修复一个循环的Bug

修复Tex文件匹配BUG
修复logging的Bug
2023-09-15 17:08:23 +08:00 · 2023-09-15 12:55:41 +08:00 · 2023-09-15 11:00:30 +08:00 · 2023-09-14 23:07:12 +08:00 · 2023-09-14 22:45:45 +08:00 · 2023-09-14 22:33:07 +08:00
115 changed files with 3809 additions and 13221 deletions
--- a/.github/workflows/build-with-all-capacity.yml
+++ b/.github/workflows/build-with-all-capacity.yml
@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: build-with-all-capacity
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_with_all_capacity
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        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@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+AllCapacity
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -0,0 +1,25 @@
+# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+#
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+
+name: 'Close stale issues and PRs'
+on:
+  schedule:
+    - cron: '*/5 * * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: read
+    
+    steps:
+      - uses: actions/stale@v8
+        with:
+          stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 1 days.'
+          days-before-stale: 100
+          days-before-close: 1
+          debug-only: true
--- a/README.md
+++ b/README.md
@@ -10,13 +10,13 @@
 **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).
+To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).

 > **Note**
 >
-> 1.请注意只有 **高亮(如红色)** 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。
+> 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[`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)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。常见问题汇总在[`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)当中。[安装方法](#installation)。
+> 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。

@@ -27,7 +27,7 @@ To translate this project to arbitary language with GPT, read and run [`multi_la

 功能（⭐= 近期新增功能） | 描述
 --- | ---
-⭐接入新模型！ | ⭐阿里达摩院[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/)
+⭐[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
@@ -53,7 +53,8 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
 [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/)
-⭐[虚空终端](https://github.com/binary-husky/void-terminal)pip包 | 脱离GUI，在Python中直接调用本项目的函数插件（开发中）
+⭐[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 | [函数插件] 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>

@@ -102,7 +103,7 @@ cd gpt_academic

 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。`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`)
+(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
@@ -148,11 +149,14 @@ python main.py

 ### 安装方法II：使用Docker

+[![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
+
 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
@@ -166,7 +170,7 @@ 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（需要熟悉Docker）
+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
@@ -174,7 +178,7 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 docker-compose up
 ```

-3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉Docker）
+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
@@ -249,10 +253,13 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
 </div>

-3. 生成报告。大部分插件都会在执行结束后，生成工作报告
+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://openreview.net/pdf?id=rJl0r3R9KX ”
+- 步骤二：点击“虚空终端”
+
 <div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="250" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="250" >
+<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
@@ -299,8 +306,12 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 </div>


+
 ### II：版本:
- version 3.5(Todo): 使用自然语言调用本项目的所有函数插件（高优先级）
+- 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
--- a/check_proxy.py
+++ b/check_proxy.py
@@ -5,7 +5,7 @@ def check_proxy(proxies):
    try:
        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
        data = response.json()
-        print(f'查询代理的地理位置，返回的结果是{data}')
+        # 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}"
--- a/config.py
+++ b/config.py
@@ -11,7 +11,7 @@
 API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"


-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
+# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
 USE_PROXY = False
 if USE_PROXY:
    """
@@ -43,7 +43,12 @@ API_URL_REDIRECT = {}
 DEFAULT_WORKER_NUM = 3


-# 对话窗的高度
+# 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"
+
+
+# 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
 CHATBOT_HEIGHT = 1115


@@ -68,14 +73,26 @@ WEB_PORT = -1
 MAX_RETRY = 2


+# 插件分类默认选项
+DEFAULT_FN_GROUPS = ['对话', '编程', '学术']
+
+
 # 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 ["qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "spark", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+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"]


-# ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
-ChatGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"
+# 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"
+
+
+# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
+CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"


 # 本地LLM模型如ChatGLM的执行方式 CPU/GPU
@@ -91,10 +108,6 @@ CONCURRENT_COUNT = 100
 AUTO_CLEAR_TXT = False


-# 色彩主体，可选 ["Default", "Chuanhu-Small-and-Beautiful"]
-THEME = "Default"
-
-
 # 加一个live2d装饰
 ADD_WAIFU = False

@@ -149,4 +162,89 @@ ANTHROPIC_API_KEY = ""


 # 自定义API KEY格式
-CUSTOM_API_KEY_PATTERN = ""
+CUSTOM_API_KEY_PATTERN = ""
+
+
+# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
+HUGGINGFACE_ACCESS_TOKEN = "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"
+
+
+# GROBID服务器地址（填写多个可以均衡负载），用于高质量地读取PDF文档
+# 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", 
+]
+
+
+# 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
+# 临时的上传文件夹位置，请勿修改
+PATH_PRIVATE_UPLOAD = "private_upload"
+# 日志文件夹的位置，请勿修改
+PATH_LOGGING = "gpt_log"
+
+"""
+在线大模型配置关联关系示意图
+│
+├── "gpt-3.5-turbo" 等openai模型
+│   ├── API_KEY
+│   ├── CUSTOM_API_KEY_PATTERN（不常用）
+│   ├── API_ORG（不常用）
+│   └── API_URL_REDIRECT（不常用）
+│
+├── "azure-gpt-3.5" 等azure模型
+│   ├── API_KEY
+│   ├── AZURE_ENDPOINT
+│   ├── AZURE_API_KEY
+│   ├── AZURE_ENGINE
+│   └── API_URL_REDIRECT
+│
+├── "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
+│   └── ANTHROPIC_API_KEY
+│
+├── "stack-claude"
+│   ├── SLACK_CLAUDE_BOT_ID
+│   └── SLACK_CLAUDE_USER_TOKEN
+│
+├── "qianfan" 百度千帆大模型库
+│   ├── BAIDU_CLOUD_QIANFAN_MODEL
+│   ├── 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
+
+    
+用户图形界面布局依赖关系示意图
+│
+├── CHATBOT_HEIGHT 对话窗的高度
+├── CODE_HIGHLIGHT 代码高亮
+├── LAYOUT 窗口布局
+├── DARK_MODE 暗色模式 / 亮色模式
+├── DEFAULT_FN_GROUPS 插件分类默认选项
+├── 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
+
+
+插件在线服务配置依赖关系示意图
+│
+├── 语音功能
+│   ├── ENABLE_AUDIO
+│   ├── ALIYUN_TOKEN
+│   ├── ALIYUN_APPKEY
+│   ├── ALIYUN_ACCESSKEY
+│   └── ALIYUN_SECRET
+│
+├── PDF文档精准解析
+│   └── GROBID_URLS
+
+"""
--- a/core_functional.py
+++ b/core_functional.py
@@ -63,6 +63,7 @@ def get_core_functions():
        "英译中": {
            "Prefix":   r"翻译成地道的中文：" + "\n\n",
            "Suffix":   r"",
+            "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，" +
@@ -78,6 +79,7 @@ def get_core_functions():
            "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,
            "Suffix":   r"",
        }
    }
--- a/crazy_functional.py
+++ b/crazy_functional.py
@@ -2,7 +2,6 @@ from toolbox import HotReload  # HotReload 的意思是热更新，修改函数


 def get_crazy_functions():
-    ###################### 第一组插件 ###########################
    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
@@ -14,7 +13,6 @@ def get_crazy_functions():
    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
@@ -24,109 +22,9 @@ def get_crazy_functions():
    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
-    function_plugins = {
-        "解析整个Python项目": {
-            "Color": "stop",    # 按钮颜色
-            "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）": {
-            "Color": "stop",
-            "AsButton":False,
-            "Function": HotReload(载入对话历史存档)
-        },
-        "删除所有本地对话历史记录（请谨慎操作）": {
-            "AsButton":False,
-            "Function": HotReload(删除所有本地对话历史记录)
-        },
-        "[测试功能] 解析Jupyter Notebook文件": {
-            "Color": "stop",
-            "AsButton":False,
-            "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
-        },
-        "批量总结Word文档": {
-            "Color": "stop",
-            "Function": HotReload(总结word文档)
-        },
-        "解析整个C++项目头文件": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个C项目的头文件)
-        },
-        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个C项目)
-        },
-        "解析整个Go项目": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个Golang项目)
-        },
-        "解析整个Rust项目": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个Rust项目)
-        },
-        "解析整个Java项目": {
-            "Color": "stop",  # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个Java项目)
-        },
-        "解析整个前端项目（js,ts,css等）": {
-            "Color": "stop",  # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个前端项目)
-        },
-        "解析整个Lua项目": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个Lua项目)
-        },
-        "解析整个CSharp项目": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析一个CSharp项目)
-        },
-        "读Tex论文写摘要": {
-            "Color": "stop",    # 按钮颜色
-            "Function": HotReload(读文章写摘要)
-        },
-        "Markdown/Readme英译中": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-            "Color": "stop",
-            "Function": HotReload(Markdown英译中)
-        },
-        "批量生成函数注释": {
-            "Color": "stop",    # 按钮颜色
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(批量生成函数注释)
-        },
-        "保存当前的对话": {
-            "Function": HotReload(对话历史存档)
-        },
-        "[多线程Demo] 解析此项目本身（源码自译解）": {
-            "AsButton": False,  # 加入下拉菜单中
-            "Function": HotReload(解析项目本身)
-        },
-        # "[老旧的Demo] 把本项目源代码切换成全英文": {
-        #     # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-        #     "AsButton": False,  # 加入下拉菜单中
-        #     "Function": HotReload(全项目切换英文)
-        # },
-        "[插件demo] 历史上的今天": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
-            "Function": HotReload(高阶功能模板函数)
-        },
-
-    }
-    ###################### 第二组插件 ###########################
-    # [第二组插件]: 经过充分测试
    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    # from crazy_functions.批量总结PDF文档pdfminer import 批量总结PDF文档pdfminer
    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
@@ -135,88 +33,248 @@ def get_crazy_functions():
    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

-    function_plugins.update({
-        "批量翻译PDF文档（多线程）": {
+
+    function_plugins = {
+        "虚空终端": {
+            "Group": "对话|编程|学术",
            "Color": "stop",
-            "AsButton": True,  # 加入下拉菜单中
+            "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",
+            "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": {
-            "Color": "stop",    # 按钮颜色
+            "Group": "对话",
+            "Color": "stop",
+            "AsButton": True,
            "Function": HotReload(同时问询)
        },
-        "[测试功能] 批量总结PDF文档": {
+        "批量总结PDF文档": {
+            "Group": "学术",
            "Color": "stop",
            "AsButton": False,  # 加入下拉菜单中
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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)
        },
-        # "[测试功能] 批量总结PDF文档pdfminer": {
-        #     "Color": "stop",
-        #     "AsButton": False,  # 加入下拉菜单中
-        #     "Function": HotReload(批量总结PDF文档pdfminer)
-        # },
        "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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）": {
-            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "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)
        },
+    }

-
-    })
-
-    ###################### 第三组插件 ###########################
-    # [第三组插件]: 尚未充分测试的函数插件
-
+    # -=--=- 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)
            }
        })
@@ -227,16 +285,20 @@ def get_crazy_functions():
        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)
            }
        })
@@ -247,10 +309,11 @@ def get_crazy_functions():
        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
+                "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)
            },
        })
@@ -261,10 +324,11 @@ def get_crazy_functions():
        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
+                "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)
            },
        })
@@ -275,10 +339,12 @@ def get_crazy_functions():
        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
+                "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)
            },
        })
@@ -289,10 +355,12 @@ def get_crazy_functions():
        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)
            }
        })
@@ -303,8 +371,10 @@ def get_crazy_functions():
        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)
            }
        })
@@ -315,6 +385,7 @@ def get_crazy_functions():
        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,
@@ -328,11 +399,12 @@ def get_crazy_functions():
    try:
        from crazy_functions.Langchain知识库 import 知识库问答
        function_plugins.update({
-            "[功能尚不稳定] 构建知识库（请先上传文件素材）": {
+            "构建知识库（先上传文件素材,再运行此插件）": {
+                "Group": "对话",
                "Color": "stop",
                "AsButton": False,
                "AdvancedArgs": True,
-                "ArgsReminder": "待注入的知识库名称id, 默认为default",
+                "ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
                "Function": HotReload(知识库问答)
            }
        })
@@ -342,21 +414,23 @@ def get_crazy_functions():
    try:
        from crazy_functions.Langchain知识库 import 读取知识库作答
        function_plugins.update({
-            "[功能尚不稳定] 知识库问答": {
+            "知识库问答（构建知识库后,再运行此插件）": {
+                "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",
+                "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要构建知识库后再运行此插件。",
                "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)
@@ -365,6 +439,94 @@ def get_crazy_functions():
    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.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档
+        function_plugins.update({
+            "精准翻译PDF文档（NOUGAT）": {
+                "Group": "学术",
+                "Color": "stop",
+                "AsButton": False,
+                "Function": HotReload(批量翻译PDF文档)
+            }
+        })
+    except:
+        print('Load function plugin failed')
+
+
+    # try:
+    #     from crazy_functions.CodeInterpreter import 虚空终端CodeInterpreter
+    #     function_plugins.update({
+    #         "CodeInterpreter（开发中，仅供测试）": {
+    #             "Group": "编程|对话",
+    #             "Color": "stop",
+    #             "AsButton": False,
+    #             "Function": HotReload(虚空终端CodeInterpreter)
+    #         }
+    #     })
+    # 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({
@@ -379,71 +541,23 @@ def get_crazy_functions():
    # 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]": {
-                "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]": {
-                "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". ',
-                "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]": {
-                "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". ',
-                "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": {
-                    "Color": "stop",
-                    "AsButton": True,
-                    "Function": HotReload(语音助手)
-                }
-            })
-    except:
-        print('Load function plugin failed')
-        
-    # try:
-    #     from crazy_functions.虚空终端 import 终端
-    #     function_plugins.update({
-    #         "超级终端": {
-    #             "Color": "stop",
-    #             "AsButton": False,
-    #             # "AdvancedArgs": True,
-    #             # "ArgsReminder": "",
-    #             "Function": HotReload(终端)
-    #         }
-    #     })
-    # except:
-    #     print('Load function plugin failed')
+    """
+    设置默认值:
+    - 默认 Group = 对话
+    - 默认 AsButton = True
+    - 默认 AdvancedArgs = False
+    - 默认 Color = secondary
+    """
+    for name, function_meta in function_plugins.items():
+        if "Group" not in function_meta:
+            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'

    return function_plugins
--- a/crazy_functions/CodeInterpreter.py
+++ b/crazy_functions/CodeInterpreter.py
@@ -0,0 +1,232 @@
+from collections.abc import Callable, Iterable, Mapping
+from typing import Any
+from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc
+from toolbox import promote_file_to_downloadzone, get_log_folder
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from .crazy_utils import input_clipping, try_install_deps
+from multiprocessing import Process, Pipe
+import os
+import time
+
+templete = """
+```python
+import ...  # Put dependencies here, e.g. import numpy as np
+
+class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
+
+    def run(self, path):    # The name of the function must be `run`, it takes only a positional argument.
+        # rewrite the function you have just written here 
+        ...
+        return generated_file_path
+```
+"""
+
+def inspect_dependency(chatbot, history):
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    return 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
+    for match in matches:
+        if 'class TerminalFunction' in match:
+            return match.strip('python') #  code block
+    raise RuntimeError("GPT is not generating proper code.")
+
+def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
+    # 输入
+    prompt_compose = [
+        f'Your job:\n'
+        f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
+        f"2. You should write this function to perform following task: " + txt + "\n",
+        f"3. Wrap the output python function with markdown codeblock."
+    ]
+    i_say = "".join(prompt_compose)
+    demo = []
+
+    # 第一步
+    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"You are a programmer."
+    )
+    history.extend([i_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+
+    # 第二步
+    prompt_compose = [
+        "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
+        templete
+    ]
+    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
+    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=history, 
+        sys_prompt= r"You are a programmer."
+    )
+    code_to_return = gpt_say
+    history.extend([i_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+    
+    # # 第三步
+    # i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
+    # i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
+    # installation_advance = 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=history, 
+    #     sys_prompt= r"You are a programmer."
+    # )
+    # # # 第三步  
+    # i_say = "Show me how to use `pip` to install packages to run the code above. "
+    # i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
+    # installation_advance = 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= r"You are a programmer."
+    # )
+    installation_advance = ""
+    
+    return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
+
+def make_module(code):
+    module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
+    with open(f'{get_log_folder()}/{module_file}.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)
+    return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}"
+
+def init_module_instance(module):
+    import importlib
+    module_, class_ = module.split('->')
+    init_f = getattr(importlib.import_module(module_), class_)
+    return init_f()
+
+def for_immediate_show_off_when_possible(file_type, fp, chatbot):
+    if file_type in ['png', 'jpg']:
+        image_path = os.path.abspath(fp)
+        chatbot.append(['这是一张图片, 展示如下:',  
+            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>'
+        ])
+    return chatbot
+
+def subprocess_worker(instance, file_path, return_dict):
+    return_dict['result'] = instance.run(file_path)
+
+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
+
+def get_recent_file_prompt_support(chatbot):
+    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
+    path = most_recent_uploaded['path']
+    return path
+
+@CatchException
+def 虚空终端CodeInterpreter(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
+    """
+    raise NotImplementedError
+
+    # 清空历史，以免输入溢出
+    history = []; clear_file_downloadzone(chatbot)
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..."
+    ])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    if have_any_recent_upload_files(chatbot):
+        file_path = get_recent_file_prompt_support(chatbot)
+    else:
+        chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 读取文件
+    if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files")
+    recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None)
+    file_path = recently_uploaded_files[-1]
+    file_type = file_path.split('.')[-1]
+
+    # 粗心检查
+    if is_the_upload_folder(txt):
+        chatbot.append([
+            "...",
+            f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）"
+        ])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    
+    # 开始干正事
+    for j in range(5):  # 最多重试5次
+        try:
+            code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
+                yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
+            code = get_code_block(code)
+            res = make_module(code)
+            instance = init_module_instance(res)
+            break
+        except Exception as e:
+            chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
+            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 代码生成结束, 开始执行
+    try:
+        import multiprocessing
+        manager = multiprocessing.Manager()
+        return_dict = manager.dict()
+
+        p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict))
+        # only has 10 seconds to run
+        p.start(); p.join(timeout=10)
+        if p.is_alive(): p.terminate(); p.join()
+        p.close()
+        res = return_dict['result']
+        # res = instance.run(file_path)
+    except Exception as e:
+        chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
+        # chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 顺利完成，收尾
+    res = str(res)
+    if os.path.exists(res):
+        chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
+        new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
+        chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+    else:
+        chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新   
+
+"""
+测试：
+    裁剪图像，保留下半部分
+    交换图像的蓝色通道和红色通道
+    将图像转为灰度图像
+    将csv文件转excel表格
+"""
--- a/crazy_functions/Langchain知识库.py
+++ b/crazy_functions/Langchain知识库.py
@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, ProxyNetworkActivate
+from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything


@@ -15,7 +15,12 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
    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。"))
+
+    # < --------------------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')
+
+    chatbot.append((f"向`{kai_id}`知识库中添加文件。", "[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
@@ -24,17 +29,12 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
        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..."]
-        )
+        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')
+        try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
+        yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
+        return

    # < --------------------读取文件--------------- >
    file_manifest = []
@@ -84,19 +84,18 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
        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'])
+        try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
+        yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
+        return

    # < -------------------  --------------- >
    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)
+    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))
+    chatbot.append((txt, f'[知识库 {kai_id}] ' + 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, 
--- a/crazy_functions/Latex全文润色.py
+++ b/crazy_functions/Latex全文润色.py
@@ -1,5 +1,5 @@
-from toolbox import update_ui, trimmed_format_exc
-from toolbox import CatchException, report_execption, write_results_to_file, zip_folder
+from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
+from toolbox import CatchException, report_execption, write_history_to_file, zip_folder


 class PaperFileGroup():
@@ -51,7 +51,7 @@ class PaperFileGroup():
        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')
+        zip_folder(folder, get_log_folder(), 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'):
@@ -126,7 +126,9 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch

    #  <-------- 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)
+    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
@@ -137,7 +139,7 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
    # 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"])
+        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky。（注意，此插件不调用Latex，如果有Latex环境，请使用“Latex英文纠错+高亮”插件）"])
    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
--- a/crazy_functions/Latex全文翻译.py
+++ b/crazy_functions/Latex全文翻译.py
@@ -1,5 +1,5 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import CatchException, report_execption, write_history_to_file
 fast_debug = False

 class PaperFileGroup():
@@ -95,7 +95,8 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch

    #  <-------- 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)
+    res = write_history_to_file(gpt_response_collection, 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
--- a/crazy_functions/Latex输出PDF结果.py
+++ b/crazy_functions/Latex输出PDF结果.py
@@ -1,4 +1,4 @@
-from toolbox import update_ui, trimmed_format_exc, get_conf, objdump, objload, promote_file_to_downloadzone
+from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, 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
@@ -6,7 +6,7 @@ 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". '
+# 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.
@@ -65,7 +65,7 @@ def move_project(project_folder, arxiv_id=None):
    if arxiv_id is not None:
        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
    else:
-        new_workfolder = f'gpt_log/{gen_time_str()}'
+        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
    try:
        shutil.rmtree(new_workfolder)
    except:
@@ -109,7 +109,7 @@ def arxiv_download(chatbot, history, txt):

    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_}"
+        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 ------------->
@@ -255,7 +255,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
        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}")
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
        return
    
@@ -291,7 +291,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
        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 ...'))
+        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)

--- a/crazy_functions/crazy_utils.py
+++ b/crazy_functions/crazy_utils.py
@@ -1,5 +1,7 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc
+from toolbox import update_ui, get_conf, trimmed_format_exc, get_log_folder
 import threading
+import os
+import logging

 def input_clipping(inputs, history, max_token_limit):
    import numpy as np
@@ -469,14 +471,16 @@ 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 = []
@@ -591,11 +595,16 @@ def get_files_from_everything(txt, type): # type='.md'
        # 网络的远程文件
        import requests
        from toolbox import get_conf
+        from toolbox import get_log_folder, gen_time_str
        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]
+        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]
@@ -698,49 +707,96 @@ class knowledge_archive_interface():
        )
        self.threadLock.release()
        return resp, prompt
+    
+@Singleton
+class nougat_interface():
+    def __init__(self):
+        self.threadLock = threading.Lock()

-def try_install_deps(deps):
+    def nougat_with_timeout(self, command, cwd, timeout=3600):
+        import subprocess
+        logging.info(f'正在执行命令 {command}')
+        process = subprocess.Popen(command, shell=True, cwd=cwd)
+        try:
+            stdout, stderr = process.communicate(timeout=timeout)
+        except subprocess.TimeoutExpired:
+            process.kill()
+            stdout, stderr = process.communicate()
+            print("Process timed out!")
+            return False
+        return True
+
+
+    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 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)
+        self.nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}"', 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 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 = """
+HTML_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 = """
+TABLE_CSS = """
 <div class="row table-row">
    <div class="column table-cell">REPLACE_A</div>
    <div class="column table-cell">REPLACE_B</div>
 </div>
-        """
+"""
+
+class construct_html():
+    def __init__(self) -> None:
+        self.css = HTML_CSS
+        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 = TABLE_CSS
        from toolbox import markdown_convertion
        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
@@ -748,6 +804,6 @@ class construct_html():


    def save_file(self, file_name):
-        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
+        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)
--- a/crazy_functions/json_fns/pydantic_io.py
+++ b/crazy_functions/json_fns/pydantic_io.py
@@ -0,0 +1,111 @@
+"""
+https://github.com/langchain-ai/langchain/blob/master/docs/extras/modules/model_io/output_parsers/pydantic.ipynb
+
+Example 1.
+
+# Define your desired data structure.
+class Joke(BaseModel):
+    setup: str = Field(description="question to set up a joke")
+    punchline: str = Field(description="answer to resolve the joke")
+
+    # You can add custom validation logic easily with Pydantic.
+    @validator("setup")
+    def question_ends_with_question_mark(cls, field):
+        if field[-1] != "?":
+            raise ValueError("Badly formed question!")
+        return field
+
+
+Example 2.
+
+# Here's another example, but with a compound typed field.
+class Actor(BaseModel):
+    name: str = Field(description="name of an actor")
+    film_names: List[str] = Field(description="list of names of films they starred in")
+"""
+
+import json, re, logging
+
+
+PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
+
+As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
+the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
+
+Here is the output schema:
+```
+{schema}
+```"""
+
+
+PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
+```
+{schema}
+```"""
+
+class JsonStringError(Exception): ...
+
+class GptJsonIO():
+
+    def __init__(self, schema, example_instruction=True):
+        self.pydantic_object = schema
+        self.example_instruction = example_instruction
+        self.format_instructions = self.generate_format_instructions()
+
+    def generate_format_instructions(self):
+        schema = self.pydantic_object.schema()
+
+        # Remove extraneous fields.
+        reduced_schema = schema
+        if "title" in reduced_schema:
+            del reduced_schema["title"]
+        if "type" in reduced_schema:
+            del reduced_schema["type"]
+        # Ensure json in context is well-formed with double quotes.
+        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)
+
+    def generate_output(self, text):
+        # Greedy search for 1st json candidate.
+        match = re.search(
+            r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL
+        )
+        json_str = ""
+        if match: json_str = match.group()
+        json_object = json.loads(json_str, strict=False)
+        final_object = self.pydantic_object.parse_obj(json_object)
+        return final_object
+
+    def generate_repair_prompt(self, broken_json, error):
+        prompt = "Fix a broken json string.\n\n" + \
+                 "(1) The broken json string need to fix is: \n\n" + \
+                 "```" + "\n" + \
+                 broken_json + "\n" + \
+                 "```" + "\n\n" + \
+                 "(2) The error message is: \n\n" + \
+                 error + "\n\n" + \
+                "Now, fix this json string. \n\n"
+        return prompt
+    
+    def generate_output_auto_repair(self, response, gpt_gen_fn):
+        """
+        response: string containing canidate json
+        gpt_gen_fn: gpt_gen_fn(inputs, sys_prompt)
+        """
+        try:
+            result = self.generate_output(response)
+        except Exception as e:
+            try:
+                logging.info(f'Repairing json：{response}')
+                repair_prompt = self.generate_repair_prompt(broken_json = response, error=repr(e))
+                result = self.generate_output(gpt_gen_fn(repair_prompt, self.format_instructions))
+                logging.info('Repaire json success.')
+            except Exception as e:
+                # 没辙了，放弃治疗
+                logging.info('Repaire json fail.')
+                raise JsonStringError('Cannot repair json.', str(e))
+        return result
+
--- a/crazy_functions/latex_fns/latex_actions.py
+++ b/crazy_functions/latex_fns/latex_actions.py
@@ -1,4 +1,4 @@
-from toolbox import update_ui, update_ui_lastest_msg    # 刷新Gradio前端界面
+from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
 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
@@ -363,7 +363,7 @@ 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')
+                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)
@@ -439,9 +439,9 @@ 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())
--- a/crazy_functions/latex_fns/latex_toolbox.py
+++ b/crazy_functions/latex_fns/latex_toolbox.py
@@ -256,6 +256,7 @@ def find_main_tex_file(file_manifest, mode):
            canidates_score.append(0)
            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
@@ -281,13 +282,20 @@ def rm_comments(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)
+    # 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)
-    # go case in-sensitive
+    # 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'):
        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):
@@ -298,9 +306,9 @@ 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_:
+            with open(fp_, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
        else:
            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
        c = merge_tex_files_(project_foler, c, mode)
@@ -420,7 +428,7 @@ def compile_latex_with_timeout(command, cwd, timeout=60):

 def merge_pdfs(pdf1_path, pdf2_path, output_path):
    import PyPDF2
-    Percent = 0.8
+    Percent = 0.95
    # Open the first PDF file
    with open(pdf1_path, 'rb') as pdf1_file:
        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
--- a/crazy_functions/live_audio/aliyunASR.py
+++ b/crazy_functions/live_audio/aliyunASR.py
@@ -1,4 +1,4 @@
-import time, threading, json
+import time, logging, json


 class AliyunASR():
@@ -12,14 +12,14 @@ class AliyunASR():
        message = json.loads(message)
        self.parsed_sentence = message['payload']['result']
        self.event_on_entence_end.set()
-        print(self.parsed_sentence)
+        # 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):
@@ -36,7 +36,6 @@ class AliyunASR():
        # 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
--- a/crazy_functions/pdf_fns/parse_pdf.py
+++ b/crazy_functions/pdf_fns/parse_pdf.py
@@ -0,0 +1,30 @@
+import requests
+import random
+from functools import lru_cache
+class GROBID_OFFLINE_EXCEPTION(Exception): pass
+
+def get_avail_grobid_url():
+    from toolbox import get_conf
+    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')
+        if res.text=='true': return _grobid_url
+        else: return None
+    except:
+        return None
+
+@lru_cache(maxsize=32)
+def parse_pdf(pdf_path, grobid_url):
+    import scipdf   # pip install scipdf_parser
+    if grobid_url.endswith('/'): grobid_url = grobid_url.rstrip('/')
+    try:
+        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
+
--- a/crazy_functions/test_project/cpp/cppipc/buffer.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/buffer.cpp
@@ -1,87 +0,0 @@
-#include "libipc/buffer.h"
-#include "libipc/utility/pimpl.h"
-
-#include <cstring>
-
-namespace ipc {
-
-bool operator==(buffer const & b1, buffer const & b2) {
-    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
-}
-
-bool operator!=(buffer const & b1, buffer const & b2) {
-    return !(b1 == b2);
-}
-
-class buffer::buffer_ : public pimpl<buffer_> {
-public:
-    void*       p_;
-    std::size_t s_;
-    void*       a_;
-    buffer::destructor_t d_;
-
-    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
-        : p_(p), s_(s), a_(a), d_(d) {
-    }
-
-    ~buffer_() {
-        if (d_ == nullptr) return;
-        d_((a_ == nullptr) ? p_ : a_, s_);
-    }
-};
-
-buffer::buffer()
-    : buffer(nullptr, 0, nullptr, nullptr) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d)
-    : p_(p_->make(p, s, d, nullptr)) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
-    : p_(p_->make(p, s, d, additional)) {
-}
-
-buffer::buffer(void* p, std::size_t s)
-    : buffer(p, s, nullptr) {
-}
-
-buffer::buffer(char const & c)
-    : buffer(const_cast<char*>(&c), 1) {
-}
-
-buffer::buffer(buffer&& rhs)
-    : buffer() {
-    swap(rhs);
-}
-
-buffer::~buffer() {
-    p_->clear();
-}
-
-void buffer::swap(buffer& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-buffer& buffer::operator=(buffer rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool buffer::empty() const noexcept {
-    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
-}
-
-void* buffer::data() noexcept {
-    return impl(p_)->p_;
-}
-
-void const * buffer::data() const noexcept {
-    return impl(p_)->p_;
-}
-
-std::size_t buffer::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/ipc.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/ipc.cpp
@@ -1,701 +0,0 @@
-
-#include <type_traits>
-#include <cstring>
-#include <algorithm>
-#include <utility>          // std::pair, std::move, std::forward
-#include <atomic>
-#include <type_traits>      // aligned_storage_t
-#include <string>
-#include <vector>
-#include <array>
-#include <cassert>
-
-#include "libipc/ipc.h"
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/pool_alloc.h"
-#include "libipc/queue.h"
-#include "libipc/policy.h"
-#include "libipc/rw_lock.h"
-#include "libipc/waiter.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/utility/id_pool.h"
-#include "libipc/utility/scope_guard.h"
-#include "libipc/utility/utility.h"
-
-#include "libipc/memory/resource.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_array.h"
-
-namespace {
-
-using msg_id_t = std::uint32_t;
-using acc_t    = std::atomic<msg_id_t>;
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t;
-
-template <std::size_t AlignSize>
-struct msg_t<0, AlignSize> {
-    msg_id_t     cc_id_;
-    msg_id_t     id_;
-    std::int32_t remain_;
-    bool         storage_;
-};
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t : msg_t<0, AlignSize> {
-    std::aligned_storage_t<DataSize, AlignSize> data_ {};
-
-    msg_t() = default;
-    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
-        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
-        if (this->storage_) {
-            if (data != nullptr) {
-                // copy storage-id
-                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
-                     *static_cast<ipc::storage_id_t const *>(data);
-            }
-        }
-        else std::memcpy(&data_, data, size);
-    }
-};
-
-template <typename T>
-ipc::buff_t make_cache(T& data, std::size_t size) {
-    auto ptr = ipc::mem::alloc(size);
-    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
-    return { ptr, size, ipc::mem::free };
-}
-
-struct cache_t {
-    std::size_t fill_;
-    ipc::buff_t buff_;
-
-    cache_t(std::size_t f, ipc::buff_t && b)
-        : fill_(f), buff_(std::move(b))
-    {}
-
-    void append(void const * data, std::size_t size) {
-        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
-        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
-        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
-        fill_ = new_fill;
-    }
-};
-
-auto cc_acc() {
-    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
-    return static_cast<acc_t*>(acc_h.get());
-}
-
-IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
-    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
-}
-
-IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
-    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
-           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
-}
-
-struct chunk_t {
-    std::atomic<ipc::circ::cc_t> &conns() noexcept {
-        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
-    }
-
-    void *data() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this)
-             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
-    }
-};
-
-struct chunk_info_t {
-    ipc::id_pool<> pool_;
-    ipc::spin_lock lock_;
-
-    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
-        return ipc::id_pool<>::max_count * chunk_size;
-    }
-
-    ipc::byte_t *chunks_mem() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this + 1);
-    }
-
-    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
-        if (id < 0) return nullptr;
-        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
-    }
-};
-
-auto& chunk_storages() {
-    class chunk_handle_t {
-        ipc::shm::handle handle_;
-
-    public:
-        chunk_info_t *get_info(std::size_t chunk_size) {
-            if (!handle_.valid() &&
-                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
-                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            auto info = static_cast<chunk_info_t*>(handle_.get());
-            if (info == nullptr) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            return info;
-        }
-    };
-    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
-    return chunk_hs;
-}
-
-chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
-    auto &storages = chunk_storages();
-    std::decay_t<decltype(storages)>::iterator it;
-    {
-        static ipc::rw_lock lock;
-        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
-        if ((it = storages.find(chunk_size)) == storages.end()) {
-            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
-            guard.unlock();
-            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
-            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
-        }
-    }
-    return it->second.get_info(chunk_size);
-}
-
-std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return {};
-
-    info->lock_.lock();
-    info->pool_.prepare();
-    // got an unique id
-    auto id = info->pool_.acquire();
-    info->lock_.unlock();
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return {};
-    chunk->conns().store(conns, std::memory_order_relaxed);
-    return { id, chunk->data() };
-}
-
-void *find_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return nullptr;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return nullptr;
-    return info->at(chunk_size, id)->data();
-}
-
-void release_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
-            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
-    return true;
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
-            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
-    auto last_conns = curr_conns & ~conn_id;
-    for (unsigned k = 0;;) {
-        auto chunk_conns  = conns.load(std::memory_order_acquire);
-        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
-            return (chunk_conns & last_conns) == 0;
-        }
-        ipc::yield(k);
-    }
-}
-
-template <typename Flag>
-void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
-    if (id < 0) {
-        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return;
-
-    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
-        return;
-    }
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <typename MsgT>
-bool clear_message(void* p) {
-    auto msg = static_cast<MsgT*>(p);
-    if (msg->storage_) {
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
-        if (r_size <= 0) {
-            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
-            return true;
-        }
-        release_storage(
-            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
-            static_cast<std::size_t>(r_size));
-    }
-    return true;
-}
-
-struct conn_info_head {
-
-    ipc::string name_;
-    msg_id_t    cc_id_; // connection-info id
-    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
-    ipc::shm::handle acc_h_;
-
-    conn_info_head(char const * name)
-        : name_     {name}
-        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
-        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
-        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
-        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
-        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
-    }
-
-    void quit_waiting() {
-        cc_waiter_.quit_waiting();
-        wt_waiter_.quit_waiting();
-        rd_waiter_.quit_waiting();
-    }
-
-    auto acc() {
-        return static_cast<acc_t*>(acc_h_.get());
-    }
-
-    auto& recv_cache() {
-        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
-        return tls;
-    }
-};
-
-template <typename W, typename F>
-bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
-    if (tm == 0) return !pred();
-    for (unsigned k = 0; pred();) {
-        bool ret = true;
-        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
-            ret = waiter.wait_if(std::forward<F>(pred), tm);
-            k   = 0;
-        });
-        if (!ret) return false; // timeout or fail
-        if (k == 0) break; // k has been reset
-    }
-    return true;
-}
-
-template <typename Policy,
-          std::size_t DataSize  = ipc::data_length,
-          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
-struct queue_generator {
-
-    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
-
-    struct conn_info_t : conn_info_head {
-        queue_t que_;
-
-        conn_info_t(char const * name)
-            : conn_info_head{name}
-            , que_{("__QU_CONN__" +
-                    ipc::to_string(DataSize) + "__" +
-                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
-        }
-
-        void disconnect_receiver() {
-            bool dis = que_.disconnect();
-            this->quit_waiting();
-            if (dis) {
-                this->recv_cache().clear();
-            }
-        }
-    };
-};
-
-template <typename Policy>
-struct detail_impl {
-
-using policy_t    = Policy;
-using flag_t      = typename policy_t::flag_t;
-using queue_t     = typename queue_generator<policy_t>::queue_t;
-using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
-
-constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
-    return static_cast<conn_info_t*>(h);
-}
-
-constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
-    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
-}
-
-/* API implementations */
-
-static void disconnect(ipc::handle_t h) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return;
-    }
-    que->shut_sending();
-    assert(info_of(h) != nullptr);
-    info_of(h)->disconnect_receiver();
-}
-
-static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
-    assert(ph != nullptr);
-    assert(*ph != nullptr);
-    auto que = queue_of(*ph);
-    if (que == nullptr) {
-        return false;
-    }
-    if (start_to_recv) {
-        que->shut_sending();
-        if (que->connect()) { // wouldn't connect twice
-            info_of(*ph)->cc_waiter_.broadcast();
-            return true;
-        }
-        return false;
-    }
-    // start_to_recv == false
-    if (que->connected()) {
-        info_of(*ph)->disconnect_receiver();
-    }
-    return que->ready_sending();
-}
-
-static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
-    assert(ph != nullptr);
-    if (*ph == nullptr) {
-        *ph = ipc::mem::alloc<conn_info_t>(name);
-    }
-    return reconnect(ph, start_to_recv);
-}
-
-static void destroy(ipc::handle_t h) {
-    disconnect(h);
-    ipc::mem::free(info_of(h));
-}
-
-static std::size_t recv_count(ipc::handle_t h) noexcept {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return ipc::invalid_value;
-    }
-    return que->conn_count();
-}
-
-static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return false;
-    }
-    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
-        return que->conn_count() < r_count;
-    }, tm);
-}
-
-template <typename F>
-static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
-    if (data == nullptr || size == 0) {
-        ipc::error("fail: send(%p, %zd)\n", data, size);
-        return false;
-    }
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: send, queue_of(h) == nullptr\n");
-        return false;
-    }
-    if (que->elems() == nullptr) {
-        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
-        return false;
-    }
-    if (!que->ready_sending()) {
-        ipc::error("fail: send, que->ready_sending() == false\n");
-        return false;
-    }
-    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
-    if (conns == 0) {
-        ipc::error("fail: send, there is no receiver on this connection.\n");
-        return false;
-    }
-    // calc a new message id
-    auto acc = info_of(h)->acc();
-    if (acc == nullptr) {
-        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
-        return false;
-    }
-    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
-    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
-    if (size > ipc::large_msg_limit) {
-        auto   dat = acquire_storage(size, conns);
-        void * buf = dat.second;
-        if (buf != nullptr) {
-            std::memcpy(buf, data, size);
-            return try_push(static_cast<std::int32_t>(size) - 
-                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
-        }
-        // try using message fragment
-        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
-    }
-    // push message fragment
-    std::int32_t offset = 0;
-    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
-        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
-            return false;
-        }
-    }
-    // if remain > 0, this is the last message fragment
-    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
-    if (remain > 0) {
-        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, 
-                      static_cast<std::size_t>(remain))) {
-            return false;
-        }
-    }
-    return true;
-}
-
-static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
-                if (!que->force_push(
-                        clear_message<typename queue_t::value_t>,
-                        info->cc_id_, msg_id, remain, data, size)) {
-                    return false;
-                }
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                return false;
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: recv, queue_of(h) == nullptr\n");
-        return {};
-    }
-    if (!que->connected()) {
-        // hasn't connected yet, just return.
-        return {};
-    }
-    auto& rc = info_of(h)->recv_cache();
-    for (;;) {
-        // pop a new message
-        typename queue_t::value_t msg;
-        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
-                return !que->pop(msg);
-            }, tm)) {
-            // pop failed, just return.
-            return {};
-        }
-        info_of(h)->wt_waiter_.broadcast();
-        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
-            continue; // ignore message to self
-        }
-        // msg.remain_ may minus & abs(msg.remain_) < data_length
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
-        if (r_size <= 0) {
-            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
-            return {};
-        }
-        std::size_t msg_size = static_cast<std::size_t>(r_size);
-        // large message
-        if (msg.storage_) {
-            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
-            void* buf = find_storage(buf_id, msg_size);
-            if (buf != nullptr) {
-                struct recycle_t {
-                    ipc::storage_id_t storage_id;
-                    ipc::circ::cc_t   curr_conns;
-                    ipc::circ::cc_t   conn_id;
-                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
-                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
-                });
-                if (r_info == nullptr) {
-                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
-                    return ipc::buff_t{buf, msg_size}; // no recycle
-                } else {
-                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
-                        auto r_info = static_cast<recycle_t *>(p_info);
-                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
-                            ipc::mem::free(r_info);
-                        });
-                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
-                    }, r_info};
-                }
-            } else {
-                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
-                continue;
-            }
-        }
-        // find cache with msg.id_
-        auto cac_it = rc.find(msg.id_);
-        if (cac_it == rc.end()) {
-            if (msg_size <= ipc::data_length) {
-                return make_cache(msg.data_, msg_size);
-            }
-            // gc
-            if (rc.size() > 1024) {
-                std::vector<msg_id_t> need_del;
-                for (auto const & pair : rc) {
-                    auto cmp = std::minmax(msg.id_, pair.first);
-                    if (cmp.second - cmp.first > 8192) {
-                        need_del.push_back(pair.first);
-                    }
-                }
-                for (auto id : need_del) rc.erase(id);
-            }
-            // cache the first message fragment
-            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
-        }
-        // has cached before this message
-        else {
-            auto& cac = cac_it->second;
-            // this is the last message fragment
-            if (msg.remain_ <= 0) {
-                cac.append(&(msg.data_), msg_size);
-                // finish this message, erase it from cache
-                auto buff = std::move(cac.buff_);
-                rc.erase(cac_it);
-                return buff;
-            }
-            // there are remain datas after this message
-            cac.append(&(msg.data_), ipc::data_length);
-        }
-    }
-}
-
-static ipc::buff_t try_recv(ipc::handle_t h) {
-    return recv(h, 0);
-}
-
-}; // detail_impl<Policy>
-
-template <typename Flag>
-using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
-
-} // internal-linkage
-
-namespace ipc {
-
-template <typename Flag>
-ipc::handle_t chan_impl<Flag>::inited() {
-    ipc::detail::waiter::init();
-    return nullptr;
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::disconnect(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::disconnect(h);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::destroy(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::destroy(h);
-}
-
-template <typename Flag>
-char const * chan_impl<Flag>::name(ipc::handle_t h) {
-    auto info = detail_impl<policy_t<Flag>>::info_of(h);
-    return (info == nullptr) ? nullptr : info->name_.c_str();
-}
-
-template <typename Flag>
-std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::recv_count(h);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::recv(h, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::try_recv(h);
-}
-
-template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
-// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
-// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
-template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
-template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
-
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/policy.h
+++ b/crazy_functions/test_project/cpp/cppipc/policy.h
@@ -1,25 +0,0 @@
-#pragma once
-
-#include <type_traits>
-
-#include "libipc/def.h"
-#include "libipc/prod_cons.h"
-
-#include "libipc/circ/elem_array.h"
-
-namespace ipc {
-namespace policy {
-
-template <template <typename, std::size_t...> class Elems, typename Flag>
-struct choose;
-
-template <typename Flag>
-struct choose<circ::elem_array, Flag> {
-    using flag_t = Flag;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    using elems_t = circ::elem_array<ipc::prod_cons_impl<flag_t>, DataSize, AlignSize>;
-};
-
-} // namespace policy
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace mem {
-
-void* pool_alloc::alloc(std::size_t size) {
-    return async_pool_alloc::alloc(size);
-}
-
-void pool_alloc::free(void* p, std::size_t size) {
-    async_pool_alloc::free(p, size);
-}
-
-} // namespace mem
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ b/crazy_functions/test_project/cpp/cppipc/prod_cons.h
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/queue.h
+++ b/crazy_functions/test_project/cpp/cppipc/queue.h
@@ -1,216 +0,0 @@
-#pragma once
-
-#include <type_traits>
-#include <new>
-#include <utility>  // [[since C++14]]: std::exchange
-#include <algorithm>
-#include <atomic>
-#include <tuple>
-#include <thread>
-#include <chrono>
-#include <string>
-#include <cassert>  // assert
-
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/rw_lock.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-
-namespace ipc {
-namespace detail {
-
-class queue_conn {
-protected:
-    circ::cc_t connected_ = 0;
-    shm::handle elems_h_;
-
-    template <typename Elems>
-    Elems* open(char const * name) {
-        if (name == nullptr || name[0] == '\0') {
-            ipc::error("fail open waiter: name is empty!\n");
-            return nullptr;
-        }
-        if (!elems_h_.acquire(name, sizeof(Elems))) {
-            return nullptr;
-        }
-        auto elems = static_cast<Elems*>(elems_h_.get());
-        if (elems == nullptr) {
-            ipc::error("fail acquire elems: %s\n", name);
-            return nullptr;
-        }
-        elems->init();
-        return elems;
-    }
-
-    void close() {
-        elems_h_.release();
-    }
-
-public:
-    queue_conn() = default;
-    queue_conn(const queue_conn&) = delete;
-    queue_conn& operator=(const queue_conn&) = delete;
-
-    bool connected() const noexcept {
-        return connected_ != 0;
-    }
-
-    circ::cc_t connected_id() const noexcept {
-        return connected_;
-    }
-
-    template <typename Elems>
-    auto connect(Elems* elems) noexcept
-                         /*needs 'optional' here*/
-     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
-        if (elems == nullptr) return {};
-        // if it's already connected, just return
-        if (connected()) return {connected(), false, 0};
-        connected_ = elems->connect_receiver();
-        return {connected(), true, elems->cursor()};
-    }
-
-    template <typename Elems>
-    bool disconnect(Elems* elems) noexcept {
-        if (elems == nullptr) return false;
-        // if it's already disconnected, just return false
-        if (!connected()) return false;
-        elems->disconnect_receiver(std::exchange(connected_, 0));
-        return true;
-    }
-};
-
-template <typename Elems>
-class queue_base : public queue_conn {
-    using base_t = queue_conn;
-
-public:
-    using elems_t  = Elems;
-    using policy_t = typename elems_t::policy_t;
-
-protected:
-    elems_t * elems_ = nullptr;
-    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
-    bool sender_flag_ = false;
-
-public:
-    using base_t::base_t;
-
-    queue_base() = default;
-
-    explicit queue_base(char const * name)
-        : queue_base{} {
-        elems_ = open<elems_t>(name);
-    }
-
-    explicit queue_base(elems_t * elems) noexcept
-        : queue_base{} {
-        assert(elems != nullptr);
-        elems_ = elems;
-    }
-
-    /* not virtual */ ~queue_base() {
-        base_t::close();
-    }
-
-    elems_t       * elems()       noexcept { return elems_; }
-    elems_t const * elems() const noexcept { return elems_; }
-
-    bool ready_sending() noexcept {
-        if (elems_ == nullptr) return false;
-        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
-    }
-
-    void shut_sending() noexcept {
-        if (elems_ == nullptr) return;
-        if (!sender_flag_) return;
-        elems_->disconnect_sender();
-    }
-
-    bool connect() noexcept {
-        auto tp = base_t::connect(elems_);
-        if (std::get<0>(tp) && std::get<1>(tp)) {
-            cursor_ = std::get<2>(tp);
-            return true;
-        }
-        return std::get<0>(tp);
-    }
-
-    bool disconnect() noexcept {
-        return base_t::disconnect(elems_);
-    }
-
-    std::size_t conn_count() const noexcept {
-        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
-    }
-
-    bool valid() const noexcept {
-        return elems_ != nullptr;
-    }
-
-    bool empty() const noexcept {
-        return !valid() || (cursor_ == elems_->cursor());
-    }
-
-    template <typename T, typename F, typename... P>
-    bool push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F, typename... P>
-    bool force_push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->force_push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
-        });
-    }
-
-    template <typename T, typename F>
-    bool pop(T& item, F&& out) {
-        if (elems_ == nullptr) {
-            return false;
-        }
-        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
-            ::new (&item) T(std::move(*static_cast<T*>(p)));
-        }, std::forward<F>(out));
-    }
-};
-
-} // namespace detail
-
-template <typename T, typename Policy>
-class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
-    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
-
-public:
-    using value_t = T;
-
-    using base_t::base_t;
-
-    template <typename... P>
-    bool push(P&&... params) {
-        return base_t::template push<T>(std::forward<P>(params)...);
-    }
-
-    template <typename... P>
-    bool force_push(P&&... params) {
-        return base_t::template force_push<T>(std::forward<P>(params)...);
-    }
-
-    bool pop(T& item) {
-        return base_t::pop(item, [](bool) {});
-    }
-
-    template <typename F>
-    bool pop(T& item, F&& out) {
-        return base_t::pop(item, std::forward<F>(out));
-    }
-};
-
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/shm.cpp
+++ b/crazy_functions/test_project/cpp/cppipc/shm.cpp
@@ -1,103 +0,0 @@
-
-#include <string>
-#include <utility>
-
-#include "libipc/shm.h"
-
-#include "libipc/utility/pimpl.h"
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace shm {
-
-class handle::handle_ : public pimpl<handle_> {
-public:
-    shm::id_t id_ = nullptr;
-    void*     m_  = nullptr;
-
-    ipc::string n_;
-    std::size_t s_ = 0;
-};
-
-handle::handle()
-    : p_(p_->make()) {
-}
-
-handle::handle(char const * name, std::size_t size, unsigned mode)
-    : handle() {
-    acquire(name, size, mode);
-}
-
-handle::handle(handle&& rhs)
-    : handle() {
-    swap(rhs);
-}
-
-handle::~handle() {
-    release();
-    p_->clear();
-}
-
-void handle::swap(handle& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-handle& handle::operator=(handle rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool handle::valid() const noexcept {
-    return impl(p_)->m_ != nullptr;
-}
-
-std::size_t handle::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-char const * handle::name() const noexcept {
-    return impl(p_)->n_.c_str();
-}
-
-std::int32_t handle::ref() const noexcept {
-    return shm::get_ref(impl(p_)->id_);
-}
-
-void handle::sub_ref() noexcept {
-    shm::sub_ref(impl(p_)->id_);
-}
-
-bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
-    release();
-    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-    return valid();
-}
-
-std::int32_t handle::release() {
-    if (impl(p_)->id_ == nullptr) return -1;
-    return shm::release(detach());
-}
-
-void* handle::get() const {
-    return impl(p_)->m_;
-}
-
-void handle::attach(id_t id) {
-    if (id == nullptr) return;
-    release();
-    impl(p_)->id_ = id;
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-}
-
-id_t handle::detach() {
-    auto old = impl(p_)->id_;
-    impl(p_)->id_ = nullptr;
-    impl(p_)->m_  = nullptr;
-    impl(p_)->s_  = 0;
-    impl(p_)->n_.clear();
-    return old;
-}
-
-} // namespace shm
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/waiter.h
+++ b/crazy_functions/test_project/cpp/cppipc/waiter.h
@@ -1,83 +0,0 @@
-#pragma once
-
-#include <utility>
-#include <string>
-#include <mutex>
-#include <atomic>
-
-#include "libipc/def.h"
-#include "libipc/mutex.h"
-#include "libipc/condition.h"
-#include "libipc/platform/detail.h"
-
-namespace ipc {
-namespace detail {
-
-class waiter {
-    ipc::sync::condition cond_;
-    ipc::sync::mutex     lock_;
-    std::atomic<bool>    quit_ {false};
-
-public:
-    static void init();
-
-    waiter() = default;
-    waiter(char const *name) {
-        open(name);
-    }
-
-    ~waiter() {
-        close();
-    }
-
-    bool valid() const noexcept {
-        return cond_.valid() && lock_.valid();
-    }
-
-    bool open(char const *name) noexcept {
-        quit_.store(false, std::memory_order_relaxed);
-        if (!cond_.open((std::string{"_waiter_cond_"} + name).c_str())) {
-            return false;
-        }
-        if (!lock_.open((std::string{"_waiter_lock_"} + name).c_str())) {
-            cond_.close();
-            return false;
-        }
-        return valid();
-    }
-
-    void close() noexcept {
-        cond_.close();
-        lock_.close();
-    }
-
-    template <typename F>
-    bool wait_if(F &&pred, std::uint64_t tm = ipc::invalid_value) noexcept {
-        IPC_UNUSED_ std::lock_guard<ipc::sync::mutex> guard {lock_};
-        while ([this, &pred] {
-                    return !quit_.load(std::memory_order_relaxed)
-                        && std::forward<F>(pred)();
-                }()) {
-            if (!cond_.wait(lock_, tm)) return false;
-        }
-        return true;
-    }
-
-    bool notify() noexcept {
-        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
-        return cond_.notify(lock_);
-    }
-
-    bool broadcast() noexcept {
-        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
-        return cond_.broadcast(lock_);
-    }
-
-    bool quit_waiting() {
-        quit_.store(true, std::memory_order_release);
-        return broadcast();
-    }
-};
-
-} // namespace detail
-} // namespace ipc
--- a/crazy_functions/test_project/cpp/cppipc/来源
+++ b/crazy_functions/test_project/cpp/cppipc/来源
@@ -1,3 +0,0 @@
-https://github.com/mutouyun/cpp-ipc
-
-A high-performance inter-process communication library using shared memory on Linux/Windows.
--- a/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ b/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
--- a/crazy_functions/test_project/cpp/libJPG/jpgd.h
+++ b/crazy_functions/test_project/cpp/libJPG/jpgd.h
@@ -1,316 +0,0 @@
-// jpgd.h - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-#ifndef JPEG_DECODER_H
-#define JPEG_DECODER_H
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <setjmp.h>
-
-namespace jpgd
-{
-  typedef unsigned char  uint8;
-  typedef   signed short int16;
-  typedef unsigned short uint16;
-  typedef unsigned int   uint;
-  typedef   signed int   int32;
-
-  // Loads a JPEG image from a memory buffer or a file.
-  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
-  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
-  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
-  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
-// BEGIN EPIC MOD
-//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
-  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
-// END EPIC MOD
-  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
-
-  // Success/failure error codes.
-  enum jpgd_status
-  {
-    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
-    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, 
-    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, 
-    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
-    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
-    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
-    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
-    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
-    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
-  };
-    
-  // Input stream interface.
-  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
-  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
-  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
-  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
-  class jpeg_decoder_stream
-  {
-  public:
-    jpeg_decoder_stream() { }
-    virtual ~jpeg_decoder_stream() { }
-
-    // The read() method is called when the internal input buffer is empty.
-    // Parameters:
-    // pBuf - input buffer
-    // max_bytes_to_read - maximum bytes that can be written to pBuf
-    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
-    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
-    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
-  };
-
-  // stdio FILE stream class.
-  class jpeg_decoder_file_stream : public jpeg_decoder_stream
-  {
-    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
-    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
-
-    FILE *m_pFile;
-    bool m_eof_flag, m_error_flag;
-
-  public:
-    jpeg_decoder_file_stream();
-    virtual ~jpeg_decoder_file_stream();
-    
-    bool open(const char *Pfilename);
-    void close();
-
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Memory stream class.
-  class jpeg_decoder_mem_stream : public jpeg_decoder_stream
-  {
-    const uint8 *m_pSrc_data;
-    uint m_ofs, m_size;
-
-  public:
-    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
-    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
-
-    virtual ~jpeg_decoder_mem_stream() { }
-
-    bool open(const uint8 *pSrc_data, uint size);
-    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
-    
-    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
-  };
-
-  // Loads JPEG file from a jpeg_decoder_stream.
-  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
-
-  enum 
-  { 
-    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, 
-    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 
-  };
-          
-  typedef int16 jpgd_quant_t;
-  typedef int16 jpgd_block_t;
-
-  class jpeg_decoder
-  {
-  public:
-    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
-    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
-    jpeg_decoder(jpeg_decoder_stream *pStream);
-
-    ~jpeg_decoder();
-
-    // Call this method after constructing the object to begin decompression.
-    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
-    int begin_decoding();
-
-    // Returns the next scan line.
-    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). 
-    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
-    // Returns JPGD_SUCCESS if a scan line has been returned.
-    // Returns JPGD_DONE if all scan lines have been returned.
-    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
-    int decode(const void** pScan_line, uint* pScan_line_len);
-    
-    inline jpgd_status get_error_code() const { return m_error_code; }
-
-    inline int get_width() const { return m_image_x_size; }
-    inline int get_height() const { return m_image_y_size; }
-
-    inline int get_num_components() const { return m_comps_in_frame; }
-
-    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
-    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
-
-    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
-    inline int get_total_bytes_read() const { return m_total_bytes_read; }
-    
-  private:
-    jpeg_decoder(const jpeg_decoder &);
-    jpeg_decoder &operator =(const jpeg_decoder &);
-
-    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
-
-    struct huff_tables
-    {
-      bool ac_table;
-      uint  look_up[256];
-      uint  look_up2[256];
-      uint8 code_size[256];
-      uint  tree[512];
-    };
-
-    struct coeff_buf
-    {
-      uint8 *pData;
-      int block_num_x, block_num_y;
-      int block_len_x, block_len_y;
-      int block_size;
-    };
-
-    struct mem_block
-    {
-      mem_block *m_pNext;
-      size_t m_used_count;
-      size_t m_size;
-      char m_data[1];
-    };
-
-    jmp_buf m_jmp_state;
-    mem_block *m_pMem_blocks;
-    int m_image_x_size;
-    int m_image_y_size;
-    jpeg_decoder_stream *m_pStream;
-    int m_progressive_flag;
-    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
-    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size
-    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size
-    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
-    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
-    int m_comps_in_frame;                         // # of components in frame
-    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor
-    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor
-    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector
-    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID
-    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
-    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
-    int m_comps_in_scan;                          // # of components in scan
-    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan
-    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector
-    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector
-    int m_spectral_start;                         // spectral selection start
-    int m_spectral_end;                           // spectral selection end
-    int m_successive_low;                         // successive approximation low
-    int m_successive_high;                        // successive approximation high
-    int m_max_mcu_x_size;                         // MCU's max. X size in pixels
-    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels
-    int m_blocks_per_mcu;
-    int m_max_blocks_per_row;
-    int m_mcus_per_row, m_mcus_per_col;
-    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
-    int m_total_lines_left;                       // total # lines left in image
-    int m_mcu_lines_left;                         // total # lines left in this MCU
-    int m_real_dest_bytes_per_scan_line;
-    int m_dest_bytes_per_scan_line;               // rounded up
-    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)
-    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
-    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
-    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
-    int m_eob_run;
-    int m_block_y_mcu[JPGD_MAX_COMPONENTS];
-    uint8* m_pIn_buf_ofs;
-    int m_in_buf_left;
-    int m_tem_flag;
-    bool m_eof_flag;
-    uint8 m_in_buf_pad_start[128];
-    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
-    uint8 m_in_buf_pad_end[128];
-    int m_bits_left;
-    uint m_bit_buf;
-    int m_restart_interval;
-    int m_restarts_left;
-    int m_next_restart_num;
-    int m_max_mcus_per_row;
-    int m_max_blocks_per_mcu;
-    int m_expanded_blocks_per_mcu;
-    int m_expanded_blocks_per_row;
-    int m_expanded_blocks_per_component;
-    bool  m_freq_domain_chroma_upsample;
-    int m_max_mcus_per_col;
-    uint m_last_dc_val[JPGD_MAX_COMPONENTS];
-    jpgd_block_t* m_pMCU_coefficients;
-    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
-    uint8* m_pSample_buf;
-    int m_crr[256];
-    int m_cbb[256];
-    int m_crg[256];
-    int m_cbg[256];
-    uint8* m_pScan_line_0;
-    uint8* m_pScan_line_1;
-    jpgd_status m_error_code;
-    bool m_ready_flag;
-    int m_total_bytes_read;
-
-    void free_all_blocks();
-    // BEGIN EPIC MOD
-    UE_NORETURN void stop_decoding(jpgd_status status);
-    // END EPIC MOD
-    void *alloc(size_t n, bool zero = false);
-    void word_clear(void *p, uint16 c, uint n);
-    void prep_in_buffer();
-    void read_dht_marker();
-    void read_dqt_marker();
-    void read_sof_marker();
-    void skip_variable_marker();
-    void read_dri_marker();
-    void read_sos_marker();
-    int next_marker();
-    int process_markers();
-    void locate_soi_marker();
-    void locate_sof_marker();
-    int locate_sos_marker();
-    void init(jpeg_decoder_stream * pStream);
-    void create_look_ups();
-    void fix_in_buffer();
-    void transform_mcu(int mcu_row);
-    void transform_mcu_expand(int mcu_row);
-    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
-    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
-    void load_next_row();
-    void decode_next_row();
-    void make_huff_table(int index, huff_tables *pH);
-    void check_quant_tables();
-    void check_huff_tables();
-    void calc_mcu_block_order();
-    int init_scan();
-    void init_frame();
-    void process_restart();
-    void decode_scan(pDecode_block_func decode_block_func);
-    void init_progressive();
-    void init_sequential();
-    void decode_start();
-    void decode_init(jpeg_decoder_stream * pStream);
-    void H2V2Convert();
-    void H2V1Convert();
-    void H1V2Convert();
-    void H1V1Convert();
-    void gray_convert();
-    void expanded_convert();
-    void find_eoi();
-    inline uint get_char();
-    inline uint get_char(bool *pPadding_flag);
-    inline void stuff_char(uint8 q);
-    inline uint8 get_octet();
-    inline uint get_bits(int num_bits);
-    inline uint get_bits_no_markers(int numbits);
-    inline int huff_decode(huff_tables *pH);
-    inline int huff_decode(huff_tables *pH, int& extrabits);
-    static inline uint8 clamp(int i);
-    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
-  };
-  
-} // namespace jpgd
-
-#endif // JPEG_DECODER_H
--- a/crazy_functions/test_project/cpp/libJPG/jpge.cpp
+++ b/crazy_functions/test_project/cpp/libJPG/jpge.cpp
--- a/crazy_functions/test_project/cpp/libJPG/jpge.h
+++ b/crazy_functions/test_project/cpp/libJPG/jpge.h
@@ -1,172 +0,0 @@
-
-// jpge.h - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Alex Evans: Added RGBA support, linear memory allocator.
-#ifndef JPEG_ENCODER_H
-#define JPEG_ENCODER_H
-
-#include <stdint.h>
-
-namespace jpge
-{
-  typedef unsigned char  uint8;
-  typedef signed short   int16;
-  typedef signed int     int32;
-  typedef unsigned short uint16;
-  typedef unsigned int   uint32;
-  typedef unsigned int   uint;
-
-  // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common.
-  enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 };
-
-  // JPEG compression parameters structure.
-  struct params
-  {
-    inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { }
-
-    inline bool check_valid() const
-    {
-      if ((m_quality < 1) || (m_quality > 100)) return false;
-      if ((uint)m_subsampling > (uint)H2V2) return false;
-      return true;
-    }
-
-    // Quality: 1-100, higher is better. Typical values are around 50-95.
-    int m_quality;
-
-    // m_subsampling:
-    // 0 = Y (grayscale) only
-    // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU)
-    // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU)
-    // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common)
-    subsampling_t m_subsampling;
-
-    // Disables CbCr discrimination - only intended for testing.
-    // If true, the Y quantization table is also used for the CbCr channels.
-    bool m_no_chroma_discrim_flag;
-
-    bool m_two_pass_flag;
-  };
-  
-  // Writes JPEG image to a file. 
-  // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels.
-  bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
-
-  // Writes JPEG image to memory buffer. 
-  // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. 
-  // If return value is true, buf_size will be set to the size of the compressed data.
-  bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
-    
-  // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. 
-  // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts.
-  class output_stream
-  {
-  public:
-    virtual ~output_stream() { };
-    virtual bool put_buf(const void* Pbuf, int64_t len) = 0;
-    template<class T> inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); }
-  };
-    
-  // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions.
-  class jpeg_encoder
-  {
-  public:
-    jpeg_encoder();
-    ~jpeg_encoder();
-
-    // Initializes the compressor.
-    // pStream: The stream object to use for writing compressed data.
-    // params - Compression parameters structure, defined above.
-    // width, height  - Image dimensions.
-    // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data.
-    // Returns false on out of memory or if a stream write fails.
-    bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params());
-    
-    const params &get_params() const { return m_params; }
-    
-    // Deinitializes the compressor, freeing any allocated memory. May be called at any time.
-    void deinit();
-
-    uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; }
-    inline uint get_cur_pass() { return m_pass_num; }
-
-    // Call this method with each source scanline.
-    // width * src_channels bytes per scanline is expected (RGB or Y format).
-    // You must call with NULL after all scanlines are processed to finish compression.
-    // Returns false on out of memory or if a stream write fails.
-    bool process_scanline(const void* pScanline);
-        
-  private:
-    jpeg_encoder(const jpeg_encoder &);
-    jpeg_encoder &operator =(const jpeg_encoder &);
-
-    typedef int32 sample_array_t;
-        
-    output_stream *m_pStream;
-    params m_params;
-    uint8 m_num_components;
-    uint8 m_comp_h_samp[3], m_comp_v_samp[3];
-    int m_image_x, m_image_y, m_image_bpp, m_image_bpl;
-    int m_image_x_mcu, m_image_y_mcu;
-    int m_image_bpl_xlt, m_image_bpl_mcu;
-    int m_mcus_per_row;
-    int m_mcu_x, m_mcu_y;
-    uint8 *m_mcu_lines[16];
-    uint8 m_mcu_y_ofs;
-    sample_array_t m_sample_array[64];
-    int16 m_coefficient_array[64];
-    int32 m_quantization_tables[2][64];
-    uint m_huff_codes[4][256];
-    uint8 m_huff_code_sizes[4][256];
-    uint8 m_huff_bits[4][17];
-    uint8 m_huff_val[4][256];
-    uint32 m_huff_count[4][256];
-    int m_last_dc_val[3];
-    enum { JPGE_OUT_BUF_SIZE = 2048 };
-    uint8 m_out_buf[JPGE_OUT_BUF_SIZE];
-    uint8 *m_pOut_buf;
-    uint m_out_buf_left;
-    uint32 m_bit_buffer;
-    uint m_bits_in;
-    uint8 m_pass_num;
-    bool m_all_stream_writes_succeeded;
-        
-    void optimize_huffman_table(int table_num, int table_len);
-    void emit_byte(uint8 i);
-    void emit_word(uint i);
-    void emit_marker(int marker);
-    void emit_jfif_app0();
-    void emit_dqt();
-    void emit_sof();
-    void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag);
-    void emit_dhts();
-    void emit_sos();
-    void emit_markers();
-    void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val);
-    void compute_quant_table(int32 *dst, int16 *src);
-    void adjust_quant_table(int32 *dst, int32 *src);
-    void first_pass_init();
-    bool second_pass_init();
-    bool jpg_open(int p_x_res, int p_y_res, int src_channels);
-    void load_block_8_8_grey(int x);
-    void load_block_8_8(int x, int y, int c);
-    void load_block_16_8(int x, int c);
-    void load_block_16_8_8(int x, int c);
-    void load_quantized_coefficients(int component_num);
-    void flush_output_buffer();
-    void put_bits(uint bits, uint len);
-    void code_coefficients_pass_one(int component_num);
-    void code_coefficients_pass_two(int component_num);
-    void code_block(int component_num);
-    void process_mcu_row();
-    bool terminate_pass_one();
-    bool terminate_pass_two();
-    bool process_end_of_image();
-    void load_mcu(const void* src);
-    void clear();
-    void init();
-  };
-
-} // namespace jpge
-
-#endif // JPEG_ENCODER
--- a/crazy_functions/test_project/cpp/libJPG/来源
+++ b/crazy_functions/test_project/cpp/libJPG/来源
@@ -1,3 +0,0 @@
-jpge.h - C++ class for JPEG compression.
-Public domain, Rich Geldreich <richgel99@gmail.com>
-Alex Evans: Added RGBA support, linear memory allocator.
--- a/crazy_functions/test_project/cpp/longcode/jpgd.cpp
+++ b/crazy_functions/test_project/cpp/longcode/jpgd.cpp
--- a/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ b/crazy_functions/test_project/cpp/longcode/jpge.cpp
--- a/crazy_functions/test_project/cpp/longcode/prod_cons.h
+++ b/crazy_functions/test_project/cpp/longcode/prod_cons.h
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
--- a/crazy_functions/test_project/latex/attention/background.tex
+++ b/crazy_functions/test_project/latex/attention/background.tex
@@ -1,58 +0,0 @@
-The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
-
-Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
-
-End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
-
-To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
-In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
-
-
-%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
-
-%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
-
-%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
-
-%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-
-
-%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
-
-%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
-
-%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
-
-%\begin{table}[h!]
-%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
-%\label{tab:op_complexities}
-%\begin{center}
-%\vspace{-5pt}
-%\scalebox{0.75}{
-
-%\begin{tabular}{l|c|c|c}
-%\hline \hline
-%Layer Type & Receptive & Complexity & Sequential  \\
-%           & Field     &            & Operations  \\
-%\hline
-%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
-%\hline
-%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
-%\hline
-%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
-%\hline \hline
-%\end{tabular}
-%}
-%\end{center}
-%\end{table}
--- a/crazy_functions/test_project/latex/attention/introduction.tex
+++ b/crazy_functions/test_project/latex/attention/introduction.tex
@@ -1,18 +0,0 @@
-Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
-
-Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
-%\marginpar{not sure if the memory constraints are understandable here}
-Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
-
-%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} 
-
-Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
-
-%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
-%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
-
-In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 
-%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
-
-% Just a standard paragraph with citations, rewrite.
-%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
--- a/crazy_functions/test_project/latex/attention/model_architecture.tex
+++ b/crazy_functions/test_project/latex/attention/model_architecture.tex
@@ -1,155 +0,0 @@
-
-\begin{figure}
-  \centering
-  \includegraphics[scale=0.6]{Figures/ModalNet-21}
-  \caption{The Transformer - model architecture.}
-  \label{fig:model-arch}
-\end{figure}
-
-% Although the primary workhorse of our model is attention, 
-%Our model maintains the encoder-decoder structure that is common to many so-called sequence-to-sequence models \citep{bahdanau2014neural,sutskever14}.  As in all such architectures, the encoder computes a representation of the input sequence, and the decoder consumes these representations along with the output tokens to autoregressively produce the output sequence.  Where, traditionally, the encoder and decoder contain stacks of recurrent or convolutional layers, our encoder and decoder stacks are composed of attention layers and position-wise feed-forward layers (Figure~\ref{fig:model-arch}).  The following sections describe the gross architecture and these particular components in detail.
-
-Most competitive neural sequence transduction models have an encoder-decoder structure \citep{cho2014learning,bahdanau2014neural,sutskever14}. Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\mathbf{z} = (z_1, ..., z_n)$. Given $\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive \citep{graves2013generating}, consuming the previously generated symbols as additional input when generating the next.
-
-The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure~\ref{fig:model-arch}, respectively.
-
-\subsection{Encoder and Decoder Stacks}
-
-\paragraph{Encoder:}The encoder is composed of a stack of $N=6$ identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.   We employ a residual connection \citep{he2016deep} around each of the two sub-layers, followed by layer normalization \cite{layernorm2016}.  That is, the output of each sub-layer is $\mathrm{LayerNorm}(x + \mathrm{Sublayer}(x))$, where $\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $\dmodel=512$.
-
-\paragraph{Decoder:}The decoder is also composed of a stack of $N=6$ identical layers.  In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.
-
-% In our model (Figure~\ref{fig:model-arch}), the encoder and decoder are composed of stacks of alternating self-attention layers (for cross-positional communication) and position-wise feed-forward layers (for in-place computation).  In addition, the decoder stack contains encoder-decoder attention layers.  Since attention is agnostic to the distances between words, our model requires a "positional encoding" to be added to the encoder and decoder input. The following sections describe all of these components in detail.
-
-\subsection{Attention} \label{sec:attention}
-An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
-
-\subsubsection{Scaled Dot-Product Attention} \label{sec:scaled-dot-prod}
-
-% \begin{figure}
-%   \centering
-%   \includegraphics[scale=0.6]{Figures/ModalNet-19}
-%   \caption{Scaled Dot-Product Attention.}
-%   \label{fig:multi-head-att}
-% \end{figure}
-
-We call our particular attention "Scaled Dot-Product Attention" (Figure~\ref{fig:multi-head-att}).   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.
-
-In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:
-
-\begin{equation}
-   \mathrm{Attention}(Q, K, V) = \mathrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V
-\end{equation}
-
-The two most commonly used attention functions are additive attention \citep{bahdanau2014neural}, and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code. 
-
-%We scale the dot products by $1/\sqrt{d_k}$ to limit the magnitude of the dot products, which works well in practice. Otherwise, we found applying the softmax to often result in weights very close to 0 or 1, and hence minuscule gradients.
-
-% Already described in the subsequent section
-%When used as part of decoder self-attention, an optional mask function is applied just before the softmax to prevent positions from attending to subsequent positions.   This mask simply sets the logits corresponding to all illegal connections (those outside of the lower triangle) to $-\infty$.
-
-%\paragraph{Comparison to Additive Attention: } We choose dot product attention over additive attention \citep{bahdanau2014neural} since it can be computed using highly optimized matrix multiplication code.  This optimization is particularly important to us, as we employ many attention layers in our model.
-
-While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ \citep{DBLP:journals/corr/BritzGLL17}. We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  \footnote{To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \cdot k = \sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.}. To counteract this effect, we scale the dot products by $\frac{1}{\sqrt{d_k}}$.
-
-
-%We suspect this to be caused by the dot products growing too large in magnitude to result in useful gradients after applying the softmax function.  To counteract this, we scale the dot product by $1/\sqrt{d_k}$.
-
-
-\subsubsection{Multi-Head Attention} \label{sec:multihead}
-
-\begin{figure}
-\begin{minipage}[t]{0.5\textwidth}
-  \centering
-  Scaled Dot-Product Attention \\
-  \vspace{0.5cm}
-  \includegraphics[scale=0.6]{Figures/ModalNet-19}
-\end{minipage}
-\begin{minipage}[t]{0.5\textwidth}
-  \centering 
-  Multi-Head Attention \\
-  \vspace{0.1cm}
-  \includegraphics[scale=0.6]{Figures/ModalNet-20}  
-\end{minipage}
-
-
-  % \centering
-
-  \caption{(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.}
-  \label{fig:multi-head-att}
-\end{figure}
-
-Instead of performing a single attention function with $\dmodel$-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values $h$ times with different, learned linear projections to $d_k$, $d_k$ and $d_v$ dimensions, respectively.
-On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding $d_v$-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure~\ref{fig:multi-head-att}.
-
-Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
-
-\begin{align*}
-    \mathrm{MultiHead}(Q, K, V) &= \mathrm{Concat}(\mathrm{head_1}, ..., \mathrm{head_h})W^O\\
-%    \mathrm{where} \mathrm{head_i} &= \mathrm{Attention}(QW_Q_i^{\dmodel \times d_q}, KW_K_i^{\dmodel \times d_k}, VW^V_i^{\dmodel \times d_v})\\
-    \text{where}~\mathrm{head_i} &= \mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)\\
-\end{align*}
-
-Where the projections are parameter matrices $W^Q_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^K_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^V_i \in \mathbb{R}^{\dmodel \times d_v}$ and $W^O \in \mathbb{R}^{hd_v \times \dmodel}$.
-
-
-%find it better (and no more expensive) to have multiple parallel attention layers (each over the full set of positions) with proportionally lower-dimensional keys, values and queries.  We call this "Multi-Head Attention" (Figure~\ref{fig:multi-head-att}).  The keys, values, and queries for each of these parallel attention layers are computed by learned linear transformations of the inputs to the multi-head attention.  We use different linear transformations across different parallel attention layers.  The output of the parallel attention layers are concatenated, and then passed through a final learned linear transformation. 
-
-In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=\dmodel/h=64$.
-Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
-
-\subsubsection{Applications of Attention in our Model}
-
-The Transformer uses multi-head attention in three different ways: 
-\begin{itemize}
- \item In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as \citep{wu2016google, bahdanau2014neural,JonasFaceNet2017}.
-
- \item The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.
-
- \item Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\infty$) all values in the input of the softmax which correspond to illegal connections.  See Figure~\ref{fig:multi-head-att}.
-
-\end{itemize}
-
-\subsection{Position-wise Feed-Forward Networks}\label{sec:ffn}
-
-In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.
-
-\begin{equation}
-   \mathrm{FFN}(x)=\max(0, xW_1 + b_1) W_2 + b_2
-\end{equation}
-
-While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $\dmodel=512$, and the inner-layer has dimensionality $d_{ff}=2048$.
-
-
-
-%In the appendix, we describe how the position-wise feed-forward network can also be seen as a form of attention.
-
-%from Jakob: The number of operations required for the model to relate signals from two arbitrary input or output positions grows in the distance between positions in input or output, linearly for ConvS2S and logarithmically for ByteNet, making it harder to learn dependencies between these positions \citep{hochreiter2001gradient}. In the transformer this is reduced to a constant number of operations, albeit at the cost of effective resolution caused by averaging attention-weighted positions, an effect we aim to counteract with multi-headed attention.
-
-
-%Figure~\ref{fig:simple-att} presents a simple attention function, $A$, with a single head, that forms the basis of our multi-head attention. $A$ takes a query key vector $\kq$, matrices of memory keys $\km$ and memory values $\vm$ ,and produces a query value vector $\vq$ as 
-%\begin{equation*} \label{eq:attention}
-%    A(\kq, \km, \vm) = {\vm}^T (Softmax(\km \kq).
-%\end{equation*}
-%We linearly transform $\kq,\,\km$, and $\vm$ with learned matrices ${\Wkq \text{,} \, \Wkm}$, and ${\Wvm}$ before calling the attention function, and transform the output query with $\Wvq$ before handing it to the feed forward layer. Each attention layer has it's own set of transformation matrices, which are shared across all query positions. $A$ is applied in parallel for each query position, and is implemented very efficiently as a batch of matrix multiplies. The self-attention and encoder-decoder attention layers use $A$, but with different arguments. For example, in encdoder self-attention, queries in encoder layer $i$ attention to memories in encoder layer $i-1$. To ensure that decoder self-attention layers do not look at future words, we add $- \inf$ to the softmax logits in positions $j+1$ to query length for query position $l$.  
-
-%In simple attention, the query value is a weighted combination of the memory values where the attention weights sum to one. Although this function performs well in practice, the constraint on attention weights can restrict the amount of information that flows from memories to queries because the query cannot focus on multiple memory positions at once, which might be desirable when translating long sequences. \marginpar{@usz, could you think of an example of this ?} We remedy this by maintaining multiple attention heads at each query position that attend to all memory positions in parallel, with a different set of parameters  per attention head $h$. 
-%\marginpar{}
-
-\subsection{Embeddings and Softmax}
-Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $\dmodel$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to \citep{press2016using}.   In the embedding layers, we multiply those weights by $\sqrt{\dmodel}$.
-
-
-\subsection{Positional Encoding}
-Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $\dmodel$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed \citep{JonasFaceNet2017}.
-
-In this work, we use sine and cosine functions of different frequencies:
-
-\begin{align*}
-    PE_{(pos,2i)} = sin(pos / 10000^{2i/\dmodel}) \\
-    PE_{(pos,2i+1)} = cos(pos / 10000^{2i/\dmodel})
-\end{align*}
-
-where $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\pi$ to $10000 \cdot 2\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.
-
-We also experimented with using learned positional embeddings \citep{JonasFaceNet2017} instead, and found that the two versions produced nearly identical results (see Table~\ref{tab:variations} row (E)).  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
--- a/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ b/crazy_functions/test_project/latex/attention/parameter_attention.tex
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
-    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
-    A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
--- a/crazy_functions/test_project/latex/attention/来源
+++ b/crazy_functions/test_project/latex/attention/来源
@@ -1,8 +0,0 @@
-chatgpt的老祖宗《Attention is all you need》
-
-Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
-
-真实的摘要如下
-The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
-
-https://arxiv.org/abs/1706.03762
--- a/crazy_functions/test_project/python/dqn/init.py
+++ b/crazy_functions/test_project/python/dqn/init.py
@@ -1,2 +0,0 @@
-from stable_baselines3.dqn.dqn import DQN
-from stable_baselines3.dqn.policies import CnnPolicy, MlpPolicy
--- a/crazy_functions/test_project/python/dqn/dqn.py
+++ b/crazy_functions/test_project/python/dqn/dqn.py
@@ -1,245 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
-
-import gym
-import numpy as np
-import torch as th
-from torch.nn import functional as F
-
-from stable_baselines3.common import logger
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.preprocessing import maybe_transpose
-from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
-from stable_baselines3.dqn.policies import DQNPolicy
-
-
-class DQN(OffPolicyAlgorithm):
-    """
-    Deep Q-Network (DQN)
-
-    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
-    Default hyperparameters are taken from the nature paper,
-    except for the optimizer and learning rate that were taken from Stable Baselines defaults.
-
-    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
-    :param env: The environment to learn from (if registered in Gym, can be str)
-    :param learning_rate: The learning rate, it can be a function
-        of the current progress remaining (from 1 to 0)
-    :param buffer_size: size of the replay buffer
-    :param learning_starts: how many steps of the model to collect transitions for before learning starts
-    :param batch_size: Minibatch size for each gradient update
-    :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
-    :param gamma: the discount factor
-    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
-        like ``(5, "step")`` or ``(2, "episode")``.
-    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
-        Set to ``-1`` means to do as many gradient steps as steps done in the environment
-        during the rollout.
-    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
-        at a cost of more complexity.
-        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
-    :param target_update_interval: update the target network every ``target_update_interval``
-        environment steps.
-    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
-    :param exploration_initial_eps: initial value of random action probability
-    :param exploration_final_eps: final value of random action probability
-    :param max_grad_norm: The maximum value for the gradient clipping
-    :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param create_eval_env: Whether to create a second environment that will be
-        used for evaluating the agent periodically. (Only available when passing string for the environment)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
-    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
-    :param seed: Seed for the pseudo random generators
-    :param device: Device (cpu, cuda, ...) on which the code should be run.
-        Setting it to auto, the code will be run on the GPU if possible.
-    :param _init_setup_model: Whether or not to build the network at the creation of the instance
-    """
-
-    def __init__(
-        self,
-        policy: Union[str, Type[DQNPolicy]],
-        env: Union[GymEnv, str],
-        learning_rate: Union[float, Schedule] = 1e-4,
-        buffer_size: int = 1000000,
-        learning_starts: int = 50000,
-        batch_size: Optional[int] = 32,
-        tau: float = 1.0,
-        gamma: float = 0.99,
-        train_freq: Union[int, Tuple[int, str]] = 4,
-        gradient_steps: int = 1,
-        optimize_memory_usage: bool = False,
-        target_update_interval: int = 10000,
-        exploration_fraction: float = 0.1,
-        exploration_initial_eps: float = 1.0,
-        exploration_final_eps: float = 0.05,
-        max_grad_norm: float = 10,
-        tensorboard_log: Optional[str] = None,
-        create_eval_env: bool = False,
-        policy_kwargs: Optional[Dict[str, Any]] = None,
-        verbose: int = 0,
-        seed: Optional[int] = None,
-        device: Union[th.device, str] = "auto",
-        _init_setup_model: bool = True,
-    ):
-
-        super(DQN, self).__init__(
-            policy,
-            env,
-            DQNPolicy,
-            learning_rate,
-            buffer_size,
-            learning_starts,
-            batch_size,
-            tau,
-            gamma,
-            train_freq,
-            gradient_steps,
-            action_noise=None,  # No action noise
-            policy_kwargs=policy_kwargs,
-            tensorboard_log=tensorboard_log,
-            verbose=verbose,
-            device=device,
-            create_eval_env=create_eval_env,
-            seed=seed,
-            sde_support=False,
-            optimize_memory_usage=optimize_memory_usage,
-            supported_action_spaces=(gym.spaces.Discrete,),
-        )
-
-        self.exploration_initial_eps = exploration_initial_eps
-        self.exploration_final_eps = exploration_final_eps
-        self.exploration_fraction = exploration_fraction
-        self.target_update_interval = target_update_interval
-        self.max_grad_norm = max_grad_norm
-        # "epsilon" for the epsilon-greedy exploration
-        self.exploration_rate = 0.0
-        # Linear schedule will be defined in `_setup_model()`
-        self.exploration_schedule = None
-        self.q_net, self.q_net_target = None, None
-
-        if _init_setup_model:
-            self._setup_model()
-
-    def _setup_model(self) -> None:
-        super(DQN, self)._setup_model()
-        self._create_aliases()
-        self.exploration_schedule = get_linear_fn(
-            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
-        )
-
-    def _create_aliases(self) -> None:
-        self.q_net = self.policy.q_net
-        self.q_net_target = self.policy.q_net_target
-
-    def _on_step(self) -> None:
-        """
-        Update the exploration rate and target network if needed.
-        This method is called in ``collect_rollouts()`` after each step in the environment.
-        """
-        if self.num_timesteps % self.target_update_interval == 0:
-            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
-
-        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
-        logger.record("rollout/exploration rate", self.exploration_rate)
-
-    def train(self, gradient_steps: int, batch_size: int = 100) -> None:
-        # Update learning rate according to schedule
-        self._update_learning_rate(self.policy.optimizer)
-
-        losses = []
-        for _ in range(gradient_steps):
-            # Sample replay buffer
-            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
-
-            with th.no_grad():
-                # Compute the next Q-values using the target network
-                next_q_values = self.q_net_target(replay_data.next_observations)
-                # Follow greedy policy: use the one with the highest value
-                next_q_values, _ = next_q_values.max(dim=1)
-                # Avoid potential broadcast issue
-                next_q_values = next_q_values.reshape(-1, 1)
-                # 1-step TD target
-                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
-
-            # Get current Q-values estimates
-            current_q_values = self.q_net(replay_data.observations)
-
-            # Retrieve the q-values for the actions from the replay buffer
-            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
-
-            # Compute Huber loss (less sensitive to outliers)
-            loss = F.smooth_l1_loss(current_q_values, target_q_values)
-            losses.append(loss.item())
-
-            # Optimize the policy
-            self.policy.optimizer.zero_grad()
-            loss.backward()
-            # Clip gradient norm
-            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
-            self.policy.optimizer.step()
-
-        # Increase update counter
-        self._n_updates += gradient_steps
-
-        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
-        logger.record("train/loss", np.mean(losses))
-
-    def predict(
-        self,
-        observation: np.ndarray,
-        state: Optional[np.ndarray] = None,
-        mask: Optional[np.ndarray] = None,
-        deterministic: bool = False,
-    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
-        """
-        Overrides the base_class predict function to include epsilon-greedy exploration.
-
-        :param observation: the input observation
-        :param state: The last states (can be None, used in recurrent policies)
-        :param mask: The last masks (can be None, used in recurrent policies)
-        :param deterministic: Whether or not to return deterministic actions.
-        :return: the model's action and the next state
-            (used in recurrent policies)
-        """
-        if not deterministic and np.random.rand() < self.exploration_rate:
-            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
-                n_batch = observation.shape[0]
-                action = np.array([self.action_space.sample() for _ in range(n_batch)])
-            else:
-                action = np.array(self.action_space.sample())
-        else:
-            action, state = self.policy.predict(observation, state, mask, deterministic)
-        return action, state
-
-    def learn(
-        self,
-        total_timesteps: int,
-        callback: MaybeCallback = None,
-        log_interval: int = 4,
-        eval_env: Optional[GymEnv] = None,
-        eval_freq: int = -1,
-        n_eval_episodes: int = 5,
-        tb_log_name: str = "DQN",
-        eval_log_path: Optional[str] = None,
-        reset_num_timesteps: bool = True,
-    ) -> OffPolicyAlgorithm:
-
-        return super(DQN, self).learn(
-            total_timesteps=total_timesteps,
-            callback=callback,
-            log_interval=log_interval,
-            eval_env=eval_env,
-            eval_freq=eval_freq,
-            n_eval_episodes=n_eval_episodes,
-            tb_log_name=tb_log_name,
-            eval_log_path=eval_log_path,
-            reset_num_timesteps=reset_num_timesteps,
-        )
-
-    def _excluded_save_params(self) -> List[str]:
-        return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
-
-    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
-        state_dicts = ["policy", "policy.optimizer"]
-
-        return state_dicts, []
--- a/crazy_functions/test_project/python/dqn/policies.py
+++ b/crazy_functions/test_project/python/dqn/policies.py
@@ -1,237 +0,0 @@
-from typing import Any, Dict, List, Optional, Type
-
-import gym
-import torch as th
-from torch import nn
-
-from stable_baselines3.common.policies import BasePolicy, register_policy
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
-from stable_baselines3.common.type_aliases import Schedule
-
-
-class QNetwork(BasePolicy):
-    """
-    Action-Value (Q-Value) network for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        features_extractor: nn.Module,
-        features_dim: int,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        normalize_images: bool = True,
-    ):
-        super(QNetwork, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor=features_extractor,
-            normalize_images=normalize_images,
-        )
-
-        if net_arch is None:
-            net_arch = [64, 64]
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.features_extractor = features_extractor
-        self.features_dim = features_dim
-        self.normalize_images = normalize_images
-        action_dim = self.action_space.n  # number of actions
-        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
-        self.q_net = nn.Sequential(*q_net)
-
-    def forward(self, obs: th.Tensor) -> th.Tensor:
-        """
-        Predict the q-values.
-
-        :param obs: Observation
-        :return: The estimated Q-Value for each action.
-        """
-        return self.q_net(self.extract_features(obs))
-
-    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        q_values = self.forward(observation)
-        # Greedy action
-        action = q_values.argmax(dim=1).reshape(-1)
-        return action
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_arch,
-                features_dim=self.features_dim,
-                activation_fn=self.activation_fn,
-                features_extractor=self.features_extractor,
-            )
-        )
-        return data
-
-
-class DQNPolicy(BasePolicy):
-    """
-    Policy class with Q-Value Net and target net for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param features_extractor_kwargs: Keyword arguments
-        to pass to the features extractor.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(DQNPolicy, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor_class,
-            features_extractor_kwargs,
-            optimizer_class=optimizer_class,
-            optimizer_kwargs=optimizer_kwargs,
-        )
-
-        if net_arch is None:
-            if features_extractor_class == FlattenExtractor:
-                net_arch = [64, 64]
-            else:
-                net_arch = []
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.normalize_images = normalize_images
-
-        self.net_args = {
-            "observation_space": self.observation_space,
-            "action_space": self.action_space,
-            "net_arch": self.net_arch,
-            "activation_fn": self.activation_fn,
-            "normalize_images": normalize_images,
-        }
-
-        self.q_net, self.q_net_target = None, None
-        self._build(lr_schedule)
-
-    def _build(self, lr_schedule: Schedule) -> None:
-        """
-        Create the network and the optimizer.
-
-        :param lr_schedule: Learning rate schedule
-            lr_schedule(1) is the initial learning rate
-        """
-
-        self.q_net = self.make_q_net()
-        self.q_net_target = self.make_q_net()
-        self.q_net_target.load_state_dict(self.q_net.state_dict())
-
-        # Setup optimizer with initial learning rate
-        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
-
-    def make_q_net(self) -> QNetwork:
-        # Make sure we always have separate networks for features extractors etc
-        net_args = self._update_features_extractor(self.net_args, features_extractor=None)
-        return QNetwork(**net_args).to(self.device)
-
-    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self._predict(obs, deterministic=deterministic)
-
-    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self.q_net._predict(obs, deterministic=deterministic)
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_args["net_arch"],
-                activation_fn=self.net_args["activation_fn"],
-                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
-                optimizer_class=self.optimizer_class,
-                optimizer_kwargs=self.optimizer_kwargs,
-                features_extractor_class=self.features_extractor_class,
-                features_extractor_kwargs=self.features_extractor_kwargs,
-            )
-        )
-        return data
-
-
-MlpPolicy = DQNPolicy
-
-
-class CnnPolicy(DQNPolicy):
-    """
-    Policy class for DQN when using images as input.
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(CnnPolicy, self).__init__(
-            observation_space,
-            action_space,
-            lr_schedule,
-            net_arch,
-            activation_fn,
-            features_extractor_class,
-            features_extractor_kwargs,
-            normalize_images,
-            optimizer_class,
-            optimizer_kwargs,
-        )
-
-
-register_policy("MlpPolicy", MlpPolicy)
-register_policy("CnnPolicy", CnnPolicy)
--- a/crazy_functions/test_project/python/dqn/来源
+++ b/crazy_functions/test_project/python/dqn/来源
@@ -1,2 +0,0 @@
-github stablebaseline3
-https://github.com/DLR-RM/stable-baselines3
--- a/crazy_functions/test_project/其他测试
+++ b/crazy_functions/test_project/其他测试
@@ -1,27 +0,0 @@
-"In practice, we found that a high-entropy initial state is more likely to increase the speed of training.
-The entropy is calculated by:
-$$H=-\sum_{k= 1}^{n_k} p(k) \cdot \log p(k), p(k)=\frac{|A_k|}{|\mathcal{A}|}$$
-where $H$ is the entropy, $|A_k|$ is the number of agent nodes in $k$-th cluster, $|\mathcal{A}|$ is the total number of agents.
-To ensure the Cooperation Graph initialization has higher entropy,
-we will randomly generate multiple initial states, 
-rank by their entropy and then pick the one with maximum $H$."
-
-```
-FROM ubuntu:latest
-
-RUN apt-get update && \
-    apt-get install -y python3 python3-pip && \
-    rm -rf /var/lib/apt/lists/*
-
-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
-
-RUN pip3 install gradio requests[socks] mdtex2html
-
-COPY . /gpt
-WORKDIR /gpt
-
-
-CMD ["python3", "main.py"]
-```
--- a/crazy_functions/vt_fns/vt_call_plugin.py
+++ b/crazy_functions/vt_fns/vt_call_plugin.py
@@ -0,0 +1,114 @@
+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 crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
+import copy, json, pickle, os, sys, time
+
+
+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_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)}
+    plugin_arr_dict_parse.update({f"F_{i}":v for i, v in enumerate(plugin_arr.values(), start=1)})
+    prompt = json.dumps(plugin_arr_info, ensure_ascii=False, indent=2)
+    prompt = "\n\nThe defination of PluginEnum:\nPluginEnum=" + prompt
+    return prompt, plugin_arr_dict, plugin_arr_dict_parse
+
+def wrap_code(txt):
+    txt = txt.replace('```','')
+    return f"\n```\n{txt}\n```\n"
+
+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
+
+def get_recent_file_prompt_support(chatbot):
+    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
+    path = most_recent_uploaded['path']
+    prompt =   "\nAdditional Information:\n"
+    prompt =   "In case that this plugin requires a path or a file as argument," 
+    prompt += f"it is important for you to know that the user has recently uploaded a file, located at: `{path}`" 
+    prompt += f"Only use it when necessary, otherwise, you can ignore this file." 
+    return prompt
+
+def get_inputs_show_user(inputs, plugin_arr_enum_prompt):
+    # remove plugin_arr_enum_prompt from inputs string
+    inputs_show_user = inputs.replace(plugin_arr_enum_prompt, "")
+    inputs_show_user += plugin_arr_enum_prompt[:200] + '...'
+    inputs_show_user += '\n...\n'
+    inputs_show_user += '...\n'
+    inputs_show_user += '...}'
+    return inputs_show_user
+
+def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
+    plugin_arr_enum_prompt, plugin_arr_dict, plugin_arr_dict_parse = read_avail_plugin_enum()
+    class Plugin(BaseModel):
+        plugin_selection: str = Field(description="The most related plugin from one of the PluginEnum.", default="F_0000")
+        reason_of_selection: str = Field(description="The reason why you should select this plugin.", default="This plugin satisfy user requirement most")
+    # ⭐ ⭐ ⭐ 选择插件
+    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
+    gpt_json_io = GptJsonIO(Plugin)
+    gpt_json_io.format_instructions = "The format of your output should be a json that can be parsed by json.loads.\n"
+    gpt_json_io.format_instructions += """Output example: {"plugin_selection":"F_1234", "reason_of_selection":"F_1234 plugin satisfy user requirement most"}\n"""
+    gpt_json_io.format_instructions += "The plugins you are authorized to use are listed below:\n"
+    gpt_json_io.format_instructions += plugin_arr_enum_prompt
+    inputs = "Choose the correct plugin according to user requirements, the user requirement is: \n\n" + \
+             ">> " + txt.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=[])
+    try:
+        gpt_reply = run_gpt_fn(inputs, "")
+        plugin_sel = gpt_json_io.generate_output_auto_repair(gpt_reply, run_gpt_fn)
+    except JsonStringError:
+        msg = f"抱歉, {llm_kwargs['llm_model']}无法理解您的需求。"
+        msg += "请求的Prompt为：\n" + wrap_code(get_inputs_show_user(inputs, plugin_arr_enum_prompt))
+        msg += "语言模型回复为：\n" + wrap_code(gpt_reply)
+        msg += "\n但您可以尝试再试一次\n"
+        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+        return
+    if plugin_sel.plugin_selection not in plugin_arr_dict_parse:
+        msg = f"抱歉, 找不到合适插件执行该任务, 或者{llm_kwargs['llm_model']}无法理解您的需求。"
+        msg += f"语言模型{llm_kwargs['llm_model']}选择了不存在的插件：\n" + wrap_code(gpt_reply)
+        msg += "\n但您可以尝试再试一次\n"
+        yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+        return
+    
+    # ⭐ ⭐ ⭐ 确认插件参数
+    if not have_any_recent_upload_files(chatbot):
+        appendix_info = ""
+    else:
+        appendix_info = get_recent_file_prompt_support(chatbot)
+
+    plugin = plugin_arr_dict_parse[plugin_sel.plugin_selection]
+    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
+    class PluginExplicit(BaseModel):
+        plugin_selection: str = plugin_sel.plugin_selection
+        plugin_arg: str = Field(description="The argument of the plugin.", default="")
+    gpt_json_io = GptJsonIO(PluginExplicit)
+    gpt_json_io.format_instructions += "The information about this plugin is:" + plugin["Info"]
+    inputs = f"A plugin named {plugin_sel.plugin_selection} is selected, " + \
+             "you should extract plugin_arg from the user requirement, the user requirement is: \n\n" + \
+             ">> " + (txt + appendix_info).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=[])
+    plugin_sel = gpt_json_io.generate_output_auto_repair(run_gpt_fn(inputs, ""), run_gpt_fn)
+
+
+    # ⭐ ⭐ ⭐ 执行插件
+    fn = plugin['Function']
+    fn_name = fn.__name__
+    msg = f'{llm_kwargs["llm_model"]}为您选择了插件: `{fn_name}`\n\n插件说明：{plugin["Info"]}\n\n插件参数：{plugin_sel.plugin_arg}\n\n假如偏离了您的要求，按停止键终止。'
+    yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
+    yield from fn(plugin_sel.plugin_arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, -1)
+    return
--- a/crazy_functions/vt_fns/vt_modify_config.py
+++ b/crazy_functions/vt_fns/vt_modify_config.py
@@ -0,0 +1,81 @@
+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 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')
+    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。", 
+            chatbot=chatbot, history=history, delay=2
+        )
+        return
+
+    # ⭐ ⭐ ⭐ 读取可配置项目条目
+    names = {}
+    from enum import Enum
+    import config
+    for k, v in config.__dict__.items():
+        if k.startswith('__'): continue
+        names.update({k:k})
+        # if len(names) > 20: break   # 限制最多前10个配置项，如果太多了会导致gpt无法理解
+
+    ConfigOptions = Enum('ConfigOptions', names)
+    class ModifyConfigurationIntention(BaseModel):
+        which_config_to_modify: ConfigOptions = Field(description="the name of the configuration to modify, you must choose from one of the ConfigOptions enum.", default=None)
+        new_option_value: str = Field(description="the new value of the option", default=None)
+
+    # ⭐ ⭐ ⭐ 分析用户意图
+    yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
+    gpt_json_io = GptJsonIO(ModifyConfigurationIntention)
+    inputs = "Analyze how to change configuration according to following user input, answer me with json: \n\n" + \
+             ">> " + txt.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=[])
+    user_intention = gpt_json_io.generate_output_auto_repair(run_gpt_fn(inputs, ""), run_gpt_fn)
+
+    explicit_conf = user_intention.which_config_to_modify.value
+
+    ok = (explicit_conf in txt)
+    if ok:
+        yield from update_ui_lastest_msg(
+            lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}", 
+            chatbot=chatbot, history=history, delay=1
+        )
+        yield from update_ui_lastest_msg(
+            lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}\n\n正在修改配置中", 
+            chatbot=chatbot, history=history, delay=2
+        )
+
+        # ⭐ ⭐ ⭐ 立即应用配置
+        from toolbox import set_conf
+        set_conf(explicit_conf, user_intention.new_option_value)
+
+        yield from update_ui_lastest_msg(
+            lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，重新页面即可生效。", chatbot=chatbot, history=history, delay=1
+        )
+    else:
+        yield from update_ui_lastest_msg(
+            lastmsg=f"失败，如果需要配置{explicit_conf}，您需要明确说明并在指令中提到它。", chatbot=chatbot, history=history, delay=5
+        )
+
+def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
+    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。", 
+            chatbot=chatbot, history=history, delay=2
+        )
+        return
+
+    yield from modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
+    yield from update_ui_lastest_msg(
+        lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，五秒后即将重启！若出现报错请无视即可。", chatbot=chatbot, history=history, delay=5
+    )
+    os.execl(sys.executable, sys.executable, *sys.argv)
--- a/crazy_functions/vt_fns/vt_state.py
+++ b/crazy_functions/vt_fns/vt_state.py
@@ -0,0 +1,28 @@
+import pickle
+
+class VoidTerminalState():
+    def __init__(self):
+        self.reset_state()
+
+    def reset_state(self):
+        self.has_provided_explaination = False
+ 
+    def lock_plugin(self, chatbot):
+        chatbot._cookies['lock_plugin'] = 'crazy_functions.虚空终端->虚空终端'
+        chatbot._cookies['plugin_state'] = pickle.dumps(self)
+
+    def unlock_plugin(self, chatbot):
+        self.reset_state()
+        chatbot._cookies['lock_plugin'] = None
+        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):
+        state = chatbot._cookies.get('plugin_state', None)
+        if state is not None:   state = pickle.loads(state)
+        else:                   state = VoidTerminalState()
+        state.chatbot = chatbot
+        return state
--- a/crazy_functions/下载arxiv论文翻译摘要.py
+++ b/crazy_functions/下载arxiv论文翻译摘要.py
@@ -1,5 +1,6 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file, get_conf
+from toolbox import update_ui, get_log_folder
+from toolbox import write_history_to_file, promote_file_to_downloadzone
+from toolbox import CatchException, report_execption, 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):
@@ -28,7 +29,7 @@ def download_arxiv_(url_pdf):
        if k in other_info['comment']:
            title = k + ' ' + title

-    download_dir = './gpt_log/arxiv/'
+    download_dir = get_log_folder(plugin_name='arxiv')
    os.makedirs(download_dir, exist_ok=True)

    title_str = title.replace('?', '？')\
@@ -40,9 +41,6 @@ def download_arxiv_(url_pdf):

    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')
@@ -61,7 +59,7 @@ def download_arxiv_(url_pdf):
        .replace('\n', '')\
        .replace('  ', ' ')\
        .replace('  ', ' ')
-    return './gpt_log/arxiv/'+title_str, other_info
+    return file_path, other_info


 def get_name(_url_):
@@ -184,11 +182,10 @@ def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, hi
    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)
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
+    promote_file_to_downloadzone(pdf_path, chatbot=chatbot)
+
    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

--- a/crazy_functions/代码重写为全英文_多线程.py
+++ b/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
--- a/crazy_functions/图片生成.py
+++ b/crazy_functions/图片生成.py
@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, get_conf, select_api_key
+from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 import datetime

@@ -33,7 +33,7 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
        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/'
+    file_path = f'{get_log_folder()}/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)
--- a/crazy_functions/对话历史存档.py
+++ b/crazy_functions/对话历史存档.py
@@ -1,4 +1,4 @@
-from toolbox import CatchException, update_ui, promote_file_to_downloadzone
+from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 import re

@@ -10,8 +10,8 @@ def write_chat_to_file(chatbot, history=None, file_name=None):
    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:
+    fp = os.path.join(get_log_folder(), file_name)
+    with open(fp, '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):
@@ -29,8 +29,8 @@ def write_chat_to_file(chatbot, history=None, file_name=None):
        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}')
+    promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
+    return '对话历史写入：' + fp

 def gen_file_preview(file_name):
    try:
@@ -106,7 +106,7 @@ def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
    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)])
+        local_history = "<br/>".join(["`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`" for f in glob.glob(f'{get_log_folder()}/**/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
@@ -132,8 +132,8 @@ def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot
    """

    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):
+    local_history = "<br/>".join(["`"+hide_cwd(f)+"`" for f in glob.glob(f'{get_log_folder()}/**/chatGPT对话历史*.html', recursive=True)])
+    for f in glob.glob(f'{get_log_folder()}/**/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
--- a/crazy_functions/总结word文档.py
+++ b/crazy_functions/总结word文档.py
@@ -1,5 +1,6 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 fast_debug = False

@@ -71,11 +72,13 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
            history.extend([i_say,gpt_say])
            this_paper_history.extend([i_say,gpt_say])

-        res = write_results_to_file(history)
+        res = write_history_to_file(history)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
        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)
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    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

--- a/crazy_functions/总结音视频.py
+++ b/crazy_functions/总结音视频.py
@@ -1,5 +1,6 @@
-from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
+from toolbox import CatchException, report_execption, select_api_key, update_ui, get_conf
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder

 def split_audio_file(filename, split_duration=1000):
    """
@@ -15,7 +16,7 @@ def split_audio_file(filename, split_duration=1000):
    """
    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
+    os.makedirs(f"{get_log_folder(plugin_name='audio')}/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)
@@ -31,8 +32,8 @@ def split_audio_file(filename, split_duration=1000):
        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")
+        split_audio.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")
+        filelist.append(f"{get_log_folder(plugin_name='audio')}/mp3/cut/{filename[0]}_{i}.mp3")

    audio.close()
    return filelist
@@ -52,7 +53,7 @@ def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
        'Authorization': f"Bearer {api_key}"
    }

-    os.makedirs('gpt_log/mp3/', exist_ok=True)
+    os.makedirs(f"{get_log_folder(plugin_name='audio')}/mp3/", exist_ok=True)
    for index, fp in enumerate(file_manifest):
        audio_history = []
        # 提取文件扩展名
@@ -60,8 +61,8 @@ def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
        # 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'
+            audio_clip.write_audiofile(f"{get_log_folder(plugin_name='audio')}/mp3/output{index}.mp3")
+            fp = f"{get_log_folder(plugin_name='audio')}/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):
@@ -113,18 +114,19 @@ def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
                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)
+        res = write_history_to_file(history)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
        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)
+    shutil.rmtree(f"{get_log_folder(plugin_name='audio')}/mp3")
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    chatbot.append(("所有音频都总结完成了吗？", res))
    yield from update_ui(chatbot=chatbot, history=history)

--- a/crazy_functions/批量Markdown翻译.py
+++ b/crazy_functions/批量Markdown翻译.py
@@ -1,7 +1,7 @@
-import glob, time, os, re
+import glob, time, os, re, logging
 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
+from toolbox import CatchException, report_execption, get_log_folder
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 fast_debug = False

 class PaperFileGroup():
@@ -34,7 +34,7 @@ class PaperFileGroup():
                    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')
+        logging.info('Segmentation: done')

    def merge_result(self):
        self.file_result = ["" for _ in range(len(self.file_paths))]
@@ -101,7 +101,7 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
        pfg.merge_result()
        pfg.write_result(language)
    except:
-        print(trimmed_format_exc())
+        logging.error(trimmed_format_exc())

    #  <-------- 整理结果，退出 ----------> 
    create_report_file_name = gen_time_str() + f"-chatgpt.md"
@@ -121,7 +121,7 @@ def get_files_from_everything(txt, preference=''):
        proxies, = get_conf('proxies')
        # 网络的远程文件
        if preference == 'Github':
-            print('正在从github下载资源 ...')
+            logging.info('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'
@@ -145,6 +145,8 @@ def get_files_from_everything(txt, preference=''):
        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
--- a/crazy_functions/批量总结PDF文档.py
+++ b/crazy_functions/批量总结PDF文档.py
@@ -1,5 +1,6 @@
 from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 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
@@ -99,8 +100,8 @@ do not have too much repetitive information, numerical values using the original
        _, 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)
+    res = write_history_to_file(file_write_buffer)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面


--- a/crazy_functions/批量总结PDF文档pdfminer.py
+++ b/crazy_functions/批量总结PDF文档pdfminer.py
@@ -1,6 +1,7 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from toolbox import write_history_to_file, promote_file_to_downloadzone

 fast_debug = False

@@ -115,7 +116,8 @@ def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbo
        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)
+        res = write_history_to_file(history)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
        chatbot.append(("完成了吗？", res))
        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面

--- a/crazy_functions/批量翻译PDF文档_NOUGAT.py
+++ b/crazy_functions/批量翻译PDF文档_NOUGAT.py
@@ -0,0 +1,203 @@
+from toolbox import CatchException, report_execption, gen_time_str
+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 os
+import math
+import logging
+
+def markdown_to_dict(article_content):
+    import markdown
+    from bs4 import BeautifulSoup
+    cur_t = ""
+    cur_c = ""
+    results = {}
+    for line in article_content:
+        if line.startswith('#'):
+            if cur_t!="":
+                if cur_t not in results:
+                    results.update({cur_t:cur_c.lstrip('\n')})
+                else:
+                    # 处理重名的章节
+                    results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')})
+            cur_t = line.rstrip('\n')
+            cur_c = ""
+        else:
+            cur_c += line
+    results_final = {}
+    for k in list(results.keys()):
+        if k.startswith('# '):
+            results_final['title'] = k.split('# ')[-1]
+            results_final['authors'] = results.pop(k).lstrip('\n')
+        if k.startswith('###### Abstract'):
+            results_final['abstract'] = results.pop(k).lstrip('\n')
+
+    results_final_sections = []
+    for k,v in results.items():
+        results_final_sections.append({
+            'heading':k.lstrip("# "),
+            'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section."
+        })
+    results_final['sections'] = results_final_sections
+    return results_final
+
+
+@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 nougat
+        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 nougat-ocr 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
+    yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+
+
+def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import copy
+    import tiktoken
+    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    generated_conclusion_files = []
+    generated_html_files = []
+    DST_LANG = "中文"
+    from crazy_functions.crazy_utils import nougat_interface, construct_html
+    nougat_handle = nougat_interface()
+    for index, fp in enumerate(file_manifest):
+        chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history)
+
+        with open(fpp, 'r', encoding='utf8') as f:
+            article_content = f.readlines()
+        article_dict = markdown_to_dict(article_content)
+        logging.info(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
+
+
--- a/crazy_functions/批量翻译PDF文档_多线程.py
+++ b/crazy_functions/批量翻译PDF文档_多线程.py
@@ -1,15 +1,19 @@
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import CatchException, report_execption, get_log_folder
+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
 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, sys_prompt, web_port):
-    import glob
-    import os
+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？",
@@ -20,30 +24,22 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_
    try:
        import fitz
        import tiktoken
+        import scipdf
    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```。")
+                         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 scipdf_parser```。")
        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 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)]
+    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:
@@ -53,22 +49,131 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_
        return

    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
+    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(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
-    import os
+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"
+    from crazy_functions.crazy_utils 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)
+        if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
+        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 = []
+    from crazy_functions.crazy_utils 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_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
        from request_llm.bridge_all import model_info
@@ -113,10 +218,11 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
        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)
+        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'./gpt_log/{create_report_file_name}')
+        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

@@ -140,8 +246,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
                    trans = k
                    ch.add_row(a=orig, b=trans)
            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-            ch.save_file(create_report_file_name)
-            generated_html_files.append(f'./gpt_log/{create_report_file_name}')
+            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())
@@ -159,49 +264,3 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
    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(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-            f.write(self.html_string.encode('utf-8', 'ignore').decode())
-
--- a/crazy_functions/生成函数注释.py
+++ b/crazy_functions/生成函数注释.py
@@ -1,5 +1,6 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 fast_debug = False

@@ -27,7 +28,8 @@ def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
            if not fast_debug: time.sleep(2)

    if not fast_debug: 
-        res = write_results_to_file(history)
+        res = write_history_to_file(history)
+        promote_file_to_downloadzone(res, chatbot=chatbot)
        chatbot.append(("完成了吗？", res))
        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面

--- a/crazy_functions/联网的ChatGPT.py
+++ b/crazy_functions/联网的ChatGPT.py
@@ -75,7 +75,11 @@ def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
    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]):
--- a/crazy_functions/联网的ChatGPT_bing版.py
+++ b/crazy_functions/联网的ChatGPT_bing版.py
@@ -75,7 +75,11 @@ def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, histor
    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]):
--- a/crazy_functions/虚空终端.py
+++ b/crazy_functions/虚空终端.py
@@ -1,119 +1,180 @@
-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
+"""
+Explanation of the Void Terminal Plugin:

-def get_fn_lib():
-    return {
-        "BatchTranslatePDFDocuments_MultiThreaded": {
-                "module": "crazy_functions.批量翻译PDF文档_多线程",  
-                "function": "批量翻译PDF文档",
-                "description": "Translate PDF Documents",
-                "arg_1_description": "A path containing pdf files.",
-            },
-        "SummarizingWordDocuments": {
-                "module": "crazy_functions.总结word文档",  
-                "function": "总结word文档",
-                "description": "Summarize Word Documents",
-                "arg_1_description": "A path containing Word files.",
-            },
-        "ImageGeneration": {
-                "module": "crazy_functions.图片生成",  
-                "function": "图片生成",
-                "description": "Generate a image that satisfies some description.",
-                "arg_1_description": "Descriptions about the image to be generated.",
-            },
-        "TranslateMarkdownFromEnglishToChinese": {
-                "module": "crazy_functions.批量Markdown翻译",  
-                "function": "Markdown中译英",
-                "description": "Translate Markdown Documents from English to Chinese.",
-                "arg_1_description": "A path containing Markdown files.",
-            },
-        "SummaryAudioVideo": {
-                "module": "crazy_functions.总结音视频",  
-                "function": "总结音视频",
-                "description": "Get text from a piece of audio and summarize this audio.",
-                "arg_1_description": "A path containing audio files.",
-            },
-    }
+Please describe in natural language what you want to do.

-functions = [
-    {
-        "name": k,
-        "description": v['description'],
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "plugin_arg_1": {
-                    "type": "string",
-                    "description": v['arg_1_description'],
-                },
-            },
-            "required": ["plugin_arg_1"],
-        },
-    } for k, v in get_fn_lib().items()
-]
+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?"

-def inspect_dependency(chatbot, history):
-    return True
+2. If you use keywords like "call the plugin xxx", "modify the configuration xxx", "please", etc., your intention can be recognized more accurately.

-def eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import importlib
-    try:
-        tmp = get_fn_lib()[code['name']]
-        fp, fn = tmp['module'], tmp['function']
-        fn_plugin = getattr(importlib.import_module(fp, fn), fn)
-        arg = json.loads(code['arguments'])['plugin_arg_1']
-        yield from fn_plugin(arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
-    except:
-        from toolbox import trimmed_format_exc
-        chatbot.append(["执行错误", f"\n```\n{trimmed_format_exc()}\n```\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+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://openreview.net/pdf?id=rJl0r3R9KX」
+    - 「把Arxiv论文翻译成中文PDF，arxiv论文的ID是1812.10695，记得用插件！」
+    - 「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」
+    - 「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"」
+    - 「请调用插件，解析python源代码项目，代码我刚刚打包拖到上传区了」
+    - 「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, is_the_upload_folder
+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

-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 终端(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 = []    
+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 = ""

-    # 基本信息：功能、贡献者
-    chatbot.append(["虚空终端插件的功能？", "根据自然语言的描述, 执行任意插件的命令."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    
-    # 输入
-    i_say = txt
-    # 开始
-    llm_kwargs_function_call = copy.deepcopy(llm_kwargs)
-    llm_kwargs_function_call['llm_model'] = 'gpt-call-fn' # 修改调用函数
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs_function_call, chatbot=chatbot, history=[], 
-        sys_prompt=functions
-    )
-
-    # 将代码转为动画
-    res = json.loads(gpt_say)['choices'][0]
-    if res['finish_reason'] == 'function_call':
-        code = json.loads(gpt_say)['choices'][0]
-        yield from eval_code(code['message']['function_call'], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
+    # 用简单的关键词检测用户意图
+    is_certain, _ = analyze_intention_with_simple_rules(txt)
+    if is_the_upload_folder(txt):
+        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:
-        chatbot.append(["无法调用相关功能", res])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        # 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
+
--- a/crazy_functions/解析JupyterNotebook.py
+++ b/crazy_functions/解析JupyterNotebook.py
@@ -1,5 +1,6 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 fast_debug = True


@@ -110,7 +111,8 @@ def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbo
    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

    #  <-------- 写入文件，退出 ---------->
-    res = write_results_to_file(history)
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    chatbot.append(("完成了吗？", res))
    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

--- a/crazy_functions/解析项目源代码.py
+++ b/crazy_functions/解析项目源代码.py
@@ -1,12 +1,13 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
+from toolbox import CatchException, report_execption, write_history_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'
+    disable_auto_promotion(chatbot=chatbot)
+
    summary_batch_isolation = True
    inputs_array = []
    inputs_show_user_array = []
@@ -22,7 +23,7 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
            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)}'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {fp}'
        # 装载请求内容
        inputs_array.append(i_say)
        inputs_show_user_array.append(i_say_show_user)
@@ -43,7 +44,8 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
    # 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)
+    res = write_history_to_file(report_part_1)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    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

@@ -97,7 +99,8 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,

    ############################## <END> ##################################
    history_to_return.extend(report_part_2)
-    res = write_results_to_file(history_to_return)
+    res = write_history_to_file(history_to_return)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
    chatbot.append(("完成了吗？", res))
    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面

@@ -106,9 +109,8 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
 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)]
+    file_manifest = [f for f in glob.glob('./*.py')] + \
+                    [f for f in glob.glob('./*/*.py')]
    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}")
--- a/crazy_functions/语音助手.py
+++ b/crazy_functions/语音助手.py
@@ -80,9 +80,9 @@ 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.parsed_text = ""   # 下个句子中已经说完的部分, 由 test_on_result_chg() 写入
+        self.parsed_sentence = ""   # 某段话的整个句子,由 test_on_sentence_end() 写入
+        self.buffered_sentence = ""    #
        self.event_on_result_chg = threading.Event()
        self.event_on_entence_end = threading.Event()
        self.event_on_commit_question = threading.Event()
@@ -132,7 +132,7 @@ class InterviewAssistant(AliyunASR):
            self.plugin_wd.feed()

            if self.event_on_result_chg.is_set(): 
-                # update audio decode result
+                # called when some words have finished
                self.event_on_result_chg.clear()
                chatbot[-1] = list(chatbot[-1])
                chatbot[-1][0] = self.buffered_sentence + self.parsed_text
@@ -144,7 +144,11 @@ class InterviewAssistant(AliyunASR):
                # called when a sentence has ended
                self.event_on_entence_end.clear()
                self.parsed_text = self.parsed_sentence
-                self.buffered_sentence += self.parsed_sentence
+                self.buffered_sentence += self.parsed_text
+                chatbot[-1] = list(chatbot[-1])
+                chatbot[-1][0] = self.buffered_sentence
+                history = chatbot2history(chatbot)
+                yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

            if self.event_on_commit_question.is_set():
                # called when a question should be commited
--- a/crazy_functions/读文章写摘要.py
+++ b/crazy_functions/读文章写摘要.py
@@ -1,7 +1,7 @@
 from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import CatchException, report_execption
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 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):
@@ -17,32 +17,29 @@ def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbo
        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)
+        msg = '正常'
+        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
+        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
+    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
+    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_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
+    chatbot.append(("完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面



--- a/crazy_functions/谷歌检索小助手.py
+++ b/crazy_functions/谷歌检索小助手.py
@@ -1,26 +1,75 @@
 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
+from toolbox import CatchException, report_execption, promote_file_to_downloadzone
+from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
+import logging
+import requests
+import time
+import random
+
+ENABLE_ALL_VERSION_SEARCH = True

 def get_meta_information(url, chatbot, history):
-    import requests
    import arxiv
    import difflib
+    import re
    from bs4 import BeautifulSoup
    from toolbox import get_conf
+    from urllib.parse import urlparse
+    session = requests.session()
+
    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',
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
+        'Accept-Encoding': 'gzip, deflate, br', 
+        'Accept-Language': 'en-US,en;q=0.9,zh-CN;q=0.8,zh;q=0.7',
+        'Cache-Control':'max-age=0',
+        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 
+        'Connection': 'keep-alive'
    }
-    # 发送 GET 请求
-    response = requests.get(url, proxies=proxies, headers=headers)
+    session.proxies.update(proxies)
+    session.headers.update(headers)

+    response = session.get(url)
    # 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()

+    if ENABLE_ALL_VERSION_SEARCH:
+        def search_all_version(url):
+            time.sleep(random.randint(1,5)) # 睡一会防止触发google反爬虫
+            response = session.get(url)
+            soup = BeautifulSoup(response.text, "html.parser")
+
+            for result in soup.select(".gs_ri"):
+                try:
+                    url = result.select_one(".gs_rt").a['href']
+                except:
+                    continue
+                arxiv_id = extract_arxiv_id(url)
+                if not arxiv_id:
+                    continue
+                search = arxiv.Search(
+                    id_list=[arxiv_id],
+                    max_results=1,
+                    sort_by=arxiv.SortCriterion.Relevance,
+                )
+                try: paper = next(search.results())
+                except: paper = None
+                return paper
+
+            return None
+
+        def extract_arxiv_id(url):
+            # 返回给定的url解析出的arxiv_id，如url未成功匹配返回None
+            pattern = r'arxiv.org/abs/([^/]+)'
+            match = re.search(pattern, url)
+            if match:
+                return match.group(1)
+            else:
+                return None
+
    profile = []
    # 获取所有文章的标题和作者
    for result in soup.select(".gs_ri"):
@@ -31,32 +80,45 @@ def get_meta_information(url, chatbot, history):
        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
+
+        # 首先在arxiv上搜索，获取文章摘要
        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:
+        try: paper = next(search.results())
+        except: paper = None
+        
+        is_match = paper is not None and string_similar(title, paper.title) > 0.90
+
+        # 如果在Arxiv上匹配失败，检索文章的历史版本的题目
+        if not is_match and ENABLE_ALL_VERSION_SEARCH:
+            other_versions_page_url = [tag['href'] for tag in result.select_one('.gs_flb').select('.gs_nph') if 'cluster' in tag['href']]
+            if len(other_versions_page_url) > 0:
+                other_versions_page_url = other_versions_page_url[0]
+                paper = search_all_version('http://' + urlparse(url).netloc + other_versions_page_url)
+                is_match = paper is not None and string_similar(title, paper.title) > 0.90
+
+        if is_match:
+            # same paper
+            abstract = paper.summary.replace('\n', ' ')
+            is_paper_in_arxiv = True
+        else:
+            # different paper
            abstract = abstract
            is_paper_in_arxiv = False
-        print(title)
-        print(author)
-        print(citation)
+
+        logging.info('[title]:' + title)
+        logging.info('[author]:' + author)
+        logging.info('[citation]:' + citation)
+
        profile.append({
-            'title':title,
-            'author':author,
-            'citation':citation,
-            'abstract':abstract,
-            'is_paper_in_arxiv':is_paper_in_arxiv,
+            '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]
@@ -65,6 +127,7 @@ def get_meta_information(url, chatbot, history):

@CatchException
 def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    disable_auto_promotion(chatbot=chatbot)
    # 基本信息：功能、贡献者
    chatbot.append([
        "函数插件功能？",
@@ -86,6 +149,9 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
    # 清空历史，以免输入溢出
    history = []
    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
+    if len(meta_paper_info_list) == 0:
+        yield from update_ui_lastest_msg(lastmsg='获取文献失败，可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
+        return
    batchsize = 5
    for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
        if len(meta_paper_info_list[:batchsize]) > 0:
@@ -107,6 +173,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
        "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)); 
+    path = write_history_to_file(history)
+    promote_file_to_downloadzone(path, chatbot=chatbot)
+    chatbot.append(("完成了吗？", path)); 
    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
--- a/crazy_functions/辅助功能.py
+++ b/crazy_functions/辅助功能.py
@@ -2,8 +2,8 @@
 # @Time   : 2023/4/19
 # @Author : Spike
 # @Descr   :
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui, get_conf
+from toolbox import CatchException
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive


@@ -25,4 +25,18 @@ def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, 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. 删除数据'])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    import shutil, os
+    PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
+    shutil.rmtree(PATH_LOGGING, ignore_errors=True)
+    shutil.rmtree(PATH_PRIVATE_UPLOAD, 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
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,7 +1,7 @@
 #【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,newbing类远程服务）
+## 【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:
@@ -13,9 +13,10 @@ services:
      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", "newbing"]                    '
+      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")]                                             '

@@ -28,7 +29,7 @@ services:


 ### ===================================================
-### 【方案二】 如果需要运行ChatGLM本地模型
+### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
 ### ===================================================
 version: '3'
 services:
@@ -36,11 +37,11 @@ services:
    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,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  '
+      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", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                                       '
+      AVAIL_LLM_MODELS:         '    ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                               '
      LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
      DEFAULT_WORKER_NUM:       '    10                                                                                             '
      WEB_PORT:                 '    12303                                                                                          '
@@ -57,6 +58,10 @@ services:
    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
 ### ===================================================
--- a/docs/Dockerfile+ChatGLM
+++ b/docs/Dockerfile+ChatGLM
@@ -1,62 +1,2 @@
-# 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
+# 此Dockerfile不再维护，请前往docs/GithubAction+ChatGLM+Moss

-# 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/chatgpt_academic.git
-WORKDIR /gpt/chatgpt_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"]
--- a/docs/Dockerfile+JittorLLM
+++ b/docs/Dockerfile+JittorLLM
@@ -1,59 +1 @@
-# 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/chatgpt_academic.git
-WORKDIR /gpt/chatgpt_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"]
+# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs
--- a/docs/Dockerfile+NoLocal+Latex
+++ b/docs/Dockerfile+NoLocal+Latex
@@ -1,27 +1 @@
-# 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"]
+# 此Dockerfile不再维护，请前往docs/GithubAction+NoLocal+Latex
--- a/docs/GithubAction+AllCapacity
+++ b/docs/GithubAction+AllCapacity
@@ -0,0 +1,37 @@
+# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacity  --network=host --build-arg http_proxy=http://localhost:10881 --build-arg https_proxy=http://localhost:10881 .
+
+# 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
+
+# use python3 as the system default python
+WORKDIR /gpt
+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
+# 准备pip依赖
+RUN python3 -m pip install openai numpy arxiv rich
+RUN python3 -m pip install colorama Markdown pygments pymupdf
+RUN python3 -m pip install python-docx moviepy pdfminer
+RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
+RUN python3 -m pip install nougat-ocr
+RUN python3 -m pip install rarfile py7zr
+RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+# 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"]
--- a/docs/GithubAction+ChatGLM+Moss
+++ b/docs/GithubAction+ChatGLM+Moss
@@ -1,7 +1,6 @@

 # 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
@@ -14,10 +13,11 @@ RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/
 # 下载分支
 WORKDIR /gpt
 RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/chatgpt_academic
+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

--- a/docs/GithubAction+JittorLLMs
+++ b/docs/GithubAction+JittorLLMs
@@ -14,7 +14,7 @@ RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/
 # 下载分支
 WORKDIR /gpt
 RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/chatgpt_academic
+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
--- a/docs/GithubAction+NoLocal+Latex
+++ b/docs/GithubAction+NoLocal+Latex
@@ -1,6 +1,6 @@
 # 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 .
+# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/GithubAction+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
@@ -10,6 +10,10 @@ WORKDIR /gpt

 RUN pip3 install gradio openai numpy arxiv rich
 RUN pip3 install colorama Markdown pygments pymupdf
+RUN pip3 install python-docx moviepy pdfminer 
+RUN pip3 install zh_langchain==0.2.1
+RUN pip3 install nougat-ocr
+RUN pip3 install aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git

 # 装载项目文件
 COPY . .
--- a/docs/README.md.German.md
+++ b/docs/README.md.German.md
@@ -15,7 +15,7 @@ Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `m
 >
 > 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/chatgpt_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/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
+> 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
 --- | ---
@@ -23,13 +23,13 @@ Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach gr
 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/chatgpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](https://github.com/binary-husky/chatgpt_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/chatgpt_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
+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/chatgpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
+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
@@ -37,7 +37,7 @@ Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfa
 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/chatgpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
+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 ……
@@ -76,8 +76,8 @@ Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokumen

 1. Download the project
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configure API_KEY
@@ -133,8 +133,8 @@ python main.py
 1. Only ChatGPT (Recommended for most people)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Download the project
-cd chatgpt_academic                                 # Enter the path
+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

@@ -164,10 +164,10 @@ docker-compose up
 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/chatgpt_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)
+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/chatgpt_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)
+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)
@@ -199,7 +199,7 @@ For example

 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/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+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
--- a/docs/README.md.Italian.md
+++ b/docs/README.md.Italian.md
@@ -13,7 +13,7 @@ Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`m
 >
 > 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
 >
-> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
+> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`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). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
 >
 > 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo. 

@@ -25,13 +25,13 @@ Correzione immediata | Supporta correzione immediata e ricerca degli errori di g
 Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic
 Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic
 [Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate
-Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](https://github.com/binary-husky/chatgpt_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) del codice sorgente di questo progetto
+Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](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)
+[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](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) del codice sorgente di questo progetto
 [Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/...
 Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto
 Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex
 Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic
-[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
+[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
 Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione
 [Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading)
 [Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF
@@ -39,7 +39,7 @@ Generazione di report di analisi di chat | [Plugin di funzioni] Generazione auto
 Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete
 Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice
 Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma
-Avvia il tema di gradio [scuro](https://github.com/binary-husky/chatgpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
+Avvia il tema di gradio [scuro](https://github.com/binary-husky/gpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
 Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto?
 Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/)
 Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento...
@@ -82,8 +82,8 @@ Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)..

 1. Scarica il progetto
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configura API_KEY
@@ -139,8 +139,8 @@ python main.py
 1. Solo ChatGPT (consigliato per la maggior parte delle persone)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # scarica il progetto
-cd chatgpt_academic                                 # entra nel percorso
+git clone https://github.com/binary-husky/gpt_academic.git  # scarica il progetto
+cd gpt_academic                                 # entra nel percorso
 nano config.py                                      # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923)
 docker build -t gpt-academic .                      # installa

@@ -171,10 +171,10 @@ docker-compose up
 Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`.

 2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud)
-Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/chatgpt_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)
+Si prega di visitare [wiki di distribuzione-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. Utilizzo di WSL2 (Windows Subsystem for Linux)
-Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/chatgpt_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)
+Si prega di visitare [wiki di distribuzione-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. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`)
 Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md)
@@ -206,7 +206,7 @@ ad esempio
 2. Plugin di funzione personalizzati

 Scrivi plugin di funzione personalizzati e esegui tutte le attività che desideri o non hai mai pensato di fare.
-La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni](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).

 ---
 # Ultimo aggiornamento
--- a/docs/README.md.Korean.md
+++ b/docs/README.md.Korean.md
@@ -13,7 +13,7 @@ GPT를 이용하여 프로젝트를 임의의 언어로 번역하려면 [`multi_
 >
 > 1. 파일을 읽기 위해 **빨간색**으로 표시된 기능 플러그인 (버튼) 만 지원됩니다. 일부 플러그인은 플러그인 영역의 **드롭다운 메뉴**에 있습니다. 또한 새로운 플러그인은 **가장 높은 우선순위**로 환영하며 처리합니다!
 >
-> 2. 이 프로젝트의 각 파일의 기능을 [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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)에서 자세히 설명합니다. 버전이 업데이트 됨에 따라 관련된 기능 플러그인을 클릭하고 GPT를 호출하여 프로젝트의 자체 분석 보고서를 다시 생성할 수도 있습니다. 자주 묻는 질문은 [`위키`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)에서 볼 수 있습니다. [설치 방법](#installation).
+> 2. 이 프로젝트의 각 파일의 기능을 [`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)에서 자세히 설명합니다. 버전이 업데이트 됨에 따라 관련된 기능 플러그인을 클릭하고 GPT를 호출하여 프로젝트의 자체 분석 보고서를 다시 생성할 수도 있습니다. 자주 묻는 질문은 [`위키`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)에서 볼 수 있습니다. [설치 방법](#installation).
 > 
 > 3. 이 프로젝트는 국내 언어 모델 chatglm과 RWKV, 판고 등의 시도와 호환 가능합니다. 여러 개의 api-key를 지원하며 설정 파일에 "API_KEY="openai-key1,openai-key2,api2d-key3""와 같이 작성할 수 있습니다. `API_KEY`를 임시로 변경해야하는 경우 입력 영역에 임시 `API_KEY`를 입력 한 후 엔터 키를 누르면 즉시 적용됩니다. 

@@ -25,13 +25,13 @@ GPT를 이용하여 프로젝트를 임의의 언어로 번역하려면 [`multi_
 한-영 키워드 | 한-영 키워드 지원
 코드 설명 | 코드 표시, 코드 설명, 코드 생성, 코드에 주석 추가
 [사용자 정의 바로 가기 키](https://www.bilibili.com/video/BV14s4y1E7jN) | 사용자 정의 바로 가기 키 지원
-모듈식 설계 | 강력한[함수 플러그인](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) 지원, 플러그인이 [램 업데이트](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)를 지원합니다.
+모듈식 설계 | 강력한[함수 플러그인](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions) 지원, 플러그인이 [램 업데이트](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)를 지원합니다.
 [자체 프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] [원 키 우드] 프로젝트 소스 코드의 내용을 이해하는 기능을 제공
 [프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] 프로젝트 트리를 분석할 수 있습니다 (Python/C/C++/Java/Lua/...)
 논문 읽기, 번역 | [함수 플러그인] LaTex/PDF 논문의 전문을 읽고 요약을 생성합니다.
 LaTeX 텍스트[번역](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [원 키워드](https://www.bilibili.com/video/BV1FT411H7c5/) | [함수 플러그인] LaTeX 논문의 번역 또는 개량을 위해 일련의 모드를 번역할 수 있습니다.
 대량의 주석 생성 | [함수 플러그인] 함수 코멘트를 대량으로 생성할 수 있습니다.
-Markdown 한-영 번역 | [함수 플러그인] 위의 5 종 언어의 [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)를 볼 수 있습니다.
+Markdown 한-영 번역 | [함수 플러그인] 위의 5 종 언어의 [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)를 볼 수 있습니다.
 chat 분석 보고서 생성 | [함수 플러그인] 수행 후 요약 보고서를 자동으로 생성합니다.
 [PDF 논문 번역](https://www.bilibili.com/video/BV1KT411x7Wn) | [함수 플러그인] PDF 논문이 제목 및 요약을 추출한 후 번역됩니다. (멀티 스레드)
 [Arxiv 도우미](https://www.bilibili.com/video/BV1LM4y1279X) | [함수 플러그인] Arxiv 논문 URL을 입력하면 요약을 번역하고 PDF를 다운로드 할 수 있습니다.
@@ -73,8 +73,8 @@ LLM 모델 추가 및[huggingface 배치](https://huggingface.co/spaces/qingxu98

 1. 프로젝트 다운로드
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. API_KEY 구성
@@ -134,8 +134,8 @@ python main.py
 1. ChatGPT 만 (대부분의 사람들이 선택하는 것을 권장합니다.)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # 다운로드
-cd chatgpt_academic                                 # 경로 이동
+git clone https://github.com/binary-husky/gpt_academic.git  # 다운로드
+cd gpt_academic                                 # 경로 이동
 nano config.py                                      # 아무 텍스트 에디터로 config.py를 열고 "Proxy","API_KEY","WEB_PORT" (예 : 50923) 등을 구성합니다.
 docker build -t gpt-academic .                      # 설치

@@ -165,10 +165,10 @@ docker-compose up
 API_URL_REDIRECT를 `config.py`에 따라 구성하면됩니다.

 2. 원격 클라우드 서버 배치 (클라우드 서버 지식과 경험이 필요합니다.)
-[배치위키-1](https://github.com/binary-husky/chatgpt_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)에 방문하십시오.
+[배치위키-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)에 방문하십시오.

 3. WSL2 사용 (Windows Subsystem for Linux 하위 시스템)
-[배치 위키-2](https://github.com/binary-husky/chatgpt_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)에 방문하십시오.
+[배치 위키-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)에 방문하십시오.

 4. 2 차 URL (예 : `http : //localhost/subpath`)에서 실행하는 방법
 [FastAPI 실행 설명서] (docs / WithFastapi.md)를 참조하십시오.
@@ -197,7 +197,7 @@ docker-compose.yml을 읽은 후 지시 사항에 따라 작업하십시오.

 2. 사용자 지정 함수 플러그인
 강력한 함수 플러그인을 작성하여 원하는 작업을 수행하십시오.
-이 프로젝트의 플러그인 작성 및 디버깅 난이도는 매우 낮으며, 일부 파이썬 기본 지식만 있으면 제공된 템플릿을 모방하여 플러그인 기능을 구현할 수 있습니다. 자세한 내용은 [함수 플러그인 가이드]를 참조하십시오. (https://github.com/binary -husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E 4%BB%B6%E6%8C%87%E5%8D%97).
+이 프로젝트의 플러그인 작성 및 디버깅 난이도는 매우 낮으며, 일부 파이썬 기본 지식만 있으면 제공된 템플릿을 모방하여 플러그인 기능을 구현할 수 있습니다. 자세한 내용은 [함수 플러그인 가이드]를 참조하십시오. (https://github.com/binary -husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E 4%BB%B6%E6%8C%87%E5%8D%97).
 ---
 # 최신 업데이트
 ## 새로운 기능 동향1. 대화 저장 기능. 
--- a/docs/README.md.Portuguese.md
+++ b/docs/README.md.Portuguese.md
@@ -14,7 +14,7 @@ Para traduzir este projeto para qualquer idioma com o GPT, leia e execute [`mult
 >
 > 1. Por favor, preste atenção que somente os plugins de funções (botões) com a cor **vermelha** podem ler arquivos. Alguns plugins estão localizados no **menu suspenso** na área de plugins. Além disso, nós damos as boas-vindas com a **maior prioridade** e gerenciamos quaisquer novos plugins PR!
 >
-> 2. As funções de cada arquivo neste projeto são detalhadas em [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
+> 2. As funções de cada arquivo neste projeto são detalhadas em [`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), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
 >
 > 3. Este projeto é compatível com e incentiva o uso de modelos de linguagem nacionais, como chatglm e RWKV, Pangolin, etc. Suporta a coexistência de várias chaves de API e pode ser preenchido no arquivo de configuração como `API_KEY="openai-key1,openai-key2,api2d-key3"`. Quando precisar alterar temporariamente o `API_KEY`, basta digitar o `API_KEY` temporário na área de entrada e pressionar Enter para que ele entre em vigor. 

@@ -26,8 +26,8 @@ Um clique de polimento | Suporte a um clique polimento, um clique encontrar erro
 Tradução chinês-inglês de um clique | Tradução chinês-inglês de um clique
 Explicação de código de um único clique | Exibir código, explicar código, gerar código, adicionar comentários ao código
 [Teclas de atalho personalizadas](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte a atalhos personalizados
-Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](https://github.com/binary-husky/chatgpt_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) o código-fonte do projeto
+Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](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)
+[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](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) o código-fonte do projeto
 [Análise do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função] Um clique pode analisar a árvore de projetos do Python/C/C++/Java/Lua/...
 Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin de função] um clique para interpretar o resumo de artigos LaTeX/PDF e gerar resumo
 Tradução completa LATEX, polimento|[Plugin de função] Uma clique para traduzir ou polir um artigo LATEX
@@ -91,8 +91,8 @@ Mais recursos novos mostrados (geração de imagens, etc.) ... | Consulte o fina
 1. Download the project

 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configure the API KEY
@@ -149,8 +149,8 @@ python main.py
 1. Apenas ChatGPT (recomendado para a maioria das pessoas)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Baixar o projeto
-cd chatgpt_academic                                 # Entrar no caminho
+git clone https://github.com/binary-husky/gpt_academic.git  # Baixar o projeto
+cd gpt_academic                                 # Entrar no caminho
 nano config.py                                       # Editar config.py com qualquer editor de texto configurando "Proxy", "API_KEY" e "WEB_PORT" (por exemplo, 50923), etc.
 docker build -t gpt-academic .                      # Instale

@@ -180,10 +180,10 @@ docker-compose up
 Basta configurar o API_URL_REDIRECT de acordo com as instruções em `config.py`.

 2. Implantação em servidores em nuvem remotos (requer conhecimento e experiência de servidores em nuvem)
-Acesse [Wiki de implementação remota do servidor em nuvem](https://github.com/binary-husky/chatgpt_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)
+Acesse [Wiki de implementação remota do servidor em nuvem](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. Usando a WSL2 (sub-sistema do Windows para Linux)
-Acesse [Wiki da implantação da WSL2](https://github.com/binary-husky/chatgpt_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)
+Acesse [Wiki da implantação da WSL2](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. Como executar em um subdiretório (ex. `http://localhost/subpath`)
 Acesse [Instruções de execução FastAPI](docs/WithFastapi.md)
@@ -214,7 +214,7 @@ Por exemplo,

 Escreva plug-ins de função poderosos para executar tarefas que você deseja e não pensava possível.
 A dificuldade geral de escrever e depurar plug-ins neste projeto é baixa e, se você tem algum conhecimento básico de python, pode implementar suas próprias funções sobre o modelo que fornecemos.
-Para mais detalhes, consulte o [Guia do plug-in de função.](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+Para mais detalhes, consulte o [Guia do plug-in de função.](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).

 ---
 # Última atualização
--- a/docs/README_EN.md
+++ b/docs/README_EN.md
@@ -14,7 +14,7 @@ To translate this project to arbitary language with GPT, read and run [`multi_la
 > Note:
 > 
 > 1. Please note that only the function plugins (buttons) marked in **red** support reading files. Some plugins are in the **drop-down menu** in the plugin area. We welcome and process any new plugins with the **highest priority**!
-> 2. The function of each file in this project is detailed in the self-translation analysis [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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). With version iteration, you can also click on related function plugins at any time to call GPT to regenerate the project's self-analysis report. Common questions are summarized in the [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installation method](#installation).
+> 2. The function of each file in this project is detailed in the self-translation analysis [`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). With version iteration, you can also click on related function plugins at any time to call GPT to regenerate the project's self-analysis report. Common questions are summarized in the [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installation method](#installation).
 > 3. This project is compatible with and encourages trying domestic large language models such as chatglm, RWKV, Pangu, etc. Multiple API keys are supported and can be filled in the configuration file like `API_KEY="openai-key1,openai-key2,api2d-key3"`. When temporarily changing `API_KEY`, enter the temporary `API_KEY` in the input area and press enter to submit, which will take effect. 

 <div align="center">
@@ -25,13 +25,13 @@ One-click polishing | Supports one-click polishing and one-click searching for g
 One-click Chinese-English translation | One-click Chinese-English translation.
 One-click code interpretation | Displays, explains, generates, and adds comments to code.
 [Custom shortcut keys](https://www.bilibili.com/video/BV14s4y1E7jN) | Supports custom shortcut keys.
-Modular design | Supports custom powerful [function plug-ins](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), plug-ins support [hot update](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-[Self-program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] [One-click understanding](https://github.com/binary-husky/chatgpt_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) of the source code of this project
+Modular design | Supports custom powerful [function plug-ins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), plug-ins support [hot update](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).
+[Self-program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] [One-click understanding](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) of the source code of this project
 [Program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] One-click profiling of other project trees in Python/C/C++/Java/Lua/...
 Reading papers, [translating](https://www.bilibili.com/video/BV1KT411x7Wn) papers | [Function Plug-in] One-click interpretation of latex/pdf full-text papers and generation of abstracts.
 Latex full-text [translation](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [polishing](https://www.bilibili.com/video/BV1FT411H7c5/) | [Function plug-in] One-click translation or polishing of latex papers.
 Batch annotation generation | [Function plug-in] One-click batch generation of function annotations.
-Markdown [Chinese-English translation](https://www.bilibili.com/video/BV1yo4y157jV/) | [Function plug-in] Have you seen the [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) in the five languages above?
+Markdown [Chinese-English translation](https://www.bilibili.com/video/BV1yo4y157jV/) | [Function plug-in] Have you seen the [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in the five languages above?
 Chat analysis report generation | [Function plug-in] Automatically generate summary reports after running.
 [PDF full-text translation function](https://www.bilibili.com/video/BV1KT411x7Wn) | [Function plug-in] PDF paper extract title & summary + translate full text (multi-threaded)
 [Arxiv Assistant](https://www.bilibili.com/video/BV1LM4y1279X) | [Function plug-in] Enter the arxiv article url and you can translate abstracts and download PDFs with one click.
@@ -39,7 +39,7 @@ Chat analysis report generation | [Function plug-in] Automatically generate summ
 Internet information aggregation+GPT | [Function plug-in] One-click [let GPT get information from the Internet first](https://www.bilibili.com/video/BV1om4y127ck), then answer questions, and let the information never be outdated.
 Formula/image/table display | Can display formulas in both [tex form and render form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), support formulas and code highlighting.
 Multi-threaded function plug-in support | Supports multi-threaded calling of chatgpt, and can process [massive text](https://www.bilibili.com/video/BV1FT411H7c5/) or programs with one click.
-Start Dark Gradio [theme](https://github.com/binary-husky/chatgpt_academic/issues/173) | Add ```/?__theme=dark``` after the browser URL to switch to the dark theme.
+Start Dark Gradio [theme](https://github.com/binary-husky/gpt_academic/issues/173) | Add ```/?__theme=dark``` after the browser URL to switch to the dark theme.
 [Multiple LLM models](https://www.bilibili.com/video/BV1wT411p7yf) support, [API2D](https://api2d.com/) interface support | The feeling of being served by GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), and [Fudan MOSS](https://github.com/OpenLMLab/MOSS) at the same time must be great, right?
 More LLM model access, support [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Add Newbing interface (New Bing), introduce Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs) to support [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) and [Panguα](https://openi.org.cn/pangu/)
 More new feature displays (image generation, etc.)…… | See the end of this document for more... 
@@ -79,8 +79,8 @@ More new feature displays (image generation, etc.)…… | See the end of this d

 1. Download the project
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configure the API_KEY
@@ -136,8 +136,8 @@ python main.py
 1. ChatGPT Only (Recommended for Most People)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Download project
-cd chatgpt_academic                                 # Enter path
+git clone https://github.com/binary-husky/gpt_academic.git  # Download project
+cd gpt_academic                                 # Enter 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

@@ -167,10 +167,10 @@ docker-compose up
 Configure API_URL_REDIRECT according to the instructions in 'config.py'.

 2. Deploy to a Remote Server (Requires Knowledge and Experience with Cloud Servers)
-Please visit [Deployment Wiki-1](https://github.com/binary-husky/chatgpt_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)
+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 WSL2 (Windows Subsystem for Linux)
-Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_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)
+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 Under a Subdomain (e.g. `http://localhost/subpath`)
 Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
@@ -202,7 +202,7 @@ For example,

 Write powerful function plugins to perform any task you can think of, even those you cannot think of.
 The difficulty of plugin writing and debugging in this project is very low. As long as you have a certain knowledge of Python, you can implement your own plug-in functions based on the template we provide.
-For details, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+For details, 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
--- a/docs/README_FR.md
+++ b/docs/README_FR.md
@@ -16,7 +16,7 @@ Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez
 >
 > 1. Veuillez noter que seuls les plugins de fonctions (boutons) **en rouge** prennent en charge la lecture de fichiers. Certains plugins se trouvent dans le **menu déroulant** de la zone de plugins. De plus, nous accueillons et traitons les nouvelles pull requests pour les plugins avec **la plus haute priorité**!
 >
-> 2. Les fonctions de chaque fichier de ce projet sont expliquées en détail dans l'auto-analyse [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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). Avec l'itération des versions, vous pouvez également cliquer sur les plugins de fonctions pertinents et appeler GPT pour régénérer le rapport d'auto-analyse du projet à tout moment. Les FAQ sont résumées dans [le wiki](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Méthode d'installation](#installation).
+> 2. Les fonctions de chaque fichier de ce projet sont expliquées en détail dans l'auto-analyse [`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). Avec l'itération des versions, vous pouvez également cliquer sur les plugins de fonctions pertinents et appeler GPT pour régénérer le rapport d'auto-analyse du projet à tout moment. Les FAQ sont résumées dans [le wiki](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Méthode d'installation](#installation).
 >
 > 3. Ce projet est compatible avec et encourage l'utilisation de grands modèles de langage nationaux tels que chatglm, RWKV, Pangu, etc. La coexistence de plusieurs clés API est prise en charge et peut être remplie dans le fichier de configuration, tel que `API_KEY="openai-key1,openai-key2,api2d-key3"`. Lorsque vous souhaitez remplacer temporairement `API_KEY`, saisissez temporairement `API_KEY` dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer. 

@@ -28,13 +28,13 @@ Révision en un clic | prend en charge la révision en un clic et la recherche d
 Traduction chinois-anglais en un clic | Traduction chinois-anglais en un clic
 Explication de code en un clic | Affichage, explication, génération et ajout de commentaires de code
 [Raccourcis personnalisés](https://www.bilibili.com/video/BV14s4y1E7jN) | prend en charge les raccourcis personnalisés
-Conception modulaire | prend en charge de puissants plugins de fonction personnalisée, les plugins prennent en charge la [mise à jour à chaud](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)  
-[Autoscanner](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] [Compréhension instantanée](https://github.com/binary-husky/chatgpt_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) du code source de ce projet
+Conception modulaire | prend en charge de puissants plugins de fonction personnalisée, les plugins prennent en charge la [mise à jour à chaud](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)  
+[Autoscanner](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] [Compréhension instantanée](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) du code source de ce projet
 [Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] Analyse en un clic de la structure d'autres projets Python / C / C ++ / Java / Lua / ...
 Lecture d'articles, [traduction](https://www.bilibili.com/video/BV1KT411x7Wn) d'articles | [Plug-in de fonction] Compréhension instantanée de l'article latex / pdf complet et génération de résumés
 [Traduction](https://www.bilibili.com/video/BV1nk4y1Y7Js/) et [révision](https://www.bilibili.com/video/BV1FT411H7c5/) complets en latex | [Plug-in de fonction] traduction ou révision en un clic d'articles en latex
 Génération de commentaires en masse | [Plug-in de fonction] Génération en un clic de commentaires de fonction en masse
-Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) en Markdown | [Plug-in de fonction] avez-vous vu la [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) pour les 5 langues ci-dessus?
+Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) en Markdown | [Plug-in de fonction] avez-vous vu la [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) pour les 5 langues ci-dessus?
 Génération de rapports d'analyse de chat | [Plug-in de fonction] Génère automatiquement un rapport de résumé après l'exécution
 [Traduction intégrale en pdf](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plug-in de fonction] Extraction de titre et de résumé de l'article pdf + traduction intégrale (multi-thread)
 [Aide à arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plug-in de fonction] Entrer l'url de l'article arxiv pour traduire et télécharger le résumé en un clic
@@ -84,8 +84,8 @@ Plus de nouvelles fonctionnalités (génération d'images, etc.) ... | Voir la f

 1. Télécharger le projet
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configuration de la clé API
@@ -141,8 +141,8 @@ python main.py
 1. ChatGPT uniquement (recommandé pour la plupart des gens)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Télécharger le projet
-cd chatgpt_academic                                 # Accéder au chemin
+git clone https://github.com/binary-husky/gpt_academic.git  # Télécharger le projet
+cd gpt_academic                                 # Accéder au chemin
 nano config.py                                      # Editez config.py avec n'importe quel éditeur de texte en configurant "Proxy", "API_KEY" et "WEB_PORT" (p. ex. 50923)
 docker build -t gpt-academic .                      # Installer

@@ -172,10 +172,10 @@ docker-compose up
 Configurez simplement API_URL_REDIRECT selon les instructions de config.py.

 2. Déploiement distant sur un serveur cloud (connaissance et expérience des serveurs cloud requises)
-Veuillez consulter [Wiki de déploiement-1] (https://github.com/binary-husky/chatgpt_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).
+Veuillez consulter [Wiki de déploiement-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. Utilisation de WSL2 (sous-système Windows pour Linux)
-Veuillez consulter [Wiki de déploiement-2] (https://github.com/binary-husky/chatgpt_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).
+Veuillez consulter [Wiki de déploiement-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. Comment exécuter sous un sous-répertoire (tel que `http://localhost/subpath`)
 Veuillez consulter les [instructions d'exécution de FastAPI] (docs/WithFastapi.md).
@@ -206,7 +206,7 @@ Par exemple

 Écrivez des plugins de fonctions puissants pour effectuer toutes les tâches que vous souhaitez ou que vous ne pouvez pas imaginer.
 Les plugins de ce projet ont une difficulté de programmation et de débogage très faible. Si vous avez des connaissances de base en Python, vous pouvez simuler la fonctionnalité de votre propre plugin en suivant le modèle que nous avons fourni.
-Veuillez consulter le [Guide du plugin de fonction] (https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) pour plus de détails.
+Veuillez consulter le [Guide du plugin de fonction] (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) pour plus de détails.

 ---
 # Latest Update
--- a/docs/README_JP.md
+++ b/docs/README_JP.md
@@ -16,7 +16,7 @@ GPTを使った任意の言語にこのプロジェクトを翻訳するには
 >
 > 1. **赤色**で表示された関数プラグイン(ボタン)のみ、ファイルの読み取りをサポートしています。一部のプラグインは、プラグインエリアの**ドロップダウンメニュー**内にあります。また、私たちはどんな新しいプラグインのPRでも、**最優先**で歓迎し、処理します！
 >
-> 2. このプロジェクトの各ファイルの機能は、自己解析の詳細説明書である[`self_analysis.md`](https://github.com/binary-husky/chatgpt_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)で説明されています。バージョンが進化するにつれて、関連する関数プラグインをいつでもクリックし、GPTを呼び出してプロジェクトの自己解析レポートを再生成することができます。よくある問題は[`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)にまとめられています。[インストール方法](#installation)。
+> 2. このプロジェクトの各ファイルの機能は、自己解析の詳細説明書である[`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)で説明されています。バージョンが進化するにつれて、関連する関数プラグインをいつでもクリックし、GPTを呼び出してプロジェクトの自己解析レポートを再生成することができます。よくある問題は[`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)にまとめられています。[インストール方法](#installation)。

 > 3. このプロジェクトは、chatglmやRWKV、パンクなど、国内の大規模自然言語モデルを利用することをサポートし、試みることを奨励します。複数のAPIキーを共存することができ、設定ファイルに`API_KEY="openai-key1,openai-key2,api2d-key3"`のように記入することができます。`API_KEY`を一時的に変更する場合は、入力エリアに一時的な`API_KEY`を入力してEnterキーを押せば、それが有効になります。

@@ -29,13 +29,13 @@ GPTを使った任意の言語にこのプロジェクトを翻訳するには
 一键中英翻訳 | 一键で中英翻訳可能
 一键コード解説 | コードを表示し、解説し、生成し、コードに注釈をつけることができる
 [自分でカスタマイズ可能なショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | 自分でカスタマイズ可能なショートカットキーをサポートする
-モジュール化された設計 | カスタマイズ可能な[強力な関数プラグイン](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions)をサポートし、プラグインは[ホットアップデート](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)に対応している
-[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] [一键読解](https://github.com/binary-husky/chatgpt_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)このプロジェクトのソースコード
+モジュール化された設計 | カスタマイズ可能な[強力な関数プラグイン](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)をサポートし、プラグインは[ホットアップデート](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)に対応している
+[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] [一键読解](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)このプロジェクトのソースコード
 プログラム解析 | [関数プラグイン] 一鍵で他のPython/C/C++/Java/Lua/...プロジェクトを分析できる
 論文の読み、[翻訳](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] LaTex/ PDF論文の全文を一鍵で読み解き、要約を生成することができる
 LaTex全文[翻訳](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[校正](https://www.bilibili.com/video/BV1FT411H7c5/) | [関数プラグイン] LaTex論文の翻訳または校正を一鍵で行うことができる
 一括で注釈を生成 | [関数プラグイン] 一鍵で関数に注釈をつけることができる
-Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数プラグイン] 上記の5種類の言語の[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)を見たことがありますか？
+Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数プラグイン] 上記の5種類の言語の[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)を見たことがありますか？
 チャット分析レポート生成 | [関数プラグイン] 実行後、自動的に概要報告書を生成する
 [PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文からタイトルと要約を抽出し、全文を翻訳する（マルチスレッド）
 [Arxivアシスタント](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] arxiv記事のURLを入力するだけで、要約を一鍵翻訳し、PDFをダウンロードできる
@@ -43,7 +43,7 @@ Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数
 インターネット情報収集＋GPT | [関数プラグイン] まずGPTに[インターネットから情報を収集](https://www.bilibili.com/video/BV1om4y127ck)してから質問に回答させ、情報が常に最新であるようにする
 数式/画像/表表示 | 数式の[tex形式とレンダリング形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)を同時に表示し、数式、コードハイライトをサポートしている
 マルチスレッド関数プラグインがサポートされている | chatgptをマルチスレッドで呼び出し、[大量のテキスト](https://www.bilibili.com/video/BV1FT411H7c5/)またはプログラムを一鍵で処理できる
-ダークグラジオ[テーマの起動](https://github.com/binary-husky/chatgpt_academic/issues/173) | ブラウザのURLの後ろに```/?__theme=dark```を追加すると、ダークテーマを切り替えることができます。
+ダークグラジオ[テーマの起動](https://github.com/binary-husky/gpt_academic/issues/173) | ブラウザのURLの後ろに```/?__theme=dark```を追加すると、ダークテーマを切り替えることができます。
 [多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)がサポートされ、[API2D](https://api2d.com/)がサポートされている | 同時にGPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)、[復旦MOSS](https://github.com/OpenLMLab/MOSS)に対応
 より多くのLLMモデルが接続され、[huggingfaceデプロイ](https://huggingface.co/spaces/qingxu98/gpt-academic)がサポートされている | Newbingインターフェイス（Newbing）、清華大学の[Jittorllm](https://github.com/Jittor/JittorLLMs)のサポート[LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV)と[盘古α](https://openi.org.cn/pangu/)
 さらに多くの新機能（画像生成など）を紹介する... | この文書の最後に示す... 
@@ -92,8 +92,8 @@ Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数
 1. Download the project.

 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configure the API_KEY.
@@ -151,8 +151,8 @@ python main.py
 1. Only ChatGPT (recommended for most people)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # Download project
-cd chatgpt_academic                                 # Enter path
+git clone https://github.com/binary-husky/gpt_academic.git  # Download project
+cd gpt_academic                                 # Enter path
 nano config.py                                      # Edit config.py with any text editor ‑ configure "Proxy," "API_KEY," "WEB_PORT" (e.g., 50923) and more
 docker build -t gpt-academic .                      # installation

@@ -182,10 +182,10 @@ docker-compose up
 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/chatgpt_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)
+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 WSL2 (Windows Subsystem for Linux Subsystem)
-Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_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)
+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 on a secondary URL (such as `http://localhost/subpath`)
 Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
@@ -216,7 +216,7 @@ example:

 Write powerful function plugins to perform any task you can and cannot think of.
 The difficulty of writing and debugging plugins in this project is low, and as long as you have a certain amount of python basic knowledge, you can follow the template provided by us to achieve your own plugin functions.
-For details, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+For details, 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
--- a/docs/README_RS.md
+++ b/docs/README_RS.md
@@ -11,7 +11,7 @@
 > 
 > 1. Обратите внимание, что только функциональные плагины (кнопки), помеченные **красным цветом**, поддерживают чтение файлов, некоторые плагины находятся в **выпадающем меню** в области плагинов. Кроме того, мы с наивысшим приоритетом рады и обрабатываем pull requests для любых новых плагинов!
 > 
-> 2. В каждом файле проекта функциональность описана в документе самоанализа [`self_analysis.md`](https://github.com/binary-husky/chatgpt_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). С каждой итерацией выполнения версии вы можете в любое время вызвать повторное создание отчета о самоанализе этого проекта, щелкнув соответствующий функциональный плагин и вызвав GPT. Вопросы сборки описаны в [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Метод установки](#installation).
+> 2. В каждом файле проекта функциональность описана в документе самоанализа [`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). С каждой итерацией выполнения версии вы можете в любое время вызвать повторное создание отчета о самоанализе этого проекта, щелкнув соответствующий функциональный плагин и вызвав GPT. Вопросы сборки описаны в [`wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Метод установки](#installation).
 > 
 > 3. Этот проект совместим и поощряет использование китайских языковых моделей chatglm и RWKV, пангу и т. Д. Поддержка нескольких api-key, которые могут существовать одновременно, может быть указан в файле конфигурации, например `API_KEY="openai-key1,openai-key2,api2d-key3"`. Если требуется временно изменить `API_KEY`, введите временный `API_KEY` в области ввода и нажмите клавишу Enter, чтобы он вступил в силу. 

@@ -33,13 +33,13 @@
 Однокнопочный перевод на английский и китайский | Однокнопочный перевод на английский и китайский
 Однокнопочное объяснение кода | Показ кода, объяснение его, генерация кода, комментирование кода
 [Настройка быстрых клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настройки быстрых клавиш
-Модульный дизайн | Поддержка пользовательских функциональных плагинов мощных [функциональных плагинов](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), плагины поддерживают [горячую замену](https://github.com/binary-husky/chatgpt_academic/wiki/Function-Plug-in-Guide)
-[Анализ своей программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Однокнопочный просмотр](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academicProject-Self-analysis-Report) исходного кода этого проекта
+Модульный дизайн | Поддержка пользовательских функциональных плагинов мощных [функциональных плагинов](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions), плагины поддерживают [горячую замену](https://github.com/binary-husky/gpt_academic/wiki/Function-Plug-in-Guide)
+[Анализ своей программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Однокнопочный просмотр](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academicProject-Self-analysis-Report) исходного кода этого проекта
 [Анализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] Однокнопочный анализ дерева других проектов Python/C/C++/Java/Lua/...
 Чтение статей, [перевод](https://www.bilibili.com/video/BV1KT411x7Wn) статей | [Функциональный плагин] Однокнопочное чтение полного текста научных статей и генерация резюме
 Полный перевод [LaTeX](https://www.bilibili.com/video/BV1nk4y1Y7Js/) и совершенствование | [Функциональный плагин] Однокнопочный перевод или совершенствование LaTeX статьи
 Автоматическое комментирование | [Функциональный плагин] Однокнопочное автоматическое генерирование комментариев функций
-[Перевод](https://www.bilibili.com/video/BV1yo4y157jV/) Markdown на английский и китайский | [Функциональный плагин] Вы видели обе версии файлов [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) для этих 5 языков?
+[Перевод](https://www.bilibili.com/video/BV1yo4y157jV/) Markdown на английский и китайский | [Функциональный плагин] Вы видели обе версии файлов [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) для этих 5 языков?
 Отчет о чат-анализе | [Функциональный плагин] После запуска будет автоматически сгенерировано сводное извещение
 Функция перевода полного текста [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) | [Функциональный плагин] Извлечение заголовка и резюме [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) и перевод всего документа (многопоточность)
 [Arxiv Helper](https://www.bilibili.com/video/BV1LM4y1279X) | [Функциональный плагин] Введите URL статьи на arxiv и одним щелчком мыши переведите резюме и загрузите PDF
@@ -81,8 +81,8 @@

 1. Download the project
 ```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
+git clone https://github.com/binary-husky/gpt_academic.git
+cd gpt_academic
 ```

 2. Configure API_KEY
@@ -138,8 +138,8 @@ python main.py
 1. ChatGPT only (recommended for most people)

 ``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # download the project
-cd chatgpt_academic                                 # enter the path
+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 to configure "Proxy", "API_KEY", and "WEB_PORT" (eg 50923)
 docker build -t gpt-academic .                      # install

@@ -169,10 +169,10 @@ docker-compose up
 Configure API_URL_REDIRECT according to the instructions in `config.py`.

 2. Remote Cloud Server Deployment (Requires Knowledge and Experience of Cloud Servers)
-Please visit [Deployment Wiki-1](https://github.com/binary-husky/chatgpt_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)
+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 WSL2 (Windows Subsystem for Linux subsystem)
-Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_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)
+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 the secondary URL (such as `http://localhost/subpath`)
 Please visit [FastAPI Operation Instructions](docs/WithFastapi.md)
@@ -204,7 +204,7 @@ For example:

 Write powerful function plugins to perform any task you can and can't imagine.
 The difficulty of debugging and writing plugins in this project is very low. As long as you have a certain knowledge of python, you can implement your own plugin function by imitating the template we provide.
-Please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) for details.
+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) for details.

 ---
 # Latest Update
--- a/docs/translate_english.json
+++ b/docs/translate_english.json
@@ -299,7 +299,6 @@
    "地址🚀": "Address 🚀",
    "感谢热情的": "Thanks to the enthusiastic",
    "开发者们❤️": "Developers ❤️",
-    "所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log": "All inquiry records will be automatically saved in the local directory ./gpt_log/chat_secrets.log",
    "请注意自我隐私保护哦！": "Please pay attention to self-privacy protection!",
    "当前模型": "Current model",
    "输入区": "Input area",
@@ -892,7 +891,6 @@
    "保存当前对话": "Save current conversation",
    "您可以调用“LoadConversationHistoryArchive”还原当下的对话": "You can call 'LoadConversationHistoryArchive' to restore the current conversation",
    "警告！被保存的对话历史可以被使用该系统的任何人查阅": "Warning! The saved conversation history can be viewed by anyone using this system",
-    "gpt_log/**/chatGPT对话历史*.html": "gpt_log/**/chatGPT conversation history *.html",
    "正在查找对话历史文件": "Looking for conversation history file",
    "html格式": "HTML format",
    "找不到任何html文件": "No HTML files found",
@@ -908,7 +906,6 @@
    "pip install pywin32 用于doc格式": "pip install pywin32 for doc format",
    "仅支持Win平台": "Only supports Win platform",
    "打开文件": "Open file",
-    "private_upload里面的文件名在解压zip后容易出现乱码": "The file name in private_upload is prone to garbled characters after unzipping",
    "rar和7z格式正常": "RAR and 7z formats are normal",
    "故可以只分析文章内容": "So you can only analyze the content of the article",
    "不输入文件名": "Do not enter the file name",
@@ -1364,7 +1361,6 @@
    "注意文章中的每一句话都要翻译": "Please translate every sentence in the article",
    "一、论文概况": "I. Overview of the paper",
    "二、论文翻译": "II. Translation of the paper",
-    "/gpt_log/总结论文-": "/gpt_log/Summary of the paper-",
    "给出输出文件清单": "Provide a list of output files",
    "第 0 步": "Step 0",
    "切割PDF": "Split PDF",
@@ -1564,7 +1560,6 @@
    "广义速度": "Generalized velocity",
    "粒子的固有": "Intrinsic of particle",
    "一个包含所有切割音频片段文件路径的列表": "A list containing the file paths of all segmented audio clips",
-    "/gpt_log/翻译-": "Translation log-",
    "计算文件总时长和切割点": "Calculate total duration and cutting points of the file",
    "总结音频": "Summarize audio",
    "作者": "Author",
@@ -2161,5 +2156,362 @@
    "在运行过程中动态地修改配置": "Dynamically modify configurations during runtime",
    "请先把模型切换至gpt-*或者api2d-*": "Please switch the model to gpt-* or api2d-* first",
    "获取简单聊天的句柄": "Get handle of simple chat",
-    "获取插件的默认参数": "Get default parameters of plugin"
+    "获取插件的默认参数": "Get default parameters of plugin",
+    "GROBID服务不可用": "GROBID service is unavailable",
+    "请问": "May I ask",
+    "如果等待时间过长": "If the waiting time is too long",
+    "编程": "programming",
+    "5. 现在": "5. Now",
+    "您不必读这个else分支": "You don't have to read this else branch",
+    "用插件实现": "Implement with plugins",
+    "插件分类默认选项": "Default options for plugin classification",
+    "填写多个可以均衡负载": "Filling in multiple can balance the load",
+    "色彩主题": "Color theme",
+    "可能附带额外依赖 -=-=-=-=-=-=-": "May come with additional dependencies -=-=-=-=-=-=-",
+    "讯飞星火认知大模型": "Xunfei Xinghuo cognitive model",
+    "ParsingLuaProject的所有源文件 | 输入参数为路径": "All source files of ParsingLuaProject | Input parameter is path",
+    "复制以下空间https": "Copy the following space https",
+    "如果意图明确": "If the intention is clear",
+    "如系统是Linux": "If the system is Linux",
+    "├── 语音功能": "├── Voice function",
+    "见Github wiki": "See Github wiki",
+    "⭐ ⭐ ⭐ 立即应用配置": "⭐ ⭐ ⭐ Apply configuration immediately",
+    "现在您只需要再次重复一次您的指令即可": "Now you just need to repeat your command again",
+    "没辙了": "No way",
+    "解析Jupyter Notebook文件 | 输入参数为路径": "Parse Jupyter Notebook file | Input parameter is path",
+    "⭐ ⭐ ⭐ 确认插件参数": "⭐ ⭐ ⭐ Confirm plugin parameters",
+    "找不到合适插件执行该任务": "Cannot find a suitable plugin to perform this task",
+    "接驳VoidTerminal": "Connect to VoidTerminal",
+    "**很好": "**Very good",
+    "对话|编程": "Conversation|Programming",
+    "对话|编程|学术": "Conversation|Programming|Academic",
+    "4. 建议使用 GPT3.5 或更强的模型": "4. It is recommended to use GPT3.5 or a stronger model",
+    "「请调用插件翻译PDF论文": "Please call the plugin to translate the PDF paper",
+    "3. 如果您使用「调用插件xxx」、「修改配置xxx」、「请问」等关键词": "3. If you use keywords such as 'call plugin xxx', 'modify configuration xxx', 'please', etc.",
+    "以下是一篇学术论文的基本信息": "The following is the basic information of an academic paper",
+    "GROBID服务器地址": "GROBID server address",
+    "修改配置": "Modify configuration",
+    "理解PDF文档的内容并进行回答 | 输入参数为路径": "Understand the content of the PDF document and answer | Input parameter is path",
+    "对于需要高级参数的插件": "For plugins that require advanced parameters",
+    "🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行": "Main process execution 🏃‍♂️🏃‍♂️🏃‍♂️",
+    "没有填写 HUGGINGFACE_ACCESS_TOKEN": "HUGGINGFACE_ACCESS_TOKEN not filled in",
+    "调度插件": "Scheduling plugin",
+    "语言模型": "Language model",
+    "├── ADD_WAIFU 加一个live2d装饰": "├── ADD_WAIFU Add a live2d decoration",
+    "初始化": "Initialization",
+    "选择了不存在的插件": "Selected a non-existent plugin",
+    "修改本项目的配置": "Modify the configuration of this project",
+    "如果输入的文件路径是正确的": "If the input file path is correct",
+    "2. 您可以打开插件下拉菜单以了解本项目的各种能力": "2. You can open the plugin dropdown menu to learn about various capabilities of this project",
+    "VoidTerminal插件说明": "VoidTerminal plugin description",
+    "无法理解您的需求": "Unable to understand your requirements",
+    "默认 AdvancedArgs = False": "Default AdvancedArgs = False",
+    "「请问Transformer网络的结构是怎样的": "What is the structure of the Transformer network?",
+    "比如1812.10695": "For example, 1812.10695",
+    "翻译README或MD": "Translate README or MD",
+    "读取新配置中": "Reading new configuration",
+    "假如偏离了您的要求": "If it deviates from your requirements",
+    "├── THEME 色彩主题": "├── THEME color theme",
+    "如果还找不到": "If still not found",
+    "问": "Ask",
+    "请检查系统字体": "Please check system fonts",
+    "如果错误": "If there is an error",
+    "作为替代": "As an alternative",
+    "ParseJavaProject的所有源文件 | 输入参数为路径": "All source files of ParseJavaProject | Input parameter is path",
+    "比对相同参数时生成的url与自己代码生成的url是否一致": "Check if the generated URL matches the one generated by your code when comparing the same parameters",
+    "清除本地缓存数据": "Clear local cache data",
+    "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": "Use Google Scholar search assistant to search for results of a specific URL | Input parameter is the URL of Google Scholar search page",
+    "运行方法": "Running method",
+    "您已经上传了文件**": "You have uploaded the file **",
+    "「给爷翻译Arxiv论文": "Translate Arxiv papers for me",
+    "请修改config中的GROBID_URL": "Please modify GROBID_URL in the config",
+    "处理特殊情况": "Handling special cases",
+    "不要自己瞎搞！」": "Don't mess around by yourself!",
+    "LoadConversationHistoryArchive | 输入参数为路径": "LoadConversationHistoryArchive | Input parameter is a path",
+    "| 输入参数是一个问题": "| Input parameter is a question",
+    "├── CHATBOT_HEIGHT 对话窗的高度": "├── CHATBOT_HEIGHT Height of the chat window",
+    "对C": "To C",
+    "默认关闭": "Default closed",
+    "当前进度": "Current progress",
+    "HUGGINGFACE的TOKEN": "HUGGINGFACE's TOKEN",
+    "查找可用插件中": "Searching for available plugins",
+    "下载LLAMA时起作用 https": "Works when downloading LLAMA https",
+    "使用 AK": "Using AK",
+    "正在执行任务": "Executing task",
+    "保存当前的对话 | 不需要输入参数": "Save current conversation | No input parameters required",
+    "对话": "Conversation",
+    "图中鲜花怒放": "Flowers blooming in the picture",
+    "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": "Batch translate Chinese to English in Markdown files | Input parameter is a path or upload a compressed package",
+    "ParsingCSharpProject的所有源文件 | 输入参数为路径": "ParsingCSharpProject's all source files | Input parameter is a path",
+    "为我翻译PDF论文": "Translate PDF papers for me",
+    "聊天对话": "Chat conversation",
+    "拼接鉴权参数": "Concatenate authentication parameters",
+    "请检查config中的GROBID_URL": "Please check the GROBID_URL in the config",
+    "拼接字符串": "Concatenate strings",
+    "您的意图可以被识别的更准确": "Your intent can be recognized more accurately",
+    "该模型有七个 bin 文件": "The model has seven bin files",
+    "但思路相同": "But the idea is the same",
+    "你需要翻译": "You need to translate",
+    "或者描述文件所在的路径": "Or the path of the description file",
+    "请您上传文件": "Please upload the file",
+    "不常用": "Not commonly used",
+    "尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-": "Experimental plugins that have not been fully tested & plugins that require additional dependencies -=--=-",
+    "⭐ ⭐ ⭐ 选择插件": "⭐ ⭐ ⭐ Select plugin",
+    "当前配置不允许被修改！如需激活本功能": "The current configuration does not allow modification! To activate this feature",
+    "正在连接GROBID服务": "Connecting to GROBID service",
+    "用户图形界面布局依赖关系示意图": "Diagram of user interface layout dependencies",
+    "是否允许通过自然语言描述修改本页的配置": "Allow modifying the configuration of this page through natural language description",
+    "self.chatbot被序列化": "self.chatbot is serialized",
+    "本地Latex论文精细翻译 | 输入参数是路径": "Locally translate Latex papers with fine-grained translation | Input parameter is the path",
+    "抱歉": "Sorry",
+    "以下这部分是最早加入的最稳定的模型 -=-=-=-=-=-=-": "The following section is the earliest and most stable model added",
+    "「用插件翻译README": "Translate README with plugins",
+    "如果不正确": "If incorrect",
+    "⭐ ⭐ ⭐ 读取可配置项目条目": "⭐ ⭐ ⭐ Read configurable project entries",
+    "开始语言对话 | 没有输入参数": "Start language conversation | No input parameters",
+    "谨慎操作 | 不需要输入参数": "Handle with caution | No input parameters required",
+    "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": "Correct the entire English Latex project | Input parameter is the path or upload compressed package",
+    "如果需要处理文件": "If file processing is required",
+    "提供图像的内容": "Provide the content of the image",
+    "查看历史上的今天事件 | 不需要输入参数": "View historical events of today | No input parameters required",
+    "这个稍微啰嗦一点": "This is a bit verbose",
+    "多线程解析并翻译此项目的源码 | 不需要输入参数": "Parse and translate the source code of this project in multi-threading | No input parameters required",
+    "此处打印出建立连接时候的url": "Print the URL when establishing the connection here",
+    "精准翻译PDF论文为中文 | 输入参数为路径": "Translate PDF papers accurately into Chinese | Input parameter is the path",
+    "检测到操作错误！当您上传文档之后": "Operation error detected! After you upload the document",
+    "在线大模型配置关联关系示意图": "Online large model configuration relationship diagram",
+    "你的填写的空间名如grobid": "Your filled space name such as grobid",
+    "获取方法": "Get method",
+    "| 输入参数为路径": "| Input parameter is the path",
+    "⭐ ⭐ ⭐ 执行插件": "⭐ ⭐ ⭐ Execute plugin",
+    "├── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置": "├── ALLOW_RESET_CONFIG Whether to allow modifying the configuration of this page through natural language description",
+    "重新页面即可生效": "Refresh the page to take effect",
+    "设为public": "Set as public",
+    "并在此处指定模型路径": "And specify the model path here",
+    "分析用户意图中": "Analyzing user intent",
+    "刷新下拉列表": "Refresh the drop-down list",
+    "失败 当前语言模型": "Failed current language model",
+    "1. 请用**自然语言**描述您需要做什么": "1. Please describe what you need to do in **natural language**",
+    "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": "Translate the full text of Latex projects from Chinese to English | Input parameter is the path or upload a compressed package",
+    "没有配置BAIDU_CLOUD_API_KEY": "No configuration for BAIDU_CLOUD_API_KEY",
+    "设置默认值": "Set default value",
+    "如果太多了会导致gpt无法理解": "If there are too many, it will cause GPT to be unable to understand",
+    "绿草如茵": "Green grass",
+    "├── LAYOUT 窗口布局": "├── LAYOUT window layout",
+    "用户意图理解": "User intent understanding",
+    "生成RFC1123格式的时间戳": "Generate RFC1123 formatted timestamp",
+    "欢迎您前往Github反馈问题": "Welcome to go to Github to provide feedback",
+    "排除已经是按钮的插件": "Exclude plugins that are already buttons",
+    "亦在下拉菜单中显示": "Also displayed in the dropdown menu",
+    "导致无法反序列化": "Causing deserialization failure",
+    "意图=": "Intent =",
+    "章节": "Chapter",
+    "调用插件": "Invoke plugin",
+    "ParseRustProject的所有源文件 | 输入参数为路径": "All source files of ParseRustProject | Input parameter is path",
+    "需要点击“函数插件区”按钮进行处理": "Need to click the 'Function Plugin Area' button for processing",
+    "默认 AsButton = True": "Default AsButton = True",
+    "收到websocket错误的处理": "Handling websocket errors",
+    "用插件": "Use Plugin",
+    "没有选择任何插件组": "No plugin group selected",
+    "答": "Answer",
+    "可修改成本地GROBID服务": "Can modify to local GROBID service",
+    "用户意图": "User intent",
+    "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": "Polish the full text of English Latex projects | Input parameters are paths or uploaded compressed packages",
+    "「我不喜欢当前的界面颜色": "I don't like the current interface color",
+    "「请调用插件": "Please call the plugin",
+    "VoidTerminal状态": "VoidTerminal status",
+    "新配置": "New configuration",
+    "支持Github链接": "Support Github links",
+    "没有配置BAIDU_CLOUD_SECRET_KEY": "No BAIDU_CLOUD_SECRET_KEY configured",
+    "获取当前VoidTerminal状态": "Get the current VoidTerminal status",
+    "刷新按钮": "Refresh button",
+    "为了防止pickle.dumps": "To prevent pickle.dumps",
+    "放弃治疗": "Give up treatment",
+    "可指定不同的生成长度、top_p等相关超参": "Can specify different generation lengths, top_p and other related hyperparameters",
+    "请将题目和摘要翻译为": "Translate the title and abstract",
+    "通过appid和用户的提问来生成请参数": "Generate request parameters through appid and user's question",
+    "ImageGeneration | 输入参数字符串": "ImageGeneration | Input parameter string",
+    "将文件拖动到文件上传区": "Drag and drop the file to the file upload area",
+    "如果意图模糊": "If the intent is ambiguous",
+    "星火认知大模型": "Spark Cognitive Big Model",
+    "默认 Color = secondary": "Default Color = secondary",
+    "此处也不需要修改": "No modification is needed here",
+    "⭐ ⭐ ⭐ 分析用户意图": "⭐ ⭐ ⭐ Analyze user intent",
+    "再试一次": "Try again",
+    "请写bash命令实现以下功能": "Please write a bash command to implement the following function",
+    "批量SummarizingWordDocuments | 输入参数为路径": "Batch SummarizingWordDocuments | Input parameter is the path",
+    "/Users/fuqingxu/Desktop/旧文件/gpt/chatgpt_academic/crazy_functions/latex_fns中的python文件进行解析": "Parse the python file in /Users/fuqingxu/Desktop/旧文件/gpt/chatgpt_academic/crazy_functions/latex_fns",
+    "当我要求你写bash命令时": "When I ask you to write a bash command",
+    "├── AUTO_CLEAR_TXT 是否在提交时自动清空输入框": "├── AUTO_CLEAR_TXT Whether to automatically clear the input box when submitting",
+    "按停止键终止": "Press the stop key to terminate",
+    "文心一言": "Original text",
+    "不能理解您的意图": "Cannot understand your intention",
+    "用简单的关键词检测用户意图": "Detect user intention with simple keywords",
+    "中文": "Chinese",
+    "解析一个C++项目的所有源文件": "Parse all source files of a C++ project",
+    "请求的Prompt为": "Requested prompt is",
+    "参考本demo的时候可取消上方打印的注释": "You can remove the comments above when referring to this demo",
+    "开始接收回复": "Start receiving replies",
+    "接入讯飞星火大模型 https": "Access to Xunfei Xinghuo large model https",
+    "用该压缩包进行反馈": "Use this compressed package for feedback",
+    "翻译Markdown或README": "Translate Markdown or README",
+    "SK 生成鉴权签名": "SK generates authentication signature",
+    "插件参数": "Plugin parameters",
+    "需要访问中文Bing": "Need to access Chinese Bing",
+    "ParseFrontendProject的所有源文件": "Parse all source files of ParseFrontendProject",
+    "现在将执行效果稍差的旧版代码": "Now execute the older version code with slightly worse performance",
+    "您需要明确说明并在指令中提到它": "You need to specify and mention it in the command",
+    "请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件": "Please set ALLOW_RESET_CONFIG=True in config.py and restart the software",
+    "按照自然语言描述生成一个动画 | 输入参数是一段话": "Generate an animation based on natural language description | Input parameter is a sentence",
+    "你的hf用户名如qingxu98": "Your hf username is qingxu98",
+    "Arixv论文精细翻译 | 输入参数arxiv论文的ID": "Fine translation of Arixv paper | Input parameter is the ID of arxiv paper",
+    "无法获取 abstract": "Unable to retrieve abstract",
+    "尽可能地仅用一行命令解决我的要求": "Try to solve my request using only one command",
+    "提取插件参数": "Extract plugin parameters",
+    "配置修改完成": "Configuration modification completed",
+    "正在修改配置中": "Modifying configuration",
+    "ParsePythonProject的所有源文件": "All source files of ParsePythonProject",
+    "请求错误": "Request error",
+    "精准翻译PDF论文": "Accurate translation of PDF paper",
+    "无法获取 authors": "Unable to retrieve authors",
+    "该插件诞生时间不长": "This plugin has not been around for long",
+    "返回项目根路径": "Return project root path",
+    "BatchSummarizePDFDocuments的内容 | 输入参数为路径": "Content of BatchSummarizePDFDocuments | Input parameter is a path",
+    "百度千帆": "Baidu Qianfan",
+    "解析一个C++项目的所有头文件": "Parse all header files of a C++ project",
+    "现在请您描述您的需求": "Now please describe your requirements",
+    "该功能具有一定的危险性": "This feature has a certain level of danger",
+    "收到websocket关闭的处理": "Processing when receiving websocket closure",
+    "读取Tex论文并写摘要 | 输入参数为路径": "Read Tex paper and write abstract | Input parameter is the path",
+    "地址为https": "The address is https",
+    "限制最多前10个配置项": "Limit up to 10 configuration items",
+    "6. 如果不需要上传文件": "6. If file upload is not needed",
+    "默认 Group = 对话": "Default Group = Conversation",
+    "五秒后即将重启！若出现报错请无视即可": "Restarting in five seconds! Please ignore if there is an error",
+    "收到websocket连接建立的处理": "Processing when receiving websocket connection establishment",
+    "批量生成函数的注释 | 输入参数为路径": "Batch generate function comments | Input parameter is the path",
+    "聊天": "Chat",
+    "但您可以尝试再试一次": "But you can try again",
+    "千帆大模型平台": "Qianfan Big Model Platform",
+    "直接运行 python tests/test_plugins.py": "Run python tests/test_plugins.py directly",
+    "或是None": "Or None",
+    "进行hmac-sha256进行加密": "Perform encryption using hmac-sha256",
+    "批量总结音频或视频 | 输入参数为路径": "Batch summarize audio or video | Input parameter is path",
+    "插件在线服务配置依赖关系示意图": "Plugin online service configuration dependency diagram",
+    "开始初始化模型": "Start initializing model",
+    "弱模型可能无法理解您的想法": "Weak model may not understand your ideas",
+    "解除大小写限制": "Remove case sensitivity restriction",
+    "跳过提示环节": "Skip prompt section",
+    "接入一些逆向工程https": "Access some reverse engineering https",
+    "执行完成": "Execution completed",
+    "如果需要配置": "If configuration is needed",
+    "此处不修改；如果使用本地或无地域限制的大模型时": "Do not modify here; if using local or region-unrestricted large models",
+    "你是一个Linux大师级用户": "You are a Linux master-level user",
+    "arxiv论文的ID是1812.10695": "The ID of the arxiv paper is 1812.10695",
+    "而不是点击“提交”按钮": "Instead of clicking the 'Submit' button",
+    "解析一个Go项目的所有源文件 | 输入参数为路径": "Parse all source files of a Go project | Input parameter is path",
+    "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": "Polish the entire text of a Chinese Latex project | Input parameter is path or upload compressed package",
+    "「生成一张图片": "Generate an image",
+    "将Markdown或README翻译为中文 | 输入参数为路径或URL": "Translate Markdown or README to Chinese | Input parameters are path or URL",
+    "训练时间": "Training time",
+    "将请求的鉴权参数组合为字典": "Combine the requested authentication parameters into a dictionary",
+    "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": "Translate the entire text of Latex project from English to Chinese | Input parameters are path or uploaded compressed package",
+    "内容如下": "The content is as follows",
+    "用于高质量地读取PDF文档": "Used for high-quality reading of PDF documents",
+    "上下文太长导致 token 溢出": "The context is too long, causing token overflow",
+    "├── DARK_MODE 暗色模式 / 亮色模式": "├── DARK_MODE Dark mode / Light mode",
+    "语言模型回复为": "The language model replies as",
+    "from crazy_functions.chatglm微调工具 import 微调数据集生成": "from crazy_functions.chatglm fine-tuning tool import fine-tuning dataset generation",
+    "为您选择了插件": "Selected plugin for you",
+    "无法获取 title": "Unable to get title",
+    "收到websocket消息的处理": "Processing of received websocket messages",
+    "2023年": "2023",
+    "清除所有缓存文件": "Clear all cache files",
+    "├── PDF文档精准解析": "├── Accurate parsing of PDF documents",
+    "论文我刚刚放到上传区了": "I just put the paper in the upload area",
+    "生成url": "Generate URL",
+    "以下部分是新加入的模型": "The following section is the newly added model",
+    "学术": "Academic",
+    "├── DEFAULT_FN_GROUPS 插件分类默认选项": "├── DEFAULT_FN_GROUPS Plugin classification default options",
+    "不推荐使用": "Not recommended for use",
+    "正在同时咨询": "Consulting simultaneously",
+    "将Markdown翻译为中文 | 输入参数为路径或URL": "Translate Markdown to Chinese | Input parameters are path or URL",
+    "Github网址是https": "The Github URL is https",
+    "试着加上.tex后缀试试": "Try adding the .tex suffix",
+    "对项目中的各个插件进行测试": "Test each plugin in the project",
+    "插件说明": "Plugin description",
+    "├── CODE_HIGHLIGHT 代码高亮": "├── CODE_HIGHLIGHT Code highlighting",
+    "记得用插件": "Remember to use the plugin",
+    "谨慎操作": "Handle with caution",
+    "private_upload里面的文件名在解压zip后容易出现乱码": "The file name inside private_upload is prone to garbled characters after unzipping",
+    "直接返回报错": "Direct return error",
+    "临时的上传文件夹位置": "Temporary upload folder location",
+    "使用latex格式 测试3 写出麦克斯韦方程组": "Write Maxwell's equations using latex format for test 3",
+    "这是一张图片": "This is an image",
+    "没有发现任何近期上传的文件": "No recent uploaded files found",
+    "如url未成功匹配返回None": "Return None if the URL does not match successfully",
+    "如果有Latex环境": "If there is a Latex environment",
+    "第一次运行时": "When running for the first time",
+    "创建工作路径": "Create a working directory",
+    "向": "To",
+    "执行中. 删除数据": "Executing. Deleting data",
+    "CodeInterpreter开源版": "CodeInterpreter open source version",
+    "建议选择更稳定的接口": "It is recommended to choose a more stable interface",
+    "现在您点击任意函数插件时": "Now when you click on any function plugin",
+    "请使用“LatexEnglishCorrection+高亮”插件": "Please use the 'LatexEnglishCorrection+Highlight' plugin",
+    "安装完成": "Installation completed",
+    "记得用插件！」": "Remember to use the plugin!",
+    "结论": "Conclusion",
+    "无法下载资源": "Unable to download resources",
+    "首先排除一个one-api没有done数据包的第三方Bug情形": "First exclude a third-party bug where one-api does not have a done data package",
+    "知识库中添加文件": "Add files to the knowledge base",
+    "处理重名的章节": "Handling duplicate chapter names",
+    "先上传文件素材": "Upload file materials first",
+    "无法从google获取信息！": "Unable to retrieve information from Google!",
+    "展示如下": "Display as follows",
+    "「把Arxiv论文翻译成中文PDF": "Translate Arxiv papers into Chinese PDF",
+    "论文我刚刚放到上传区了」": "I just put the paper in the upload area",
+    "正在下载Gradio主题": "Downloading Gradio themes",
+    "再运行此插件": "Run this plugin again",
+    "记录近期文件": "Record recent files",
+    "粗心检查": "Careful check",
+    "更多主题": "More themes",
+    "//huggingface.co/spaces/gradio/theme-gallery 可选": "//huggingface.co/spaces/gradio/theme-gallery optional",
+    "由 test_on_result_chg": "By test_on_result_chg",
+    "所有问询记录将自动保存在本地目录./": "All inquiry records will be automatically saved in the local directory ./",
+    "正在解析论文": "Analyzing the paper",
+    "逐个文件转移到目标路径": "Move each file to the target path",
+    "最多重试5次": "Retry up to 5 times",
+    "日志文件夹的位置": "Location of the log folder",
+    "我们暂时无法解析此PDF文档": "We are temporarily unable to parse this PDF document",
+    "文件检索": "File retrieval",
+    "/**/chatGPT对话历史*.html": "/**/chatGPT conversation history*.html",
+    "非OpenAI官方接口返回了错误": "Non-OpenAI official interface returned an error",
+    "如果在Arxiv上匹配失败": "If the match fails on Arxiv",
+    "文件进入知识库后可长期保存": "Files can be saved for a long time after entering the knowledge base",
+    "您可以再次重试": "You can try again",
+    "整理文件集合": "Organize file collection",
+    "检测到有缺陷的非OpenAI官方接口": "Detected defective non-OpenAI official interface",
+    "此插件不调用Latex": "This plugin does not call Latex",
+    "移除过时的旧文件从而节省空间&保护隐私": "Remove outdated old files to save space & protect privacy",
+    "代码我刚刚打包拖到上传区了」": "I just packed the code and dragged it to the upload area",
+    "将图像转为灰度图像": "Convert the image to grayscale",
+    "待排除": "To be excluded",
+    "请勿修改": "Please do not modify",
+    "crazy_functions/代码重写为全英文_多线程.py": "crazy_functions/code rewritten to all English_multi-threading.py",
+    "开发中": "Under development",
+    "请查阅Gradio主题商店": "Please refer to the Gradio theme store",
+    "输出消息": "Output message",
+    "其他情况": "Other situations",
+    "获取文献失败": "Failed to retrieve literature",
+    "可以通过再次调用本插件的方式": "You can use this plugin again by calling it",
+    "保留下半部分": "Keep the lower half",
+    "排除问题": "Exclude the problem",
+    "知识库": "Knowledge base",
+    "ParsePDF失败": "ParsePDF failed",
+    "向知识库追加更多文档": "Append more documents to the knowledge base",
+    "此处待注入的知识库名称id": "The knowledge base name ID to be injected here",
+    "您需要构建知识库后再运行此插件": "You need to build the knowledge base before running this plugin",
+    "判定是否为公式 | 测试1 写出洛伦兹定律": "Determine whether it is a formula | Test 1 write out the Lorentz law",
+    "构建知识库后": "After building the knowledge base"
 }
--- a/docs/translate_japanese.json
+++ b/docs/translate_japanese.json
@@ -301,7 +301,6 @@
    "缺少的依赖": "不足している依存関係",
    "紫色": "紫色",
    "唤起高级参数输入区": "高度なパラメータ入力エリアを呼び出す",
-    "所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log": "すべての問い合わせ記録は自動的にローカルディレクトリ./gpt_log/chat_secrets.logに保存されます",
    "则换行符更有可能表示段落分隔": "したがって、改行記号は段落の区切りを表す可能性がより高いです",
    "；4、引用数量": "；4、引用数量",
    "中转网址预览": "中継ウェブサイトのプレビュー",
@@ -448,7 +447,6 @@
    "表示函数是否成功执行": "関数が正常に実行されたかどうかを示す",
    "一般原样传递下去就行": "通常はそのまま渡すだけでよい",
    "琥珀色": "琥珀色",
-    "gpt_log/**/chatGPT对话历史*.html": "gpt_log/**/chatGPT対話履歴*.html",
    "jittorllms 没有 sys_prompt 接口": "jittorllmsにはsys_promptインターフェースがありません",
    "清除": "クリア",
    "小于正文的": "本文より小さい",
@@ -1234,7 +1232,6 @@
    "找不到任何前端相关文件": "No frontend-related files can be found",
    "Not enough point. API2D账户点数不足": "Not enough points. API2D account points are insufficient",
    "当前版本": "Current version",
-    "/gpt_log/总结论文-": "/gpt_log/Summary paper-",
    "1. 临时解决方案": "1. Temporary solution",
    "第8步": "Step 8",
    "历史": "History",
--- a/docs/translate_std.json
+++ b/docs/translate_std.json
@@ -83,5 +83,12 @@
    "图片生成": "ImageGeneration",
    "动画生成": "AnimationGeneration",
    "语音助手": "VoiceAssistant",
-    "启动微调": "StartFineTuning"
+    "启动微调": "StartFineTuning",
+    "清除缓存": "ClearCache",
+    "辅助功能": "Accessibility",
+    "虚空终端": "VoidTerminal",
+    "解析PDF_基于GROBID": "ParsePDF_BasedOnGROBID",
+    "虚空终端主路由": "VoidTerminalMainRoute",
+    "批量翻译PDF文档_NOUGAT": "BatchTranslatePDFDocuments_NOUGAT",
+    "解析PDF_基于NOUGAT": "ParsePDF_NOUGAT"
 }
--- a/docs/translate_traditionalchinese.json
+++ b/docs/translate_traditionalchinese.json
@@ -314,7 +314,6 @@
    "请用markdown格式输出": "請用 Markdown 格式輸出",
    "模仿ChatPDF": "模仿 ChatPDF",
    "等待多久判定为超时": "等待多久判定為超時",
-    "/gpt_log/总结论文-": "/gpt_log/總結論文-",
    "请结合互联网信息回答以下问题": "請結合互聯網信息回答以下問題",
    "IP查询频率受限": "IP查詢頻率受限",
    "高级参数输入区的显示提示": "高級參數輸入區的顯示提示",
@@ -511,7 +510,6 @@
    "將生成的報告自動投射到文件上傳區": "將生成的報告自動上傳到文件區",
    "函數插件作者": "函數插件作者",
    "將要匹配的模式": "將要匹配的模式",
-    "所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log": "所有詢問記錄將自動保存在本地目錄./gpt_log/chat_secrets.log",
    "正在分析一个项目的源代码": "正在分析一個專案的源代碼",
    "使每个段落之间有两个换行符分隔": "使每個段落之間有兩個換行符分隔",
    "并在被装饰的函数上执行": "並在被裝飾的函數上執行",
@@ -1059,7 +1057,6 @@
    "重试中": "重試中",
    "月": "月份",
    "localhost意思是代理软件安装在本机上": "localhost意思是代理軟體安裝在本機上",
-    "gpt_log/**/chatGPT对话历史*.html": "gpt_log/**/chatGPT對話歷史*.html",
    "的长度必须小于 2500 个 Token": "長度必須小於 2500 個 Token",
    "抽取可用的api-key": "提取可用的api-key",
    "增强报告的可读性": "增強報告的可讀性",
--- a/main.py
+++ b/main.py
@@ -6,27 +6,27 @@ def main():
    from request_llm.bridge_all import predict
    from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-    proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
-        get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
-    ENABLE_AUDIO, AUTO_CLEAR_TXT = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT')
+    proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
+    CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
+    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING')
+
    # 如果WEB_PORT是-1, 则随机选取WEB端口
    PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
-    if not AUTHENTICATION: AUTHENTICATION = None
-
    from check_proxy import get_current_version
    from themes.theme import adjust_theme, advanced_css, theme_declaration
    initial_prompt = "Serve me as a writing and programming assistant."
    title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
-    description =  """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)"""
+    description =  "代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic)，"
+    description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)"

    # 问询记录, python 版本建议3.9+（越新越好）
    import logging, uuid
-    os.makedirs("gpt_log", exist_ok=True)
-    try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    os.makedirs(PATH_LOGGING, exist_ok=True)
+    try:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    except:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
    # Disable logging output from the 'httpx' logger
    logging.getLogger("httpx").setLevel(logging.WARNING)
-    print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
+    print(f"所有问询记录将自动保存在本地目录./{PATH_LOGGING}/chat_secrets.log, 请注意自我隐私保护哦！")

    # 一些普通功能模块
    from core_functional import get_core_functions
@@ -34,7 +34,10 @@ def main():

    # 高级函数插件
    from crazy_functional import get_crazy_functions
-    crazy_fns = get_crazy_functions()
+    DEFAULT_FN_GROUPS, = get_conf('DEFAULT_FN_GROUPS')
+    plugins = get_crazy_functions()
+    all_plugin_groups = list(set([g for _, plugin in plugins.items() for g in plugin['Group'].split('|')]))
+    match_group = lambda tags, groups: any([g in groups for g in tags.split('|')])

    # 处理markdown文本格式的转变
    gr.Chatbot.postprocess = format_io
@@ -83,25 +86,33 @@ def main():
                            if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
                            variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
                            functional[k]["Button"] = gr.Button(k, variant=variant)
+                            functional[k]["Button"].style(size="sm")
                with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
                    with gr.Row():
                        gr.Markdown("插件可读取“输入区”文本/路径作为参数（上传文件自动修正路径）")
+                    with gr.Row(elem_id="input-plugin-group"):
+                        plugin_group_sel = gr.Dropdown(choices=all_plugin_groups, label='', show_label=False, value=DEFAULT_FN_GROUPS, 
+                                                      multiselect=True, interactive=True, elem_classes='normal_mut_select').style(container=False)
                    with gr.Row():
-                        for k in crazy_fns:
-                            if not crazy_fns[k].get("AsButton", True): continue
-                            variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
-                            crazy_fns[k]["Button"] = gr.Button(k, variant=variant)
-                            crazy_fns[k]["Button"].style(size="sm")
+                        for k, plugin in plugins.items():
+                            if not plugin.get("AsButton", True): continue
+                            visible = True if match_group(plugin['Group'], DEFAULT_FN_GROUPS) else False
+                            variant = plugins[k]["Color"] if "Color" in plugin else "secondary"
+                            plugin['Button'] = plugins[k]['Button'] = gr.Button(k, variant=variant, visible=visible).style(size="sm")
                    with gr.Row():
                        with gr.Accordion("更多函数插件", open=True):
-                            dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
+                            dropdown_fn_list = []
+                            for k, plugin in plugins.items():
+                                if not match_group(plugin['Group'], DEFAULT_FN_GROUPS): continue
+                                if not plugin.get("AsButton", True): dropdown_fn_list.append(k)     # 排除已经是按钮的插件
+                                elif plugin.get('AdvancedArgs', False): dropdown_fn_list.append(k)  # 对于需要高级参数的插件，亦在下拉菜单中显示
                            with gr.Row():
                                dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="", show_label=False).style(container=False)
                            with gr.Row():
                                plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False, 
                                                                 placeholder="这里是特殊函数插件的高级参数输入区").style(container=False)
                            with gr.Row():
-                                switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
+                                switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary").style(size="sm")
                    with gr.Row():
                        with gr.Accordion("点击展开“文件上传区”。上传本地文件/压缩包供函数插件调用。", open=False) as area_file_up:
                            file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
@@ -112,7 +123,15 @@ def main():
                    max_length_sl = gr.Slider(minimum=256, maximum=8192, value=4096, step=1, interactive=True, label="Local LLM MaxLength",)
                    checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
                    md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
-
+                    dark_mode_btn = gr.Button("Toggle Dark Mode ☀", variant="secondary").style(size="sm")
+                    dark_mode_btn.click(None, None, None, _js="""() => {
+                            if (document.querySelectorAll('.dark').length) {
+                                document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
+                            } else {
+                                document.querySelector('body').classList.add('dark');
+                            }
+                        }""",
+                    )
                    gr.Markdown(description)
                with gr.Accordion("备选输入区", open=True, visible=False, elem_id="input-panel2") as area_input_secondary:
                    with gr.Row():
@@ -123,6 +142,7 @@ def main():
                        resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm")
                        stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
                        clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
+
        # 功能区显示开关与功能区的互动
        def fn_area_visibility(a):
            ret = {}
@@ -139,7 +159,7 @@ def main():
        # 整理反复出现的控件句柄组合
        input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
        output_combo = [cookies, chatbot, history, status]
-        predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
+        predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True)], outputs=output_combo)
        # 提交按钮、重置按钮
        cancel_handles.append(txt.submit(**predict_args))
        cancel_handles.append(txt2.submit(**predict_args))
@@ -160,19 +180,19 @@ def main():
            click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
            cancel_handles.append(click_handle)
        # 文件上传区，接收文件后与chatbot的互动
-        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2])
+        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
        # 函数插件-固定按钮区
-        for k in crazy_fns:
-            if not crazy_fns[k].get("AsButton", True): continue
-            click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo)
+        for k in plugins:
+            if not plugins[k].get("AsButton", True): continue
+            click_handle = plugins[k]["Button"].click(ArgsGeneralWrapper(plugins[k]["Function"]), [*input_combo], output_combo)
            click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
            cancel_handles.append(click_handle)
        # 函数插件-下拉菜单与随变按钮的互动
        def on_dropdown_changed(k):
-            variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
+            variant = plugins[k]["Color"] if "Color" in plugins[k] else "secondary"
            ret = {switchy_bt: gr.update(value=k, variant=variant)}
-            if crazy_fns[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
-                ret.update({plugin_advanced_arg: gr.update(visible=True,  label=f"插件[{k}]的高级参数说明：" + crazy_fns[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
+            if plugins[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
+                ret.update({plugin_advanced_arg: gr.update(visible=True,  label=f"插件[{k}]的高级参数说明：" + plugins[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
            else:
                ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")})
            return ret
@@ -183,13 +203,26 @@ def main():
        # 随变按钮的回调函数注册
        def route(request: gr.Request, k, *args, **kwargs):
            if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
-            yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(request, *args, **kwargs)
-        click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
+            yield from ArgsGeneralWrapper(plugins[k]["Function"])(request, *args, **kwargs)
+        click_handle = switchy_bt.click(route,[switchy_bt, *input_combo], output_combo)
        click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
        cancel_handles.append(click_handle)
        # 终止按钮的回调函数注册
        stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
        stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
+        plugins_as_btn = {name:plugin for name, plugin in plugins.items() if plugin.get('Button', None)}
+        def on_group_change(group_list):
+            btn_list = []
+            fns_list = []
+            if not group_list: # 处理特殊情况：没有选择任何插件组
+                return [*[plugin['Button'].update(visible=False) for _, plugin in plugins_as_btn.items()], gr.Dropdown.update(choices=[])]
+            for k, plugin in plugins.items():
+                if plugin.get("AsButton", True): 
+                    btn_list.append(plugin['Button'].update(visible=match_group(plugin['Group'], group_list))) # 刷新按钮
+                    if plugin.get('AdvancedArgs', False): dropdown_fn_list.append(k) # 对于需要高级参数的插件，亦在下拉菜单中显示
+                elif match_group(plugin['Group'], group_list): fns_list.append(k) # 刷新下拉列表
+            return [*btn_list, gr.Dropdown.update(choices=fns_list)]
+        plugin_group_sel.select(fn=on_group_change, inputs=[plugin_group_sel], outputs=[*[plugin['Button'] for name, plugin in plugins_as_btn.items()], dropdown])
        if ENABLE_AUDIO: 
            from crazy_functions.live_audio.audio_io import RealtimeAudioDistribution
            rad = RealtimeAudioDistribution()
@@ -221,8 +254,10 @@ def main():

    auto_opentab_delay()
    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-        server_name="0.0.0.0", server_port=PORT,
-        favicon_path="docs/logo.png", auth=AUTHENTICATION,
+        server_name="0.0.0.0", 
+        server_port=PORT,
+        favicon_path="docs/logo.png", 
+        auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None,
        blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])

    # 如果需要在二级路径下运行
--- a/multi_language.py
+++ b/multi_language.py
@@ -33,9 +33,11 @@ import functools
 import re
 import pickle
 import time
+from toolbox import get_conf

-CACHE_FOLDER = "gpt_log"
-blacklist = ['multi-language', 'gpt_log', '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv']
+CACHE_FOLDER, = get_conf('PATH_LOGGING')
+
+blacklist = ['multi-language', CACHE_FOLDER, '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv']

 # LANG = "TraditionalChinese"
 # TransPrompt = f"Replace each json value `#` with translated results in Traditional Chinese, e.g., \"原始文本\":\"翻譯後文字\". Keep Json format. Do not answer #."
@@ -478,6 +480,8 @@ def step_2_core_key_translate():
    up = trans_json(need_translate, language=LANG, special=False)
    map_to_json(up, language=LANG)
    cached_translation = read_map_from_json(language=LANG)
+    LANG_STD = 'std'
+    cached_translation.update(read_map_from_json(language=LANG_STD))
    cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0])))

    # ===============================================
--- a/request_llm/bridge_all.py
+++ b/request_llm/bridge_all.py
@@ -19,6 +19,12 @@ from .bridge_chatgpt import predict as chatgpt_ui
 from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
 from .bridge_chatglm import predict as chatglm_ui

+from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
+from .bridge_chatglm import predict as chatglm_ui
+
+from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
+from .bridge_qianfan import predict as qianfan_ui
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']

 class LazyloadTiktoken(object):
@@ -165,7 +171,14 @@ model_info = {
        "tokenizer": tokenizer_gpt35,
        "token_cnt": get_token_num_gpt35,
    },
-
+    "qianfan": {
+        "fn_with_ui": qianfan_ui,
+        "fn_without_ui": qianfan_noui,
+        "endpoint": None,
+        "max_token": 2000,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 }

 # -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
@@ -361,7 +374,7 @@ if "chatgpt_website" in AVAIL_LLM_MODELS:   # 接入一些逆向工程https://gi
            "chatgpt_website": {
                "fn_with_ui": chatgpt_website_ui,
                "fn_without_ui": chatgpt_website_noui,
-                "endpoint": None,
+                "endpoint": openai_endpoint,
                "max_token": 4096,
                "tokenizer": tokenizer_gpt35,
                "token_cnt": get_token_num_gpt35,
@@ -385,6 +398,38 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
        })
    except:
        print(trimmed_format_exc())
+if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+    try:
+        from .bridge_spark import predict_no_ui_long_connection as spark_noui
+        from .bridge_spark import predict as spark_ui
+        model_info.update({
+            "sparkv2": {
+                "fn_with_ui": spark_ui,
+                "fn_without_ui": spark_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
+if "llama2" in AVAIL_LLM_MODELS:   # llama2
+    try:
+        from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
+        from .bridge_llama2 import predict as llama2_ui
+        model_info.update({
+            "llama2": {
+                "fn_with_ui": llama2_ui,
+                "fn_without_ui": llama2_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())



--- a/request_llm/bridge_chatglmft.py
+++ b/request_llm/bridge_chatglmft.py
@@ -63,9 +63,9 @@ class GetGLMFTHandle(Process):
                    # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
                    # with open(conf, 'r', encoding='utf8') as f:
                    #     model_args = json.loads(f.read())
-                    ChatGLM_PTUNING_CHECKPOINT, = get_conf('ChatGLM_PTUNING_CHECKPOINT')
-                    assert os.path.exists(ChatGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
-                    conf = os.path.join(ChatGLM_PTUNING_CHECKPOINT, "config.json")
+                    CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT')
+                    assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
+                    conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
                    with open(conf, 'r', encoding='utf8') as f:
                        model_args = json.loads(f.read())
                    if 'model_name_or_path' not in model_args:
@@ -78,9 +78,9 @@ class GetGLMFTHandle(Process):
                    config.pre_seq_len = model_args['pre_seq_len']
                    config.prefix_projection = model_args['prefix_projection']

-                    print(f"Loading prefix_encoder weight from {ChatGLM_PTUNING_CHECKPOINT}")
+                    print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
                    model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
-                    prefix_state_dict = torch.load(os.path.join(ChatGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
+                    prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
                    new_prefix_state_dict = {}
                    for k, v in prefix_state_dict.items():
                        if k.startswith("transformer.prefix_encoder."):
--- a/request_llm/bridge_chatgpt.py
+++ b/request_llm/bridge_chatgpt.py
@@ -21,7 +21,7 @@ import importlib

 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')

@@ -72,6 +72,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",

    stream_response =  response.iter_lines()
    result = ''
+    json_data = None
    while True:
        try: chunk = next(stream_response).decode()
        except StopIteration: 
@@ -90,20 +91,21 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
        delta = json_data["delta"]
        if len(delta) == 0: break
        if "role" in delta: continue
-        if "content" in delta: 
+        if "content" in delta:
            result += delta["content"]
            if not console_slience: print(delta["content"], end='')
            if observe_window is not None: 
                # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += delta["content"]
+                if len(observe_window) >= 1:
+                    observe_window[0] += delta["content"]
                # 看门狗，如果超过期限没有喂狗，则终止
-                if len(observe_window) >= 2:  
+                if len(observe_window) >= 2:
                    if (time.time()-observe_window[1]) > watch_dog_patience:
                        raise RuntimeError("用户取消了程序。")
        else: raise RuntimeError("意外Json结构："+delta)
-    if json_data['finish_reason'] == 'content_filter':
+    if json_data and json_data['finish_reason'] == 'content_filter':
        raise RuntimeError("由于提问含不合规内容被Azure过滤。")
-    if json_data['finish_reason'] == 'length':
+    if json_data and json_data['finish_reason'] == 'length':
        raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
    return result

@@ -128,6 +130,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
        yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
        return

+    user_input = inputs
    if additional_fn is not None:
        from core_functional import handle_core_functionality
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
@@ -137,6 +140,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
    chatbot.append((inputs, ""))
    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面

+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
    try:
        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
    except RuntimeError as e:
@@ -173,18 +182,22 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
                chunk_decoded = chunk.decode()
                error_msg = chunk_decoded
+                # 首先排除一个one-api没有done数据包的第三方Bug情形
+                if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0: 
+                    yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
+                    break
+                # 其他情况，直接返回报错
                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
+                yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
                return
            
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
+            chunk_decoded = chunk.decode()
+            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
                # 数据流的第一帧不携带content
                is_head_of_the_stream = False; continue
            
            if chunk:
                try:
-                    chunk_decoded = chunk.decode()
                    # 前者是API2D的结束条件，后者是OPENAI的结束条件
                    if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
                        # 判定为数据流的结束，gpt_replying_buffer也写完了
@@ -192,9 +205,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                        break
                    # 处理数据流的主体
                    chunkjson = json.loads(chunk_decoded[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
+                    status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
-                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk_decoded[6:])['choices'][0]["delta"]["content"]
+                    gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
                    history[-1] = gpt_replying_buffer
                    chatbot[-1] = (history[-2], history[-1])
                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
@@ -216,7 +229,6 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
-                        # history = []    # 清除历史
    elif "does not exist" in error_msg:
        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
    elif "Incorrect API key" in error_msg:
--- a/request_llm/bridge_chatgpt_website.py
+++ b/request_llm/bridge_chatgpt_website.py
@@ -118,16 +118,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
    additional_fn代表点击的哪个按钮，按钮见functional.py
    """
-    if is_any_api_key(inputs):
-        chatbot._cookies['api_key'] = inputs
-        chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
-        yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
-        return
-    elif not is_any_api_key(chatbot._cookies['api_key']):
-        chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
-        yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
-        return
-
    if additional_fn is not None:
        from core_functional import handle_core_functionality
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
@@ -245,14 +235,9 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
    if not is_any_api_key(llm_kwargs['api_key']):
        raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")

-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-
    headers = {
        "Content-Type": "application/json",
-        "Authorization": f"Bearer {api_key}"
    }
-    if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
-    if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})

    conversation_cnt = len(history) // 2

--- a/request_llm/bridge_llama2.py
+++ b/request_llm/bridge_llama2.py
@@ -0,0 +1,91 @@
+model_name = "LLaMA"
+cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from toolbox import update_ui, get_conf, ProxyNetworkActivate
+from multiprocessing import Process, Pipe
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from threading import Thread
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+@SingletonLocalLLM
+class GetONNXGLMHandle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        import os, glob
+        import os
+        import platform
+        huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
+        assert len(huggingface_token) != 0, "没有填写 HUGGINGFACE_ACCESS_TOKEN"
+        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
+            f.write(huggingface_token)
+        model_id = 'meta-llama/Llama-2-7b-chat-hf'
+        with ProxyNetworkActivate():
+            self._tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=huggingface_token)
+            # use fp16
+            model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=huggingface_token).eval()
+            if device.startswith('cuda'): model = model.half().to(device)
+            self._model = model
+
+            return self._model, self._tokenizer
+
+    def llm_stream_generator(self, **kwargs):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        def adaptor(kwargs):
+            query = kwargs['query']
+            max_length = kwargs['max_length']
+            top_p = kwargs['top_p']
+            temperature = kwargs['temperature']
+            history = kwargs['history']
+            console_slience = kwargs.get('console_slience', True)
+            return query, max_length, top_p, temperature, history, console_slience
+        
+        def convert_messages_to_prompt(query, history):
+            prompt = ""
+            for a, b in history:
+                prompt += f"\n[INST]{a}[/INST]"
+                prompt += "\n{b}" + b
+            prompt += f"\n[INST]{query}[/INST]"
+            return prompt
+        
+        query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
+        prompt = convert_messages_to_prompt(query, history)
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        # code from transformers.llama
+        streamer = TextIteratorStreamer(self._tokenizer)
+        # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
+        inputs = self._tokenizer([prompt], return_tensors="pt")
+        prompt_tk_back = self._tokenizer.batch_decode(inputs['input_ids'])[0]
+
+        generation_kwargs = dict(inputs.to(self._model.device), streamer=streamer, max_new_tokens=max_length)
+        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
+        thread.start()
+        generated_text = ""
+        for new_text in streamer: 
+            generated_text += new_text
+            if not console_slience: print(new_text, end='')
+            yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
+        if not console_slience: print()
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        
+    def try_to_import_special_deps(self, **kwargs):
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        import importlib
+        importlib.import_module('transformers')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name)
--- a/request_llm/bridge_qianfan.py
+++ b/request_llm/bridge_qianfan.py
@@ -0,0 +1,165 @@
+
+import time, requests, json
+from multiprocessing import Process, Pipe
+from functools import wraps
+from datetime import datetime, timedelta
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, get_conf
+
+model_name = '千帆大模型平台'
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+
+def cache_decorator(timeout):
+    cache = {}
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            key = (func.__name__, args, frozenset(kwargs.items()))
+            # Check if result is already cached and not expired
+            if key in cache:
+                result, timestamp = cache[key]
+                if datetime.now() - timestamp < timedelta(seconds=timeout):
+                    return result
+
+            # Call the function and cache the result
+            result = func(*args, **kwargs)
+            cache[key] = (result, datetime.now())
+            return result
+        return wrapper
+    return decorator
+
+@cache_decorator(timeout=3600)
+def get_access_token():
+    """
+    使用 AK，SK 生成鉴权签名（Access Token）
+    :return: access_token，或是None(如果错误)
+    """
+    # if (access_token_cache is None) or (time.time() - last_access_token_obtain_time > 3600):
+    BAIDU_CLOUD_API_KEY, BAIDU_CLOUD_SECRET_KEY = get_conf('BAIDU_CLOUD_API_KEY', 'BAIDU_CLOUD_SECRET_KEY')
+
+    if len(BAIDU_CLOUD_SECRET_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_SECRET_KEY")
+    if len(BAIDU_CLOUD_API_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_API_KEY")
+
+    url = "https://aip.baidubce.com/oauth/2.0/token"
+    params = {"grant_type": "client_credentials", "client_id": BAIDU_CLOUD_API_KEY, "client_secret": BAIDU_CLOUD_SECRET_KEY}
+    access_token_cache = str(requests.post(url, params=params).json().get("access_token"))
+    return access_token_cache
+    # else:
+    #     return access_token_cache
+
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+    conversation_cnt = len(history) // 2
+    if system_prompt == "": system_prompt = "Hello"
+    messages = [{"role": "user", "content": system_prompt}]
+    messages.append({"role": "assistant", "content": 'Certainly!'})
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index] if history[index]!="" else "Hello"
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1] if history[index]!="" else "Hello"
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    return messages
+
+
+def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
+    BAIDU_CLOUD_QIANFAN_MODEL,  = get_conf('BAIDU_CLOUD_QIANFAN_MODEL')
+
+    url_lib = {
+        "ERNIE-Bot":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions" ,
+        "ERNIE-Bot-turbo":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant"  ,
+        "BLOOMZ-7B":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1",
+
+        "Llama-2-70B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b",
+        "Llama-2-13B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_13b",
+        "Llama-2-7B-Chat":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_7b",
+    }
+
+    url = url_lib[BAIDU_CLOUD_QIANFAN_MODEL]
+
+    url += "?access_token=" + get_access_token()
+
+
+    payload = json.dumps({
+        "messages": generate_message_payload(inputs, llm_kwargs, history, system_prompt),
+        "stream": True
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload, stream=True)
+    buffer = ""
+    for line in response.iter_lines():
+        if len(line) == 0: continue
+        try:
+            dec = line.decode().lstrip('data:')
+            dec = json.loads(dec)
+            incoming = dec['result']
+            buffer += incoming
+            yield buffer
+        except:
+            if ('error_code' in dec) and ("max length" in dec['error_msg']):
+                raise ConnectionAbortedError(dec['error_msg'])  # 上下文太长导致 token 溢出
+            elif ('error_code' in dec):
+                raise RuntimeError(dec['error_msg'])
+
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llm/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llm/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    yield from update_ui(chatbot=chatbot, history=history)
+    # 开始接收回复
+    try:
+        for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
+            chatbot[-1] = (inputs, response)
+            yield from update_ui(chatbot=chatbot, history=history)
+    except ConnectionAbortedError as e:
+        from .bridge_all import model_info
+        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
+        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
+                    max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
+        yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
+        return
+    
+    # 总结输出
+    response = f"[Local Message]: {model_name}响应异常 ..."
+    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+        response = f"[Local Message]: {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)
--- a/request_llm/bridge_spark.py
+++ b/request_llm/bridge_spark.py
@@ -2,11 +2,17 @@
 import time
 import threading
 import importlib
-from toolbox import update_ui, get_conf
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
 from multiprocessing import Process, Pipe

 model_name = '星火认知大模型'

+def validate_key():
+    XFYUN_APPID,  = get_conf('XFYUN_APPID', )
+    if XFYUN_APPID == '00000000' or XFYUN_APPID == '': 
+        return False
+    return True
+
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
    """
        ⭐多线程方法
@@ -15,6 +21,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
    watch_dog_patience = 5
    response = ""

+    if validate_key() is False:
+        raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
+
    from .com_sparkapi import SparkRequestInstance
    sri = SparkRequestInstance()
    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
@@ -30,6 +39,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
        函数的说明请见 request_llm/bridge_all.py
    """
    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+
+    if validate_key() is False:
+        yield from update_ui_lastest_msg(lastmsg="[Local Message]: 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
+        return

    if additional_fn is not None:
        from core_functional import handle_core_functionality
--- a/request_llm/com_sparkapi.py
+++ b/request_llm/com_sparkapi.py
@@ -58,11 +58,13 @@ class Ws_Param(object):
 class SparkRequestInstance():
    def __init__(self):
        XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY')
-
+        if XFYUN_APPID == '00000000' or XFYUN_APPID == '': raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
        self.appid = XFYUN_APPID
        self.api_secret = XFYUN_API_SECRET
        self.api_key = XFYUN_API_KEY
        self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
+        self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
+
        self.time_to_yield_event = threading.Event()
        self.time_to_exit_event = threading.Event()

@@ -83,7 +85,12 @@ class SparkRequestInstance():


    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
-        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, self.gpt_url)
+        if llm_kwargs['llm_model'] == 'sparkv2':
+            gpt_url = self.gpt_url_v2
+        else:
+            gpt_url = self.gpt_url
+
+        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
        websocket.enableTrace(False)
        wsUrl = wsParam.create_url()

@@ -102,6 +109,7 @@ class SparkRequestInstance():
            code = data['header']['code']
            if code != 0:
                print(f'请求错误: {code}, {data}')
+                self.result_buf += str(data)
                ws.close()
                self.time_to_exit_event.set()
            else:
@@ -167,7 +175,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
        },
        "parameter": {
            "chat": {
-                "domain": "general",
+                "domain": "generalv2" if llm_kwargs['llm_model'] == 'sparkv2' else "general",
                "temperature": llm_kwargs["temperature"],
                "random_threshold": 0.5,
                "max_tokens": 4096,
--- a/request_llm/local_llm_class.py
+++ b/request_llm/local_llm_class.py
@@ -128,7 +128,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):

        # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
        history_feedin = []
-        history_feedin.append(["What can I do?", sys_prompt])
+        history_feedin.append([sys_prompt, "Certainly!"])
        for i in range(len(history)//2):
            history_feedin.append([history[2*i], history[2*i+1]] )

@@ -161,7 +161,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):

        # 处理历史信息
        history_feedin = []
-        history_feedin.append(["What can I do?", system_prompt] )
+        history_feedin.append([system_prompt, "Certainly!"])
        for i in range(len(history)//2):
            history_feedin.append([history[2*i], history[2*i+1]] )

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
binary-husky	760ff1840c	修复一个循环的Bug	2023-09-15 17:08:23 +08:00
binary-husky	9905122fc2	修复Tex文件匹配BUG	2023-09-15 12:55:41 +08:00
binary-husky	abea0d07ac	修复logging的Bug	2023-09-15 11:00:30 +08:00
binary-husky	16ff5ddcdc	版本3.52	2023-09-14 23:07:12 +08:00
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binary-husky	5ba8ea27d1	用logging取代print	2023-09-14 22:33:07 +08:00
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binary-husky	0937f37388	Predict按钮参数修正	2023-09-14 11:02:40 +08:00
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binary-husky	ab7999c71a	修正本项目源码范围	2023-09-14 01:00:38 +08:00
binary-husky	544771db9a	隐藏历史对话绝对路径	2023-09-14 00:53:15 +08:00
binary-husky	ec9d030457	把上传文件路径和日志路径修改为统一可配置的变量	2023-09-14 00:51:25 +08:00
binary-husky	14de282302	给nougat加线程锁合并冗余代码	2023-09-13 23:21:00 +08:00
binary-husky	fb5467b85b	更新插件系统提示	2023-09-12 19:13:36 +08:00
binary-husky	c4c6465927	解决issues #1097	2023-09-12 18:57:50 +08:00
qingxu fu	99a1cd6f9f	添加pypinyin依赖	2023-09-12 12:20:05 +08:00
qingxu fu	7e73a255f4	修改知识库插件的提示信息	2023-09-12 11:47:34 +08:00
qingxu fu	4b5f13bff2	修复知识库的依赖问题	2023-09-12 11:35:31 +08:00
qingxu fu	d495b73456	支持更多UI皮肤外观，加入暗色亮色切换键	2023-09-11 22:55:32 +08:00
qingxu fu	e699b6b13f	Merge branch 'master' of https://github.com/binary-husky/chatgpt_academic into master	2023-09-11 14:49:37 +08:00
qingxu fu	eb150987f0	兼容一个one-api没有done数据包的第三方Bug情形	2023-09-11 14:49:30 +08:00
binary-husky	34784333dc	融合PDF左右比例调整到95%	2023-09-10 17:22:35 +08:00
binary-husky	28d777a96b	修正报错消息	2023-09-10 16:52:35 +08:00
qingxu fu	c45fa88684	update translation matrix	2023-09-09 21:57:24 +08:00
binary-husky	ad9807dd14	更新虚空终端的提示	2023-09-09 20:32:44 +08:00
binary-husky	2a51715075	修复Dockerfile	2023-09-09 20:15:46 +08:00
binary-husky	7c307d8964	修复源代码解析模块与虚空终端的兼容性	2023-09-09 19:33:05 +08:00
binary-husky	baaacc5a7b	Update README.md	2023-09-09 19:11:21 +08:00
binary-husky	6faf5947c9	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-09-09 18:30:59 +08:00
binary-husky	571335cbc4	fix docker file	2023-09-09 18:30:43 +08:00
binary-husky	7d5abb6d69	Merge pull request #1077 from jsz14897502/master 更改谷歌学术搜索助手获取摘要的逻辑	2023-09-09 18:24:30 +08:00
binary-husky	a0f592308a	Merge branch 'master' into jsz14897502-master	2023-09-09 18:22:29 +08:00
binary-husky	e512d99879	添加一定的延迟，防止触发反爬虫机制	2023-09-09 18:22:22 +08:00
binary-husky	e70b636513	修复数学公式判定的Bug	2023-09-09 17:50:38 +08:00
binary-husky	408b8403fe	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-09-08 12:10:22 +08:00
binary-husky	74f8cb3511	update dockerfile	2023-09-08 12:10:16 +08:00
qingxu fu	2202cf3701	remove proxy message	2023-09-08 11:11:53 +08:00
qingxu fu	cce69beee9	update error message	2023-09-08 11:08:02 +08:00
qingxu fu	347124c967	update scipdf_parser dep	2023-09-08 10:43:20 +08:00
qingxu fu	77a6105a9a	修改demo案例	2023-09-08 09:52:29 +08:00
qingxu fu	13c9606af7	修正下载PDF失败时产生的错误提示	2023-09-08 09:47:29 +08:00
binary-husky	bac6810e75	修改操作提示	2023-09-08 09:38:16 +08:00
binary-husky	c176187d24	修复因为函数返回值导致的不准确错误提示	2023-09-07 23:46:54 +08:00
binary-husky	31d5ee6ccc	Update README.md	2023-09-07 23:05:54 +08:00
binary-husky	5e0dc9b9ad	修复PDF下载路径时间戳的问题	2023-09-07 18:51:09 +08:00
binary-husky	4c6f3aa427	CodeInterpreter	2023-09-07 17:45:44 +08:00
binary-husky	d7331befc1	add note	2023-09-07 17:42:47 +08:00
binary-husky	63219baa21	修正语音对话时句子末尾显示异常的问题	2023-09-07 17:04:40 +08:00
binary-husky	97cb9a4adc	full capacity docker file	2023-09-07 15:09:38 +08:00
binary-husky	24f41b0a75	new docker file	2023-09-07 00:45:03 +08:00
binary-husky	bfec29e9bc	new docker file	2023-09-07 00:43:31 +08:00
binary-husky	dd9e624761	add new dockerfile	2023-09-07 00:40:11 +08:00
binary-husky	7855325ff9	update dockerfiles	2023-09-06 23:33:15 +08:00
binary-husky	2c039ff5c9	add session	2023-09-06 22:19:32 +08:00
binary-husky	9a5ee86434	Merge pull request #1084 from eltociear/patch-2 Update README.md	2023-09-06 21:56:39 +08:00
binary-husky	d6698db257	nougat翻译PDF论文	2023-09-06 15:32:11 +08:00
Ikko Eltociear Ashimine	b2d03bf2a3	Update README.md arbitary -> arbitrary	2023-09-06 15:30:12 +09:00
binary-husky	2f83b60fb3	添加搜索失败时的提示	2023-09-06 12:36:59 +08:00
binary-husky	d183e34461	添加一个全版本搜索的开关	2023-09-06 11:42:29 +08:00
binary-husky	fb78569335	Merge branch 'master' of https://github.com/jsz14897502/gpt_academic into jsz14897502-master	2023-09-06 10:27:52 +08:00
qingxu fu	12c8cd75ee	Merge branch 'master' of https://github.com/binary-husky/chatgpt_academic into master	2023-09-06 10:24:14 +08:00
qingxu fu	0e21e3e2e7	修复没填写讯飞APPID无报错提示的问题	2023-09-06 10:24:11 +08:00
binary-husky	fda1e87278	Update stale.yml	2023-09-06 10:19:21 +08:00
binary-husky	1092031d77	Create stale.yml	2023-09-06 10:15:52 +08:00
binary-husky	f0482d3bae	Update docker-compose.yml	2023-09-04 12:39:25 +08:00
binary-husky	b6ac3d0d6c	Update README.md	2023-09-04 12:34:55 +08:00
binary-husky	3344ffcb8b	Update README.md	2023-09-04 11:41:52 +08:00
binary-husky	82936f71b6	Update README.md	2023-09-04 11:37:47 +08:00
binary-husky	51e809c09e	Update README.md	2023-09-04 11:34:46 +08:00
qingxu fu	713df396dc	Merge branch 'master' of https://github.com/binary-husky/chatgpt_academic into master	2023-09-03 16:46:30 +08:00
qingxu fu	23a42d93df	update translation matrix	2023-09-03 16:46:27 +08:00
binary-husky	0ef06683dc	Update README.md	2023-09-03 16:35:03 +08:00
qingxu fu	843113ba0f	fix minor bugs	2023-09-03 16:20:05 +08:00
binary-husky	79080290c6	Merge pull request #1074 from Kilig947/plugin_classification 插件分区新增插件分类选择	2023-09-03 15:41:45 +08:00
qingxu fu	9bd2023a8e	revise version check	2023-09-03 15:40:41 +08:00
qingxu fu	0d6e32d31a	version 3.5 release	2023-09-03 15:38:10 +08:00
qingxu fu	0418257218	Merge branch 'master' into Kilig947-plugin_classification	2023-09-03 15:35:16 +08:00
qingxu fu	a3e6fc0141	修复文心一言的接口问题	2023-09-03 15:32:39 +08:00
qingxu fu	1dd165a3cd	ui layout improve	2023-09-03 14:47:22 +08:00
qingxu fu	e666b5269e	改进虚空终端	2023-09-03 00:53:57 +08:00
qingxu fu	0b70e9df7b	优化虚空终端调用流程	2023-09-02 23:49:56 +08:00
qingxu fu	1639796041	support file implementation	2023-09-02 22:22:41 +08:00
jsz14	03164bcb6f	fix:没有获取到所有版本时的处理	2023-09-02 19:58:24 +08:00
qingxu fu	d0af074225	change layout	2023-09-02 18:19:19 +08:00
binary-husky	6d7f3feab3	优化主题外观，新增high-contrast主题	2023-09-01 10:45:22 +08:00
binary-husky	045b7f6312	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-09-01 10:34:33 +08:00
binary-husky	116b7ce12f	支持星火认知大模型v2	2023-09-01 10:34:26 +08:00
qingxu fu	8b0905c076	提高虚空终端的成功率	2023-08-31 18:04:31 +08:00
qingxu fu	b69140307b	修复对话框对齐的问题	2023-08-31 16:24:00 +08:00
qingxu fu	b31abbcad3	每个插件可以归属多个Group	2023-08-31 15:59:19 +08:00
qingxu fu	2d5a1fbc12	修改前端代码	2023-08-31 00:21:24 +08:00
jsz14	d052d425af	更改谷歌学术搜索助手获取摘要的逻辑	2023-08-30 19:14:01 +08:00
qingxu fu	89de49f31e	修改变量命名，整理配置清单	2023-08-30 16:00:27 +08:00
w_xiaolizu	a208782049	新增插件分类	2023-08-30 14:46:34 +08:00
qingxu fu	eb802ee975	implement two stage plugin selection	2023-08-29 23:53:47 +08:00
qingxu fu	f40d48b014	fix typing problems	2023-08-29 23:46:40 +08:00
qingxu fu	ef4203f5ca	Merge branch 'master' of https://github.com/binary-husky/chatgpt_academic into master	2023-08-29 23:25:10 +08:00
qingxu fu	adf93195e8	尝试使用自然语言调度各个插件	2023-08-29 23:25:06 +08:00
binary-husky	3e5cdbaf68	Update README.md	2023-08-29 18:29:45 +08:00
binary-husky	27cab3b38a	Update README.md	2023-08-29 18:29:16 +08:00
qingxu fu	09d38e4abf	出于安全性考虑，默认禁用动态配置修改	2023-08-29 17:50:45 +08:00
qingxu fu	7efb5cb6f5	移除早期引入的测试样本	2023-08-29 17:43:55 +08:00
qingxu fu	31ff6e1e7a	支持自然语言修改项目本身的配置	2023-08-29 17:37:41 +08:00
qingxu fu	2fa3d47887	fix json read error	2023-08-29 12:42:06 +08:00
binary-husky	2cca46375c	Update crazy_functional.py	2023-08-28 17:47:37 +08:00
binary-husky	06410b593c	Update config.py	2023-08-28 16:16:30 +08:00
binary-husky	545c9f47de	Update README.md	2023-08-28 11:59:23 +08:00
binary-husky	973ad41bde	add a space	2023-08-28 02:03:30 +08:00
binary-husky	3fa7416eb2	notify dummy action	2023-08-28 01:56:15 +08:00
binary-husky	ec76d3dcc4	支持借助GROBID实现PDF高精度翻译	2023-08-28 01:25:44 +08:00
binary-husky	3f27bec94b	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-08-28 01:22:26 +08:00
binary-husky	ed11269aef	支持借助GROBID实现PDF高精度翻译	2023-08-28 01:22:20 +08:00
qingxu fu	6c653734ec	Fix 3rd part chatgpt compat	2023-08-26 17:57:59 +08:00
qingxu fu	19bd0c35ed	修复latex input命令解析问题	2023-08-25 21:20:15 +08:00
binary-husky	3f4c4ebc29	调整注释	2023-08-25 13:16:18 +08:00
binary-husky	6cc7d4ed69	修复文心一言最大文本长度限制带来的问题	2023-08-25 13:09:08 +08:00
binary-husky	67fff17917	3.49 接入百度千帆平台和文心一言	2023-08-25 12:45:08 +08:00
binary-husky	8fce49fa02	支持百度云千帆和文心一言	2023-08-25 12:31:51 +08:00
binary-husky	30f28b37c3	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-08-21 22:09:05 +08:00
binary-husky	6a5681dd0a	add llama2	2023-08-21 22:08:57 +08:00
binary-husky	dacc282763	Update README.md	2023-08-21 22:00:51 +08:00
binary-husky	9720bec5e5	Interface with LLaMa2 from huggingface	2023-08-21 21:54:21 +08:00
binary-husky	8b3b883fce	Update README.md	2023-08-17 10:02:55 +08:00
qingxu fu	4dc0f8e57a	修改dockercompose，添加对阿里qwen的支持	2023-08-17 10:00:42 +08:00
qingxu fu	5e48fc98ed	添加本地缓存删除功能	2023-08-16 22:49:46 +08:00
qingxu fu	2ff8dc787e	interface with ChatGPT-to-API	2023-08-16 22:21:51 +08:00
qingxu fu	cd38d1697c	fix missing finish_reason problem	2023-08-16 21:40:34 +08:00
qingxu fu	00f63cb0bc	configure utf8 encoding	2023-08-16 21:29:16 +08:00
binary-husky	dc7fab3c19	Merge branch 'master' of github.com:binary-husky/chatgpt_academic	2023-08-14 17:27:33 +08:00
binary-husky	d1b5359e2b	fix github action	2023-08-14 17:27:13 +08:00
binary-husky	0597ffea2e	Update README.md	2023-08-14 16:37:07 +08:00