        promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
+        chatbot.append((None, f"完成解析 {md_zip_path} ..."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return md_zip_path
+
+    def deliver_to_markdown_plugin(md_zip_path, user_request):
+        from crazy_functions.Markdown_Translate import Markdown英译中
+        import shutil, re
+
+        time_tag = gen_time_str()
+        target_path_base = get_log_folder(chatbot.get_user())
+        file_origin_name = os.path.basename(md_zip_path)
+        this_file_path = os.path.join(target_path_base, file_origin_name)
+        os.makedirs(target_path_base, exist_ok=True)
+        shutil.copyfile(md_zip_path, this_file_path)
+        ex_folder = this_file_path + ".extract"
+        extract_archive(
+            file_path=this_file_path, dest_dir=ex_folder
+        )
+
+        # edit markdown files
+        success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md')
+        for generated_fp in file_manifest:
+            # 修正一些公式问题
+            with open(generated_fp, 'r', encoding='utf8') as f:
+                content = f.read()
+            # 将公式中的\[ \]替换成$$
+            content = content.replace(r'\[', r'$$').replace(r'\]', r'$$')
+            # 将公式中的\( \)替换成$
+            content = content.replace(r'\(', r'$').replace(r'\)', r'$')
+            content = content.replace('```markdown', '\n').replace('```', '\n')
+            with open(generated_fp, 'w', encoding='utf8') as f:
+                f.write(content)
+            promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
+            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+            # 生成在线预览html
+            file_name = '在线预览翻译（原文）' + gen_time_str() + '.html'
+            preview_fp = os.path.join(ex_folder, file_name)
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
+            with open(generated_fp, "r", encoding="utf-8") as f:
+                md = f.read()
+            #     # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
+            #     md = re.sub(r'^<table>', r'.<table>', md, flags=re.MULTILINE)
+            html = markdown_convertion_for_file(md)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
+            chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
+            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
+
+
+
+        chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
+        plugin_kwargs['markdown_expected_output_dir'] = ex_folder
+
+        translated_f_name = 'translated_markdown.md'
+        generated_fp = plugin_kwargs['markdown_expected_output_path'] = os.path.join(ex_folder, translated_f_name)
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        yield from Markdown英译中(ex_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+        if os.path.exists(generated_fp):
+            # 修正一些公式问题
+            with open(generated_fp, 'r', encoding='utf8') as f: content = f.read()
+            content = content.replace('```markdown', '\n').replace('```', '\n')
+            # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
+            # content = re.sub(r'^<table>', r'.<table>', content, flags=re.MULTILINE)
+            with open(generated_fp, 'w', encoding='utf8') as f: f.write(content)
+            # 生成在线预览html
+            file_name = '在线预览翻译' + gen_time_str() + '.html'
+            preview_fp = os.path.join(ex_folder, file_name)
+            from shared_utils.advanced_markdown_format import markdown_convertion_for_file
+            with open(generated_fp, "r", encoding="utf-8") as f:
+                md = f.read()
+            html = markdown_convertion_for_file(md)
+            with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
+            promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
+            # 生成包含图片的压缩包
+            dest_folder = get_log_folder(chatbot.get_user())
+            zip_name = '翻译后的带图文档.zip'
+            zip_folder(source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name)
+            zip_fp = os.path.join(dest_folder, zip_name)
+            promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
+            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    md_zip_path = yield from pdf2markdown(fp)
+    yield from deliver_to_markdown_plugin(md_zip_path, user_request)
+
+def 解析PDF_基于DOC2X(file_manifest, *args):
+    for index, fp in enumerate(file_manifest):
+        yield from 解析PDF_DOC2X_单文件(fp, *args)
+    return
+
+

crazy_functions/pdf_fns/parse_word.py

@@ -0,0 +1,85 @@
+from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
+import os
+import re
+def extract_text_from_files(txt, chatbot, history):
+    """
+    查找pdf/md/word并获取文本内容并返回状态以及文本
+
+    输入参数 Args:
+        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
+        history (list): List of chat history （历史，对话历史列表）
+
+    输出 Returns:
+        文件是否存在(bool)
+        final_result(list):文本内容
+        page_one(list):第一页内容/摘要
+        file_manifest(list):文件路径
+        excption(string):需要用户手动处理的信息,如没出错则保持为空
+    """
+
+    final_result = []
+    page_one = []
+    file_manifest = []
+    excption = ""
+
+    if txt == "":
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    #查找输入区内容中的文件
+    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
+    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
+    file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx')
+    file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')
+
+    if file_doc:
+        excption = "word"
+        return False, final_result, page_one, file_manifest, excption
+
+    file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
+    if file_num == 0:
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    if file_pdf:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            import fitz
+        except:
+            excption = "pdf"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(pdf_manifest):
+            file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
+            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+            pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
+            final_result.append(file_content)
+            page_one.append(pdf_one)
+            file_manifest.append(os.path.relpath(fp, folder_pdf))
+
+    if file_md:
+        for index, fp in enumerate(md_manifest):
+            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+                file_content = f.read()
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE)  #接下来提取md中的一级/二级标题作为摘要
+            if len(headers) > 0:
+                page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
+            else:
+                page_one.append("")
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_md))
+
+    if file_word:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            from docx import Document
+        except:
+            excption = "word_pip"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(word_manifest):
+            doc = Document(fp)
+            file_content = '\n'.join([p.text for p in doc.paragraphs])
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            page_one.append(file_content[:200])
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_word))
+
+    return True, final_result, page_one, file_manifest, excption

crazy_functions/pdf_fns/report_gen_html.py

@@ -0,0 +1,58 @@
+from toolbox import update_ui, get_conf, trimmed_format_exc, get_log_folder
+import os
+
+
+
+
+class construct_html():
+    def __init__(self) -> None:
+        self.html_string = ""
+
+    def add_row(self, a, b):
+        from toolbox import markdown_convertion
+        template = """
+            {
+                primary_col: {
+                    header: String.raw`__PRIMARY_HEADER__`,
+                    msg: String.raw`__PRIMARY_MSG__`,
+                },
+                secondary_rol: {
+                    header: String.raw`__SECONDARY_HEADER__`,
+                    msg: String.raw`__SECONDARY_MSG__`,
+                }
+            },
+        """
+        def std(str):
+            str = str.replace(r'`',r'`')
+            if str.endswith("\\"): str += ' '
+            if str.endswith("}"): str += ' '
+            if str.endswith("$"): str += ' '
+            return str
+
+        template_ = template
+        a_lines = a.split('\n')
+        b_lines = b.split('\n')
+
+        if len(a_lines) == 1 or len(a_lines[0]) > 50:
+            template_ = template_.replace("__PRIMARY_HEADER__", std(a[:20]))
+            template_ = template_.replace("__PRIMARY_MSG__", std(markdown_convertion(a)))
+        else:
+            template_ = template_.replace("__PRIMARY_HEADER__", std(a_lines[0]))
+            template_ = template_.replace("__PRIMARY_MSG__", std(markdown_convertion('\n'.join(a_lines[1:]))))
+
+        if len(b_lines) == 1 or len(b_lines[0]) > 50:
+            template_ = template_.replace("__SECONDARY_HEADER__", std(b[:20]))
+            template_ = template_.replace("__SECONDARY_MSG__", std(markdown_convertion(b)))
+        else:
+            template_ = template_.replace("__SECONDARY_HEADER__", std(b_lines[0]))
+            template_ = template_.replace("__SECONDARY_MSG__", std(markdown_convertion('\n'.join(b_lines[1:]))))
+        self.html_string += template_
+
+    def save_file(self, file_name):
+        from toolbox import get_log_folder
+        with open('crazy_functions/pdf_fns/report_template.html', 'r', encoding='utf8') as f:
+            html_template = f.read()
+        html_template = html_template.replace("__TF_ARR__", self.html_string)
+        with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f:
+            f.write(html_template.encode('utf-8', 'ignore').decode())
+        return os.path.join(get_log_folder(), file_name)

crazy_functions/pdf_fns/report_template_v2.html

@@ -0,0 +1,73 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
+    <title>GPT-Academic 翻译报告书</title>
+    <style>
+        .centered-a {
+            color: red;
+            text-align: center;
+            margin-bottom: 2%;
+            font-size: 1.5em;
+        }
+        .centered-b {
+            color: red;
+            text-align: center;
+            margin-top: 10%;
+            margin-bottom: 20%;
+            font-size: 1.5em;
+        }
+        .centered-c {
+            color: rgba(255, 0, 0, 0);
+            text-align: center;
+            margin-top: 2%;
+            margin-bottom: 20%;
+            font-size: 7em;
+        }
+    </style>
+<script>
+        // Configure MathJax settings
+        MathJax = {
+            tex: {
+                inlineMath: [
+                    ['$', '$'],
+                    ['\(', '\)']
+                ]
+            }
+        }
+        addEventListener('zero-md-rendered', () => {MathJax.typeset(); console.log('MathJax typeset!');})
+    </script>
+    <!-- Load MathJax library -->
+    <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
+    <script
+        type="module"
+        src="https://cdn.jsdelivr.net/gh/zerodevx/zero-md@2/dist/zero-md.min.js"
+    ></script>
+
+</head>
+
+<body>
+    <div class="test_temp1" style="width:10%; height: 500px; float:left;">
+
+    </div>
+    <div class="test_temp2" style="width:80%; height: 500px; float:left;">
+        <!-- Simply set the `src` attribute to your MD file and win -->
+        <div class="centered-a">
+            请按Ctrl+S保存此页面，否则该页面可能在几分钟后失效。
+        </div>
+        <zero-md src="translated_markdown.md" no-shadow>
+        </zero-md>
+        <div class="centered-b">
+            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
+        </div>
+        <div class="centered-c">
+            本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
+        </div>
+    </div>
+    <div class="test_temp3" style="width:10%; height: 500px; float:left;">
+    </div>
+
+    </body>
+
+</html>

crazy_functions/plugin_template/plugin_class_template.py

@@ -0,0 +1,52 @@
+import os, json, base64
+from pydantic import BaseModel, Field
+from textwrap import dedent
+from typing import List
+
+class ArgProperty(BaseModel): # PLUGIN_ARG_MENU
+    title: str = Field(description="The title", default="")
+    description: str = Field(description="The description", default="")
+    default_value: str = Field(description="The default value", default="")
+    type: str = Field(description="The type", default="")   # currently we support ['string', 'dropdown']
+    options: List[str] = Field(default=[], description="List of options available for the argument") # only used when type is 'dropdown'
+
+class GptAcademicPluginTemplate():
+    def __init__(self):
+        # please note that `execute` method may run in different threads,
+        # thus you should not store any state in the plugin instance,
+        # which may be accessed by multiple threads
+        pass
+
+
+    def define_arg_selection_menu(self):
+        """
+        An example as below:
+            ```
+            def define_arg_selection_menu(self):
+                gui_definition = {
+                    "main_input":
+                        ArgProperty(title="main input", description="description", default_value="default_value", type="string").model_dump_json(),
+                    "advanced_arg":
+                        ArgProperty(title="advanced arguments", description="description", default_value="default_value", type="string").model_dump_json(),
+                    "additional_arg_01":
+                        ArgProperty(title="additional", description="description", default_value="default_value", type="string").model_dump_json(),
+                }
+                return gui_definition
+            ```
+        """
+        raise NotImplementedError("You need to implement this method in your plugin class")
+
+
+    def get_js_code_for_generating_menu(self, btnName):
+        define_arg_selection = self.define_arg_selection_menu()
+
+        if len(define_arg_selection.keys()) > 8:
+            raise ValueError("You can only have up to 8 arguments in the define_arg_selection")
+        # if "main_input" not in define_arg_selection:
+        #     raise ValueError("You must have a 'main_input' in the define_arg_selection")
+
+        DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
+        return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
+
+    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        raise NotImplementedError("You need to implement this method in your plugin class")

crazy_functions/prompts/internet.py

@@ -0,0 +1,87 @@
+SearchOptimizerPrompt="""作为一个网页搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高网页检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
+历史记录: 
+"
+Q: 对话背景。
+A: 当前对话是关于 Nginx 的介绍和在Ubuntu上的使用等。
+"
+原问题: 怎么下载
+检索词: ["Nginx 下载","Ubuntu Nginx","Ubuntu安装Nginx"]
+----------------
+历史记录: 
+"
+Q: 对话背景。
+A: 当前对话是关于 Nginx 的介绍和使用等。
+Q: 报错 "no connection"
+A: 报错"no connection"可能是因为……
+"
+原问题: 怎么解决
+检索词: ["Nginx报错"no connection" 解决","Nginx'no connection'报错 原因","Nginx提示'no connection'"]
+----------------
+历史记录:
+"
+
+"
+原问题: 你知道 Python 么？
+检索词: ["Python","Python 使用教程。","Python 特点和优势"]
+----------------
+历史记录:
+"
+Q: 列出Java的三种特点？
+A: 1. Java 是一种编译型语言。
+   2. Java 是一种面向对象的编程语言。
+   3. Java 是一种跨平台的编程语言。
+"
+原问题: 介绍下第2点。
+检索词: ["Java 面向对象特点","Java 面向对象编程优势。","Java 面向对象编程"]
+----------------
+现在有历史记录:
+"
+{history}
+"
+有其原问题: {query}
+直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
+"""
+
+SearchAcademicOptimizerPrompt="""作为一个学术论文搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高学术论文检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
+历史记录: 
+"
+Q: 对话背景。
+A: 当前对话是关于深度学习的介绍和在图像识别中的应用等。
+"
+原问题: 怎么下载相关论文
+检索词: ["深度学习 图像识别 论文下载","图像识别 深度学习 研究论文","深度学习 图像识别 论文资源","Deep Learning Image Recognition Paper Download","Image Recognition Deep Learning Research Paper"]
+----------------
+历史记录: 
+"
+Q: 对话背景。
+A: 当前对话是关于深度学习的介绍和应用等。
+Q: 报错 "模型不收敛"
+A: 报错"模型不收敛"可能是因为……
+"
+原问题: 怎么解决
+检索词: ["深度学习 模型不收敛 解决方案 论文","深度学习 模型不收敛 原因 研究","深度学习 模型不收敛 论文","Deep Learning Model Convergence Issue Solution Paper","Deep Learning Model Convergence Problem Research"]
+----------------
+历史记录:
+"
+
+"
+原问题: 你知道 GAN 么？
+检索词: ["生成对抗网络 论文","GAN 使用教程 论文","GAN 特点和优势 研究","Generative Adversarial Network Paper","GAN Usage Tutorial Paper"]
+----------------
+历史记录:
+"
+Q: 列出机器学习的三种应用？
+A: 1. 机器学习在图像识别中的应用。
+   2. 机器学习在自然语言处理中的应用。
+   3. 机器学习在推荐系统中的应用。
+"
+原问题: 介绍下第2点。
+检索词: ["机器学习 自然语言处理 应用 论文","机器学习 自然语言处理 研究","机器学习 NLP 应用 论文","Machine Learning Natural Language Processing Application Paper","Machine Learning NLP Research"]
+----------------
+现在有历史记录:
+"
+{history}
+"
+有其原问题: {query}
+直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
+"""

crazy_functions/rag_fns/llama_index_worker.py

@@ -0,0 +1,138 @@
+import atexit
+from loguru import logger
+from typing import List
+
+from llama_index.core import Document
+from llama_index.core.ingestion import run_transformations
+from llama_index.core.schema import TextNode
+
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+
+DEFAULT_QUERY_GENERATION_PROMPT = """\
+Now, you have context information as below:
+---------------------
+{context_str}
+---------------------
+Answer the user request below (use the context information if necessary, otherwise you can ignore them):
+---------------------
+{query_str}
+"""
+
+QUESTION_ANSWER_RECORD = """\
+{{
+    "type": "This is a previous conversation with the user",
+    "question": "{question}",
+    "answer": "{answer}",
+}}
+"""
+
+
+class SaveLoad():
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        logger.info(f'saving vector store to: {checkpoint_dir}')
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            logger.info('loading checkpoint from disk')
+            from llama_index.core import StorageContext, load_index_from_storage
+            storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
+            self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
+            return self.vs_index
+        else:
+            return self.create_new_vs()
+
+    def create_new_vs(self):
+        return GptacVectorStoreIndex.default_vector_store(embed_model=self.embed_model)
+
+    def purge(self):
+        import shutil
+        shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
+        self.vs_index = self.create_new_vs(self.checkpoint_dir)
+
+
+class LlamaIndexRagWorker(SaveLoad):
+    def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
+        self.debug_mode = True
+        self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
+        self.user_name = user_name
+        self.checkpoint_dir = checkpoint_dir
+        if auto_load_checkpoint:
+            self.vs_index = self.load_from_checkpoint(checkpoint_dir)
+        else:
+            self.vs_index = self.create_new_vs()
+        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
+
+    def assign_embedding_model(self):
+        pass
+
+    def inspect_vector_store(self):
+        # This function is for debugging
+        self.vs_index.storage_context.index_store.to_dict()
+        docstore = self.vs_index.storage_context.docstore.docs
+        vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
+        logger.info('\n++ --------inspect_vector_store begin--------')
+        logger.info(vector_store_preview)
+        logger.info('oo --------inspect_vector_store end--------')
+        return vector_store_preview
+
+    def add_documents_to_vector_store(self, document_list: List[Document]):
+        """
+        Adds a list of Document objects to the vector store after processing.
+        """
+        documents = document_list
+        documents_nodes = run_transformations(
+            documents,  # type: ignore
+            self.vs_index._transformations,
+            show_progress=True
+        )
+        self.vs_index.insert_nodes(documents_nodes)
+        if self.debug_mode:
+            self.inspect_vector_store()
+
+    def add_text_to_vector_store(self, text: str):
+        node = TextNode(text=text)
+        documents_nodes = run_transformations(
+            [node],
+            self.vs_index._transformations,
+            show_progress=True
+        )
+        self.vs_index.insert_nodes(documents_nodes)
+        if self.debug_mode:
+            self.inspect_vector_store()
+
+    def remember_qa(self, question, answer):
+        formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
+        self.add_text_to_vector_store(formatted_str)
+
+    def retrieve_from_store_with_query(self, query):
+        if self.debug_mode:
+            self.inspect_vector_store()
+        retriever = self.vs_index.as_retriever()
+        return retriever.retrieve(query)
+
+    def build_prompt(self, query, nodes):
+        context_str = self.generate_node_array_preview(nodes)
+        return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
+
+    def generate_node_array_preview(self, nodes):
+        buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
+        if self.debug_mode: logger.info(buf)
+        return buf
+
+    def purge_vector_store(self):
+        """
+        Purges the current vector store and creates a new one.
+        """
+        self.purge()

crazy_functions/rag_fns/milvus_worker.py

@@ -0,0 +1,108 @@
+import llama_index
+import os
+import atexit
+from typing import List
+from loguru import logger
+from llama_index.core import Document
+from llama_index.core.schema import TextNode
+from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+from shared_utils.connect_void_terminal import get_chat_default_kwargs
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from llama_index.core.ingestion import run_transformations
+from llama_index.core import PromptTemplate
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.core import StorageContext
+from llama_index.vector_stores.milvus import MilvusVectorStore
+from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+
+DEFAULT_QUERY_GENERATION_PROMPT = """\
+Now, you have context information as below:
+---------------------
+{context_str}
+---------------------
+Answer the user request below (use the context information if necessary, otherwise you can ignore them):
+---------------------
+{query_str}
+"""
+
+QUESTION_ANSWER_RECORD = """\
+{{
+    "type": "This is a previous conversation with the user",
+    "question": "{question}",
+    "answer": "{answer}",
+}}
+"""
+
+
+class MilvusSaveLoad():
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        logger.info(f'saving vector store to: {checkpoint_dir}')
+        # if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        # self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            logger.info('loading checkpoint from disk')
+            from llama_index.core import StorageContext, load_index_from_storage
+            storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
+            try:
+                self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
+                return self.vs_index
+            except:
+                return self.create_new_vs(checkpoint_dir)
+        else:
+            return self.create_new_vs(checkpoint_dir)
+
+    def create_new_vs(self, checkpoint_dir, overwrite=False):
+        vector_store = MilvusVectorStore(
+            uri=os.path.join(checkpoint_dir, "milvus_demo.db"), 
+            dim=self.embed_model.embedding_dimension(),
+            overwrite=overwrite
+        )
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+        index = GptacVectorStoreIndex.default_vector_store(storage_context=storage_context, embed_model=self.embed_model)
+        return index
+
+    def purge(self):
+        self.vs_index = self.create_new_vs(self.checkpoint_dir, overwrite=True)
+
+class MilvusRagWorker(MilvusSaveLoad, LlamaIndexRagWorker):
+
+    def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
+        self.debug_mode = True
+        self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
+        self.user_name = user_name
+        self.checkpoint_dir = checkpoint_dir
+        if auto_load_checkpoint:
+            self.vs_index = self.load_from_checkpoint(checkpoint_dir)
+        else:
+            self.vs_index = self.create_new_vs(checkpoint_dir)
+        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
+
+    def inspect_vector_store(self):
+        # This function is for debugging
+        try:
+            self.vs_index.storage_context.index_store.to_dict()
+            docstore = self.vs_index.storage_context.docstore.docs
+            if not docstore.items():
+                raise ValueError("cannot inspect")
+            vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
+        except:
+            dummy_retrieve_res: List["NodeWithScore"] = self.vs_index.as_retriever().retrieve(' ')
+            vector_store_preview = "\n".join(
+                [f"{node.id_} | {node.text}" for node in dummy_retrieve_res]
+            )
+        logger.info('\n++ --------inspect_vector_store begin--------')
+        logger.info(vector_store_preview)
+        logger.info('oo --------inspect_vector_store end--------')
+        return vector_store_preview

crazy_functions/rag_fns/rag_file_support.py

@@ -0,0 +1,22 @@
+import os
+from llama_index.core import SimpleDirectoryReader
+
+supports_format = ['.csv', '.docx', '.epub', '.ipynb',  '.mbox', '.md', '.pdf',  '.txt', '.ppt',
+                   '.pptm', '.pptx']
+
+
+# 修改后的 extract_text 函数，结合 SimpleDirectoryReader 和自定义解析逻辑
+def extract_text(file_path):
+    _, ext = os.path.splitext(file_path.lower())
+
+    # 使用 SimpleDirectoryReader 处理它支持的文件格式
+    if ext in supports_format:
+        try:
+            reader = SimpleDirectoryReader(input_files=[file_path])
+            documents = reader.load_data()
+            if len(documents) > 0:
+                return documents[0].text
+        except Exception as e:
+            pass
+
+    return None

crazy_functions/rag_fns/vector_store_index.py

@@ -0,0 +1,58 @@
+from llama_index.core import VectorStoreIndex
+from typing import Any,  List, Optional
+
+from llama_index.core.callbacks.base import CallbackManager
+from llama_index.core.schema import TransformComponent
+from llama_index.core.service_context import ServiceContext
+from llama_index.core.settings import (
+    Settings,
+    callback_manager_from_settings_or_context,
+    transformations_from_settings_or_context,
+)
+from llama_index.core.storage.storage_context import StorageContext
+
+
+class GptacVectorStoreIndex(VectorStoreIndex):
+    
+    @classmethod
+    def default_vector_store(
+        cls,
+        storage_context: Optional[StorageContext] = None,
+        show_progress: bool = False,
+        callback_manager: Optional[CallbackManager] = None,
+        transformations: Optional[List[TransformComponent]] = None,
+        # deprecated
+        service_context: Optional[ServiceContext] = None,
+        embed_model = None,
+        **kwargs: Any,
+    ):
+        """Create index from documents.
+
+        Args:
+            documents (Optional[Sequence[BaseDocument]]): List of documents to
+                build the index from.
+
+        """
+        storage_context = storage_context or StorageContext.from_defaults()
+        docstore = storage_context.docstore
+        callback_manager = (
+            callback_manager
+            or callback_manager_from_settings_or_context(Settings, service_context)
+        )
+        transformations = transformations or transformations_from_settings_or_context(
+            Settings, service_context
+        )
+
+        with callback_manager.as_trace("index_construction"):
+
+            return cls(
+                nodes=[],
+                storage_context=storage_context,
+                callback_manager=callback_manager,
+                show_progress=show_progress,
+                transformations=transformations,
+                service_context=service_context,
+                embed_model=embed_model,
+                **kwargs,
+            )
+

crazy_functions/vector_fns/general_file_loader.py

@@ -0,0 +1,70 @@
+# From project chatglm-langchain
+
+
+from langchain.document_loaders import UnstructuredFileLoader
+from langchain.text_splitter import CharacterTextSplitter
+import re
+from typing import List
+
+class ChineseTextSplitter(CharacterTextSplitter):
+    def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
+        super().__init__(**kwargs)
+        self.pdf = pdf
+        self.sentence_size = sentence_size
+
+    def split_text1(self, text: str) -> List[str]:
+        if self.pdf:
+            text = re.sub(r"\n{3,}", "\n", text)
+            text = re.sub('\s', ' ', text)
+            text = text.replace("\n\n", "")
+        sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')  # del ：；
+        sent_list = []
+        for ele in sent_sep_pattern.split(text):
+            if sent_sep_pattern.match(ele) and sent_list:
+                sent_list[-1] += ele
+            elif ele:
+                sent_list.append(ele)
+        return sent_list
+
+    def split_text(self, text: str) -> List[str]:   ##此处需要进一步优化逻辑
+        if self.pdf:
+            text = re.sub(r"\n{3,}", r"\n", text)
+            text = re.sub('\s', " ", text)
+            text = re.sub("\n\n", "", text)
+
+        text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text)  # 单字符断句符
+        text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text)  # 英文省略号
+        text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text)  # 中文省略号
+        text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
+        # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
+        text = text.rstrip()  # 段尾如果有多余的\n就去掉它
+        # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
+        ls = [i for i in text.split("\n") if i]
+        for ele in ls:
+            if len(ele) > self.sentence_size:
+                ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
+                ele1_ls = ele1.split("\n")
+                for ele_ele1 in ele1_ls:
+                    if len(ele_ele1) > self.sentence_size:
+                        ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
+                        ele2_ls = ele_ele2.split("\n")
+                        for ele_ele2 in ele2_ls:
+                            if len(ele_ele2) > self.sentence_size:
+                                ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
+                                ele2_id = ele2_ls.index(ele_ele2)
+                                ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
+                                                                                                       ele2_id + 1:]
+                        ele_id = ele1_ls.index(ele_ele1)
+                        ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
+
+                id = ls.index(ele)
+                ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
+        return ls
+
+def load_file(filepath, sentence_size):
+    loader = UnstructuredFileLoader(filepath, mode="elements")
+    textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
+    docs = loader.load_and_split(text_splitter=textsplitter)
+    # write_check_file(filepath, docs)
+    return docs
+

crazy_functions/vector_fns/vector_database.py

@@ -0,0 +1,339 @@
+# From project chatglm-langchain
+
+import os
+import os
+import uuid
+import tqdm
+import shutil
+import threading
+import numpy as np
+from toolbox import Singleton
+from loguru import logger
+from langchain.vectorstores import FAISS
+from langchain.docstore.document import Document
+from typing import List, Tuple
+from crazy_functions.vector_fns.general_file_loader import load_file
+
+embedding_model_dict = {
+    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
+    "ernie-base": "nghuyong/ernie-3.0-base-zh",
+    "text2vec-base": "shibing624/text2vec-base-chinese",
+    "text2vec": "GanymedeNil/text2vec-large-chinese",
+}
+
+# Embedding model name
+EMBEDDING_MODEL = "text2vec"
+
+# Embedding running device
+EMBEDDING_DEVICE = "cpu"
+
+# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
+PROMPT_TEMPLATE = """已知信息：
+{context}
+
+根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
+
+# 文本分句长度
+SENTENCE_SIZE = 100
+
+# 匹配后单段上下文长度
+CHUNK_SIZE = 250
+
+# LLM input history length
+LLM_HISTORY_LEN = 3
+
+# return top-k text chunk from vector store
+VECTOR_SEARCH_TOP_K = 5
+
+# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
+VECTOR_SEARCH_SCORE_THRESHOLD = 0
+
+NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
+
+FLAG_USER_NAME = uuid.uuid4().hex
+
+# 是否开启跨域，默认为False，如果需要开启，请设置为True
+# is open cross domain
+OPEN_CROSS_DOMAIN = False
+
+def similarity_search_with_score_by_vector(
+        self, embedding: List[float], k: int = 4
+) -> List[Tuple[Document, float]]:
+
+    def seperate_list(ls: List[int]) -> List[List[int]]:
+        lists = []
+        ls1 = [ls[0]]
+        for i in range(1, len(ls)):
+            if ls[i - 1] + 1 == ls[i]:
+                ls1.append(ls[i])
+            else:
+                lists.append(ls1)
+                ls1 = [ls[i]]
+        lists.append(ls1)
+        return lists
+
+    scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+    docs = []
+    id_set = set()
+    store_len = len(self.index_to_docstore_id)
+    for j, i in enumerate(indices[0]):
+        if i == -1 or 0 < self.score_threshold < scores[0][j]:
+            # This happens when not enough docs are returned.
+            continue
+        _id = self.index_to_docstore_id[i]
+        doc = self.docstore.search(_id)
+        if not self.chunk_conent:
+            if not isinstance(doc, Document):
+                raise ValueError(f"Could not find document for id {_id}, got {doc}")
+            doc.metadata["score"] = int(scores[0][j])
+            docs.append(doc)
+            continue
+        id_set.add(i)
+        docs_len = len(doc.page_content)
+        for k in range(1, max(i, store_len - i)):
+            break_flag = False
+            for l in [i + k, i - k]:
+                if 0 <= l < len(self.index_to_docstore_id):
+                    _id0 = self.index_to_docstore_id[l]
+                    doc0 = self.docstore.search(_id0)
+                    if docs_len + len(doc0.page_content) > self.chunk_size:
+                        break_flag = True
+                        break
+                    elif doc0.metadata["source"] == doc.metadata["source"]:
+                        docs_len += len(doc0.page_content)
+                        id_set.add(l)
+            if break_flag:
+                break
+    if not self.chunk_conent:
+        return docs
+    if len(id_set) == 0 and self.score_threshold > 0:
+        return []
+    id_list = sorted(list(id_set))
+    id_lists = seperate_list(id_list)
+    for id_seq in id_lists:
+        for id in id_seq:
+            if id == id_seq[0]:
+                _id = self.index_to_docstore_id[id]
+                doc = self.docstore.search(_id)
+            else:
+                _id0 = self.index_to_docstore_id[id]
+                doc0 = self.docstore.search(_id0)
+                doc.page_content += " " + doc0.page_content
+        if not isinstance(doc, Document):
+            raise ValueError(f"Could not find document for id {_id}, got {doc}")
+        doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
+        doc.metadata["score"] = int(doc_score)
+        docs.append(doc)
+    return docs
+
+
+class LocalDocQA:
+    llm: object = None
+    embeddings: object = None
+    top_k: int = VECTOR_SEARCH_TOP_K
+    chunk_size: int = CHUNK_SIZE
+    chunk_conent: bool = True
+    score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
+
+    def init_cfg(self,
+                 top_k=VECTOR_SEARCH_TOP_K,
+                 ):
+
+        self.llm = None
+        self.top_k = top_k
+
+    def init_knowledge_vector_store(self,
+                                    filepath,
+                                    vs_path: str or os.PathLike = None,
+                                    sentence_size=SENTENCE_SIZE,
+                                    text2vec=None):
+        loaded_files = []
+        failed_files = []
+        if isinstance(filepath, str):
+            if not os.path.exists(filepath):
+                logger.error("路径不存在")
+                return None
+            elif os.path.isfile(filepath):
+                file = os.path.split(filepath)[-1]
+                try:
+                    docs = load_file(filepath, SENTENCE_SIZE)
+                    logger.info(f"{file} 已成功加载")
+                    loaded_files.append(filepath)
+                except Exception as e:
+                    logger.error(e)
+                    logger.error(f"{file} 未能成功加载")
+                    return None
+            elif os.path.isdir(filepath):
+                docs = []
+                for file in tqdm(os.listdir(filepath), desc="加载文件"):
+                    fullfilepath = os.path.join(filepath, file)
+                    try:
+                        docs += load_file(fullfilepath, SENTENCE_SIZE)
+                        loaded_files.append(fullfilepath)
+                    except Exception as e:
+                        logger.error(e)
+                        failed_files.append(file)
+
+                if len(failed_files) > 0:
+                    logger.error("以下文件未能成功加载：")
+                    for file in failed_files:
+                        logger.error(f"{file}\n")
+
+        else:
+            docs = []
+            for file in filepath:
+                docs += load_file(file, SENTENCE_SIZE)
+                logger.info(f"{file} 已成功加载")
+                loaded_files.append(file)
+
+        if len(docs) > 0:
+            logger.info("文件加载完毕，正在生成向量库")
+            if vs_path and os.path.isdir(vs_path):
+                try:
+                    self.vector_store = FAISS.load_local(vs_path, text2vec)
+                    self.vector_store.add_documents(docs)
+                except:
+                    self.vector_store = FAISS.from_documents(docs, text2vec)
+            else:
+                self.vector_store = FAISS.from_documents(docs, text2vec)  # docs 为Document列表
+
+            self.vector_store.save_local(vs_path)
+            return vs_path, loaded_files
+        else:
+            raise RuntimeError("文件加载失败，请检查文件格式是否正确")
+
+    def get_loaded_file(self, vs_path):
+        ds = self.vector_store.docstore
+        return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
+
+
+    # query      查询内容
+    # vs_path    知识库路径
+    # chunk_conent   是否启用上下文关联
+    # score_threshold    搜索匹配score阈值
+    # vector_search_top_k   搜索知识库内容条数，默认搜索5条结果
+    # chunk_sizes    匹配单段内容的连接上下文长度
+    def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
+                                        score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
+                                        vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
+                                        text2vec=None):
+        self.vector_store = FAISS.load_local(vs_path, text2vec)
+        self.vector_store.chunk_conent = chunk_conent
+        self.vector_store.score_threshold = score_threshold
+        self.vector_store.chunk_size = chunk_size
+
+        embedding = self.vector_store.embedding_function.embed_query(query)
+        related_docs_with_score = similarity_search_with_score_by_vector(self.vector_store, embedding, k=vector_search_top_k)
+
+        if not related_docs_with_score:
+            response = {"query": query,
+                        "source_documents": []}
+            return response, ""
+        # prompt = f"{query}. You should answer this question using information from following documents: \n\n"
+        prompt = f"{query}. 你必须利用以下文档中包含的信息回答这个问题: \n\n---\n\n"
+        prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
+        prompt += "\n\n---\n\n"
+        prompt = prompt.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+        # logger.info(prompt)
+        response = {"query": query, "source_documents": related_docs_with_score}
+        return response, prompt
+
+
+
+
+def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
+    for file in files:
+        assert os.path.exists(file), "输入文件不存在：" + file
+    import nltk
+    if NLTK_DATA_PATH not in nltk.data.path: nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+    local_doc_qa = LocalDocQA()
+    local_doc_qa.init_cfg()
+    filelist = []
+    if not os.path.exists(os.path.join(vs_path, vs_id)):
+        os.makedirs(os.path.join(vs_path, vs_id))
+    for file in files:
+        file_name = file.name if not isinstance(file, str) else file
+        filename = os.path.split(file_name)[-1]
+        shutil.copyfile(file_name, os.path.join(vs_path, vs_id, filename))
+        filelist.append(os.path.join(vs_path, vs_id, filename))
+    vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, os.path.join(vs_path, vs_id), sentence_size, text2vec)
+
+    if len(loaded_files):
+        file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
+    else:
+        pass
+        # file_status = "文件未成功加载，请重新上传文件"
+    # logger.info(file_status)
+    return local_doc_qa, vs_path
+
+@Singleton
+class knowledge_archive_interface():
+    def __init__(self) -> None:
+        self.threadLock = threading.Lock()
+        self.current_id = ""
+        self.kai_path = None
+        self.qa_handle = None
+        self.text2vec_large_chinese = None
+
+    def get_chinese_text2vec(self):
+        if self.text2vec_large_chinese is None:
+            # < -------------------预热文本向量化模组--------------- >
+            from toolbox import ProxyNetworkActivate
+            logger.info('Checking Text2vec ...')
+            from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+            with ProxyNetworkActivate('Download_LLM'):    # 临时地激活代理网络
+                self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
+
+        return self.text2vec_large_chinese
+
+
+    def feed_archive(self, file_manifest, vs_path, id="default"):
+        self.threadLock.acquire()
+        # import uuid
+        self.current_id = id
+        self.qa_handle, self.kai_path = construct_vector_store(
+            vs_id=self.current_id,
+            vs_path=vs_path,
+            files=file_manifest,
+            sentence_size=100,
+            history=[],
+            one_conent="",
+            one_content_segmentation="",
+            text2vec = self.get_chinese_text2vec(),
+        )
+        self.threadLock.release()
+
+    def get_current_archive_id(self):
+        return self.current_id
+
+    def get_loaded_file(self, vs_path):
+        return self.qa_handle.get_loaded_file(vs_path)
+
+    def answer_with_archive_by_id(self, txt, id, vs_path):
+        self.threadLock.acquire()
+        if not self.current_id == id:
+            self.current_id = id
+            self.qa_handle, self.kai_path = construct_vector_store(
+                vs_id=self.current_id,
+                vs_path=vs_path,
+                files=[],
+                sentence_size=100,
+                history=[],
+                one_conent="",
+                one_content_segmentation="",
+                text2vec = self.get_chinese_text2vec(),
+            )
+        VECTOR_SEARCH_SCORE_THRESHOLD = 0
+        VECTOR_SEARCH_TOP_K = 4
+        CHUNK_SIZE = 512
+        resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
+            query = txt,
+            vs_path = self.kai_path,
+            score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
+            vector_search_top_k=VECTOR_SEARCH_TOP_K,
+            chunk_conent=True,
+            chunk_size=CHUNK_SIZE,
+            text2vec = self.get_chinese_text2vec(),
+        )
+        self.threadLock.release()
+        return resp, prompt

crazy_functions/vt_fns/vt_call_plugin.py

@@ -1,7 +1,7 @@
 from pydantic import BaseModel, Field
 from typing import List
 from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from request_llm.bridge_all import predict_no_ui_long_connection
+from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
 import copy, json, pickle, os, sys, time
 
@@ -10,7 +10,7 @@ def read_avail_plugin_enum():
     from crazy_functional import get_crazy_functions
     plugin_arr = get_crazy_functions()
     # remove plugins with out explaination
-    plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
+    plugin_arr = {k:v for k, v in plugin_arr.items() if ('Info' in v) and ('Function' in v)}
     plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
     plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}

crazy_functions/vt_fns/vt_modify_config.py

@@ -1,13 +1,13 @@
 from pydantic import BaseModel, Field
 from typing import List
 from toolbox import update_ui_lastest_msg, get_conf
-from request_llm.bridge_all import predict_no_ui_long_connection
+from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.json_fns.pydantic_io import GptJsonIO
 import copy, json, pickle, os, sys
 
 
 def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    ALLOW_RESET_CONFIG, = get_conf('ALLOW_RESET_CONFIG')
+    ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
     if not ALLOW_RESET_CONFIG:
         yield from update_ui_lastest_msg(
             lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
@@ -66,7 +66,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         )
 
 def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    ALLOW_RESET_CONFIG, = get_conf('ALLOW_RESET_CONFIG')
+    ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
     if not ALLOW_RESET_CONFIG:
         yield from update_ui_lastest_msg(
             lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",

crazy_functions/下载arxiv论文翻译摘要.py

@@ -1,194 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file, get_conf
-import re, requests, unicodedata, os
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-def download_arxiv_(url_pdf):
-    if 'arxiv.org' not in url_pdf:
-        if ('.' in url_pdf) and ('/' not in url_pdf):
-            new_url = 'https://arxiv.org/abs/'+url_pdf
-            print('下载编号：', url_pdf, '自动定位：', new_url)
-            # download_arxiv_(new_url)
-            return download_arxiv_(new_url)
-        else:
-            print('不能识别的URL！')
-            return None
-    if 'abs' in url_pdf:
-        url_pdf = url_pdf.replace('abs', 'pdf')
-        url_pdf = url_pdf + '.pdf'
-
-    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
-    title, other_info = get_name(_url_=url_abs)
-
-    paper_id = title.split()[0]  # '[1712.00559]'
-    if '2' in other_info['year']:
-        title = other_info['year'] + ' ' + title
-
-    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
-    for k in known_conf:
-        if k in other_info['comment']:
-            title = k + ' ' + title
-
-    download_dir = './gpt_log/arxiv/'
-    os.makedirs(download_dir, exist_ok=True)
-
-    title_str = title.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-
-    requests_pdf_url = url_pdf
-    file_path = download_dir+title_str
-    # if os.path.exists(file_path):
-    #     print('返回缓存文件')
-    #     return './gpt_log/arxiv/'+title_str
-
-    print('下载中')
-    proxies, = get_conf('proxies')
-    r = requests.get(requests_pdf_url, proxies=proxies)
-    with open(file_path, 'wb+') as f:
-        f.write(r.content)
-    print('下载完成')
-
-    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
-    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
-
-    x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
-    x = x.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-    return './gpt_log/arxiv/'+title_str, other_info
-
-
-def get_name(_url_):
-    import os
-    from bs4 import BeautifulSoup
-    print('正在获取文献名！')
-    print(_url_)
-
-    # arxiv_recall = {}
-    # if os.path.exists('./arxiv_recall.pkl'):
-    #     with open('./arxiv_recall.pkl', 'rb') as f:
-    #         arxiv_recall = pickle.load(f)
-
-    # if _url_ in arxiv_recall:
-    #     print('在缓存中')
-    #     return arxiv_recall[_url_]
-
-    proxies, = get_conf('proxies')
-    res = requests.get(_url_, proxies=proxies)
-
-    bs = BeautifulSoup(res.text, 'html.parser')
-    other_details = {}
-
-    # get year
-    try:
-        year = bs.find_all(class_='dateline')[0].text
-        year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
-        other_details['year'] = year
-        abstract = bs.find_all(class_='abstract mathjax')[0].text
-        other_details['abstract'] = abstract
-    except:
-        other_details['year'] = ''
-        print('年份获取失败')
-
-    # get author
-    try:
-        authors = bs.find_all(class_='authors')[0].text
-        authors = authors.split('Authors:')[1]
-        other_details['authors'] = authors
-    except:
-        other_details['authors'] = ''
-        print('authors获取失败')
-
-    # get comment
-    try:
-        comment = bs.find_all(class_='metatable')[0].text
-        real_comment = None
-        for item in comment.replace('\n', ' ').split('   '):
-            if 'Comments' in item:
-                real_comment = item
-        if real_comment is not None:
-            other_details['comment'] = real_comment
-        else:
-            other_details['comment'] = ''
-    except:
-        other_details['comment'] = ''
-        print('年份获取失败')
-
-    title_str = BeautifulSoup(
-        res.text, 'html.parser').find('title').contents[0]
-    print('获取成功：', title_str)
-    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
-    # with open('./arxiv_recall.pkl', 'wb') as f:
-    #     pickle.dump(arxiv_recall, f)
-
-    return title_str+'.pdf', other_details
-
-
-
-@CatchException
-def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-
-    CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import bs4
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 提取摘要，下载PDF文档
-    try:
-        pdf_path, info = download_arxiv_(txt)
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"下载pdf文件未成功")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # 翻译摘要等
-    i_say =            f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
-    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
-    chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    msg = '正常'
-    # ** gpt request **
-    # 单线，获取文章meta信息
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say,
-        inputs_show_user=i_say_show_user,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot, history=[],
-        sys_prompt="Your job is to collect information from materials and translate to Chinese。",
-    )
-
-    chatbot[-1] = (i_say_show_user, gpt_say)
-    history.append(i_say_show_user); history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    # 写入文件
-    import shutil
-    # 重置文件的创建时间
-    shutil.copyfile(pdf_path, f'./gpt_log/{os.path.basename(pdf_path)}'); os.remove(pdf_path)
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res + "\n\nPDF文件也已经下载"))
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-

crazy_functions/交互功能函数模板.py

@@ -1,63 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-
-@CatchException
-def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数, 如温度和top_p等, 一般原样传递下去就行
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
-
-    if state is None:
-        chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数'      # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数
-        chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword'                              # 赋予插件状态
-
-        chatbot.append(("第一次调用：", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if state == 'wait_user_keyword':
-        chatbot._cookies['lock_plugin'] = None          # 解除插件锁定，避免遗忘导致死锁
-        chatbot._cookies['plugin_state_0001'] = None    # 解除插件状态，避免遗忘导致死锁
-
-        # 解除插件锁定
-        chatbot.append((f"获取关键词：{txt}", ""))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        page_return = get_image_page_by_keyword(txt)
-        inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=inputs, inputs_show_user=inputs_show_user,
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="When you want to show an image, use markdown format. e.g. ![image_description](image_url). If there are no image url provided, answer 'no image url provided'"
-        )
-        chatbot[-1] = [chatbot[-1][0], gpt_say]
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-
-
-# ---------------------------------------------------------------------------------
-
-def get_image_page_by_keyword(keyword):
-    import requests
-    from bs4 import BeautifulSoup
-    response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
-    res = "image urls: \n"
-    for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
-        try:
-            res += image_element["data-src"]
-            res += "\n"
-        except:
-            pass
-    return res

crazy_functions/代码重写为全英文_多线程.py

@@ -1,138 +0,0 @@
-import threading
-from request_llm.bridge_all import predict_no_ui_long_connection
-from toolbox import update_ui
-from toolbox import CatchException, write_results_to_file, report_execption
-from .crazy_utils import breakdown_txt_to_satisfy_token_limit
-
-def extract_code_block_carefully(txt):
-    splitted = txt.split('```')
-    n_code_block_seg = len(splitted) - 1
-    if n_code_block_seg <= 1: return txt
-    # 剩下的情况都开头除去 ``` 结尾除去一次 ```
-    txt_out = '```'.join(splitted[1:-1])
-    return txt_out
-
-
-
-def break_txt_into_half_at_some_linebreak(txt):
-    lines = txt.split('\n')
-    n_lines = len(lines)
-    pre = lines[:(n_lines//2)]
-    post = lines[(n_lines//2):]
-    return "\n".join(pre), "\n".join(post)
-
-
-@CatchException
-def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    # 第1步：清空历史，以免输入溢出
-    history = []
-
-    # 第2步：尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 第3步：集合文件
-    import time, glob, os, shutil, re
-    os.makedirs('gpt_log/generated_english_version', exist_ok=True)
-    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    # file_manifest = ['./toolbox.py']
-    i_say_show_user_buffer = []
-
-    # 第4步：随便显示点什么防止卡顿的感觉
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'
-        i_say_show_user_buffer.append(i_say_show_user)
-        chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-    # 第5步：Token限制下的截断与处理
-    MAX_TOKEN = 3000
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-
-
-    # 第6步：任务函数
-    mutable_return = [None for _ in file_manifest]
-    observe_window = [[""] for _ in file_manifest]
-    def thread_worker(fp,index):
-        if index > 10: 
-            time.sleep(60)
-            print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
-        try:
-            gpt_say = ""
-            # 分解代码文件
-            file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
-            for file_content_partial in file_content_breakdown:
-                i_say = i_say_template(fp, file_content_partial)
-                # # ** gpt request **
-                gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
-                gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
-                gpt_say += gpt_say_partial
-            mutable_return[index] = gpt_say
-        except ConnectionAbortedError as token_exceed_err:
-            print('至少一个线程任务Token溢出而失败', e)
-        except Exception as e:
-            print('至少一个线程任务意外失败', e)
-
-    # 第7步：所有线程同时开始执行任务函数
-    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
-    for h in handles:
-        h.daemon = True
-        h.start()
-    chatbot.append(('开始了吗？', f'多线程操作已经开始'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第8步：循环轮询各个线程是否执行完毕
-    cnt = 0
-    while True:
-        cnt += 1
-        time.sleep(0.2)
-        th_alive = [h.is_alive() for h in handles]
-        if not any(th_alive): break
-        # 更好的UI视觉效果
-        observe_win = []
-        for thread_index, alive in enumerate(th_alive): 
-            observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
-        stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
-        stat_str = ''.join(stat)
-        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1)))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 第9步：把结果写入文件
-    for index, h in enumerate(handles):
-        h.join() # 这里其实不需要join了，肯定已经都结束了
-        fp = file_manifest[index]
-        gpt_say = mutable_return[index]
-        i_say_show_user = i_say_show_user_buffer[index]
-
-        where_to_relocate = f'gpt_log/generated_english_version/{fp}'
-        if gpt_say is not None:
-            with open(where_to_relocate, 'w+', encoding='utf-8') as f:  
-                f.write(gpt_say)
-        else:  # 失败
-            shutil.copyfile(file_manifest[index], where_to_relocate)
-        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
-        history.append(i_say_show_user); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        time.sleep(1)
-
-    # 第10步：备份一个文件
-    res = write_results_to_file(history)
-    chatbot.append(("生成一份任务执行报告", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

crazy_functions/命令行助手.py

@@ -1,31 +0,0 @@
-from toolbox import CatchException, update_ui, gen_time_str
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import input_clipping
-import copy, json
-
-@CatchException
-def 命令行助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本, 例如需要翻译的一段话, 再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
-    plugin_kwargs   插件模型的参数, 暂时没有用武之地
-    chatbot         聊天显示框的句柄, 用于显示给用户
-    history         聊天历史, 前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    # 清空历史, 以免输入溢出
-    history = []
-
-    # 输入
-    i_say = "请写bash命令实现以下功能：" + txt
-    # 开始
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-        sys_prompt="你是一个Linux大师级用户。注意，当我要求你写bash命令时，尽可能地仅用一行命令解决我的要求。"
-    )
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-

crazy_functions/图片生成.py

@@ -1,69 +0,0 @@
-from toolbox import CatchException, update_ui, get_conf, select_api_key
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-
-
-def gen_image(llm_kwargs, prompt, resolution="256x256"):
-    import requests, json, time, os
-    from request_llm.bridge_all import model_info
-
-    proxies, = get_conf('proxies')
-    # Set up OpenAI API key and model 
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-    # 'https://api.openai.com/v1/chat/completions'
-    img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
-    # # Generate the image
-    url = img_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}",
-        'Content-Type': 'application/json'
-    }
-    data = {
-        'prompt': prompt,
-        'n': 1,
-        'size': resolution,
-        'response_format': 'url'
-    }
-    response = requests.post(url, headers=headers, json=data, proxies=proxies)
-    print(response.content)
-    try:
-        image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-    except:
-        raise RuntimeError(response.content.decode())
-    # 文件保存到本地
-    r = requests.get(image_url, proxies=proxies)
-    file_path = 'gpt_log/image_gen/'
-    os.makedirs(file_path, exist_ok=True)
-    file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
-    with open(file_path+file_name, 'wb+') as f: f.write(r.content)
-
-
-    return image_url, file_path+file_name
-
-
-
-@CatchException
-def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 请尝试英文Prompt。正在处理中 ....."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    resolution = plugin_kwargs.get("advanced_arg", '256x256')
-    image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
-    chatbot.append([prompt,  
-        f'图像中转网址: <br/>`{image_url}`<br/>'+
-        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
-        f'本地文件地址: <br/>`{image_path}`<br/>'+
-        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

crazy_functions/对话历史存档.py

@@ -1,142 +0,0 @@
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import re
-
-def write_chat_to_file(chatbot, history=None, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        file_name = 'chatGPT对话历史' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        from themes.theme import advanced_css
-        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
-        for i, contents in enumerate(chatbot):
-            for j, content in enumerate(contents):
-                try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                    if type(content) != str: content = str(content)
-                except:
-                    continue
-                f.write(content)
-                if j == 0:
-                    f.write('<hr style="border-top: dotted 3px #ccc;">')
-            f.write('<hr color="red"> \n\n')
-        f.write('<hr color="blue"> \n\n raw chat context:\n')
-        f.write('<code>')
-        for h in history:
-            f.write("\n>>>" + h)
-        f.write('</code>')
-    promote_file_to_downloadzone(f'./gpt_log/{file_name}', rename_file=file_name, chatbot=chatbot)
-    return '对话历史写入：' + os.path.abspath(f'./gpt_log/{file_name}')
-
-def gen_file_preview(file_name):
-    try:
-        with open(file_name, 'r', encoding='utf8') as f:
-            file_content = f.read()
-        # pattern to match the text between <head> and </head>
-        pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-        file_content = re.sub(pattern, '', file_content)
-        html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-        history = history.strip('<code>')
-        history = history.strip('</code>')
-        history = history.split("\n>>>")
-        return list(filter(lambda x:x!="", history))[0][:100]
-    except:
-        return ""
-
-def read_file_to_chat(chatbot, history, file_name):
-    with open(file_name, 'r', encoding='utf8') as f:
-        file_content = f.read()
-    # pattern to match the text between <head> and </head>
-    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
-    file_content = re.sub(pattern, '', file_content)
-    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
-    history = history.strip('<code>')
-    history = history.strip('</code>')
-    history = history.split("\n>>>")
-    history = list(filter(lambda x:x!="", history))
-    html = html.split('<hr color="red"> \n\n')
-    html = list(filter(lambda x:x!="", html))
-    chatbot.clear()
-    for i, h in enumerate(html):
-        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
-        chatbot.append([i_say, gpt_say])
-    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
-    return chatbot, history    
-
-@CatchException
-def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-
-    chatbot.append(("保存当前对话", 
-        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用“载入对话历史存档”还原当下的对话。\n警告！被保存的对话历史可以被使用该系统的任何人查阅。"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-def hide_cwd(str):
-    import os
-    current_path = os.getcwd()
-    replace_path = "."
-    return str.replace(current_path, replace_path)
-
-@CatchException
-def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, _ = get_files_from_everything(txt, type='.html')
-
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-        import glob
-        local_history = "<br/>".join(["`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
-        chatbot.append([f"正在查找对话历史文件（html格式）: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    try:
-        chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    except:
-        chatbot.append([f"载入对话历史文件", f"对话历史文件损坏！"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-@CatchException
-def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-
-    import glob, os
-    local_history = "<br/>".join(["`"+hide_cwd(f)+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
-    for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True):
-        os.remove(f)
-    chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return
-
-

crazy_functions/总结word文档.py

@@ -1,129 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    # pip install python-docx 用于docx格式，跨平台
-    # pip install pywin32 用于doc格式，仅支持Win平台
-    for index, fp in enumerate(file_manifest):
-        if fp.split(".")[-1] == "docx":
-            from docx import Document
-            doc = Document(fp)
-            file_content = "\n".join([para.text for para in doc.paragraphs])
-        else:
-            try:
-                import win32com.client
-                word = win32com.client.Dispatch("Word.Application")
-                word.visible = False
-                # 打开文件
-                doc = word.Documents.Open(os.getcwd() + '/' + fp)
-                # file_content = doc.Content.Text
-                doc = word.ActiveDocument
-                file_content = doc.Range().Text
-                doc.Close()
-                word.Quit()
-            except:
-                raise RuntimeError('请先将.doc文档转换为.docx文档。')
-
-        print(file_content)
-        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        max_token = model_info[llm_kwargs['llm_model']]['max_token']
-        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  
-            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], 
-            limit=TOKEN_LIMIT_PER_FRAGMENT
-        )
-        this_paper_history = []
-        for i, paper_frag in enumerate(paper_fragments):
-            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
-            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user,gpt_say])
-            this_paper_history.extend([i_say_show_user,gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
-        if len(paper_fragments) > 1:
-            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=this_paper_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say,gpt_say])
-            this_paper_history.extend([i_say,gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    res = write_results_to_file(history)
-    chatbot.append(("所有文件都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        from docx import Document
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    if txt.endswith('.docx') or txt.endswith('.doc'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
-                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/总结音视频.py

@@ -1,184 +0,0 @@
-from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def split_audio_file(filename, split_duration=1000):
-    """
-    根据给定的切割时长将音频文件切割成多个片段。
-
-    Args:
-        filename (str): 需要被切割的音频文件名。
-        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
-
-    Returns:
-        filelist (list): 一个包含所有切割音频片段文件路径的列表。
-
-    """
-    from moviepy.editor import AudioFileClip
-    import os
-    os.makedirs('gpt_log/mp3/cut/', exist_ok=True)  # 创建存储切割音频的文件夹
-
-    # 读取音频文件
-    audio = AudioFileClip(filename)
-
-    # 计算文件总时长和切割点
-    total_duration = audio.duration
-    split_points = list(range(0, int(total_duration), split_duration))
-    split_points.append(int(total_duration))
-    filelist = []
-
-    # 切割音频文件
-    for i in range(len(split_points) - 1):
-        start_time = split_points[i]
-        end_time = split_points[i + 1]
-        split_audio = audio.subclip(start_time, end_time)
-        split_audio.write_audiofile(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-        filelist.append(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-
-    audio.close()
-    return filelist
-
-def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
-    import os, requests
-    from moviepy.editor import AudioFileClip
-    from request_llm.bridge_all import model_info
-
-    # 设置OpenAI密钥和模型
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-
-    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
-    url = whisper_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}"
-    }
-
-    os.makedirs('gpt_log/mp3/', exist_ok=True)
-    for index, fp in enumerate(file_manifest):
-        audio_history = []
-        # 提取文件扩展名
-        ext = os.path.splitext(fp)[1]
-        # 提取视频中的音频
-        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
-            audio_clip = AudioFileClip(fp)
-            audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')
-            fp = f'gpt_log/mp3/output{index}.mp3'
-        # 调用whisper模型音频转文字
-        voice = split_audio_file(fp)
-        for j, i in enumerate(voice):
-            with open(i, 'rb') as f:
-                file_content = f.read()  # 读取文件内容到内存
-                files = {
-                    'file': (os.path.basename(i), file_content),
-                }
-                data = {
-                    "model": "whisper-1",
-                    "prompt": parse_prompt,
-                    'response_format': "text"
-                }
-
-            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-            proxies, = get_conf('proxies')
-            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
-
-            chatbot.append(["音频解析结果", response])
-            history.extend(["音频解析结果", response])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
-            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt=f"总结音频。音频文件名{fp}"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user, gpt_say])
-            audio_history.extend([i_say_show_user, gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了
-        result = "".join(audio_history)
-        if len(audio_history) > 1:
-            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
-            i_say_show_user = f'第{index + 1}段音频的主要内容：'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=audio_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say, gpt_say])
-            audio_history.extend([i_say, gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # 删除中间文件夹
-    import shutil
-    shutil.rmtree('gpt_log/mp3')
-    res = write_results_to_file(history)
-    chatbot.append(("所有音频都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history)
-
-
-@CatchException
-def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    try:
-        from moviepy.editor import AudioFileClip
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
-
-    if txt.endswith(tuple(extensions)):
-        file_manifest = [txt]
-    else:
-        file_manifest = []
-        for extension in extensions:
-            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 开始正式执行任务
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
-    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
-
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

crazy_functions/批量Markdown翻译.py

@@ -1,261 +0,0 @@
-import glob, time, os, re
-from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
-from toolbox import CatchException, report_execption, write_history_to_file
-from toolbox import promote_file_to_downloadzone, get_log_folder
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
-        print('Segmentation: done')
-
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self, language):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')
-            with open(dst_file, 'w', encoding='utf8') as f:
-                manifest.append(dst_file)
-                f.write(res)
-        return manifest
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Markdown文件，删除其中的所有注释 ----------> 
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(file_content)
-
-    #  <-------- 拆分过长的Markdown文件 ----------> 
-    pfg.run_file_split(max_token_limit=1500)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 多线程翻译开始 ----------> 
-    if language == 'en->zh':
-        inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    else:
-        inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-    try:
-        pfg.sp_file_result = []
-        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-        pfg.write_result(language)
-    except:
-        print(trimmed_format_exc())
-
-    #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = gen_time_str() + f"-chatgpt.md"
-    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def get_files_from_everything(txt, preference=''):
-    if txt == "": return False, None, None
-    success = True
-    if txt.startswith('http'):
-        import requests
-        from toolbox import get_conf
-        proxies, = get_conf('proxies')
-        # 网络的远程文件
-        if preference == 'Github':
-            print('正在从github下载资源 ...')
-            if not txt.endswith('.md'):
-                # Make a request to the GitHub API to retrieve the repository information
-                url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'
-                response = requests.get(url, proxies=proxies)
-                txt = response.json()['download_url']
-            else:
-                txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
-                txt = txt.replace("/blob/", "/")
-
-        r = requests.get(txt, proxies=proxies)
-        download_local = f'{get_log_folder(plugin_name="批量Markdown翻译")}/raw-readme-{gen_time_str()}.md'
-        project_folder = f'{get_log_folder(plugin_name="批量Markdown翻译")}'
-        with open(download_local, 'wb+') as f: f.write(r.content)
-        file_manifest = [download_local]
-    elif txt.endswith('.md'):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder
-
-
-@CatchException
-def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-
-    success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
-
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
-
-
-@CatchException
-def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    language = plugin_kwargs.get("advanced_arg", 'Chinese')
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)

crazy_functions/批量总结PDF文档.py

@@ -1,149 +0,0 @@
-from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import input_clipping
-
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    file_write_buffer = []
-    for file_name in file_manifest:
-        print('begin analysis on:', file_name)
-        ############################## <第 0 步，切割PDF> ##################################
-        # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-        # 的长度必须小于 2500 个 Token
-        file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-        
-        TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        
-        ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-        final_results = []
-        final_results.append(paper_meta)
-
-        ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-        i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-        chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-        iteration_results = []
-        last_iteration_result = paper_meta  # 初始值是摘要
-        MAX_WORD_TOTAL = 4096 * 0.7
-        n_fragment = len(paper_fragments)
-        if n_fragment >= 20: print('文章极长，不能达到预期效果')
-        for i in range(n_fragment):
-            NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-            i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
-            i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                                llm_kwargs, chatbot, 
-                                                                                history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                                sys_prompt="Extract the main idea of this section with Chinese."  # 提示
-                                                                                ) 
-            iteration_results.append(gpt_say)
-            last_iteration_result = gpt_say
-
-        ############################## <第 3 步，整理history，提取总结> ##################################
-        final_results.extend(iteration_results)
-        final_results.append(f'Please conclude this paper discussed above。')
-        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
-        NUM_OF_WORD = 1000
-        i_say = """
-1. Mark the title of the paper (with Chinese translation)
-2. list all the authors' names (use English)
-3. mark the first author's affiliation (output Chinese translation only)
-4. mark the keywords of this article (use English)
-5. link to the paper, Github code link (if available, fill in Github:None if not)
-6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)
-    - (1):What is the research background of this article?
-    - (2):What are the past methods? What are the problems with them? Is the approach well motivated?
-    - (3):What is the research methodology proposed in this paper?
-    - (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?
-Follow the format of the output that follows:                  
-1. Title: xxx\n\n
-2. Authors: xxx\n\n
-3. Affiliation: xxx\n\n
-4. Keywords: xxx\n\n
-5. Urls: xxx or xxx , xxx \n\n
-6. Summary: \n\n
-    - (1):xxx;\n 
-    - (2):xxx;\n 
-    - (3):xxx;\n
-    - (4):xxx.\n\n
-Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible,
-do not have too much repetitive information, numerical values using the original numbers.
-        """
-        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
-        file_write_buffer.extend(final_results)
-        i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000)
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user='开始最终总结', 
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results, 
-            sys_prompt= f"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters"
-        )
-        final_results.append(gpt_say)
-        file_write_buffer.extend([i_say, gpt_say])
-        ############################## <第 4 步，设置一个token上限> ##################################
-        _, final_results = input_clipping("", final_results, max_token_limit=3200)
-        yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-    res = write_results_to_file(file_write_buffer, file_name=gen_time_str())
-    promote_file_to_downloadzone(res.split('\t')[-1], chatbot=chatbot)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面
-
-
-@CatchException
-def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/批量总结PDF文档pdfminer.py

@@ -1,160 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-fast_debug = False
-
-def readPdf(pdfPath):
-    """
-    读取pdf文件，返回文本内容
-    """
-    import pdfminer
-    from pdfminer.pdfparser import PDFParser
-    from pdfminer.pdfdocument import PDFDocument
-    from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
-    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
-    from pdfminer.pdfdevice import PDFDevice
-    from pdfminer.layout import LAParams
-    from pdfminer.converter import PDFPageAggregator
-
-    fp = open(pdfPath, 'rb')
-
-    # Create a PDF parser object associated with the file object
-    parser = PDFParser(fp)
-
-    # Create a PDF document object that stores the document structure.
-    # Password for initialization as 2nd parameter
-    document = PDFDocument(parser)
-    # Check if the document allows text extraction. If not, abort.
-    if not document.is_extractable:
-        raise PDFTextExtractionNotAllowed
-
-    # Create a PDF resource manager object that stores shared resources.
-    rsrcmgr = PDFResourceManager()
-
-    # Create a PDF device object.
-    # device = PDFDevice(rsrcmgr)
-
-    # BEGIN LAYOUT ANALYSIS.
-    # Set parameters for analysis.
-    laparams = LAParams(
-        char_margin=10.0,
-        line_margin=0.2,
-        boxes_flow=0.2,
-        all_texts=False,
-    )
-    # Create a PDF page aggregator object.
-    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
-    # Create a PDF interpreter object.
-    interpreter = PDFPageInterpreter(rsrcmgr, device)
-
-    # loop over all pages in the document
-    outTextList = []
-    for page in PDFPage.create_pages(document):
-        # read the page into a layout object
-        interpreter.process_page(page)
-        layout = device.get_result()
-        for obj in layout._objs:
-            if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
-                # print(obj.get_text())
-                outTextList.append(obj.get_text())
-
-    return outTextList
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    from bs4 import BeautifulSoup
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        if ".tex" in fp:
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-        if ".pdf" in fp.lower():
-            file_content = readPdf(fp)
-            file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug:
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug:
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, 
-            inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, 
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import pdfminer, bs4
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,309 +0,0 @@
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
-from toolbox import write_history_to_file, get_log_folder
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url
-from colorful import *
-import glob
-import os
-import math
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-
-    disable_auto_promotion(chatbot)
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    from .crazy_utils import get_files_from_everything
-    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if not success:
-        if txt == "": txt = '空空如也的输入栏'
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    grobid_url = get_avail_grobid_url()
-    if grobid_url is not None:
-        yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
-    else:
-        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
-        yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
-    import copy
-    import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
-    generated_conclusion_files = []
-    generated_html_files = []
-    DST_LANG = "中文"
-    for index, fp in enumerate(file_manifest):
-        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        article_dict = parse_pdf(fp, grobid_url)
-        print(article_dict)
-        prompt = "以下是一篇学术论文的基本信息:\n"
-        # title
-        title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
-        # authors
-        authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n'
-        # abstract
-        abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
-        # command
-        prompt += f"请将题目和摘要翻译为{DST_LANG}。"
-        meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
-
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=prompt,
-            inputs_show_user=prompt,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="You are an academic paper reader。",
-        )
-
-        # 多线，翻译
-        inputs_array = []
-        inputs_show_user_array = []
-
-        # get_token_num
-        from request_llm.bridge_all import model_info
-        enc = model_info[llm_kwargs['llm_model']]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-
-        def break_down(txt):
-            raw_token_num = get_token_num(txt)
-            if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
-                return [txt]
-            else:
-                # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
-                # find a smooth token limit to achieve even seperation
-                count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
-                token_limit_smooth = raw_token_num // count + count
-                return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
-
-        for section in article_dict.get('sections'):
-            if len(section['text']) == 0: continue
-            section_frags = break_down(section['text'])
-            for i, fragment in enumerate(section_frags):
-                heading = section['heading']
-                if len(section_frags) > 1: heading += f' Part-{i+1}'
-                inputs_array.append(
-                    f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
-                )
-                inputs_show_user_array.append(
-                    f"# {heading}\n\n{fragment}"
-                )
-
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[meta for _ in inputs_array],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
-        )
-        res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None)
-        promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
-        generated_conclusion_files.append(res_path)
-
-        ch = construct_html() 
-        orig = ""
-        trans = ""
-        gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-        for i,k in enumerate(gpt_response_collection_html): 
-            if i%2==0:
-                gpt_response_collection_html[i] = inputs_show_user_array[i//2]
-            else:
-                gpt_response_collection_html[i] = gpt_response_collection_html[i]
-
-        final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
-        final.extend(gpt_response_collection_html)
-        for i, k in enumerate(final): 
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-        html_file = ch.save_file(create_report_file_name)
-        generated_html_files.append(html_file)
-        promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
-
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import copy
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
-    generated_conclusion_files = []
-    generated_html_files = []
-    for index, fp in enumerate(file_manifest):
-        # 读取PDF文件
-        file_content, page_one = read_and_clean_pdf_text(fp)
-        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
-
-        # 递归地切割PDF文件
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
-            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="Your job is to collect information from materials。",
-        )
-
-        # 多线，翻译
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[
-                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
-            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[paper_meta] for _ in paper_fragments],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
-            # max_workers=5  # OpenAI所允许的最大并行过载
-        )
-        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
-        # 整理报告的格式
-        for i,k in enumerate(gpt_response_collection_md): 
-            if i%2==0:
-                gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
-            else:
-                gpt_response_collection_md[i] = gpt_response_collection_md[i]
-        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
-        final.extend(gpt_response_collection_md)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
-        res = write_results_to_file(final, file_name=create_report_file_name)
-
-        # 更新UI
-        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
-        chatbot.append((f"{fp}完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        # write html
-        try:
-            ch = construct_html() 
-            orig = ""
-            trans = ""
-            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-            for i,k in enumerate(gpt_response_collection_html): 
-                if i%2==0:
-                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
-                else:
-                    gpt_response_collection_html[i] = gpt_response_collection_html[i]
-            final = ["论文概况", paper_meta_info.replace('# ', '### '),  "二、论文翻译",  ""]
-            final.extend(gpt_response_collection_html)
-            for i, k in enumerate(final): 
-                if i%2==0:
-                    orig = k
-                if i%2==1:
-                    trans = k
-                    ch.add_row(a=orig, b=trans)
-            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-            generated_html_files.append(ch.save_file(create_report_file_name))
-        except:
-            from toolbox import trimmed_format_exc
-            print('writing html result failed:', trimmed_format_exc())
-
-    # 准备文件的下载
-    for pdf_path in generated_conclusion_files:
-        # 重命名文件
-        rename_file = f'翻译-{os.path.basename(pdf_path)}'
-        promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
-    for html_path in generated_html_files:
-        # 重命名文件
-        rename_file = f'翻译-{os.path.basename(html_path)}'
-        promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-class construct_html():
-    def __init__(self) -> None:
-        self.css = """
-.row {
-  display: flex;
-  flex-wrap: wrap;
-}
-
-.column {
-  flex: 1;
-  padding: 10px;
-}
-
-.table-header {
-  font-weight: bold;
-  border-bottom: 1px solid black;
-}
-
-.table-row {
-  border-bottom: 1px solid lightgray;
-}
-
-.table-cell {
-  padding: 5px;
-}
-        """
-        self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
-
-
-    def add_row(self, a, b):
-        tmp = """
-<div class="row table-row">
-    <div class="column table-cell">REPLACE_A</div>
-    <div class="column table-cell">REPLACE_B</div>
-</div>
-        """
-        from toolbox import markdown_convertion
-        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
-        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
-        self.html_string += tmp
-
-
-    def save_file(self, file_name):
-        with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f:
-            f.write(self.html_string.encode('utf-8', 'ignore').decode())
-        return os.path.join(get_log_folder(), file_name)

crazy_functions/数学动画生成manim.py

@@ -1,187 +0,0 @@
-from toolbox import CatchException, update_ui, gen_time_str
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import input_clipping
-
-def inspect_dependency(chatbot, history):
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import manim
-        return True
-    except:
-        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install manim manimgl```"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return False
-
-def eval_manim(code):
-    import subprocess, sys, os, shutil
-
-    with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f:
-        f.write(code)
-
-    def get_class_name(class_string):
-        import re
-        # Use regex to extract the class name
-        class_name = re.search(r'class (\w+)\(', class_string).group(1)
-        return class_name
-
-    class_name = get_class_name(code)
-
-    try: 
-        subprocess.check_output([sys.executable, '-c', f"from gpt_log.MyAnimation import {class_name}; {class_name}().render()"])
-        shutil.move('media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{gen_time_str()}.mp4')
-        return f'gpt_log/{gen_time_str()}.mp4'
-    except subprocess.CalledProcessError as e:
-        output = e.output.decode()
-        print(f"Command returned non-zero exit status {e.returncode}: {output}.")
-        return f"Evaluating python script failed: {e.output}."
-    except: 
-        print('generating mp4 failed')
-        return "Generating mp4 failed."
-
-
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) != 1: 
-        raise RuntimeError("GPT is not generating proper code.")
-    return matches[0].strip('python') #  code block
-
-@CatchException
-def 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    # 清空历史，以免输入溢出
-    history = []    
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..."
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖, 如果缺少依赖, 则给出安装建议
-    dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
-    if not dep_ok: return
-    
-    # 输入
-    i_say = f'Generate a animation to show: ' + txt
-    demo = ["Here is some examples of manim", examples_of_manim()]
-    _, demo = input_clipping(inputs="", history=demo, max_token_limit=2560)
-    # 开始
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, 
-        sys_prompt=
-        r"Write a animation script with 3blue1brown's manim. "+
-        r"Please begin with `from manim import *`. " + 
-        r"Answer me with a code block wrapped by ```."
-    )
-    chatbot.append(["开始生成动画", "..."])
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-    
-    # 将代码转为动画
-    code = get_code_block(gpt_say)
-    res = eval_manim(code)
-
-    chatbot.append(("生成的视频文件路径", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-# 在这里放一些网上搜集的demo，辅助gpt生成代码
-def examples_of_manim():
-    return r"""
-
-
-```
-
-class MovingGroupToDestination(Scene):
-    def construct(self):
-        group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4)
-        dest = Dot([4, 3, 0], color=YELLOW)
-        self.add(group, dest)
-        self.play(group.animate.shift(dest.get_center() - group[2].get_center()))
-        self.wait(0.5)
-
-```
-
-
-```
-
-class LatexWithMovingFramebox(Scene):
-    def construct(self):
-        text=MathTex(
-            "\\frac{d}{dx}f(x)g(x)=","f(x)\\frac{d}{dx}g(x)","+",
-            "g(x)\\frac{d}{dx}f(x)"
-        )
-        self.play(Write(text))
-        framebox1 = SurroundingRectangle(text[1], buff = .1)
-        framebox2 = SurroundingRectangle(text[3], buff = .1)
-        self.play(
-            Create(framebox1),
-        )
-        self.wait()
-        self.play(
-            ReplacementTransform(framebox1,framebox2),
-        )
-        self.wait()
-
-```
-
-
-
-```
-
-class PointWithTrace(Scene):
-    def construct(self):
-        path = VMobject()
-        dot = Dot()
-        path.set_points_as_corners([dot.get_center(), dot.get_center()])
-        def update_path(path):
-            previous_path = path.copy()
-            previous_path.add_points_as_corners([dot.get_center()])
-            path.become(previous_path)
-        path.add_updater(update_path)
-        self.add(path, dot)
-        self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))
-        self.wait()
-        self.play(dot.animate.shift(UP))
-        self.play(dot.animate.shift(LEFT))
-        self.wait()
-
-```
-
-```
-
-# do not use get_graph, this funciton is deprecated
-
-class ExampleFunctionGraph(Scene):
-    def construct(self):
-        cos_func = FunctionGraph(
-            lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t),
-            color=RED,
-        )
-
-        sin_func_1 = FunctionGraph(
-            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
-            color=BLUE,
-        )
-
-        sin_func_2 = FunctionGraph(
-            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
-            x_range=[-4, 4],
-            color=GREEN,
-        ).move_to([0, 1, 0])
-
-        self.add(cos_func, sin_func_1, sin_func_2)
-
-```
-"""

crazy_functions/理解PDF文档内容.py

@@ -1,114 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-    file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-    page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
-    
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-    
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot, 
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section."  # 提示
-                                                                        ) 
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/生成函数注释.py

@@ -1,54 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
-        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    if not fast_debug: 
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
-
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/联网的ChatGPT.py

@@ -1,106 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
-import requests
-from bs4 import BeautifulSoup
-from request_llm.bridge_all import model_info
-
-def google(query, proxies):
-    query = query # 在此处替换您要搜索的关键词
-    url = f"https://www.google.com/search?q={query}"
-    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    response = requests.get(url, headers=headers, proxies=proxies)
-    soup = BeautifulSoup(response.content, 'html.parser')
-    results = []
-    for g in soup.find_all('div', class_='g'):
-        anchors = g.find_all('a')
-        if anchors:
-            link = anchors[0]['href']
-            if link.startswith('/url?q='):
-                link = link[7:]
-            if not link.startswith('http'):
-                continue
-            title = g.find('h3').text
-            item = {'title': title, 'link': link}
-            results.append(item)
-
-    for r in results:
-        print(r['link'])
-    return results
-
-def scrape_text(url, proxies) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
-        'Content-Type': 'text/plain',
-    }
-    try: 
-        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
-        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
-    except: 
-        return "无法连接到该网页"
-    soup = BeautifulSoup(response.text, "html.parser")
-    for script in soup(["script", "style"]):
-        script.extract()
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = "\n".join(chunk for chunk in chunks if chunk)
-    return text
-
-@CatchException
-def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((f"请结合互联网信息回答以下问题：{txt}", 
-                    "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    urls = google(txt, proxies)
-    history = []
-    if len(urls) == 0:
-        chatbot.append((f"结论：{txt}",
-                        "[Local Message] 受到google限制，无法从google获取信息！"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-        return
-    # ------------- < 第2步：依次访问网页 > -------------
-    max_search_result = 5   # 最多收纳多少个网页的结果
-    for index, url in enumerate(urls[:max_search_result]):
-        res = scrape_text(url['link'], proxies)
-        history.extend([f"第{index}份搜索结果：", res])
-        chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第3步：ChatGPT综合 > -------------
-    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
-    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token
-        inputs=i_say, 
-        history=history, 
-        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
-    )
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
-    )
-    chatbot[-1] = (i_say, gpt_say)
-    history.append(i_say);history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-

crazy_functions/联网的ChatGPT_bing版.py

@@ -1,106 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
-import requests
-from bs4 import BeautifulSoup
-from request_llm.bridge_all import model_info
-
-
-def bing_search(query, proxies=None):
-    query = query
-    url = f"https://cn.bing.com/search?q={query}"
-    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    response = requests.get(url, headers=headers, proxies=proxies)
-    soup = BeautifulSoup(response.content, 'html.parser')
-    results = []
-    for g in soup.find_all('li', class_='b_algo'):
-        anchors = g.find_all('a')
-        if anchors:
-            link = anchors[0]['href']
-            if not link.startswith('http'):
-                continue
-            title = g.find('h2').text
-            item = {'title': title, 'link': link}
-            results.append(item)
-
-    for r in results:
-        print(r['link'])
-    return results
-
-
-def scrape_text(url, proxies) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
-        'Content-Type': 'text/plain',
-    }
-    try:
-        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
-        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
-    except:
-        return "无法连接到该网页"
-    soup = BeautifulSoup(response.text, "html.parser")
-    for script in soup(["script", "style"]):
-        script.extract()
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = "\n".join(chunk for chunk in chunks if chunk)
-    return text
-
-@CatchException
-def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((f"请结合互联网信息回答以下问题：{txt}",
-                    "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    urls = bing_search(txt, proxies)
-    history = []
-    if len(urls) == 0:
-        chatbot.append((f"结论：{txt}",
-                        "[Local Message] 受到bing限制，无法从bing获取信息！"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-        return
-    # ------------- < 第2步：依次访问网页 > -------------
-    max_search_result = 8   # 最多收纳多少个网页的结果
-    for index, url in enumerate(urls[:max_search_result]):
-        res = scrape_text(url['link'], proxies)
-        history.extend([f"第{index}份搜索结果：", res])
-        chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第3步：ChatGPT综合 > -------------
-    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
-    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token
-        inputs=i_say,
-        history=history,
-        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
-    )
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
-        sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
-    )
-    chatbot[-1] = (i_say, gpt_say)
-    history.append(i_say);history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-

crazy_functions/虚空终端.py

@@ -1,179 +0,0 @@
-"""
-Explanation of the Void Terminal Plugin:
-
-Please describe in natural language what you want to do.
-
-1. You can open the plugin's dropdown menu to explore various capabilities of this project, and then describe your needs in natural language, for example:
-- "Please call the plugin to translate a PDF paper for me. I just uploaded the paper to the upload area."
-- "Please use the plugin to translate a PDF paper, with the address being https://www.nature.com/articles/s41586-019-1724-z.pdf."
-- "Generate an image with blooming flowers and lush green grass using the plugin."
-- "Translate the README using the plugin. The GitHub URL is https://github.com/facebookresearch/co-tracker."
-- "Translate an Arxiv paper for me. The Arxiv ID is 1812.10695. Remember to use the plugin and don't do it manually!"
-- "I don't like the current interface color. Modify the configuration and change the theme to THEME="High-Contrast"."
-- "Could you please explain the structure of the Transformer network?"
-
-2. If you use keywords like "call the plugin xxx", "modify the configuration xxx", "please", etc., your intention can be recognized more accurately.
-
-3. Your intention can be recognized more accurately when using powerful models like GPT4. This plugin is relatively new, so please feel free to provide feedback on GitHub.
-
-4. Now, if you need to process a file, please upload the file (drag the file to the file upload area) or describe the path to the file.
-
-5. If you don't need to upload a file, you can simply repeat your command again.
-"""
-explain_msg = """
-## 虚空终端插件说明:
-
-1. 请用**自然语言**描述您需要做什么。例如：
-    - 「请调用插件，为我翻译PDF论文，论文我刚刚放到上传区了。」
-    - 「请调用插件翻译PDF论文，地址为https://www.nature.com/articles/s41586-019-1724-z.pdf」
-    - 「生成一张图片，图中鲜花怒放，绿草如茵，用插件实现。」
-    - 「用插件翻译README，Github网址是https://github.com/facebookresearch/co-tracker」
-    - 「给爷翻译Arxiv论文，arxiv论文的ID是1812.10695，记得用插件，不要自己瞎搞！」
-    - 「我不喜欢当前的界面颜色，修改配置，把主题THEME更换为THEME="High-Contrast"。」
-    - 「请问Transformer网络的结构是怎样的？」
-
-2. 您可以打开插件下拉菜单以了解本项目的各种能力。    
-
-3. 如果您使用「调用插件xxx」、「修改配置xxx」、「请问」等关键词，您的意图可以被识别的更准确。
-
-4. 建议使用 GPT3.5 或更强的模型，弱模型可能无法理解您的想法。该插件诞生时间不长，欢迎您前往Github反馈问题。
-
-5. 现在，如果需要处理文件，请您上传文件（将文件拖动到文件上传区），或者描述文件所在的路径。
-
-6. 如果不需要上传文件，现在您只需要再次重复一次您的指令即可。
-"""
-
-from pydantic import BaseModel, Field
-from typing import List
-from toolbox import CatchException, update_ui, gen_time_str
-from toolbox import update_ui_lastest_msg, disable_auto_promotion
-from request_llm.bridge_all import predict_no_ui_long_connection
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from crazy_functions.crazy_utils import input_clipping
-from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
-from crazy_functions.vt_fns.vt_state import VoidTerminalState
-from crazy_functions.vt_fns.vt_modify_config import modify_configuration_hot
-from crazy_functions.vt_fns.vt_modify_config import modify_configuration_reboot
-from crazy_functions.vt_fns.vt_call_plugin import execute_plugin
-
-class UserIntention(BaseModel):
-    user_prompt: str = Field(description="the content of user input", default="")
-    intention_type: str = Field(description="the type of user intention, choose from ['ModifyConfiguration', 'ExecutePlugin', 'Chat']", default="ExecutePlugin")
-    user_provide_file: bool = Field(description="whether the user provides a path to a file", default=False)
-    user_provide_url: bool = Field(description="whether the user provides a url", default=False)
-
-
-def chat(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=txt, inputs_show_user=txt,
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-        sys_prompt=system_prompt
-    )
-    chatbot[-1] = [txt, gpt_say]
-    history.extend([txt, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    pass
-
-
-explain_intention_to_user = {
-    'Chat': "聊天对话",
-    'ExecutePlugin': "调用插件",
-    'ModifyConfiguration': "修改配置",
-}
-
-
-def analyze_intention_with_simple_rules(txt):
-    user_intention = UserIntention()
-    user_intention.user_prompt = txt
-    is_certain = False
-
-    if '请问' in txt:
-        is_certain = True
-        user_intention.intention_type = 'Chat'
-
-    if '用插件' in txt:
-        is_certain = True
-        user_intention.intention_type = 'ExecutePlugin'
-
-    if '修改配置' in txt:
-        is_certain = True
-        user_intention.intention_type = 'ModifyConfiguration'
-
-    return is_certain, user_intention
-
-
-@CatchException
-def 虚空终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    disable_auto_promotion(chatbot=chatbot)
-    # 获取当前虚空终端状态
-    state = VoidTerminalState.get_state(chatbot)
-    appendix_msg = ""
-
-    # 用简单的关键词检测用户意图
-    is_certain, _ = analyze_intention_with_simple_rules(txt)
-    if txt.startswith('private_upload/') and len(txt) == 34:
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=False)
-        appendix_msg = "\n\n**很好，您已经上传了文件**，现在请您描述您的需求。"
-        
-    if is_certain or (state.has_provided_explaination):
-        # 如果意图明确，跳过提示环节
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
-        state.unlock_plugin(chatbot=chatbot)
-        yield from update_ui(chatbot=chatbot, history=history)
-        yield from 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
-        return
-    else:
-        # 如果意图模糊，提示
-        state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
-        state.lock_plugin(chatbot=chatbot)
-        chatbot.append(("虚空终端状态:", explain_msg+appendix_msg))
-        yield from update_ui(chatbot=chatbot, history=history)
-        return
-
-
-
-def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []
-    chatbot.append(("虚空终端状态: ", f"正在执行任务: {txt}"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # ⭐ ⭐ ⭐ 分析用户意图
-    is_certain, user_intention = analyze_intention_with_simple_rules(txt)
-    if not is_certain:
-        yield from update_ui_lastest_msg(
-            lastmsg=f"正在执行任务: {txt}\n\n分析用户意图中", chatbot=chatbot, history=history, delay=0)
-        gpt_json_io = GptJsonIO(UserIntention)
-        rf_req = "\nchoose from ['ModifyConfiguration', 'ExecutePlugin', 'Chat']"
-        inputs = "Analyze the intention of the user according to following user input: \n\n" + \
-            ">> " + (txt+rf_req).rstrip('\n').replace('\n','\n>> ') + '\n\n' + gpt_json_io.format_instructions
-        run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(
-            inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
-        analyze_res = run_gpt_fn(inputs, "")
-        try:
-            user_intention = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
-            lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}", 
-        except JsonStringError as e:
-            yield from update_ui_lastest_msg(
-                lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 失败 当前语言模型（{llm_kwargs['llm_model']}）不能理解您的意图", chatbot=chatbot, history=history, delay=0)
-            return
-    else:
-        pass
-
-    yield from update_ui_lastest_msg(
-        lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}", 
-        chatbot=chatbot, history=history, delay=0)
-
-    # 用户意图: 修改本项目的配置
-    if user_intention.intention_type == 'ModifyConfiguration':
-        yield from modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
-
-    # 用户意图: 调度插件
-    if user_intention.intention_type == 'ExecutePlugin':
-        yield from execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
-
-    # 用户意图: 聊天
-    if user_intention.intention_type == 'Chat':
-        yield from chat(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
-
-    return
-

crazy_functions/解析JupyterNotebook.py

@@ -1,146 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = True
-
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(
-            enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-                    file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(
-                        self.file_paths[index] + f".part-{j}.txt")
-
-
-
-def parseNotebook(filename, enable_markdown=1):
-    import json
-
-    CodeBlocks = []
-    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
-        notebook = json.load(f)
-    for cell in notebook['cells']:
-        if cell['cell_type'] == 'code' and cell['source']:
-            # remove blank lines
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("".join(cell['source']))
-        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("Markdown:"+"".join(cell['source']))
-
-    Code = ""
-    for idx, code in enumerate(CodeBlocks):
-        Code += f"This is {idx+1}th code block: \n"
-        Code += code+"\n"
-
-    return Code 
-
-
-def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    enable_markdown = plugin_kwargs.get("advanced_arg", "1")
-    try:
-        enable_markdown = int(enable_markdown)
-    except ValueError:
-        enable_markdown = 1
-
-    pfg = PaperFileGroup()
-
-    for fp in file_manifest:
-        file_content = parseNotebook(fp, enable_markdown=enable_markdown)
-        pfg.file_paths.append(fp)
-        pfg.file_contents.append(file_content)
-
-    #  <-------- 拆分过长的IPynb文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
-                    r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
-                    r"Start a new line for a block and block num use Chinese." +
-                    f"\n\n{frag}" for frag in pfg.sp_file_contents]
-    inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
-    sys_prompt_array = ["You are a professional programmer."] * n_split
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len=80
-    )
-
-    #  <-------- 整理结果，退出 ---------->
-    block_result = "  \n".join(gpt_response_collection)
-    chatbot.append(("解析的结果如下", block_result))
-    history.extend(["解析的结果如下", block_result])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    #  <-------- 写入文件，退出 ---------->
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-@CatchException
-def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    chatbot.append([
-        "函数插件功能？",
-        "对IPynb文件进行解析。Contributor: codycjy."])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    history = []    # 清空历史
-    import glob
-    import os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    if txt.endswith('.ipynb'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(
-            f'{project_folder}/**/*.ipynb', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )

crazy_functions/解析项目源代码.py

@@ -1,352 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import input_clipping
-
-def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import os, copy
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-    msg = '正常'
-    summary_batch_isolation = True
-    inputs_array = []
-    inputs_show_user_array = []
-    history_array = []
-    sys_prompt_array = []
-    report_part_1 = []
-
-    assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
-    ############################## <第一步，逐个文件分析，多线程> ##################################
-    for index, fp in enumerate(file_manifest):
-        # 读取文件
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        # 装载请求内容
-        inputs_array.append(i_say)
-        inputs_show_user_array.append(i_say_show_user)
-        history_array.append([])
-        sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
-
-    # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array = inputs_array,
-        inputs_show_user_array = inputs_show_user_array,
-        history_array = history_array,
-        sys_prompt_array = sys_prompt_array,
-        llm_kwargs = llm_kwargs,
-        chatbot = chatbot,
-        show_user_at_complete = True
-    )
-
-    # 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
-    report_part_1 = copy.deepcopy(gpt_response_collection)
-    history_to_return = report_part_1
-    res = write_results_to_file(report_part_1)
-    chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
-    batchsize = 16  # 10个文件为一组
-    report_part_2 = []
-    previous_iteration_files = []
-    last_iteration_result = ""
-    while True:
-        if len(file_manifest) == 0: break
-        this_iteration_file_manifest = file_manifest[:batchsize]
-        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
-        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
-        # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
-        for index, content in enumerate(this_iteration_gpt_response_collection):
-            if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
-        this_iteration_files = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
-        previous_iteration_files.extend(this_iteration_files)
-        previous_iteration_files_string = ', '.join(previous_iteration_files)
-        current_iteration_focus = ', '.join(this_iteration_files)
-        if summary_batch_isolation: focus = current_iteration_focus
-        else:                       focus = previous_iteration_files_string
-        i_say = f'用一张Markdown表格简要描述以下文件的功能：{focus}。根据以上分析，用一句话概括程序的整体功能。'
-        if last_iteration_result != "":
-            sys_prompt_additional = "已知某些代码的局部作用是:" + last_iteration_result + "\n请继续分析其他源代码，从而更全面地理解项目的整体功能。"
-        else:
-            sys_prompt_additional = ""
-        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
-        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
-        this_iteration_history.append(last_iteration_result)
-        # 裁剪input
-        inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)
-        result = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
-            history=this_iteration_history_feed,   # 迭代之前的分析
-            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
-        
-        summary = "请用一句话概括这些文件的整体功能"
-        summary_result = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=summary, 
-            inputs_show_user=summary, 
-            llm_kwargs=llm_kwargs, 
-            chatbot=chatbot,
-            history=[i_say, result],   # 迭代之前的分析
-            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
-
-        report_part_2.extend([i_say, result])
-        last_iteration_result = summary_result
-        file_manifest = file_manifest[batchsize:]
-        gpt_response_collection = gpt_response_collection[batchsize*2:]
-
-    ############################## <END> ##################################
-    history_to_return.extend(report_part_2)
-    res = write_results_to_file(history_to_return)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-
-@CatchException
-def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \
-                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    project_folder = './'
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.vue', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.less', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sass', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.wxml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.wxss', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    txt_pattern = plugin_kwargs.get("advanced_arg")
-    txt_pattern = txt_pattern.replace("，", ",")
-    # 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
-    pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
-    if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
-    # 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
-    pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
-    pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
-    # 将要忽略匹配的文件名(例如: ^README.md)
-    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
-    # 生成正则表达式
-    pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
-    pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
-
-    history.clear()
-    import glob, os, re
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    # 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
-    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
-    if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):
-        extract_folder_path = maybe_dir[0]
-    else:
-        extract_folder_path = project_folder
-    # 按输入的匹配模式寻找上传的非压缩文件和已解压的文件
-    file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
-                      os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/询问多个大语言模型.py

@@ -1,62 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=txt, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt=system_prompt,
-        retry_times_at_unknown_error=0
-    )
-
-    history.append(txt)
-    history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-@CatchException
-def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-
-    chatbot.append((txt, f"正在同时咨询{llm_kwargs['llm_model']}"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=txt, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt=system_prompt,
-        retry_times_at_unknown_error=0
-    )
-
-    history.append(txt)
-    history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

crazy_functions/语音助手.py

@@ -1,195 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, get_conf, markdown_convertion
-from crazy_functions.crazy_utils import input_clipping
-from request_llm.bridge_all import predict_no_ui_long_connection
-import threading, time
-import numpy as np
-from .live_audio.aliyunASR import AliyunASR
-import json
-
-class WatchDog():
-    def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
-        self.last_feed = None
-        self.timeout = timeout
-        self.bark_fn = bark_fn
-        self.interval = interval
-        self.msg = msg
-        self.kill_dog = False
-    
-    def watch(self):
-        while True:
-            if self.kill_dog: break
-            if time.time() - self.last_feed > self.timeout:
-                if len(self.msg) > 0: print(self.msg)
-                self.bark_fn()
-                break
-            time.sleep(self.interval)
-
-    def begin_watch(self):
-        self.last_feed = time.time()
-        th = threading.Thread(target=self.watch)
-        th.daemon = True
-        th.start()
-
-    def feed(self):
-        self.last_feed = time.time()
-
-def chatbot2history(chatbot):
-    history = []
-    for c in chatbot:
-        for q in c:
-            if q not in ["[请讲话]", "[等待GPT响应]", "[正在等您说完问题]"]:
-                history.append(q.strip('<div class="markdown-body">').strip('</div>').strip('<p>').strip('</p>'))
-    return history
-
-class AsyncGptTask():
-    def __init__(self) -> None:
-        self.observe_future = []
-        self.observe_future_chatbot_index = []
-
-    def gpt_thread_worker(self, i_say, llm_kwargs, history, sys_prompt, observe_window, index):
-        try:
-            MAX_TOKEN_ALLO = 2560
-            i_say, history = input_clipping(i_say, history, max_token_limit=MAX_TOKEN_ALLO)
-            gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt, 
-                                                            observe_window=observe_window[index], console_slience=True)
-        except ConnectionAbortedError as token_exceed_err:
-            print('至少一个线程任务Token溢出而失败', e)
-        except Exception as e:
-            print('至少一个线程任务意外失败', e)
-
-    def add_async_gpt_task(self, i_say, chatbot_index, llm_kwargs, history, system_prompt):
-        self.observe_future.append([""])
-        self.observe_future_chatbot_index.append(chatbot_index)
-        cur_index = len(self.observe_future)-1
-        th_new = threading.Thread(target=self.gpt_thread_worker, args=(i_say, llm_kwargs, history, system_prompt, self.observe_future, cur_index))
-        th_new.daemon = True
-        th_new.start()
-
-    def update_chatbot(self, chatbot):
-        for of, ofci in zip(self.observe_future, self.observe_future_chatbot_index):
-            try:
-                chatbot[ofci] = list(chatbot[ofci])
-                chatbot[ofci][1] = markdown_convertion(of[0])
-            except:
-                self.observe_future = []
-                self.observe_future_chatbot_index = []
-        return chatbot
-
-class InterviewAssistant(AliyunASR):
-    def __init__(self):
-        self.capture_interval = 0.5 # second
-        self.stop = False
-        self.parsed_text = ""
-        self.parsed_sentence = ""
-        self.buffered_sentence = ""
-        self.event_on_result_chg = threading.Event()
-        self.event_on_entence_end = threading.Event()
-        self.event_on_commit_question = threading.Event()
-
-    def __del__(self):
-        self.stop = True
-        self.stop_msg = ""
-        self.commit_wd.kill_dog = True
-        self.plugin_wd.kill_dog = True
-
-    def init(self, chatbot):
-        # 初始化音频采集线程
-        self.captured_audio = np.array([])
-        self.keep_latest_n_second = 10
-        self.commit_after_pause_n_second = 2.0
-        self.ready_audio_flagment = None
-        self.stop = False
-        self.plugin_wd = WatchDog(timeout=5, bark_fn=self.__del__, msg="程序终止")
-        self.aut = threading.Thread(target=self.audio_convertion_thread, args=(chatbot._cookies['uuid'],))
-        self.aut.daemon = True
-        self.aut.start()
-        # th2 = threading.Thread(target=self.audio2txt_thread, args=(chatbot._cookies['uuid'],))
-        # th2.daemon = True
-        # th2.start()
-
-    def no_audio_for_a_while(self):
-        if len(self.buffered_sentence) < 7: # 如果一句话小于7个字，暂不提交
-            self.commit_wd.begin_watch()
-        else:
-            self.event_on_commit_question.set()
-
-    def begin(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-        # main plugin function
-        self.init(chatbot)
-        chatbot.append(["[请讲话]", "[正在等您说完问题]"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        self.plugin_wd.begin_watch()
-        self.agt = AsyncGptTask()
-        self.commit_wd = WatchDog(timeout=self.commit_after_pause_n_second, bark_fn=self.no_audio_for_a_while, interval=0.2)
-        self.commit_wd.begin_watch()
-
-        while not self.stop:
-            self.event_on_result_chg.wait(timeout=0.25)  # run once every 0.25 second
-            chatbot = self.agt.update_chatbot(chatbot)   # 将子线程的gpt结果写入chatbot
-            history = chatbot2history(chatbot)
-            yield from update_ui(chatbot=chatbot, history=history)      # 刷新界面
-            self.plugin_wd.feed()
-
-            if self.event_on_result_chg.is_set(): 
-                # update audio decode result
-                self.event_on_result_chg.clear()
-                chatbot[-1] = list(chatbot[-1])
-                chatbot[-1][0] = self.buffered_sentence + self.parsed_text
-                history = chatbot2history(chatbot)
-                yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-                self.commit_wd.feed()
-
-            if self.event_on_entence_end.is_set():
-                # called when a sentence has ended
-                self.event_on_entence_end.clear()
-                self.parsed_text = self.parsed_sentence
-                self.buffered_sentence += self.parsed_sentence
-
-            if self.event_on_commit_question.is_set():
-                # called when a question should be commited
-                self.event_on_commit_question.clear()
-                if len(self.buffered_sentence) == 0: raise RuntimeError
-
-                self.commit_wd.begin_watch()
-                chatbot[-1] = list(chatbot[-1])
-                chatbot[-1] = [self.buffered_sentence, "[等待GPT响应]"]
-                yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-                # add gpt task 创建子线程请求gpt，避免线程阻塞
-                history = chatbot2history(chatbot)
-                self.agt.add_async_gpt_task(self.buffered_sentence, len(chatbot)-1, llm_kwargs, history, system_prompt)
-                
-                self.buffered_sentence = ""
-                chatbot.append(["[请讲话]", "[正在等您说完问题]"])
-                yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if len(self.stop_msg) != 0:
-            raise RuntimeError(self.stop_msg)
-
-
-
-@CatchException
-def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # pip install -U openai-whisper
-    chatbot.append(["对话助手函数插件：使用时，双手离开鼠标键盘吧", "音频助手, 正在听您讲话（点击“停止”键可终止程序）..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import nls
-        from scipy import io
-    except:
-        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    APPKEY = get_conf('ALIYUN_APPKEY')
-    if APPKEY == "":
-        chatbot.append(["导入依赖失败", "没有阿里云语音识别APPKEY和TOKEN, 详情见https://help.aliyun.com/document_detail/450255.html"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    ia = InterviewAssistant()
-    yield from ia.begin(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-

crazy_functions/读文章写摘要.py

@@ -1,67 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/谷歌检索小助手.py

@@ -1,112 +0,0 @@
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-
-def get_meta_information(url, chatbot, history):
-    import requests
-    import arxiv
-    import difflib
-    from bs4 import BeautifulSoup
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
-    }
-    # 发送 GET 请求
-    response = requests.get(url, proxies=proxies, headers=headers)
-
-    # 解析网页内容
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    def string_similar(s1, s2):
-        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
-
-    profile = []
-    # 获取所有文章的标题和作者
-    for result in soup.select(".gs_ri"):
-        title = result.a.text.replace('\n', ' ').replace('  ', ' ')
-        author = result.select_one(".gs_a").text
-        try:
-            citation = result.select_one(".gs_fl > a[href*='cites']").text  # 引用次数是链接中的文本，直接取出来
-        except:
-            citation = 'cited by 0'
-        abstract = result.select_one(".gs_rs").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格
-        search = arxiv.Search(
-            query = title,
-            max_results = 1,
-            sort_by = arxiv.SortCriterion.Relevance,
-        )
-        try:
-            paper = next(search.results())
-            if string_similar(title, paper.title) > 0.90: # same paper
-                abstract = paper.summary.replace('\n', ' ')
-                is_paper_in_arxiv = True
-            else:   # different paper
-                abstract = abstract
-                is_paper_in_arxiv = False
-            paper = next(search.results())
-        except:
-            abstract = abstract
-            is_paper_in_arxiv = False
-        print(title)
-        print(author)
-        print(citation)
-        profile.append({
-            'title':title,
-            'author':author,
-            'citation':citation,
-            'abstract':abstract,
-            'is_paper_in_arxiv':is_paper_in_arxiv,
-        })
-
-        chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    return profile
-
-@CatchException
-def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import arxiv
-        import math
-        from bs4 import BeautifulSoup
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
-    batchsize = 5
-    for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
-        if len(meta_paper_info_list[:batchsize]) > 0:
-            i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
-            "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
-            f"以下是信息源：{str(meta_paper_info_list[:batchsize])}" 
-
-            inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, inputs_show_user=inputs_show_user,
-                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
-                sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
-            )
-
-            history.extend([ f"第{batch+1}批", gpt_say ])
-            meta_paper_info_list = meta_paper_info_list[batchsize:]
-
-    chatbot.append(["状态？", 
-        "已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
-    msg = '正常'
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res)); 
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面

crazy_functions/辅助功能.py

@@ -1,43 +0,0 @@
-# encoding: utf-8
-# @Time   : 2023/4/19
-# @Author : Spike
-# @Descr   :
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file, get_log_folder
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-
-@CatchException
-def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    if txt:
-        show_say = txt
-        prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
-    else:
-        prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
-        show_say = '分析上述回答，再列出用户可能提出的三个问题。'
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=prompt,
-        inputs_show_user=show_say,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history=history,
-        sys_prompt=system_prompt
-    )
-    chatbot[-1] = (show_say, gpt_say)
-    history.extend([show_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-
-@CatchException
-def 清除缓存(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    chatbot.append(['清除本地缓存数据', '执行中. 删除 gpt_log & private_upload'])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    import shutil, os
-    gpt_log_dir = os.path.join(os.path.dirname(__file__), '..', 'gpt_log')
-    private_upload_dir = os.path.join(os.path.dirname(__file__), '..', 'private_upload')
-    shutil.rmtree(gpt_log_dir, ignore_errors=True)
-    shutil.rmtree(private_upload_dir, ignore_errors=True)
-
-    chatbot.append(['清除本地缓存数据', '执行完成'])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

crazy_functions/高级功能函数模板.py

@@ -1,29 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-    for i in range(5):
-        currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
-        currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
-        i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
-        )
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say);history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

docker-compose.yml

@@ -1,155 +0,0 @@
-#【请修改完参数后，删除此行】请在以下方案中选择一种，然后删除其他的方案，最后docker-compose up运行 | Please choose from one of these options below, delete other options as well as This Line
-
-## ===================================================
-## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
-## ===================================================
-version: '3'
-services:
-  gpt_academic_nolocalllms:
-    image: ghcr.io/binary-husky/gpt_academic_nolocal:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal)
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                                            '
-      USE_PROXY:                '    True                                                                                           '
-      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
-      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "sparkv2", "qianfan"]         '
-      WEB_PORT:                 '    22303                                                                                          '
-      ADD_WAIFU:                '    True                                                                                           '
-      # THEME:                    '    Chuanhu-Small-and-Beautiful                                                                    '
-      # DEFAULT_WORKER_NUM:       '    10                                                                                             '
-      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
-
-    # 与宿主的网络融合
-    network_mode: "host"
-
-    # 不使用代理网络拉取最新代码
-    command: >
-      bash -c "python3 -u main.py"
-
-
-### ===================================================
-### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
-### ===================================================
-version: '3'
-services:
-  gpt_academic_with_chatglm:
-    image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master  # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM)
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                                            '
-      USE_PROXY:                '    True                                                                                           '
-      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
-      AVAIL_LLM_MODELS:         '    ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                               '
-      LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
-      DEFAULT_WORKER_NUM:       '    10                                                                                             '
-      WEB_PORT:                 '    12303                                                                                          '
-      ADD_WAIFU:                '    True                                                                                           '
-      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
-
-    # 显卡的使用，nvidia0指第0个GPU
-    runtime: nvidia
-    devices:
-      - /dev/nvidia0:/dev/nvidia0
-      
-    # 与宿主的网络融合
-    network_mode: "host"
-    command: >
-      bash -c "python3 -u main.py"
-
-    # P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
-    # command: >
-    #   bash -c "pip install -r request_llm/requirements_qwen.txt && python3 -u main.py"
-
-### ===================================================
-### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
-### ===================================================
-version: '3'
-services:
-  gpt_academic_with_rwkv:
-    image: ghcr.io/binary-husky/gpt_academic_jittorllms:master
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  '
-      USE_PROXY:                '    True                                                                                           '
-      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
-      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "newbing", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]   '
-      LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
-      DEFAULT_WORKER_NUM:       '    10                                                                                             '
-      WEB_PORT:                 '    12305                                                                                          '
-      ADD_WAIFU:                '    True                                                                                           '
-      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
-
-    # 显卡的使用，nvidia0指第0个GPU
-    runtime: nvidia
-    devices:
-      - /dev/nvidia0:/dev/nvidia0
-      
-    # 与宿主的网络融合
-    network_mode: "host"
-
-    # 不使用代理网络拉取最新代码
-    command: >
-      python3 -u main.py
-
-
-## ===================================================
-## 【方案四】 ChatGPT + Latex
-## ===================================================
-version: '3'
-services:
-  gpt_academic_with_latex:
-    image: ghcr.io/binary-husky/gpt_academic_with_latex:master  # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex)
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                              '
-      USE_PROXY:                '    True                                                                             '
-      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }   '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                                    '
-      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "gpt-4"]                                                       '
-      LOCAL_MODEL_DEVICE:       '    cuda                                                                             '
-      DEFAULT_WORKER_NUM:       '    10                                                                               '
-      WEB_PORT:                 '    12303                                                                            '
-
-    # 与宿主的网络融合
-    network_mode: "host"
-
-    # 不使用代理网络拉取最新代码
-    command: >
-      bash -c "python3 -u main.py"
-
-
-## ===================================================
-## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
-## ===================================================
-version: '3'
-services:
-  gpt_academic_with_audio:
-    image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS                        '
-      USE_PROXY:                '    False                                                            '
-      proxies:                  '    None                                                             '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                    '
-      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "gpt-4"]                                       '
-      ENABLE_AUDIO:             '    True                                                             '
-      LOCAL_MODEL_DEVICE:       '    cuda                                                             '
-      DEFAULT_WORKER_NUM:       '    20                                                               '
-      WEB_PORT:                 '    12343                                                            '
-      ADD_WAIFU:                '    True                                                             '
-      THEME:                    '    Chuanhu-Small-and-Beautiful                                      '
-      ALIYUN_APPKEY:            '    RoP1ZrM84DnAFkZK                                                 '
-      ALIYUN_TOKEN:             '    f37f30e0f9934c34a992f6f64f7eba4f                                 '
-      # (无需填写) ALIYUN_ACCESSKEY:         '    LTAI5q6BrFUzoRXVGUWnekh1                                         '
-      # (无需填写) ALIYUN_SECRET:            '    eHmI20AVWIaQZ0CiTD2bGQVsaP9i68                                   '
-
-    # 与宿主的网络融合
-    network_mode: "host"
-
-    # 不使用代理网络拉取最新代码
-    command: >
-      bash -c "python3 -u main.py"
-

docs/Dockerfile+ChatGLM

@@ -1,62 +0,0 @@
-# How to build | 如何构建: docker build -t gpt-academic --network=host  -f Dockerfile+ChatGLM .
-# How to run | (1) 我想直接一键运行（选择0号GPU）: docker run --rm -it --net=host --gpus \"device=0\" gpt-academic
-# How to run | (2) 我想运行之前进容器做一些调整（选择1号GPU）: docker run --rm -it --net=host --gpus \"device=1\" gpt-academic bash
- 
-# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
-FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
-ARG useProxyNetwork=''
-RUN apt-get update
-RUN apt-get install -y curl proxychains curl 
-RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
-
-# 配置代理网络（构建Docker镜像时使用）
-# # comment out below if you do not need proxy network | 如果不需要翻墙 - 从此行向下删除
-RUN $useProxyNetwork curl cip.cc
-RUN sed -i '$ d' /etc/proxychains.conf
-RUN sed -i '$ d' /etc/proxychains.conf
-# 在这里填写主机的代理协议（用于从github拉取代码）
-RUN echo "socks5 127.0.0.1 10880" >> /etc/proxychains.conf
-ARG useProxyNetwork=proxychains
-# # comment out above if you do not need proxy network | 如果不需要翻墙 - 从此行向上删除
-
-
-# use python3 as the system default python
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
-# 下载pytorch
-RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
-# 下载分支
-WORKDIR /gpt
-RUN $useProxyNetwork git clone https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/gpt_academic
-RUN $useProxyNetwork python3 -m pip install -r requirements.txt
-RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
-RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_newbing.txt
-
-# 预热CHATGLM参数（非必要 可选步骤）
-RUN echo ' \n\
-from transformers import AutoModel, AutoTokenizer \n\
-chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) \n\
-chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float() ' >> warm_up_chatglm.py
-RUN python3 -u warm_up_chatglm.py
-
-# 禁用缓存，确保更新代码
-ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
-RUN $useProxyNetwork git pull
-
-# 预热Tiktoken模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 为chatgpt-academic配置代理和API-KEY （非必要 可选步骤）
-# 可同时填写多个API-KEY，支持openai的key和api2d的key共存，用英文逗号分割，例如API_KEY = "sk-openaikey1,fkxxxx-api2dkey2,........"
-# LLM_MODEL 是选择初始的模型
-# LOCAL_MODEL_DEVICE 是选择chatglm等本地模型运行的设备，可选 cpu 和 cuda
-# [说明: 以下内容与`config.py`一一对应，请查阅config.py来完成一下配置的填写]
-RUN echo ' \n\
-API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
-USE_PROXY = True \n\
-LLM_MODEL = "chatglm" \n\
-LOCAL_MODEL_DEVICE = "cuda" \n\
-proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/Dockerfile+JittorLLM

@@ -1,59 +0,0 @@
-# How to build | 如何构建: docker build -t gpt-academic-jittor --network=host  -f Dockerfile+ChatGLM .
-# How to run | (1) 我想直接一键运行（选择0号GPU）: docker run --rm -it --net=host --gpus \"device=0\" gpt-academic-jittor bash
-# How to run | (2) 我想运行之前进容器做一些调整（选择1号GPU）: docker run --rm -it --net=host --gpus \"device=1\" gpt-academic-jittor bash
- 
-# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
-FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
-ARG useProxyNetwork=''
-RUN apt-get update
-RUN apt-get install -y curl proxychains curl g++
-RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
-
-# 配置代理网络（构建Docker镜像时使用）
-# # comment out below if you do not need proxy network | 如果不需要翻墙 - 从此行向下删除
-RUN $useProxyNetwork curl cip.cc
-RUN sed -i '$ d' /etc/proxychains.conf
-RUN sed -i '$ d' /etc/proxychains.conf
-# 在这里填写主机的代理协议（用于从github拉取代码）
-RUN echo "socks5 127.0.0.1 10880" >> /etc/proxychains.conf
-ARG useProxyNetwork=proxychains
-# # comment out above if you do not need proxy network | 如果不需要翻墙 - 从此行向上删除
-
-
-# use python3 as the system default python
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
-# 下载pytorch
-RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
-# 下载分支
-WORKDIR /gpt
-RUN $useProxyNetwork git clone https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/gpt_academic
-RUN $useProxyNetwork python3 -m pip install -r requirements.txt
-RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
-RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_newbing.txt
-RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
-
-# 下载JittorLLMs
-RUN $useProxyNetwork git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
-
-# 禁用缓存，确保更新代码
-ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
-RUN $useProxyNetwork git pull
-
-# 预热Tiktoken模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 为chatgpt-academic配置代理和API-KEY （非必要 可选步骤）
-# 可同时填写多个API-KEY，支持openai的key和api2d的key共存，用英文逗号分割，例如API_KEY = "sk-openaikey1,fkxxxx-api2dkey2,........"
-# LLM_MODEL 是选择初始的模型
-# LOCAL_MODEL_DEVICE 是选择chatglm等本地模型运行的设备，可选 cpu 和 cuda
-# [说明: 以下内容与`config.py`一一对应，请查阅config.py来完成一下配置的填写]
-RUN echo ' \n\
-API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
-USE_PROXY = True \n\
-LLM_MODEL = "chatglm" \n\
-LOCAL_MODEL_DEVICE = "cuda" \n\
-proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/Dockerfile+NoLocal+Latex

@@ -1,27 +0,0 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
-# - 1 修改 `config.py`
-# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/Dockerfile+NoLocal+Latex .
-# - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
-
-FROM fuqingxu/python311_texlive_ctex:latest
-
-# 指定路径
-WORKDIR /gpt
-
-ARG useProxyNetwork=''
-
-RUN $useProxyNetwork pip3 install gradio openai numpy arxiv rich -i https://pypi.douban.com/simple/
-RUN $useProxyNetwork pip3 install colorama Markdown pygments pymupdf -i https://pypi.douban.com/simple/
-
-# 装载项目文件
-COPY . .
-
-
-# 安装依赖
-RUN $useProxyNetwork pip3 install -r requirements.txt -i https://pypi.douban.com/simple/
-
-# 可选步骤，用于预热模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+ChatGLM+Moss

@@ -1,31 +0,0 @@
-
-# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
-FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
-ARG useProxyNetwork=''
-RUN apt-get update
-RUN apt-get install -y curl proxychains curl gcc
-RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
-
-
-# use python3 as the system default python
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
-# 下载pytorch
-RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
-# 下载分支
-WORKDIR /gpt
-RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/gpt_academic
-RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss
-RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llm/requirements_moss.txt
-RUN python3 -m pip install -r request_llm/requirements_qwen.txt
-RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llm/requirements_newbing.txt
-
-
-
-# 预热Tiktoken模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+JittorLLMs

@@ -1,34 +0,0 @@
-# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
-FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
-ARG useProxyNetwork=''
-RUN apt-get update
-RUN apt-get install -y curl proxychains curl g++
-RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
-
-# use python3 as the system default python
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
-
-# 下载pytorch
-RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
-
-# 下载分支
-WORKDIR /gpt
-RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/gpt_academic
-RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llm/requirements_newbing.txt
-RUN python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
-
-# 下载JittorLLMs
-RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
-
-# 禁用缓存，确保更新代码
-ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
-RUN git pull
-
-# 预热Tiktoken模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal

@@ -1,20 +0,0 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
-# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
-# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
-FROM python:3.11
-
-# 指定路径
-WORKDIR /gpt
-
-# 装载项目文件
-COPY . .
-
-# 安装依赖
-RUN pip3 install -r requirements.txt
-
-
-# 可选步骤，用于预热模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+AudioAssistant

@@ -1,22 +0,0 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
-# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
-# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
-FROM python:3.11
-
-# 指定路径
-WORKDIR /gpt
-
-# 装载项目文件
-COPY . .
-
-# 安装依赖
-RUN pip3 install -r requirements.txt
-
-# 安装语音插件的额外依赖
-RUN pip3 install pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
-
-# 可选步骤，用于预热模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+Latex

@@ -1,25 +0,0 @@
-# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
-# - 1 修改 `config.py`
-# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/Dockerfile+NoLocal+Latex .
-# - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
-
-FROM fuqingxu/python311_texlive_ctex:latest
-
-# 指定路径
-WORKDIR /gpt
-
-RUN pip3 install gradio openai numpy arxiv rich
-RUN pip3 install colorama Markdown pygments pymupdf
-
-# 装载项目文件
-COPY . .
-
-
-# 安装依赖
-RUN pip3 install -r requirements.txt
-
-# 可选步骤，用于预热模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/README.md.German.md

@@ -1,307 +0,0 @@
-> **Hinweis**
->
-> Bei der Installation von Abhängigkeiten sollten nur die in **requirements.txt** **angegebenen Versionen** streng ausgewählt werden.
->
-> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
-
-# <img src="docs/logo.png" width="40" > GPT Akademisch optimiert (GPT Academic)
-
-**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Stern; wenn Sie bessere Tastenkombinationen oder Funktions-Plugins entwickelt haben, können Sie gerne einen Pull Request eröffnen.**
-
-Wenn Sie dieses Projekt mögen, geben Sie ihm bitte einen Stern. Wenn Sie weitere nützliche wissenschaftliche Abkürzungen oder funktionale Plugins entwickelt haben, können Sie gerne ein Problem oder eine Pull-Anforderung öffnen. Wir haben auch ein README in [Englisch|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md), das von diesem Projekt selbst übersetzt wurde.
-Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `multi_language.py` (experimentell).
-
-> **Hinweis**
->
-> 1. Beachten Sie bitte, dass nur Funktionserweiterungen (Schaltflächen) mit **roter Farbe** Dateien lesen können und einige Erweiterungen im **Dropdown-Menü** des Erweiterungsbereichs zu finden sind. Außerdem begrüßen wir jede neue Funktionserweiterung mit **höchster Priorität** und bearbeiten sie.
->
-> 2. Die Funktionalität jeder Datei in diesem Projekt wird in der Selbstanalyse [`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) detailliert beschrieben. Mit der Weiterentwicklung der Versionen können Sie jederzeit die zugehörigen Funktions-Erweiterungen aufrufen, um durch Aufruf von GPT einen Selbstanalysebericht des Projekts zu erstellen. Häufig gestellte Fragen finden Sie in der [`Wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
->
-> 3. Dieses Projekt ist kompatibel und fördert die Verwendung von inländischen Sprachmodellen wie ChatGLM und RWKV, Pangu, etc. Es unterstützt das Vorhandensein mehrerer api-keys, die in der Konfigurationsdatei wie folgt angegeben werden können: `API_KEY="openai-key1,openai-key2,api2d-key3"`. Wenn ein `API_KEY` temporär geändert werden muss, geben Sie den temporären `API_KEY` im Eingabebereich ein und drücken Sie dann die Eingabetaste, um ihn zu übernehmen.Funktion | Beschreibung
---- | ---
-Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten
-Ein-Klick Chinesisch-Englisch Übersetzung | Ein-Klick Chinesisch-Englisch Übersetzung
-Ein-Klick-Code-Erklärung | Zeigt Code, erklärt Code, erzeugt Code und fügt Kommentare zum Code hinzu
-[Benutzerdefinierte Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) | Unterstützt benutzerdefinierte Tastenkombinationen
-Modulare Gestaltung | Unterstützt leistungsstarke individuelle [Funktions-Plugins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Selbstprogramm-Analyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] [Ein-Klick Verstehen](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) der Quellcode dieses Projekts
-[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] Ein-Klick-Analyse des Projektbaums anderer Python/C/C++/Java/Lua/...-Projekte
-Lesen von Papieren, [Übersetzen](https://www.bilibili.com/video/BV1KT411x7Wn) von Papieren | [Funktions-Plugin] Ein-Klick Erklärung des gesamten LaTeX/PDF-Artikels und Erstellung einer Zusammenfassung
-LaTeX-Volltext-Übersetzung und [Polieren](https://www.bilibili.com/video/BV1FT411H7c5/) | [Funktions-Plugin] Ein-Klick-Übersetzung oder-Polieren des LaTeX-Artikels
-Bulk-Kommentargenerierung | [Funktions-Plugin] Ein-Klick Massenerstellung von Funktionskommentaren
-Markdown [Chinesisch-Englisch Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Funktions-Plugin] Haben Sie die [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
-Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfassung nach der Ausführung
-[Funktion zur vollständigen Übersetzung von PDF-Artikeln](https://www.bilibili.com/video/BV1KT411x7Wn) | [Funktions-Plugin] Extrahiert Titel und Zusammenfassung der PDF-Artikel und übersetzt den gesamten Text (mehrere Threads)
-[Arxiv-Assistent](https://www.bilibili.com/video/BV1LM4y1279X) | [Funktions-Plugin] Geben Sie die Arxiv-Artikel-URL ein und klicken Sie auf Eine-Klick-Übersetzung-Zusammenfassung + PDF-Download
-[Google Scholar Integrations-Assistent](https://www.bilibili.com/video/BV19L411U7ia) | [Funktions-Plugin] Geben Sie eine beliebige Google Scholar Such-URL ein und lassen Sie gpt Ihnen bei der Erstellung von [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) helfen
-Internet-Informationen Aggregation + GPT | [Funktions-Plugin] Lassen Sie GPT eine Frage beantworten, indem es [zuerst Informationen aus dem Internet](https://www.bilibili.com/video/BV1om4y127ck/) sammelt und so die Informationen nie veralten
-Anzeige von Formeln / Bildern / Tabellen | Zeigt Formeln in beiden Formen, [TeX-Format und gerendeter Form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), unterstützt Formeln und Code-Highlights
-Unterstützung von PlugIns mit mehreren Threads | Unterstützt den Aufruf mehrerer Threads in Chatgpt, um Text oder Programme [Batch zu verarbeiten](https://www.bilibili.com/video/BV1FT411H7c5/)
-Starten Sie das dunkle Gradio-[Thema](https://github.com/binary-husky/gpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
-[Unterstützung für mehrere LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) Interface-Unterstützung | Das Gefühl, gleichzeitig von GPT3.5, GPT4, [Tshinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) bedient zu werden, muss toll sein, oder?
-Zugriff auf weitere LLM-Modelle, Unterstützung von [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der Unterstützung von [Jittorllms](https://github.com/Jittor/JittorLLMs) der Tsinghua-Universität, [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) und [Pangu alpha](https://openi.org.cn/pangu/)
-Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokuments ……
-
-- Neue Oberfläche (Ändern Sie die LAYOUT-Option in `config.py`, um zwischen "Seitenlayout" und "Oben-unten-Layout" zu wechseln)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>- All buttons are dynamically generated by reading `functional.py`, and custom functions can be easily added, freeing up the clipboard.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- Proofreading/Correcting
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- If the output contains formulas, they will be displayed in both tex format and rendered format for easy copying and reading.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- Don't feel like reading the project code? Show off the entire project to chatgpt. 
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- Multiple large language models are mixed and called together (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4).
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
----
-# Installation
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download the project
-```sh
-git clone https://github.com/binary-husky/gpt_academic.git
-cd gpt_academic
-```
-
-2. Configure API_KEY
-
-Configure API KEY and other settings in `config.py`. [Special Network Environment Settings](https://github.com/binary-husky/gpt_academic/issues/1).
-
-(P.S. When the program is running, it will first check whether there is a "config_private.py" private configuration file, and use the configuration defined in it to override the configuration of "config.py". Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named "config_private.py" next to "config.py" and transfer (copy) the configurations in "config.py" to "config_private.py". "config_private.py" is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` >`config.py`)
-
-
-3. Install dependencies
-```sh
-# (Option I: If familar with Python) (Python version 3.9 or above, the newer the better), Note: Use the official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Option II: If not familiar with Python) Use anaconda with similar steps (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11    # Create an anaconda environment
-conda activate gptac_venv                 # Activate the anaconda environment
-python -m pip install -r requirements.txt # Same step as pip installation
-```
-
-<details><summary>Click to expand if supporting Tsinghua ChatGLM/Fudan MOSS as backend</summary>
-<p>
-
-[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
-```sh
-# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
-
-# [Optional Step II] Support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
-
-# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-</p>
-</details>
-
-
-
-4. Run
-```sh
-python main.py
-```5. Testing Function Plugin
-```
-- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
-   Click "[Function Plugin Template Demo] Today in History"
-```
-
-## Installation-Method 2: Using Docker
-
-1. Only ChatGPT (Recommended for most people)
-
-``` sh
-git clone https://github.com/binary-husky/gpt_academic.git  # Download the project
-cd gpt_academic                                 # Enter the path
-nano config.py                                      # Edit config.py with any text editor, Configure "Proxy","API_KEY"and"WEB_PORT" (e.g 50923) etc.
-docker build -t gpt-academic .                      # Install
-
-# (Last step-option 1) Under Linux environment, use `--net=host` is more convenient and quick
-docker run --rm -it --net=host gpt-academic
-# (Last step-option 2) Under macOS/windows environment, can only use the -p option to expose the container's port(eg.50923) to the port on the host.
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (Requires familiarity with Docker)
-
-``` sh
-# Modify docker-compose.yml, delete solution 1 and solution 3, and retain solution 2. Modify the configuration of solution 2 in docker-compose.yml, referring to the comments in it.
-docker-compose up
-```
-
-3. ChatGPT+LLAMA+Pangu+RWKV(Requires familiarity with Docker)
-``` sh
-# Modify docker-compose.yml, delete solution 1 and solution 2, and retain solution 3. Modify the configuration of solution 3 in docker-compose.yml, referring to the comments in it.
-docker-compose up
-```
-
-
-## Installation-Method 3: Other Deployment Options
-
-1. How to use reverse proxy URL/Microsoft Azure API
-Configure API_URL_REDIRECT according to the instructions in `config.py`.
-
-2. Remote cloud server deployment (requires cloud server knowledge and experience)
-Please visit [Deployment wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-3. Using WSL 2 (Windows subsystem for Linux)
-Please visit [Deployment wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-4. How to run at a secondary URL (such as `http://localhost/subpath`)
-Please visit [FastAPI operating instructions](docs/WithFastapi.md)
-
-5. Use docker-compose to run
-Please read docker-compose.yml and follow the prompts to operate.
-
----
-# Advanced Usage
-## Customize new convenience buttons / custom function plugins.
-
-1. Customize new convenience buttons (Academic Shortcut Keys)
-Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, then the prefix and suffix can be hot-modified, and it will take effect without restarting the program.)
-For example
-```
-"Super English to Chinese": {
-   # Prefix, will be added before your input. For example, used to describe your requirements, such as translation, explaining code, polishing, etc.
-    "Prefix": "Please translate the following content into Chinese, and then use a markdown table to explain the proper nouns that appear in the text one by one:\n\n", 
-    
-    # Suffix, will be added after your input. For example, combined with prefix, you can enclose your input content in quotes.
-    "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-2. Custom function plugins
-
-Write powerful function plugins to perform any task you want and can't think of.
-The difficulty of plugin writing and debugging is very low in this project. As long as you have a certain knowledge of Python, you can implement your own plugin functions by imitating the template we provided.
-For more information, please refer to the [Function Plugin Guide](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-
----
-# Latest Update
-## New feature dynamics1. Funktion zur Speicherung von Dialogen. Rufen Sie im Bereich der Funktions-Plugins "Aktuellen Dialog speichern" auf, um den aktuellen Dialog als lesbares und wiederherstellbares HTML-Datei zu speichern. Darüber hinaus können Sie im Funktions-Plugin-Bereich (Dropdown-Menü) "Laden von Dialogverlauf" aufrufen, um den vorherigen Dialog wiederherzustellen. Tipp: Wenn Sie keine Datei angeben und stattdessen direkt auf "Laden des Dialogverlaufs" klicken, können Sie das HTML-Cache-Archiv anzeigen. Durch Klicken auf "Löschen aller lokalen Dialogverlaufsdatensätze" können alle HTML-Archiv-Caches gelöscht werden.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
-</div>
-
-2. Berichterstellung. Die meisten Plugins generieren nach Abschluss der Ausführung einen Arbeitsbericht.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-3. Modularisierte Funktionsgestaltung, einfache Schnittstellen mit leistungsstarken Funktionen.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-4. Dies ist ein Open-Source-Projekt, das sich "selbst übersetzen" kann.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
-</div>
-
-5. Die Übersetzung anderer Open-Source-Projekte ist kein Problem.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
-</div>
-
-6. Dekorieren Sie [`live2d`](https://github.com/fghrsh/live2d_demo) mit kleinen Funktionen (standardmäßig deaktiviert, Änderungen an `config.py` erforderlich).
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
-</div>
-
-7. Neue MOSS-Sprachmodellunterstützung.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
-</div>
-
-8. OpenAI-Bildgenerierung.
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
-</div>
-
-9. OpenAI-Audio-Analyse und Zusammenfassung.
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
-</div>
-
-10. Latex-Proofreading des gesamten Textes.
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
-</div>
-
-
-## Version:
-- Version 3.5 (Todo): Rufen Sie alle Funktionserweiterungen dieses Projekts mit natürlicher Sprache auf (hohe Priorität).
-- Version 3.4 (Todo): Verbesserte Unterstützung mehrerer Threads für Local Large Model (LLM).
-- Version 3.3: + Internet-Informationssynthese-Funktion
-- Version 3.2: Funktionserweiterungen unterstützen mehr Parameter-Schnittstellen (Speicherung von Dialogen, Interpretation beliebigen Sprachcodes + gleichzeitige Abfrage jeder LLM-Kombination)
-- Version 3.1: Unterstützung mehrerer GPT-Modelle gleichzeitig! Unterstützung für API2D, Unterstützung für Lastenausgleich von mehreren API-Schlüsseln.
-- Version 3.0: Unterstützung von Chatglm und anderen kleinen LLMs
-- Version 2.6: Umstrukturierung der Plugin-Struktur zur Verbesserung der Interaktivität, Einführung weiterer Plugins
-- Version 2.5: Automatische Aktualisierung, Problembehebung bei Quelltexten großer Projekte, wenn der Text zu lang ist oder Token überlaufen.
-- Version 2.4: (1) Neue Funktion zur Übersetzung des gesamten PDF-Texts; (2) Neue Funktion zum Wechseln der Position des Eingabebereichs; (3) Neue Option für vertikales Layout; (4) Optimierung von Multithread-Funktions-Plugins.
-- Version 2.3: Verbesserte Interaktivität mit mehreren Threads
-- Version 2.2: Funktionserweiterungen unterstützen "Hot-Reload"
-- Version 2.1: Faltbares Layout
-- Version 2.0: Einführung von modularisierten Funktionserweiterungen
-- Version 1.0: Grundlegende Funktionengpt_academic Entwickler QQ-Gruppe-2: 610599535
-
-- Bekannte Probleme
-    - Einige Browser-Übersetzungs-Plugins können die Frontend-Ausführung dieser Software stören.
-    - Sowohl eine zu hohe als auch eine zu niedrige Version von Gradio führt zu verschiedenen Ausnahmen.
-
-## Referenz und Lernen
-
-```
-Der Code bezieht sich auf viele Designs von anderen herausragenden Projekten, insbesondere:
-
-# Projekt 1: ChatGLM-6B der Tsinghua Universität:
-https://github.com/THUDM/ChatGLM-6B
-
-# Projekt 2: JittorLLMs der Tsinghua Universität:
-https://github.com/Jittor/JittorLLMs
-
-# Projekt 3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Projekt 4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Projekt 5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Mehr:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```