Compare commits
2 Commits
mini-core
...
hongyi-zha
| Author | SHA1 | Date | |
|---|---|---|---|
|
e8c17a099e | ||
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3f36cfea38 |
@@ -1,14 +1,14 @@
|
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# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
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name: build-with-latex-arm
|
||||
name: build-with-all-capacity-beta
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
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- "master"
|
||||
- 'master'
|
||||
|
||||
env:
|
||||
REGISTRY: ghcr.io
|
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IMAGE_NAME: ${{ github.repository }}_with_latex_arm
|
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IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
|
||||
|
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jobs:
|
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build-and-push-image:
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@@ -18,17 +18,11 @@ jobs:
|
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packages: write
|
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|
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steps:
|
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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|
||||
- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
|
||||
|
||||
- name: Checkout repository
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uses: actions/checkout@v4
|
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uses: actions/checkout@v3
|
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|
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- name: Log in to the Container registry
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uses: docker/login-action@v3
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uses: docker/login-action@v2
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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@@ -41,11 +35,10 @@ jobs:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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- name: Build and push Docker image
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uses: docker/build-push-action@v6
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uses: docker/build-push-action@v4
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with:
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context: .
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push: true
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platforms: linux/arm64
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file: docs/GithubAction+NoLocal+Latex
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file: docs/GithubAction+AllCapacityBeta
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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labels: ${{ steps.meta.outputs.labels }}
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44
.github/workflows/build-with-jittorllms.yml
vendored
Normal file
44
.github/workflows/build-with-jittorllms.yml
vendored
Normal file
@@ -0,0 +1,44 @@
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# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
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name: build-with-jittorllms
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|
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on:
|
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push:
|
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branches:
|
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- 'master'
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|
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env:
|
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REGISTRY: ghcr.io
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IMAGE_NAME: ${{ github.repository }}_jittorllms
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|
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jobs:
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build-and-push-image:
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runs-on: ubuntu-latest
|
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permissions:
|
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contents: read
|
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packages: write
|
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|
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steps:
|
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- name: Checkout repository
|
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uses: actions/checkout@v3
|
||||
|
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- name: Log in to the Container registry
|
||||
uses: docker/login-action@v2
|
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with:
|
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registry: ${{ env.REGISTRY }}
|
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username: ${{ github.actor }}
|
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password: ${{ secrets.GITHUB_TOKEN }}
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|
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- name: Extract metadata (tags, labels) for Docker
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id: meta
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uses: docker/metadata-action@v4
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with:
|
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
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|
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- name: Build and push Docker image
|
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uses: docker/build-push-action@v4
|
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with:
|
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context: .
|
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push: true
|
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file: docs/GithubAction+JittorLLMs
|
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tags: ${{ steps.meta.outputs.tags }}
|
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labels: ${{ steps.meta.outputs.labels }}
|
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10
.gitignore
vendored
10
.gitignore
vendored
@@ -131,9 +131,6 @@ dmypy.json
|
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# Pyre type checker
|
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.pyre/
|
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|
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# macOS files
|
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.DS_Store
|
||||
|
||||
.vscode
|
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.idea
|
||||
|
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@@ -156,10 +153,3 @@ media
|
||||
flagged
|
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request_llms/ChatGLM-6b-onnx-u8s8
|
||||
.pre-commit-config.yaml
|
||||
test.*
|
||||
temp.*
|
||||
objdump*
|
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*.min.*.js
|
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TODO
|
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experimental_mods
|
||||
search_results
|
||||
|
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@@ -12,16 +12,11 @@ RUN echo '[global]' > /etc/pip.conf && \
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echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
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|
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|
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# 语音输出功能（以下两行，第一行更换阿里源，第二行安装ffmpeg，都可以删除）
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RUN UBUNTU_VERSION=$(awk -F= '/^VERSION_CODENAME=/{print $2}' /etc/os-release); echo "deb https://mirrors.aliyun.com/debian/ $UBUNTU_VERSION main non-free contrib" > /etc/apt/sources.list; apt-get update
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RUN apt-get install ffmpeg -y
|
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|
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|
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# 进入工作路径（必要）
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WORKDIR /gpt
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|
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|
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# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下两行，可以删除）
|
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# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下三行，可以删除）
|
||||
COPY requirements.txt ./
|
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RUN pip3 install -r requirements.txt
|
||||
|
||||
|
||||
444
README.md
Normal file
444
README.md
Normal file
@@ -0,0 +1,444 @@
|
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> [!IMPORTANT]
|
||||
> 2024.1.18: 更新3.70版本，支持Mermaid绘图库（让大模型绘制脑图）
|
||||
> 2024.1.17: 恭迎GLM4，全力支持Qwen、GLM、DeepseekCoder等国内中文大语言基座模型！
|
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> 2024.1.17: 某些依赖包尚不兼容python 3.12，推荐python 3.11。
|
||||
> 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
|
||||
|
||||
<br>
|
||||
|
||||
<div align=center>
|
||||
<h1 aligh="center">
|
||||
<img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)
|
||||
</h1>
|
||||
|
||||
[![Github][Github-image]][Github-url]
|
||||
[![License][License-image]][License-url]
|
||||
[![Releases][Releases-image]][Releases-url]
|
||||
[![Installation][Installation-image]][Installation-url]
|
||||
[![Wiki][Wiki-image]][Wiki-url]
|
||||
[![PR][PRs-image]][PRs-url]
|
||||
|
||||
[Github-image]: https://img.shields.io/badge/github-12100E.svg?style=flat-square
|
||||
[License-image]: https://img.shields.io/github/license/binary-husky/gpt_academic?label=License&style=flat-square&color=orange
|
||||
[Releases-image]: https://img.shields.io/github/release/binary-husky/gpt_academic?label=Release&style=flat-square&color=blue
|
||||
[Installation-image]: https://img.shields.io/badge/dynamic/json?color=blue&url=https://raw.githubusercontent.com/binary-husky/gpt_academic/master/version&query=$.version&label=Installation&style=flat-square
|
||||
[Wiki-image]: https://img.shields.io/badge/wiki-项目文档-black?style=flat-square
|
||||
[PRs-image]: https://img.shields.io/badge/PRs-welcome-pink?style=flat-square
|
||||
|
||||
[Github-url]: https://github.com/binary-husky/gpt_academic
|
||||
[License-url]: https://github.com/binary-husky/gpt_academic/blob/master/LICENSE
|
||||
[Releases-url]: https://github.com/binary-husky/gpt_academic/releases
|
||||
[Installation-url]: https://github.com/binary-husky/gpt_academic#installation
|
||||
[Wiki-url]: https://github.com/binary-husky/gpt_academic/wiki
|
||||
[PRs-url]: https://github.com/binary-husky/gpt_academic/pulls
|
||||
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
**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插件，欢迎发pull requests！**
|
||||
|
||||
If you like this project, please give it a Star.
|
||||
Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanese.md) | [한국어](docs/README.Korean.md) | [Русский](docs/README.Russian.md) | [Français](docs/README.French.md). All translations have been provided by the project itself. To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
|
||||
<br>
|
||||
|
||||
> [!NOTE]
|
||||
> 1.本项目中每个文件的功能都在[自译解报告](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)`self_analysis.md`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。常见问题请查阅wiki。
|
||||
> [](#installation) [](https://github.com/binary-husky/gpt_academic/releases) [](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明) []([https://github.com/binary-husky/gpt_academic/wiki/项目配置说明](https://github.com/binary-husky/gpt_academic/wiki))
|
||||
>
|
||||
> 2.本项目兼容并鼓励尝试国内中文大语言基座模型如通义千问，智谱GLM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`然后回车键提交即可生效。
|
||||
|
||||
<br><br>
|
||||
|
||||
<div align="center">
|
||||
|
||||
功能（⭐= 近期新增功能） | 描述
|
||||
--- | ---
|
||||
⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](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)，[智谱GLM4](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
|
||||
⭐支持mermaid图像渲染 | 支持让GPT生成[流程图](https://www.bilibili.com/video/BV18c41147H9/)、状态转移图、甘特图、饼状图、GitGraph等等（3.7版本）
|
||||
⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
|
||||
⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
|
||||
⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
|
||||
⭐虚空终端插件 | [插件] 能够使用自然语言直接调度本项目其他插件
|
||||
润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
|
||||
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
|
||||
模块化设计 | 支持自定义强大的[插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [插件] 一键剖析Python/C/C++/Java/Lua/...项目树 或 [自我剖析](https://www.bilibili.com/video/BV1cj411A7VW)
|
||||
读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
|
||||
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
|
||||
批量注释生成 | [插件] 一键批量生成函数注释
|
||||
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？就是出自他的手笔
|
||||
[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
|
||||
[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
|
||||
Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
|
||||
[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
|
||||
互联网信息聚合+GPT | [插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
|
||||
公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
|
||||
启动暗色[主题](https://github.com/binary-husky/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)伺候的感觉一定会很不错吧？
|
||||
更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
|
||||
⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
|
||||
更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
|
||||
</div>
|
||||
|
||||
|
||||
- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/279702205-d81137c3-affd-4cd1-bb5e-b15610389762.gif" width="700" >
|
||||
</div>
|
||||
|
||||
|
||||
- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放剪贴板
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
|
||||
</div>
|
||||
|
||||
- 润色/纠错
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
|
||||
</div>
|
||||
|
||||
- 如果输出包含公式，会以tex形式和渲染形式同时显示，方便复制和阅读
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
|
||||
</div>
|
||||
|
||||
- 懒得看项目代码？直接把整个工程炫ChatGPT嘴里
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
|
||||
</div>
|
||||
|
||||
- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + GPT4）
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
|
||||
</div>
|
||||
|
||||
<br><br>
|
||||
|
||||
# Installation
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A{"安装方法"} --> W1("I. 🔑直接运行 (Windows, Linux or MacOS)")
|
||||
W1 --> W11["1. Python pip包管理依赖"]
|
||||
W1 --> W12["2. Anaconda包管理依赖（推荐⭐）"]
|
||||
|
||||
A --> W2["II. 🐳使用Docker (Windows, Linux or MacOS)"]
|
||||
|
||||
W2 --> k1["1. 部署项目全部能力的大镜像（推荐⭐）"]
|
||||
W2 --> k2["2. 仅在线模型（GPT, GLM4等）镜像"]
|
||||
W2 --> k3["3. 在线模型 + Latex的大镜像"]
|
||||
|
||||
A --> W4["IV. 🚀其他部署方法"]
|
||||
W4 --> C1["1. Windows/MacOS 一键安装运行脚本（推荐⭐）"]
|
||||
W4 --> C2["2. Huggingface, Sealos远程部署"]
|
||||
W4 --> C4["3. ... 其他 ..."]
|
||||
```
|
||||
|
||||
### 安装方法I：直接运行 (Windows, Linux or MacOS)
|
||||
|
||||
1. 下载项目
|
||||
|
||||
```sh
|
||||
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||
cd gpt_academic
|
||||
```
|
||||
|
||||
2. 配置API_KEY等变量
|
||||
|
||||
在`config.py`中，配置API KEY等变量。[特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1)、[Wiki-项目配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
|
||||
|
||||
「 程序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。如您能理解以上读取逻辑，我们强烈建议您在`config.py`同路径下创建一个名为`config_private.py`的新配置文件，并使用`config_private.py`配置项目，从而确保自动更新时不会丢失配置 」。
|
||||
|
||||
「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。配置读取优先级: `环境变量` > `config_private.py` > `config.py` 」。
|
||||
|
||||
|
||||
3. 安装依赖
|
||||
```sh
|
||||
# （选择I: 如熟悉python, python推荐版本 3.9 ~ 3.11）备注：使用官方pip源或者阿里pip源, 临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
|
||||
python -m pip install -r requirements.txt
|
||||
|
||||
# （选择II: 使用Anaconda）步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
|
||||
conda create -n gptac_venv python=3.11 # 创建anaconda环境
|
||||
conda activate gptac_venv # 激活anaconda环境
|
||||
python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步骤
|
||||
```
|
||||
|
||||
|
||||
<details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
|
||||
<p>
|
||||
|
||||
【可选步骤】如果需要支持清华ChatGLM3/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
|
||||
|
||||
```sh
|
||||
# 【可选步骤I】支持清华ChatGLM3。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# 【可选步骤II】支持复旦MOSS
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # 注意执行此行代码时，必须处于项目根路径
|
||||
|
||||
# 【可选步骤III】支持RWKV Runner
|
||||
参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
|
||||
|
||||
# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
|
||||
pip install bitsandbyte
|
||||
# windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
|
||||
python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
|
||||
pip install -U git+https://github.com/huggingface/transformers.git
|
||||
pip install -U git+https://github.com/huggingface/accelerate.git
|
||||
pip install peft
|
||||
```
|
||||
|
||||
</p>
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
4. 运行
|
||||
```sh
|
||||
python main.py
|
||||
```
|
||||
|
||||
### 安装方法II：使用Docker
|
||||
|
||||
0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小，则不推荐该方法部署完整项目）
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
|
||||
|
||||
``` sh
|
||||
# 修改docker-compose.yml，保留方案0并删除其他方案。然后运行：
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
1. 仅ChatGPT + GLM4 + 文心一言+spark等在线模型（推荐大多数人选择）
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
|
||||
|
||||
``` sh
|
||||
# 修改docker-compose.yml，保留方案1并删除其他方案。然后运行：
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用方案4或者方案0获取Latex功能。
|
||||
|
||||
2. ChatGPT + GLM3 + 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）
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
|
||||
|
||||
``` sh
|
||||
# 修改docker-compose.yml，保留方案2并删除其他方案。然后运行：
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
|
||||
### 安装方法III：其他部署方法
|
||||
1. **Windows一键运行脚本**。
|
||||
完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。脚本贡献来源：[oobabooga](https://github.com/oobabooga/one-click-installers)。
|
||||
|
||||
2. 使用第三方API、Azure等、文心一言、星火等，见[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
|
||||
|
||||
3. 云服务器远程部署避坑指南。
|
||||
请访问[云服务器远程部署wiki](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)
|
||||
|
||||
4. 在其他平台部署&二级网址部署
|
||||
- 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
|
||||
- 使用WSL2（Windows Subsystem for Linux 子系统）。请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
|
||||
- 如何在二级网址（如`http://localhost/subpath`）下运行。请访问[FastAPI运行说明](docs/WithFastapi.md)
|
||||
|
||||
<br><br>
|
||||
|
||||
# Advanced Usage
|
||||
### I：自定义新的便捷按钮（学术快捷键）
|
||||
|
||||
任意文本编辑器打开`core_functional.py`，添加如下条目，然后重启程序。（如果按钮已存在，那么可以直接修改（前缀、后缀都已支持热修改），无需重启程序即可生效。）
|
||||
例如
|
||||
|
||||
```python
|
||||
"超级英译中": {
|
||||
# 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
|
||||
"Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n",
|
||||
|
||||
# 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来。
|
||||
"Suffix": "",
|
||||
},
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
|
||||
</div>
|
||||
|
||||
### II：自定义函数插件
|
||||
编写强大的函数插件来执行任何你想得到的和想不到的任务。
|
||||
本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。
|
||||
详情请参考[函数插件指南](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
|
||||
|
||||
<br><br>
|
||||
|
||||
# Updates
|
||||
### I：动态
|
||||
|
||||
1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件，
|
||||
另外在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。
|
||||
Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史html存档缓存。
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
|
||||
</div>
|
||||
|
||||
2. ⭐Latex/Arxiv论文翻译功能⭐
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
|
||||
</div>
|
||||
|
||||
3. 虚空终端（从自然语言输入中，理解用户意图+自动调用其他插件）
|
||||
|
||||
- 步骤一：输入 “ 请调用插件翻译PDF论文，地址为https://openreview.net/pdf?id=rJl0r3R9KX ”
|
||||
- 步骤二：点击“虚空终端”
|
||||
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
|
||||
</div>
|
||||
|
||||
4. 模块化功能设计，简单的接口却能支持强大的功能
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
|
||||
</div>
|
||||
|
||||
5. 译解其他开源项目
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
|
||||
</div>
|
||||
|
||||
6. 装饰[live2d](https://github.com/fghrsh/live2d_demo)的小功能（默认关闭，需要修改`config.py`）
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
|
||||
</div>
|
||||
|
||||
7. OpenAI图像生成
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
|
||||
</div>
|
||||
|
||||
8. 基于mermaid的流图、脑图绘制
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/c518b82f-bd53-46e2-baf5-ad1b081c1da4" width="500" >
|
||||
</div>
|
||||
|
||||
9. Latex全文校对纠错
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
|
||||
</div>
|
||||
|
||||
10. 语言、主题切换
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
### II：版本:
|
||||
- version 3.80(TODO): 优化AutoGen插件主题并设计一系列衍生插件
|
||||
- version 3.70: 引入Mermaid绘图，实现GPT画脑图等功能
|
||||
- version 3.60: 引入AutoGen作为新一代插件的基石
|
||||
- version 3.57: 支持GLM3，星火v3，文心一言v4，修复本地模型的并发BUG
|
||||
- version 3.56: 支持动态追加基础功能按钮，新汇报PDF汇总页面
|
||||
- version 3.55: 重构前端界面，引入悬浮窗口与菜单栏
|
||||
- version 3.54: 新增动态代码解释器（Code Interpreter）（待完善）
|
||||
- version 3.53: 支持动态选择不同界面主题，提高稳定性&解决多用户冲突问题
|
||||
- version 3.50: 使用自然语言调用本项目的所有函数插件（虚空终端），支持插件分类，改进UI，设计新主题
|
||||
- version 3.49: 支持百度千帆平台和文心一言
|
||||
- version 3.48: 支持阿里达摩院通义千问，上海AI-Lab书生，讯飞星火
|
||||
- version 3.46: 支持完全脱手操作的实时语音对话
|
||||
- version 3.45: 支持自定义ChatGLM2微调模型
|
||||
- version 3.44: 正式支持Azure，优化界面易用性
|
||||
- version 3.4: +arxiv论文翻译、latex论文批改功能
|
||||
- version 3.3: +互联网信息综合功能
|
||||
- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
|
||||
- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡
|
||||
- version 3.0: 对chatglm和其他小型llm的支持
|
||||
- version 2.6: 重构了插件结构，提高了交互性，加入更多插件
|
||||
- version 2.5: 自更新，解决总结大工程源代码时文本过长、token溢出的问题
|
||||
- version 2.4: 新增PDF全文翻译功能; 新增输入区切换位置的功能
|
||||
- version 2.3: 增强多线程交互性
|
||||
- version 2.2: 函数插件支持热重载
|
||||
- version 2.1: 可折叠式布局
|
||||
- version 2.0: 引入模块化函数插件
|
||||
- version 1.0: 基础功能
|
||||
|
||||
GPT Academic开发者QQ群：`610599535`
|
||||
|
||||
- 已知问题
|
||||
- 某些浏览器翻译插件干扰此软件前端的运行
|
||||
- 官方Gradio目前有很多兼容性问题，请**务必使用`requirement.txt`安装Gradio**
|
||||
|
||||
```mermaid
|
||||
timeline LR
|
||||
title GPT-Academic项目发展历程
|
||||
section 2.x
|
||||
1.0~2.2: 基础功能: 引入模块化函数插件: 可折叠式布局: 函数插件支持热重载
|
||||
2.3~2.5: 增强多线程交互性: 新增PDF全文翻译功能: 新增输入区切换位置的功能: 自更新
|
||||
2.6: 重构了插件结构: 提高了交互性: 加入更多插件
|
||||
section 3.x
|
||||
3.0~3.1: 对chatglm支持: 对其他小型llm支持: 支持同时问询多个gpt模型: 支持多个apikey负载均衡
|
||||
3.2~3.3: 函数插件支持更多参数接口: 保存对话功能: 解读任意语言代码: 同时询问任意的LLM组合: 互联网信息综合功能
|
||||
3.4: 加入arxiv论文翻译: 加入latex论文批改功能
|
||||
3.44: 正式支持Azure: 优化界面易用性
|
||||
3.46: 自定义ChatGLM2微调模型: 实时语音对话
|
||||
3.49: 支持阿里达摩院通义千问: 上海AI-Lab书生: 讯飞星火: 支持百度千帆平台 & 文心一言
|
||||
3.50: 虚空终端: 支持插件分类: 改进UI: 设计新主题
|
||||
3.53: 动态选择不同界面主题: 提高稳定性: 解决多用户冲突问题
|
||||
3.55: 动态代码解释器: 重构前端界面: 引入悬浮窗口与菜单栏
|
||||
3.56: 动态追加基础功能按钮: 新汇报PDF汇总页面
|
||||
3.57: GLM3, 星火v3: 支持文心一言v4: 修复本地模型的并发BUG
|
||||
3.60: 引入AutoGen
|
||||
3.70: 引入Mermaid绘图: 实现GPT画脑图等功能
|
||||
3.80(TODO): 优化AutoGen插件主题: 设计衍生插件
|
||||
|
||||
```
|
||||
|
||||
|
||||
### III：主题
|
||||
可以通过修改`THEME`选项（config.py）变更主题
|
||||
1. `Chuanhu-Small-and-Beautiful` [网址](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
|
||||
|
||||
|
||||
### IV：本项目的开发分支
|
||||
|
||||
1. `master` 分支: 主分支，稳定版
|
||||
2. `frontier` 分支: 开发分支，测试版
|
||||
3. 如何[接入其他大模型](request_llms/README.md)
|
||||
4. 访问GPT-Academic的[在线服务并支持我们](https://github.com/binary-husky/gpt_academic/wiki/online)
|
||||
|
||||
### V：参考与学习
|
||||
|
||||
```
|
||||
代码中参考了很多其他优秀项目中的设计，顺序不分先后：
|
||||
|
||||
# 清华ChatGLM2-6B:
|
||||
https://github.com/THUDM/ChatGLM2-6B
|
||||
|
||||
# 清华JittorLLMs:
|
||||
https://github.com/Jittor/JittorLLMs
|
||||
|
||||
# ChatPaper:
|
||||
https://github.com/kaixindelele/ChatPaper
|
||||
|
||||
# Edge-GPT:
|
||||
https://github.com/acheong08/EdgeGPT
|
||||
|
||||
# ChuanhuChatGPT:
|
||||
https://github.com/GaiZhenbiao/ChuanhuChatGPT
|
||||
|
||||
# Oobabooga one-click installer:
|
||||
https://github.com/oobabooga/one-click-installers
|
||||
|
||||
# More：
|
||||
https://github.com/gradio-app/gradio
|
||||
https://github.com/fghrsh/live2d_demo
|
||||
```
|
||||
161
check_proxy.py
161
check_proxy.py
@@ -1,77 +1,37 @@
|
||||
from loguru import logger
|
||||
|
||||
def check_proxy(proxies, return_ip=False):
|
||||
"""
|
||||
检查代理配置并返回结果。
|
||||
|
||||
Args:
|
||||
proxies (dict): 包含http和https代理配置的字典。
|
||||
return_ip (bool, optional): 是否返回代理的IP地址。默认为False。
|
||||
|
||||
Returns:
|
||||
str or None: 检查的结果信息或代理的IP地址（如果`return_ip`为True）。
|
||||
"""
|
||||
def check_proxy(proxies):
|
||||
import requests
|
||||
proxies_https = proxies['https'] if proxies is not None else '无'
|
||||
ip = None
|
||||
try:
|
||||
response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4) # ⭐ 执行GET请求以获取代理信息
|
||||
response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
|
||||
data = response.json()
|
||||
if 'country_name' in data:
|
||||
country = data['country_name']
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：{country}"
|
||||
if 'ip' in data:
|
||||
ip = data['ip']
|
||||
elif 'error' in data:
|
||||
alternative, ip = _check_with_backup_source(proxies) # ⭐ 调用备用方法检查代理配置
|
||||
alternative = _check_with_backup_source(proxies)
|
||||
if alternative is None:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
|
||||
else:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
|
||||
else:
|
||||
result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
|
||||
|
||||
if not return_ip:
|
||||
logger.warning(result)
|
||||
return result
|
||||
else:
|
||||
return ip
|
||||
print(result)
|
||||
return result
|
||||
except:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
|
||||
if not return_ip:
|
||||
logger.warning(result)
|
||||
return result
|
||||
else:
|
||||
return ip
|
||||
print(result)
|
||||
return result
|
||||
|
||||
def _check_with_backup_source(proxies):
|
||||
"""
|
||||
通过备份源检查代理，并获取相应信息。
|
||||
|
||||
Args:
|
||||
proxies (dict): 包含代理信息的字典。
|
||||
|
||||
Returns:
|
||||
tuple: 代理信息(geo)和IP地址(ip)的元组。
|
||||
"""
|
||||
import random, string, requests
|
||||
random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
|
||||
try:
|
||||
res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json() # ⭐ 执行代理检查和备份源请求
|
||||
return res_json['dns']['geo'], res_json['dns']['ip']
|
||||
except:
|
||||
return None, None
|
||||
try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
|
||||
except: return None
|
||||
|
||||
def backup_and_download(current_version, remote_version):
|
||||
"""
|
||||
一键更新协议：备份当前版本，下载远程版本并解压缩。
|
||||
|
||||
Args:
|
||||
current_version (str): 当前版本号。
|
||||
remote_version (str): 远程版本号。
|
||||
|
||||
Returns:
|
||||
str: 新版本目录的路径。
|
||||
一键更新协议：备份和下载
|
||||
"""
|
||||
from toolbox import get_conf
|
||||
import shutil
|
||||
@@ -87,8 +47,8 @@ def backup_and_download(current_version, remote_version):
|
||||
shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
|
||||
proxies = get_conf('proxies')
|
||||
try: r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
|
||||
except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
|
||||
zip_file_path = backup_dir+'/master.zip' # ⭐ 保存备份文件的路径
|
||||
except: r = requests.get('https://public.gpt-academic.top/publish/master.zip', proxies=proxies, stream=True)
|
||||
zip_file_path = backup_dir+'/master.zip'
|
||||
with open(zip_file_path, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
dst_path = new_version_dir
|
||||
@@ -104,17 +64,6 @@ def backup_and_download(current_version, remote_version):
|
||||
def patch_and_restart(path):
|
||||
"""
|
||||
一键更新协议：覆盖和重启
|
||||
|
||||
Args:
|
||||
path (str): 新版本代码所在的路径
|
||||
|
||||
注意事项:
|
||||
如果您的程序没有使用config_private.py私密配置文件，则会将config.py重命名为config_private.py以避免配置丢失。
|
||||
|
||||
更新流程:
|
||||
- 复制最新版本代码到当前目录
|
||||
- 更新pip包依赖
|
||||
- 如果更新失败，则提示手动安装依赖库并重启
|
||||
"""
|
||||
from distutils import dir_util
|
||||
import shutil
|
||||
@@ -122,44 +71,33 @@ def patch_and_restart(path):
|
||||
import sys
|
||||
import time
|
||||
import glob
|
||||
from shared_utils.colorful import log亮黄, log亮绿, log亮红
|
||||
|
||||
from colorful import print亮黄, print亮绿, print亮红
|
||||
# if not using config_private, move origin config.py as config_private.py
|
||||
if not os.path.exists('config_private.py'):
|
||||
log亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
|
||||
print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
|
||||
'另外您可以随时在history子文件夹下找回旧版的程序。')
|
||||
shutil.copyfile('config.py', 'config_private.py')
|
||||
|
||||
path_new_version = glob.glob(path + '/*-master')[0]
|
||||
dir_util.copy_tree(path_new_version, './') # ⭐ 将最新版本代码复制到当前目录
|
||||
|
||||
log亮绿('代码已经更新，即将更新pip包依赖……')
|
||||
for i in reversed(range(5)): time.sleep(1); log亮绿(i)
|
||||
|
||||
try:
|
||||
dir_util.copy_tree(path_new_version, './')
|
||||
print亮绿('代码已经更新，即将更新pip包依赖……')
|
||||
for i in reversed(range(5)): time.sleep(1); print(i)
|
||||
try:
|
||||
import subprocess
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
|
||||
except:
|
||||
log亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
|
||||
log亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
|
||||
log亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
log亮绿(' ------------------------------ -----------------------------------')
|
||||
|
||||
for i in reversed(range(8)): time.sleep(1); log亮绿(i)
|
||||
os.execl(sys.executable, sys.executable, *sys.argv) # 重启程序
|
||||
print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
|
||||
print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
print(' ------------------------------ -----------------------------------')
|
||||
for i in reversed(range(8)): time.sleep(1); print(i)
|
||||
os.execl(sys.executable, sys.executable, *sys.argv)
|
||||
|
||||
|
||||
def get_current_version():
|
||||
"""
|
||||
获取当前的版本号。
|
||||
|
||||
Returns:
|
||||
str: 当前的版本号。如果无法获取版本号，则返回空字符串。
|
||||
"""
|
||||
import json
|
||||
try:
|
||||
with open('./version', 'r', encoding='utf8') as f:
|
||||
current_version = json.loads(f.read())['version'] # ⭐ 从读取的json数据中提取版本号
|
||||
current_version = json.loads(f.read())['version']
|
||||
except:
|
||||
current_version = ""
|
||||
return current_version
|
||||
@@ -168,12 +106,6 @@ def get_current_version():
|
||||
def auto_update(raise_error=False):
|
||||
"""
|
||||
一键更新协议：查询版本和用户意见
|
||||
|
||||
Args:
|
||||
raise_error (bool, optional): 是否在出错时抛出错误。默认为 False。
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
try:
|
||||
from toolbox import get_conf
|
||||
@@ -181,7 +113,7 @@ def auto_update(raise_error=False):
|
||||
import json
|
||||
proxies = get_conf('proxies')
|
||||
try: response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
|
||||
except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
|
||||
except: response = requests.get("https://public.gpt-academic.top/publish/version", proxies=proxies, timeout=5)
|
||||
remote_json_data = json.loads(response.text)
|
||||
remote_version = remote_json_data['version']
|
||||
if remote_json_data["show_feature"]:
|
||||
@@ -192,22 +124,22 @@ def auto_update(raise_error=False):
|
||||
current_version = f.read()
|
||||
current_version = json.loads(current_version)['version']
|
||||
if (remote_version - current_version) >= 0.01-1e-5:
|
||||
from shared_utils.colorful import log亮黄
|
||||
log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}') # ⭐ 在控制台打印新版本信息
|
||||
logger.info('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
|
||||
from colorful import print亮黄
|
||||
print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
|
||||
print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
|
||||
user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
|
||||
if user_instruction in ['Y', 'y']:
|
||||
path = backup_and_download(current_version, remote_version) # ⭐ 备份并下载文件
|
||||
path = backup_and_download(current_version, remote_version)
|
||||
try:
|
||||
patch_and_restart(path) # ⭐ 执行覆盖并重启操作
|
||||
patch_and_restart(path)
|
||||
except:
|
||||
msg = '更新失败。'
|
||||
if raise_error:
|
||||
from toolbox import trimmed_format_exc
|
||||
msg += trimmed_format_exc()
|
||||
logger.warning(msg)
|
||||
print(msg)
|
||||
else:
|
||||
logger.info('自动更新程序：已禁用')
|
||||
print('自动更新程序：已禁用')
|
||||
return
|
||||
else:
|
||||
return
|
||||
@@ -216,13 +148,10 @@ def auto_update(raise_error=False):
|
||||
if raise_error:
|
||||
from toolbox import trimmed_format_exc
|
||||
msg += trimmed_format_exc()
|
||||
logger.info(msg)
|
||||
print(msg)
|
||||
|
||||
def warm_up_modules():
|
||||
"""
|
||||
预热模块，加载特定模块并执行预热操作。
|
||||
"""
|
||||
logger.info('正在执行一些模块的预热 ...')
|
||||
print('正在执行一些模块的预热 ...')
|
||||
from toolbox import ProxyNetworkActivate
|
||||
from request_llms.bridge_all import model_info
|
||||
with ProxyNetworkActivate("Warmup_Modules"):
|
||||
@@ -230,28 +159,18 @@ def warm_up_modules():
|
||||
enc.encode("模块预热", disallowed_special=())
|
||||
enc = model_info["gpt-4"]['tokenizer']
|
||||
enc.encode("模块预热", disallowed_special=())
|
||||
|
||||
|
||||
def warm_up_vectordb():
|
||||
"""
|
||||
执行一些模块的预热操作。
|
||||
|
||||
本函数主要用于执行一些模块的预热操作，确保在后续的流程中能够顺利运行。
|
||||
|
||||
⭐ 关键作用：预热模块
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
logger.info('正在执行一些模块的预热 ...')
|
||||
print('正在执行一些模块的预热 ...')
|
||||
from toolbox import ProxyNetworkActivate
|
||||
with ProxyNetworkActivate("Warmup_Modules"):
|
||||
import nltk
|
||||
with ProxyNetworkActivate("Warmup_Modules"): nltk.download("punkt")
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import os
|
||||
os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
check_proxy(proxies)
|
||||
check_proxy(proxies)
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import platform
|
||||
from sys import stdout
|
||||
from loguru import logger
|
||||
|
||||
if platform.system()=="Linux":
|
||||
pass
|
||||
else:
|
||||
else:
|
||||
from colorama import init
|
||||
init()
|
||||
|
||||
@@ -60,29 +59,3 @@ def sprint亮紫(*kw):
|
||||
return "\033[1;35m"+' '.join(kw)+"\033[0m"
|
||||
def sprint亮靛(*kw):
|
||||
return "\033[1;36m"+' '.join(kw)+"\033[0m"
|
||||
|
||||
def log红(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint红(*kw))
|
||||
def log绿(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint绿(*kw))
|
||||
def log黄(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint黄(*kw))
|
||||
def log蓝(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint蓝(*kw))
|
||||
def log紫(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint紫(*kw))
|
||||
def log靛(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint靛(*kw))
|
||||
|
||||
def log亮红(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮红(*kw))
|
||||
def log亮绿(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮绿(*kw))
|
||||
def log亮黄(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮黄(*kw))
|
||||
def log亮蓝(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮蓝(*kw))
|
||||
def log亮紫(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮紫(*kw))
|
||||
def log亮靛(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮靛(*kw))
|
||||
177
config.py
177
config.py
@@ -2,8 +2,8 @@
|
||||
以下所有配置也都支持利用环境变量覆写，环境变量配置格式见docker-compose.yml。
|
||||
读取优先级：环境变量 > config_private.py > config.py
|
||||
--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
|
||||
All the following configurations also support using environment variables to override,
|
||||
and the environment variable configuration format can be seen in docker-compose.yml.
|
||||
All the following configurations also support using environment variables to override,
|
||||
and the environment variable configuration format can be seen in docker-compose.yml.
|
||||
Configuration reading priority: environment variable > config_private.py > config.py
|
||||
"""
|
||||
|
||||
@@ -30,44 +30,11 @@ if USE_PROXY:
|
||||
else:
|
||||
proxies = None
|
||||
|
||||
# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
|
||||
LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
|
||||
AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
|
||||
"gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
|
||||
"gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
|
||||
"gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
|
||||
"gemini-1.5-pro", "chatglm3"
|
||||
]
|
||||
|
||||
EMBEDDING_MODEL = "text-embedding-3-small"
|
||||
|
||||
# --- --- --- ---
|
||||
# P.S. 其他可用的模型还包括
|
||||
# AVAIL_LLM_MODELS = [
|
||||
# "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
|
||||
# "qianfan", "deepseekcoder",
|
||||
# "spark", "sparkv2", "sparkv3", "sparkv3.5", "sparkv4",
|
||||
# "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
|
||||
# "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
|
||||
# "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
|
||||
# "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
|
||||
# "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
|
||||
# "deepseek-chat" ,"deepseek-coder",
|
||||
# "gemini-1.5-flash",
|
||||
# "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
|
||||
# ]
|
||||
# --- --- --- ---
|
||||
# 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
|
||||
# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
|
||||
# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
|
||||
# --- --- --- ---
|
||||
|
||||
|
||||
# --------------- 以下配置可以优化体验 ---------------
|
||||
# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
|
||||
|
||||
# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
|
||||
# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
|
||||
# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
|
||||
# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
|
||||
# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
|
||||
API_URL_REDIRECT = {}
|
||||
|
||||
|
||||
@@ -99,7 +66,7 @@ LAYOUT = "LEFT-RIGHT" # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下
|
||||
|
||||
|
||||
# 暗色模式 / 亮色模式
|
||||
DARK_MODE = True
|
||||
DARK_MODE = True
|
||||
|
||||
|
||||
# 发送请求到OpenAI后，等待多久判定为超时
|
||||
@@ -110,10 +77,6 @@ TIMEOUT_SECONDS = 30
|
||||
WEB_PORT = -1
|
||||
|
||||
|
||||
# 是否自动打开浏览器页面
|
||||
AUTO_OPEN_BROWSER = True
|
||||
|
||||
|
||||
# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
|
||||
MAX_RETRY = 2
|
||||
|
||||
@@ -122,6 +85,20 @@ MAX_RETRY = 2
|
||||
DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
|
||||
|
||||
|
||||
# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
|
||||
LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
|
||||
AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
|
||||
"gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
|
||||
"gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
|
||||
"gemini-pro", "chatglm3", "claude-2", "zhipuai"]
|
||||
# P.S. 其他可用的模型还包括 [
|
||||
# "moss", "qwen-turbo", "qwen-plus", "qwen-max"
|
||||
# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613",
|
||||
# "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
|
||||
# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
|
||||
# ]
|
||||
|
||||
|
||||
# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
|
||||
MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
|
||||
|
||||
@@ -139,7 +116,7 @@ DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
|
||||
# 百度千帆（LLM_MODEL="qianfan"）
|
||||
BAIDU_CLOUD_API_KEY = ''
|
||||
BAIDU_CLOUD_SECRET_KEY = ''
|
||||
BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
|
||||
BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
|
||||
|
||||
|
||||
# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
|
||||
@@ -150,7 +127,6 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
|
||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
||||
LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
|
||||
|
||||
|
||||
# 设置gradio的并行线程数（不需要修改）
|
||||
CONCURRENT_COUNT = 100
|
||||
|
||||
@@ -168,8 +144,7 @@ ADD_WAIFU = False
|
||||
AUTHENTICATION = []
|
||||
|
||||
|
||||
# 如果需要在二级路径下运行（常规情况下，不要修改!!）
|
||||
# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
|
||||
# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
|
||||
CUSTOM_PATH = "/"
|
||||
|
||||
|
||||
@@ -183,7 +158,7 @@ API_ORG = ""
|
||||
|
||||
|
||||
# 如果需要使用Slack Claude，使用教程详情见 request_llms/README.md
|
||||
SLACK_CLAUDE_BOT_ID = ''
|
||||
SLACK_CLAUDE_BOT_ID = ''
|
||||
SLACK_CLAUDE_USER_TOKEN = ''
|
||||
|
||||
|
||||
@@ -197,8 +172,14 @@ AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.
|
||||
AZURE_CFG_ARRAY = {}
|
||||
|
||||
|
||||
# 阿里云实时语音识别 配置难度较高
|
||||
# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
|
||||
# 使用Newbing (不推荐使用，未来将删除)
|
||||
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
|
||||
NEWBING_COOKIES = """
|
||||
put your new bing cookies here
|
||||
"""
|
||||
|
||||
|
||||
# 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
|
||||
ENABLE_AUDIO = False
|
||||
ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
||||
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
||||
@@ -206,12 +187,6 @@ ALIYUN_ACCESSKEY="" # （无需填写）
|
||||
ALIYUN_SECRET="" # （无需填写）
|
||||
|
||||
|
||||
# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
|
||||
TTS_TYPE = "EDGE_TTS" # EDGE_TTS / LOCAL_SOVITS_API / DISABLE
|
||||
GPT_SOVITS_URL = ""
|
||||
EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
|
||||
|
||||
|
||||
# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
|
||||
XFYUN_APPID = "00000000"
|
||||
XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
|
||||
@@ -220,38 +195,19 @@ XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
|
||||
|
||||
# 接入智谱大模型
|
||||
ZHIPUAI_API_KEY = ""
|
||||
ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
|
||||
ZHIPUAI_MODEL = "glm-4" # 可选 "glm-3-turbo" "glm-4"
|
||||
|
||||
|
||||
# # 火山引擎YUNQUE大模型
|
||||
# YUNQUE_SECRET_KEY = ""
|
||||
# YUNQUE_ACCESS_KEY = ""
|
||||
# YUNQUE_MODEL = ""
|
||||
|
||||
|
||||
# Claude API KEY
|
||||
ANTHROPIC_API_KEY = ""
|
||||
|
||||
|
||||
# 月之暗面 API KEY
|
||||
MOONSHOT_API_KEY = ""
|
||||
|
||||
|
||||
# 零一万物(Yi Model) API KEY
|
||||
YIMODEL_API_KEY = ""
|
||||
|
||||
|
||||
# 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
|
||||
DEEPSEEK_API_KEY = ""
|
||||
|
||||
|
||||
# 紫东太初大模型 https://ai-maas.wair.ac.cn
|
||||
TAICHU_API_KEY = ""
|
||||
|
||||
|
||||
# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
|
||||
MATHPIX_APPID = ""
|
||||
MATHPIX_APPKEY = ""
|
||||
|
||||
|
||||
# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
|
||||
DOC2X_API_KEY = ""
|
||||
|
||||
|
||||
# 自定义API KEY格式
|
||||
CUSTOM_API_KEY_PATTERN = ""
|
||||
|
||||
@@ -268,15 +224,11 @@ HUGGINGFACE_ACCESS_TOKEN = "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"
|
||||
# 获取方法：复制以下空间https://huggingface.co/spaces/qingxu98/grobid，设为public，然后GROBID_URL = "https://(你的hf用户名如qingxu98)-(你的填写的空间名如grobid).hf.space"
|
||||
GROBID_URLS = [
|
||||
"https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space",
|
||||
"https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space",
|
||||
"https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space",
|
||||
"https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space",
|
||||
"https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space",
|
||||
]
|
||||
|
||||
|
||||
# Searxng互联网检索服务
|
||||
SEARXNG_URL = "https://cloud-1.agent-matrix.com/"
|
||||
|
||||
|
||||
# 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
|
||||
ALLOW_RESET_CONFIG = False
|
||||
|
||||
@@ -285,21 +237,21 @@ ALLOW_RESET_CONFIG = False
|
||||
AUTOGEN_USE_DOCKER = False
|
||||
|
||||
|
||||
# 临时的上传文件夹位置，请尽量不要修改
|
||||
# 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，请尽量不要修改
|
||||
# 日志文件夹的位置，请勿修改
|
||||
PATH_LOGGING = "gpt_log"
|
||||
|
||||
|
||||
# 存储翻译好的arxiv论文的路径，请尽量不要修改
|
||||
ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
|
||||
# 除了连接OpenAI之外，还有哪些场合允许使用代理，请勿修改
|
||||
WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
|
||||
"Warmup_Modules", "Nougat_Download", "AutoGen"]
|
||||
|
||||
|
||||
# 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
|
||||
WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
|
||||
"Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
|
||||
# *实验性功能*: 自动检测并屏蔽失效的KEY，请勿使用
|
||||
BLOCK_INVALID_APIKEY = False
|
||||
|
||||
|
||||
# 启用插件热加载
|
||||
@@ -309,15 +261,7 @@ PLUGIN_HOT_RELOAD = False
|
||||
# 自定义按钮的最大数量限制
|
||||
NUM_CUSTOM_BASIC_BTN = 4
|
||||
|
||||
|
||||
# 媒体智能体的服务地址（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
|
||||
DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
|
||||
|
||||
|
||||
"""
|
||||
--------------- 配置关联关系说明 ---------------
|
||||
|
||||
在线大模型配置关联关系示意图
|
||||
│
|
||||
├── "gpt-3.5-turbo" 等openai模型
|
||||
@@ -341,7 +285,7 @@ DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
│ ├── XFYUN_API_SECRET
|
||||
│ └── XFYUN_API_KEY
|
||||
│
|
||||
├── "claude-3-opus-20240229" 等claude模型
|
||||
├── "claude-1-100k" 等claude模型
|
||||
│ └── ANTHROPIC_API_KEY
|
||||
│
|
||||
├── "stack-claude"
|
||||
@@ -353,11 +297,9 @@ DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
│ ├── BAIDU_CLOUD_API_KEY
|
||||
│ └── BAIDU_CLOUD_SECRET_KEY
|
||||
│
|
||||
├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
|
||||
│ └── ZHIPUAI_API_KEY
|
||||
│
|
||||
├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
|
||||
│ └── YIMODEL_API_KEY
|
||||
├── "zhipuai" 智谱AI大模型chatglm_turbo
|
||||
│ ├── ZHIPUAI_API_KEY
|
||||
│ └── ZHIPUAI_MODEL
|
||||
│
|
||||
├── "qwen-turbo" 等通义千问大模型
|
||||
│ └── DASHSCOPE_API_KEY
|
||||
@@ -365,12 +307,11 @@ DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
├── "Gemini"
|
||||
│ └── GEMINI_API_KEY
|
||||
│
|
||||
└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
|
||||
├── AVAIL_LLM_MODELS
|
||||
├── API_KEY
|
||||
└── API_URL_REDIRECT
|
||||
|
||||
└── "newbing" Newbing接口不再稳定，不推荐使用
|
||||
├── NEWBING_STYLE
|
||||
└── NEWBING_COOKIES
|
||||
|
||||
|
||||
本地大模型示意图
|
||||
│
|
||||
├── "chatglm3"
|
||||
@@ -402,9 +343,6 @@ DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
|
||||
插件在线服务配置依赖关系示意图
|
||||
│
|
||||
├── 互联网检索
|
||||
│ └── SEARXNG_URL
|
||||
│
|
||||
├── 语音功能
|
||||
│ ├── ENABLE_AUDIO
|
||||
│ ├── ALIYUN_TOKEN
|
||||
@@ -413,9 +351,6 @@ DAAS_SERVER_URL = "https://hamercity-bbdown.hf.space/stream"
|
||||
│ └── ALIYUN_SECRET
|
||||
│
|
||||
└── PDF文档精准解析
|
||||
├── GROBID_URLS
|
||||
├── MATHPIX_APPID
|
||||
└── MATHPIX_APPKEY
|
||||
|
||||
└── GROBID_URLS
|
||||
|
||||
"""
|
||||
|
||||
@@ -17,7 +17,7 @@ def get_core_functions():
|
||||
text_show_english=
|
||||
r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
|
||||
r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
|
||||
r"Firstly, you should provide the polished paragraph (in English). "
|
||||
r"Firstly, you should provide the polished paragraph. "
|
||||
r"Secondly, you should list all your modification and explain the reasons to do so in markdown table.",
|
||||
text_show_chinese=
|
||||
r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，"
|
||||
@@ -33,19 +33,17 @@ def get_core_functions():
|
||||
"AutoClearHistory": False,
|
||||
# [6] 文本预处理 （可选参数，默认 None，举例：写个函数移除所有的换行符）
|
||||
"PreProcess": None,
|
||||
# [7] 模型选择 （可选参数。如不设置，则使用当前全局模型；如设置，则用指定模型覆盖全局模型。）
|
||||
# "ModelOverride": "gpt-3.5-turbo", # 主要用途：强制点击此基础功能按钮时，使用指定的模型。
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"总结绘制脑图": {
|
||||
# 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
|
||||
"Prefix": '''"""\n\n''',
|
||||
"Prefix": r"",
|
||||
# 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
|
||||
"Suffix":
|
||||
# dedent() 函数用于去除多行字符串的缩进
|
||||
dedent("\n\n"+r'''
|
||||
"""
|
||||
dedent("\n"+r'''
|
||||
==============================
|
||||
|
||||
使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
|
||||
|
||||
@@ -59,15 +57,15 @@ def get_core_functions():
|
||||
C --> |"箭头名2"| F["节点名6"]
|
||||
```
|
||||
|
||||
注意：
|
||||
警告：
|
||||
（1）使用中文
|
||||
（2）节点名字使用引号包裹，如["Laptop"]
|
||||
（3）`|` 和 `"`之间不要存在空格
|
||||
（4）根据情况选择flowchart LR（从左到右）或者flowchart TD（从上到下）
|
||||
'''),
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"查找语法错误": {
|
||||
"Prefix": r"Help me ensure that the grammar and the spelling is correct. "
|
||||
r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good. "
|
||||
@@ -87,14 +85,14 @@ def get_core_functions():
|
||||
"Suffix": r"",
|
||||
"PreProcess": clear_line_break, # 预处理：清除换行符
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"中译英": {
|
||||
"Prefix": r"Please translate following sentence to English:" + "\n\n",
|
||||
"Suffix": r"",
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"学术英中互译": {
|
||||
"Prefix": build_gpt_academic_masked_string_langbased(
|
||||
text_show_chinese=
|
||||
@@ -114,29 +112,29 @@ def get_core_functions():
|
||||
) + "\n\n",
|
||||
"Suffix": r"",
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"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，"
|
||||
r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
|
||||
"Suffix": r"",
|
||||
"Visible": False,
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"解释代码": {
|
||||
"Prefix": r"请解释以下代码：" + "\n```\n",
|
||||
"Suffix": "\n```\n",
|
||||
},
|
||||
|
||||
|
||||
|
||||
|
||||
"参考文献转Bib": {
|
||||
"Prefix": r"Here are some bibliography items, please transform them into bibtex style."
|
||||
r"Note that, reference styles maybe more than one kind, you should transform each item correctly."
|
||||
|
||||
@@ -1,20 +1,695 @@
|
||||
from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
|
||||
from toolbox import trimmed_format_exc
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_crazy_functions():
|
||||
from crazy_functions.AntFin import AntFinTest
|
||||
from crazy_functions.读文章写摘要 import 读文章写摘要
|
||||
from crazy_functions.生成函数注释 import 批量生成函数注释
|
||||
from crazy_functions.解析项目源代码 import 解析项目本身
|
||||
from crazy_functions.解析项目源代码 import 解析一个Python项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Matlab项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
|
||||
from crazy_functions.解析项目源代码 import 解析一个C项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Golang项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Rust项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Java项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个前端项目
|
||||
from crazy_functions.高级功能函数模板 import 高阶功能模板函数
|
||||
from crazy_functions.Latex全文润色 import Latex英文润色
|
||||
from crazy_functions.询问多个大语言模型 import 同时问询
|
||||
from crazy_functions.解析项目源代码 import 解析一个Lua项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个CSharp项目
|
||||
from crazy_functions.总结word文档 import 总结word文档
|
||||
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
|
||||
from crazy_functions.对话历史存档 import 对话历史存档
|
||||
from crazy_functions.对话历史存档 import 载入对话历史存档
|
||||
from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
|
||||
from crazy_functions.辅助功能 import 清除缓存
|
||||
from crazy_functions.批量Markdown翻译 import Markdown英译中
|
||||
from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
|
||||
from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
|
||||
from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
|
||||
from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
|
||||
from crazy_functions.Latex全文润色 import Latex中文润色
|
||||
from crazy_functions.Latex全文润色 import Latex英文纠错
|
||||
from crazy_functions.批量Markdown翻译 import Markdown中译英
|
||||
from crazy_functions.虚空终端 import 虚空终端
|
||||
from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表
|
||||
|
||||
function_plugins = {
|
||||
"蚂小财测试": {
|
||||
"Group": "智能体",
|
||||
"虚空终端": {
|
||||
"Group": "对话|编程|学术|智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Function": HotReload(虚空终端),
|
||||
},
|
||||
"解析整个Python项目": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Info": "解析一个Python项目的所有源文件(.py) | 输入参数为路径",
|
||||
"Function": HotReload(解析一个Python项目),
|
||||
},
|
||||
"载入对话历史存档（先上传存档或输入路径）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "蚂小财测试",
|
||||
"Function": HotReload(AntFinTest),
|
||||
"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),
|
||||
},
|
||||
"生成多种Mermaid图表(从当前对话或文件(.pdf/.md)中生产图表）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info" : "基于当前对话或PDF生成多种Mermaid图表,图表类型由模型判断",
|
||||
"Function": HotReload(生成多种Mermaid图表),
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
|
||||
},
|
||||
"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Matlab项目": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "解析一个Matlab项目的所有源文件(.m) | 输入参数为路径",
|
||||
"Function": HotReload(解析一个Matlab项目),
|
||||
},
|
||||
"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),
|
||||
},
|
||||
"历史上的今天": {
|
||||
"Group": "对话",
|
||||
"AsButton": True,
|
||||
"Info": "查看历史上的今天事件 (这是一个面向开发者的插件Demo) | 不需要输入参数",
|
||||
"Function": HotReload(高阶功能模板函数),
|
||||
},
|
||||
"精准翻译PDF论文": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Info": "精准翻译PDF论文为中文 | 输入参数为路径",
|
||||
"Function": HotReload(批量翻译PDF文档),
|
||||
},
|
||||
"询问多个GPT模型": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Function": HotReload(同时问询),
|
||||
},
|
||||
"批量总结PDF文档": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "批量总结PDF文档的内容 | 输入参数为路径",
|
||||
"Function": HotReload(批量总结PDF文档),
|
||||
},
|
||||
"谷歌学术检索助手（输入谷歌学术搜索页url）": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
|
||||
"Function": HotReload(谷歌检索小助手),
|
||||
},
|
||||
"理解PDF文档内容 （模仿ChatPDF）": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
|
||||
"Function": HotReload(理解PDF文档内容标准文件输入),
|
||||
},
|
||||
"英文Latex项目全文润色（输入路径或上传压缩包）": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "对英文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
|
||||
"Function": HotReload(Latex英文润色),
|
||||
},
|
||||
|
||||
"中文Latex项目全文润色（输入路径或上传压缩包）": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "对中文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
|
||||
"Function": HotReload(Latex中文润色),
|
||||
},
|
||||
# 已经被新插件取代
|
||||
# "英文Latex项目全文纠错（输入路径或上传压缩包）": {
|
||||
# "Group": "学术",
|
||||
# "Color": "stop",
|
||||
# "AsButton": False, # 加入下拉菜单中
|
||||
# "Info": "对英文Latex项目全文进行纠错处理 | 输入参数为路径或上传压缩包",
|
||||
# "Function": HotReload(Latex英文纠错),
|
||||
# },
|
||||
# 已经被新插件取代
|
||||
# "Latex项目全文中译英（输入路径或上传压缩包）": {
|
||||
# "Group": "学术",
|
||||
# "Color": "stop",
|
||||
# "AsButton": False, # 加入下拉菜单中
|
||||
# "Info": "对Latex项目全文进行中译英处理 | 输入参数为路径或上传压缩包",
|
||||
# "Function": HotReload(Latex中译英)
|
||||
# },
|
||||
# 已经被新插件取代
|
||||
# "Latex项目全文英译中（输入路径或上传压缩包）": {
|
||||
# "Group": "学术",
|
||||
# "Color": "stop",
|
||||
# "AsButton": False, # 加入下拉菜单中
|
||||
# "Info": "对Latex项目全文进行英译中处理 | 输入参数为路径或上传压缩包",
|
||||
# "Function": HotReload(Latex英译中)
|
||||
# },
|
||||
"批量Markdown中译英（输入路径或上传压缩包）": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
|
||||
"Function": HotReload(Markdown中译英),
|
||||
},
|
||||
}
|
||||
|
||||
# -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
|
||||
try:
|
||||
from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
# "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
|
||||
"Function": HotReload(下载arxiv论文并翻译摘要),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.联网的ChatGPT import 连接网络回答问题
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
# "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
|
||||
"Function": HotReload(连接网络回答问题),
|
||||
}
|
||||
}
|
||||
)
|
||||
from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
|
||||
"Function": HotReload(连接bing搜索回答问题),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.解析项目源代码 import 解析任意code项目
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"解析项目源代码（手动指定和筛选源代码文件类型）": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": '输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: "*.c, ^*.cpp, config.toml, ^*.toml"', # 高级参数输入区的显示提示
|
||||
"Function": HotReload(解析任意code项目),
|
||||
},
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"询问多个GPT模型（手动指定询问哪些模型）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&gpt-4", # 高级参数输入区的显示提示
|
||||
"Function": HotReload(同时问询_指定模型),
|
||||
},
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"图片生成_DALLE2 （先切换模型到gpt-*）": {
|
||||
"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1024x1024（默认），支持 256x256, 512x512, 1024x1024", # 高级参数输入区的显示提示
|
||||
"Info": "使用DALLE2生成图片 | 输入参数字符串，提供图像的内容",
|
||||
"Function": HotReload(图片生成_DALLE2),
|
||||
},
|
||||
}
|
||||
)
|
||||
function_plugins.update(
|
||||
{
|
||||
"图片生成_DALLE3 （先切换模型到gpt-*）": {
|
||||
"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": "在这里输入自定义参数「分辨率-质量(可选)-风格(可选)」, 参数示例「1024x1024-hd-vivid」 || 分辨率支持 「1024x1024」(默认) /「1792x1024」/「1024x1792」 || 质量支持 「-standard」(默认) /「-hd」 || 风格支持 「-vivid」(默认) /「-natural」", # 高级参数输入区的显示提示
|
||||
"Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
|
||||
"Function": HotReload(图片生成_DALLE3),
|
||||
},
|
||||
}
|
||||
)
|
||||
function_plugins.update(
|
||||
{
|
||||
"图片修改_DALLE2 （先切换模型到gpt-*）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": False, # 调用时，唤起高级参数输入区（默认False）
|
||||
# "Info": "使用DALLE2修改图片 | 输入参数字符串，提供图像的内容",
|
||||
"Function": HotReload(图片修改_DALLE2),
|
||||
},
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.总结音视频 import 总结音视频
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"批量总结音视频（输入路径或上传压缩包）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
|
||||
"Info": "批量总结音频或视频 | 输入参数为路径",
|
||||
"Function": HotReload(总结音视频),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.数学动画生成manim import 动画生成
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"数学动画生成（Manim）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
|
||||
"Function": HotReload(动画生成),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"Markdown翻译（指定翻译成何种语言）": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": "请输入要翻译成哪种语言，默认为Chinese。",
|
||||
"Function": HotReload(Markdown翻译指定语言),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.知识库问答 import 知识库文件注入
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"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": "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(知识库文件注入),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.知识库问答 import 读取知识库作答
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"知识库文件注入（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, 您需要构建知识库后再运行此插件。",
|
||||
"Function": HotReload(读取知识库作答),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
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Demo函数（查找wallhaven.cc的壁纸）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(交互功能模板函数),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
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
|
||||
from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
|
||||
|
||||
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),
|
||||
},
|
||||
"Arxiv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),
|
||||
},
|
||||
"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(trimmed_format_exc())
|
||||
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(
|
||||
{
|
||||
"实时语音对话": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Info": "这是一个时刻聆听着的语音对话助手 | 没有输入参数",
|
||||
"Function": HotReload(语音助手),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
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(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.函数动态生成 import 函数动态生成
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"动态代码解释器（CodeInterpreter）": {
|
||||
"Group": "智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(函数动态生成),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.多智能体 import 多智能体终端
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"AutoGen多智能体终端（仅供测试）": {
|
||||
"Group": "智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(多智能体终端),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.互动小游戏 import 随机小游戏
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"随机互动小游戏（仅供测试）": {
|
||||
"Group": "智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(随机小游戏),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
# try:
|
||||
# from crazy_functions.高级功能函数模板 import 测试图表渲染
|
||||
# function_plugins.update({
|
||||
# "绘制逻辑关系（测试图表渲染）": {
|
||||
# "Group": "智能体",
|
||||
# "Color": "stop",
|
||||
# "AsButton": True,
|
||||
# "Function": HotReload(测试图表渲染)
|
||||
# }
|
||||
# })
|
||||
# except:
|
||||
# print(trimmed_format_exc())
|
||||
# print('Load function plugin failed')
|
||||
|
||||
# try:
|
||||
# from crazy_functions.chatglm微调工具 import 微调数据集生成
|
||||
# function_plugins.update({
|
||||
# "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
|
||||
# "Color": "stop",
|
||||
# "AsButton": False,
|
||||
# "AdvancedArgs": True,
|
||||
# "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
|
||||
# "Function": HotReload(微调数据集生成)
|
||||
# }
|
||||
# })
|
||||
# except:
|
||||
# print('Load function plugin failed')
|
||||
|
||||
"""
|
||||
设置默认值:
|
||||
@@ -34,15 +709,3 @@ def get_crazy_functions():
|
||||
function_plugins[name]["Color"] = "secondary"
|
||||
|
||||
return function_plugins
|
||||
|
||||
|
||||
def get_multiplex_button_functions():
|
||||
"""多路复用主提交按钮的功能映射
|
||||
"""
|
||||
return {
|
||||
"常规对话":
|
||||
"",
|
||||
|
||||
"蚂小财测试":
|
||||
"蚂小财测试", # 映射到上面的 `询问多个GPT模型` 插件
|
||||
}
|
||||
|
||||
@@ -1,9 +0,0 @@
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
|
||||
|
||||
|
||||
@CatchException
|
||||
def AntFinTest(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
chatbot.append(("AntFin Test", "AntFin Test"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
|
||||
@@ -1,43 +0,0 @@
|
||||
|
||||
from toolbox import get_conf, update_ui
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
from crazy_functions.AntFin import AntFinTest
|
||||
|
||||
|
||||
class ImageGen_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
|
||||
第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="输入图片描述", description="需要生成图像的文本描述，尽量使用英文", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"model_name":
|
||||
ArgProperty(title="模型", options=["DALLE2", "DALLE3"], default_value="DALLE3", description="无", type="dropdown").model_dump_json(),
|
||||
"resolution":
|
||||
ArgProperty(title="分辨率", options=["256x256(限DALLE2)", "512x512(限DALLE2)", "1024x1024", "1792x1024(限DALLE3)", "1024x1792(限DALLE3)"], default_value="1024x1024", description="无", type="dropdown").model_dump_json(),
|
||||
"quality (仅DALLE3生效)":
|
||||
ArgProperty(title="质量", options=["standard", "hd"], default_value="standard", description="无", type="dropdown").model_dump_json(),
|
||||
"style (仅DALLE3生效)":
|
||||
ArgProperty(title="风格", options=["vivid", "natural"], default_value="vivid", description="无", type="dropdown").model_dump_json(),
|
||||
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
yield from AntFinTest(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
|
||||
232
crazy_functions/CodeInterpreter.py
Normal file
232
crazy_functions/CodeInterpreter.py
Normal file
@@ -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, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
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
|
||||
"""
|
||||
245
crazy_functions/Latex全文润色.py
Normal file
245
crazy_functions/Latex全文润色.py
Normal file
@@ -0,0 +1,245 @@
|
||||
from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
|
||||
from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
|
||||
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
self.file_paths = []
|
||||
self.file_contents = []
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
self.get_token_num = get_token_num
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
for index, file_content in enumerate(self.file_contents):
|
||||
if self.get_token_num(file_content) < max_token_limit:
|
||||
self.sp_file_contents.append(file_content)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
|
||||
|
||||
print('Segmentation: done')
|
||||
def merge_result(self):
|
||||
self.file_result = ["" for _ in range(len(self.file_paths))]
|
||||
for r, k in zip(self.sp_file_result, self.sp_file_index):
|
||||
self.file_result[k] += r
|
||||
|
||||
def write_result(self):
|
||||
manifest = []
|
||||
for path, res in zip(self.file_paths, self.file_result):
|
||||
with open(path + '.polish.tex', 'w', encoding='utf8') as f:
|
||||
manifest.append(path + '.polish.tex')
|
||||
f.write(res)
|
||||
return manifest
|
||||
|
||||
def zip_result(self):
|
||||
import os, time
|
||||
folder = os.path.dirname(self.file_paths[0])
|
||||
t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
|
||||
zip_folder(folder, 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'):
|
||||
import time, os, re
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
|
||||
# <-------- 读取Latex文件，删除其中的所有注释 ---------->
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
# 定义注释的正则表达式
|
||||
comment_pattern = r'(?<!\\)%.*'
|
||||
# 使用正则表达式查找注释，并替换为空字符串
|
||||
clean_tex_content = re.sub(comment_pattern, '', file_content)
|
||||
# 记录删除注释后的文本
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(clean_tex_content)
|
||||
|
||||
# <-------- 拆分过长的latex文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
|
||||
# <-------- 多线程润色开始 ---------->
|
||||
if language == 'en':
|
||||
if mode == 'polish':
|
||||
inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " +
|
||||
"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
else:
|
||||
inputs_array = [r"Below is a section from an academic paper, proofread this section." +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
|
||||
r"Answer me only with the revised text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"Polish {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
|
||||
elif language == 'zh':
|
||||
if mode == 'polish':
|
||||
inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
else:
|
||||
inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]
|
||||
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
inputs_show_user_array=inputs_show_user_array,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[""] for _ in range(n_split)],
|
||||
sys_prompt_array=sys_prompt_array,
|
||||
# max_workers=5, # 并行任务数量限制，最多同时执行5个，其他的排队等待
|
||||
scroller_max_len = 80
|
||||
)
|
||||
|
||||
# <-------- 文本碎片重组为完整的tex文件，整理结果为压缩包 ---------->
|
||||
try:
|
||||
pfg.sp_file_result = []
|
||||
for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
|
||||
pfg.sp_file_result.append(gpt_say)
|
||||
pfg.merge_result()
|
||||
pfg.write_result()
|
||||
pfg.zip_result()
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
|
||||
res = write_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
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 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。（注意，此插件不调用Latex，如果有Latex环境，请使用「Latex英文纠错+高亮修正位置(需Latex)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
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行纠错。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')
|
||||
|
||||
|
||||
|
||||
176
crazy_functions/Latex全文翻译.py
Normal file
176
crazy_functions/Latex全文翻译.py
Normal file
@@ -0,0 +1,176 @@
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, write_history_to_file
|
||||
fast_debug = False
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
self.file_paths = []
|
||||
self.file_contents = []
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
self.get_token_num = get_token_num
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
for index, file_content in enumerate(self.file_contents):
|
||||
if self.get_token_num(file_content) < max_token_limit:
|
||||
self.sp_file_contents.append(file_content)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
|
||||
|
||||
print('Segmentation: done')
|
||||
|
||||
def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
|
||||
import time, os, re
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
# <-------- 读取Latex文件，删除其中的所有注释 ---------->
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
# 定义注释的正则表达式
|
||||
comment_pattern = r'(?<!\\)%.*'
|
||||
# 使用正则表达式查找注释，并替换为空字符串
|
||||
clean_tex_content = re.sub(comment_pattern, '', file_content)
|
||||
# 记录删除注释后的文本
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(clean_tex_content)
|
||||
|
||||
# <-------- 拆分过长的latex文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
# <-------- 抽取摘要 ---------->
|
||||
# if language == 'en':
|
||||
# abs_extract_inputs = f"Please write an abstract for this paper"
|
||||
|
||||
# # 单线，获取文章meta信息
|
||||
# paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
# inputs=abs_extract_inputs,
|
||||
# inputs_show_user=f"正在抽取摘要信息。",
|
||||
# llm_kwargs=llm_kwargs,
|
||||
# chatbot=chatbot, history=[],
|
||||
# sys_prompt="Your job is to collect information from materials。",
|
||||
# )
|
||||
|
||||
# <-------- 多线程润色开始 ---------->
|
||||
if language == 'en->zh':
|
||||
inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
elif language == 'zh->en':
|
||||
inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
inputs_show_user_array=inputs_show_user_array,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[""] for _ in range(n_split)],
|
||||
sys_prompt_array=sys_prompt_array,
|
||||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||||
scroller_max_len = 80
|
||||
)
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
|
||||
res = write_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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
|
||||
313
crazy_functions/Latex输出PDF结果.py
Normal file
313
crazy_functions/Latex输出PDF结果.py
Normal file
@@ -0,0 +1,313 @@
|
||||
from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
|
||||
from functools import partial
|
||||
import glob, os, requests, time, tarfile
|
||||
pj = os.path.join
|
||||
ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
# 专业词汇声明 = 'If the term "agent" is used in this section, it should be translated to "智能体". '
|
||||
def switch_prompt(pfg, mode, more_requirement):
|
||||
"""
|
||||
Generate prompts and system prompts based on the mode for proofreading or translating.
|
||||
Args:
|
||||
- pfg: Proofreader or Translator instance.
|
||||
- mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
|
||||
|
||||
Returns:
|
||||
- inputs_array: A list of strings containing prompts for users to respond to.
|
||||
- sys_prompt_array: A list of strings containing prompts for system prompts.
|
||||
"""
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
if mode == 'proofread_en':
|
||||
inputs_array = [r"Below is a section from an academic paper, proofread this section." +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
|
||||
r"Answer me only with the revised text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
|
||||
elif mode == 'translate_zh':
|
||||
inputs_array = [r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
|
||||
r"Answer me only with the translated text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
|
||||
else:
|
||||
assert False, "未知指令"
|
||||
return inputs_array, sys_prompt_array
|
||||
|
||||
def desend_to_extracted_folder_if_exist(project_folder):
|
||||
"""
|
||||
Descend into the extracted folder if it exists, otherwise return the original folder.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
|
||||
"""
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir) == 0: return project_folder
|
||||
if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
|
||||
return project_folder
|
||||
|
||||
def move_project(project_folder, arxiv_id=None):
|
||||
"""
|
||||
Create a new work folder and copy the project folder to it.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path of the project.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the new work folder.
|
||||
"""
|
||||
import shutil, time
|
||||
time.sleep(2) # avoid time string conflict
|
||||
if arxiv_id is not None:
|
||||
new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
|
||||
else:
|
||||
new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
|
||||
try:
|
||||
shutil.rmtree(new_workfolder)
|
||||
except:
|
||||
pass
|
||||
|
||||
# align subfolder if there is a folder wrapper
|
||||
items = glob.glob(pj(project_folder,'*'))
|
||||
items = [item for item in items if os.path.basename(item)!='__MACOSX']
|
||||
if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:
|
||||
if os.path.isdir(items[0]): project_folder = items[0]
|
||||
|
||||
shutil.copytree(src=project_folder, dst=new_workfolder)
|
||||
return new_workfolder
|
||||
|
||||
def arxiv_download(chatbot, history, txt, allow_cache=True):
|
||||
def check_cached_translation_pdf(arxiv_id):
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
|
||||
if not os.path.exists(translation_dir):
|
||||
os.makedirs(translation_dir)
|
||||
target_file = pj(translation_dir, 'translate_zh.pdf')
|
||||
if os.path.exists(target_file):
|
||||
promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
|
||||
target_file_compare = pj(translation_dir, 'comparison.pdf')
|
||||
if os.path.exists(target_file_compare):
|
||||
promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
|
||||
return target_file
|
||||
return False
|
||||
def is_float(s):
|
||||
try:
|
||||
float(s)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt.strip()
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt[:10]
|
||||
if not txt.startswith('https://arxiv.org'):
|
||||
return txt, None # 是本地文件，跳过下载
|
||||
|
||||
# <-------------- inspect format ------------->
|
||||
chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
|
||||
url_ = txt # https://arxiv.org/abs/1707.06690
|
||||
if not txt.startswith('https://arxiv.org/abs/'):
|
||||
msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
|
||||
yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
|
||||
return msg, None
|
||||
# <-------------- set format ------------->
|
||||
arxiv_id = url_.split('/abs/')[-1]
|
||||
if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
|
||||
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
||||
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
||||
|
||||
url_tar = url_.replace('/abs/', '/e-print/')
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
||||
os.makedirs(translation_dir, exist_ok=True)
|
||||
|
||||
# <-------------- download arxiv source file ------------->
|
||||
dst = pj(translation_dir, arxiv_id+'.tar')
|
||||
if os.path.exists(dst):
|
||||
yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面
|
||||
else:
|
||||
yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(url_tar, proxies=proxies)
|
||||
with open(dst, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
# <-------------- extract file ------------->
|
||||
yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history) # 刷新界面
|
||||
from toolbox import extract_archive
|
||||
extract_archive(file_path=dst, dest_dir=extract_dst)
|
||||
return extract_dst, arxiv_id
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([ "函数插件功能？",
|
||||
"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([ f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = desend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
project_folder = move_project(project_folder, arxiv_id=None)
|
||||
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)
|
||||
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
|
||||
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
@CatchException
|
||||
def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
no_cache = more_req.startswith("--no-cache")
|
||||
if no_cache: more_req.lstrip("--no-cache")
|
||||
allow_cache = not no_cache
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([ f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
try:
|
||||
txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
|
||||
except tarfile.ReadError as e:
|
||||
yield from update_ui_lastest_msg(
|
||||
"无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
|
||||
chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
if txt.endswith('.pdf'):
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(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
|
||||
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = desend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
project_folder = move_project(project_folder, arxiv_id)
|
||||
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)
|
||||
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
@@ -1,5 +1,4 @@
|
||||
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
|
||||
from loguru import logger
|
||||
|
||||
class EchoDemo(PluginMultiprocessManager):
|
||||
def subprocess_worker(self, child_conn):
|
||||
@@ -17,4 +16,4 @@ class EchoDemo(PluginMultiprocessManager):
|
||||
elif msg.cmd == "terminate":
|
||||
self.child_conn.send(PipeCom("done", ""))
|
||||
break
|
||||
logger.info('[debug] subprocess_worker terminated')
|
||||
print('[debug] subprocess_worker terminated')
|
||||
@@ -1,6 +1,5 @@
|
||||
from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_file_to_downloadzone
|
||||
from crazy_functions.agent_fns.watchdog import WatchDog
|
||||
from loguru import logger
|
||||
import time, os
|
||||
|
||||
class PipeCom:
|
||||
@@ -48,7 +47,7 @@ class PluginMultiprocessManager:
|
||||
def terminate(self):
|
||||
self.p.terminate()
|
||||
self.alive = False
|
||||
logger.info("[debug] instance terminated")
|
||||
print("[debug] instance terminated")
|
||||
|
||||
def subprocess_worker(self, child_conn):
|
||||
# ⭐⭐ run in subprocess
|
||||
@@ -73,7 +72,7 @@ class PluginMultiprocessManager:
|
||||
if file_type.lower() in ['png', 'jpg']:
|
||||
image_path = os.path.abspath(fp)
|
||||
self.chatbot.append([
|
||||
'检测到新生图像:',
|
||||
'检测到新生图像:',
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
yield from update_ui(chatbot=self.chatbot, history=self.history)
|
||||
@@ -115,21 +114,21 @@ class PluginMultiprocessManager:
|
||||
self.cnt = 1
|
||||
self.parent_conn = self.launch_subprocess_with_pipe() # ⭐⭐⭐
|
||||
repeated, cmd_to_autogen = self.send_command(txt)
|
||||
if txt == 'exit':
|
||||
if txt == 'exit':
|
||||
self.chatbot.append([f"结束", "结束信号已明确，终止AutoGen程序。"])
|
||||
yield from update_ui(chatbot=self.chatbot, history=self.history)
|
||||
self.terminate()
|
||||
return "terminate"
|
||||
|
||||
|
||||
# patience = 10
|
||||
|
||||
|
||||
while True:
|
||||
time.sleep(0.5)
|
||||
if not self.alive:
|
||||
# the heartbeat watchdog might have it killed
|
||||
self.terminate()
|
||||
return "terminate"
|
||||
if self.parent_conn.poll():
|
||||
if self.parent_conn.poll():
|
||||
self.feed_heartbeat_watchdog()
|
||||
if "[GPT-Academic] 等待中" in self.chatbot[-1][-1]:
|
||||
self.chatbot.pop(-1) # remove the last line
|
||||
@@ -153,8 +152,8 @@ class PluginMultiprocessManager:
|
||||
yield from update_ui(chatbot=self.chatbot, history=self.history)
|
||||
if msg.cmd == "interact":
|
||||
yield from self.overwatch_workdir_file_change()
|
||||
self.chatbot.append([f"程序抵达用户反馈节点.", msg.content +
|
||||
"\n\n等待您的进一步指令." +
|
||||
self.chatbot.append([f"程序抵达用户反馈节点.", msg.content +
|
||||
"\n\n等待您的进一步指令." +
|
||||
"\n\n(1) 一般情况下您不需要说什么, 清空输入区, 然后直接点击“提交”以继续. " +
|
||||
"\n\n(2) 如果您需要补充些什么, 输入要反馈的内容, 直接点击“提交”以继续. " +
|
||||
"\n\n(3) 如果您想终止程序, 输入exit, 直接点击“提交”以终止AutoGen并解锁. "
|
||||
|
||||
@@ -1,457 +0,0 @@
|
||||
import datetime
|
||||
import re
|
||||
import os
|
||||
from loguru import logger
|
||||
from textwrap import dedent
|
||||
from toolbox import CatchException, update_ui
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
# TODO: 解决缩进问题
|
||||
|
||||
find_function_end_prompt = '''
|
||||
Below is a page of code that you need to read. This page may not yet complete, you job is to split this page to sperate functions, class functions etc.
|
||||
- Provide the line number where the first visible function ends.
|
||||
- Provide the line number where the next visible function begins.
|
||||
- If there are no other functions in this page, you should simply return the line number of the last line.
|
||||
- Only focus on functions declared by `def` keyword. Ignore inline functions. Ignore function calls.
|
||||
|
||||
------------------ Example ------------------
|
||||
INPUT:
|
||||
|
||||
```
|
||||
L0000 |import sys
|
||||
L0001 |import re
|
||||
L0002 |
|
||||
L0003 |def trimmed_format_exc():
|
||||
L0004 | import os
|
||||
L0005 | import traceback
|
||||
L0006 | str = traceback.format_exc()
|
||||
L0007 | current_path = os.getcwd()
|
||||
L0008 | replace_path = "."
|
||||
L0009 | return str.replace(current_path, replace_path)
|
||||
L0010 |
|
||||
L0011 |
|
||||
L0012 |def trimmed_format_exc_markdown():
|
||||
L0013 | ...
|
||||
L0014 | ...
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
```
|
||||
<first_function_end_at>L0009</first_function_end_at>
|
||||
<next_function_begin_from>L0012</next_function_begin_from>
|
||||
```
|
||||
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ------------------
|
||||
```
|
||||
{THE_TAGGED_CODE}
|
||||
```
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
revise_funtion_prompt = '''
|
||||
You need to read the following code, and revise the source code ({FILE_BASENAME}) according to following instructions:
|
||||
1. You should analyze the purpose of the functions (if there are any).
|
||||
2. You need to add docstring for the provided functions (if there are any).
|
||||
|
||||
Be aware:
|
||||
1. You must NOT modify the indent of code.
|
||||
2. You are NOT authorized to change or translate non-comment code, and you are NOT authorized to add empty lines either, toggle qu.
|
||||
3. Use {LANG} to add comments and docstrings. Do NOT translate Chinese that is already in the code.
|
||||
4. Besides adding a docstring, use the ⭐ symbol to annotate the most core and important line of code within the function, explaining its role.
|
||||
|
||||
------------------ Example ------------------
|
||||
INPUT:
|
||||
```
|
||||
L0000 |
|
||||
L0001 |def zip_result(folder):
|
||||
L0002 | t = gen_time_str()
|
||||
L0003 | zip_folder(folder, get_log_folder(), f"result.zip")
|
||||
L0004 | return os.path.join(get_log_folder(), f"result.zip")
|
||||
L0005 |
|
||||
L0006 |
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
<instruction_1_purpose>
|
||||
This function compresses a given folder, and return the path of the resulting `zip` file.
|
||||
</instruction_1_purpose>
|
||||
<instruction_2_revised_code>
|
||||
```
|
||||
def zip_result(folder):
|
||||
"""
|
||||
Compresses the specified folder into a zip file and stores it in the log folder.
|
||||
|
||||
Args:
|
||||
folder (str): The path to the folder that needs to be compressed.
|
||||
|
||||
Returns:
|
||||
str: The path to the created zip file in the log folder.
|
||||
"""
|
||||
t = gen_time_str()
|
||||
zip_folder(folder, get_log_folder(), f"result.zip") # ⭐ Execute the zipping of folder
|
||||
return os.path.join(get_log_folder(), f"result.zip")
|
||||
```
|
||||
</instruction_2_revised_code>
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
|
||||
```
|
||||
{THE_CODE}
|
||||
```
|
||||
{INDENT_REMINDER}
|
||||
{BRIEF_REMINDER}
|
||||
{HINT_REMINDER}
|
||||
'''
|
||||
|
||||
|
||||
revise_funtion_prompt_chinese = '''
|
||||
您需要阅读以下代码，并根据以下说明修订源代码({FILE_BASENAME}):
|
||||
1. 如果源代码中包含函数的话, 你应该分析给定函数实现了什么功能
|
||||
2. 如果源代码中包含函数的话, 你需要为函数添加docstring, docstring必须使用中文
|
||||
|
||||
请注意：
|
||||
1. 你不得修改代码的缩进
|
||||
2. 你无权更改或翻译代码中的非注释部分，也不允许添加空行
|
||||
3. 使用 {LANG} 添加注释和文档字符串。不要翻译代码中已有的中文
|
||||
4. 除了添加docstring之外, 使用⭐符号给该函数中最核心、最重要的一行代码添加注释，并说明其作用
|
||||
|
||||
------------------ 示例 ------------------
|
||||
INPUT:
|
||||
```
|
||||
L0000 |
|
||||
L0001 |def zip_result(folder):
|
||||
L0002 | t = gen_time_str()
|
||||
L0003 | zip_folder(folder, get_log_folder(), f"result.zip")
|
||||
L0004 | return os.path.join(get_log_folder(), f"result.zip")
|
||||
L0005 |
|
||||
L0006 |
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
<instruction_1_purpose>
|
||||
该函数用于压缩指定文件夹，并返回生成的`zip`文件的路径。
|
||||
</instruction_1_purpose>
|
||||
<instruction_2_revised_code>
|
||||
```
|
||||
def zip_result(folder):
|
||||
"""
|
||||
该函数将指定的文件夹压缩成ZIP文件, 并将其存储在日志文件夹中。
|
||||
|
||||
输入参数:
|
||||
folder (str): 需要压缩的文件夹的路径。
|
||||
返回值:
|
||||
str: 日志文件夹中创建的ZIP文件的路径。
|
||||
"""
|
||||
t = gen_time_str()
|
||||
zip_folder(folder, get_log_folder(), f"result.zip") # ⭐ 执行文件夹的压缩
|
||||
return os.path.join(get_log_folder(), f"result.zip")
|
||||
```
|
||||
</instruction_2_revised_code>
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
|
||||
```
|
||||
{THE_CODE}
|
||||
```
|
||||
{INDENT_REMINDER}
|
||||
{BRIEF_REMINDER}
|
||||
{HINT_REMINDER}
|
||||
'''
|
||||
|
||||
|
||||
class PythonCodeComment():
|
||||
|
||||
def __init__(self, llm_kwargs, plugin_kwargs, language, observe_window_update) -> None:
|
||||
self.original_content = ""
|
||||
self.full_context = []
|
||||
self.full_context_with_line_no = []
|
||||
self.current_page_start = 0
|
||||
self.page_limit = 100 # 100 lines of code each page
|
||||
self.ignore_limit = 20
|
||||
self.llm_kwargs = llm_kwargs
|
||||
self.plugin_kwargs = plugin_kwargs
|
||||
self.language = language
|
||||
self.observe_window_update = observe_window_update
|
||||
if self.language == "chinese":
|
||||
self.core_prompt = revise_funtion_prompt_chinese
|
||||
else:
|
||||
self.core_prompt = revise_funtion_prompt
|
||||
self.path = None
|
||||
self.file_basename = None
|
||||
self.file_brief = ""
|
||||
|
||||
def generate_tagged_code_from_full_context(self):
|
||||
for i, code in enumerate(self.full_context):
|
||||
number = i
|
||||
padded_number = f"{number:04}"
|
||||
result = f"L{padded_number}"
|
||||
self.full_context_with_line_no.append(f"{result} | {code}")
|
||||
return self.full_context_with_line_no
|
||||
|
||||
def read_file(self, path, brief):
|
||||
with open(path, 'r', encoding='utf8') as f:
|
||||
self.full_context = f.readlines()
|
||||
self.original_content = ''.join(self.full_context)
|
||||
self.file_basename = os.path.basename(path)
|
||||
self.file_brief = brief
|
||||
self.full_context_with_line_no = self.generate_tagged_code_from_full_context()
|
||||
self.path = path
|
||||
|
||||
def find_next_function_begin(self, tagged_code:list, begin_and_end):
|
||||
begin, end = begin_and_end
|
||||
THE_TAGGED_CODE = ''.join(tagged_code)
|
||||
self.llm_kwargs['temperature'] = 0
|
||||
result = predict_no_ui_long_connection(
|
||||
inputs=find_function_end_prompt.format(THE_TAGGED_CODE=THE_TAGGED_CODE),
|
||||
llm_kwargs=self.llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt="",
|
||||
observe_window=[],
|
||||
console_slience=True
|
||||
)
|
||||
|
||||
def extract_number(text):
|
||||
# 使用正则表达式匹配模式
|
||||
match = re.search(r'<next_function_begin_from>L(\d+)</next_function_begin_from>', text)
|
||||
if match:
|
||||
# 提取匹配的数字部分并转换为整数
|
||||
return int(match.group(1))
|
||||
return None
|
||||
|
||||
line_no = extract_number(result)
|
||||
if line_no is not None:
|
||||
return line_no
|
||||
else:
|
||||
return end
|
||||
|
||||
def _get_next_window(self):
|
||||
#
|
||||
current_page_start = self.current_page_start
|
||||
|
||||
if self.current_page_start == len(self.full_context) + 1:
|
||||
raise StopIteration
|
||||
|
||||
# 如果剩余的行数非常少，一鼓作气处理掉
|
||||
if len(self.full_context) - self.current_page_start < self.ignore_limit:
|
||||
future_page_start = len(self.full_context) + 1
|
||||
self.current_page_start = future_page_start
|
||||
return current_page_start, future_page_start
|
||||
|
||||
|
||||
tagged_code = self.full_context_with_line_no[ self.current_page_start: self.current_page_start + self.page_limit]
|
||||
line_no = self.find_next_function_begin(tagged_code, [self.current_page_start, self.current_page_start + self.page_limit])
|
||||
|
||||
if line_no > len(self.full_context) - 5:
|
||||
line_no = len(self.full_context) + 1
|
||||
|
||||
future_page_start = line_no
|
||||
self.current_page_start = future_page_start
|
||||
|
||||
# ! consider eof
|
||||
return current_page_start, future_page_start
|
||||
|
||||
def dedent(self, text):
|
||||
"""Remove any common leading whitespace from every line in `text`.
|
||||
"""
|
||||
# Look for the longest leading string of spaces and tabs common to
|
||||
# all lines.
|
||||
margin = None
|
||||
_whitespace_only_re = re.compile('^[ \t]+$', re.MULTILINE)
|
||||
_leading_whitespace_re = re.compile('(^[ \t]*)(?:[^ \t\n])', re.MULTILINE)
|
||||
text = _whitespace_only_re.sub('', text)
|
||||
indents = _leading_whitespace_re.findall(text)
|
||||
for indent in indents:
|
||||
if margin is None:
|
||||
margin = indent
|
||||
|
||||
# Current line more deeply indented than previous winner:
|
||||
# no change (previous winner is still on top).
|
||||
elif indent.startswith(margin):
|
||||
pass
|
||||
|
||||
# Current line consistent with and no deeper than previous winner:
|
||||
# it's the new winner.
|
||||
elif margin.startswith(indent):
|
||||
margin = indent
|
||||
|
||||
# Find the largest common whitespace between current line and previous
|
||||
# winner.
|
||||
else:
|
||||
for i, (x, y) in enumerate(zip(margin, indent)):
|
||||
if x != y:
|
||||
margin = margin[:i]
|
||||
break
|
||||
|
||||
# sanity check (testing/debugging only)
|
||||
if 0 and margin:
|
||||
for line in text.split("\n"):
|
||||
assert not line or line.startswith(margin), \
|
||||
"line = %r, margin = %r" % (line, margin)
|
||||
|
||||
if margin:
|
||||
text = re.sub(r'(?m)^' + margin, '', text)
|
||||
return text, len(margin)
|
||||
else:
|
||||
return text, 0
|
||||
|
||||
def get_next_batch(self):
|
||||
current_page_start, future_page_start = self._get_next_window()
|
||||
return ''.join(self.full_context[current_page_start: future_page_start]), current_page_start, future_page_start
|
||||
|
||||
def tag_code(self, fn, hint):
|
||||
code = fn
|
||||
_, n_indent = self.dedent(code)
|
||||
indent_reminder = "" if n_indent == 0 else "(Reminder: as you can see, this piece of code has indent made up with {n_indent} whitespace, please preseve them in the OUTPUT.)"
|
||||
brief_reminder = "" if self.file_brief == "" else f"({self.file_basename} abstract: {self.file_brief})"
|
||||
hint_reminder = "" if hint is None else f"(Reminder: do not ignore or modify code such as `{hint}`, provide complete code in the OUTPUT.)"
|
||||
self.llm_kwargs['temperature'] = 0
|
||||
result = predict_no_ui_long_connection(
|
||||
inputs=self.core_prompt.format(
|
||||
LANG=self.language,
|
||||
FILE_BASENAME=self.file_basename,
|
||||
THE_CODE=code,
|
||||
INDENT_REMINDER=indent_reminder,
|
||||
BRIEF_REMINDER=brief_reminder,
|
||||
HINT_REMINDER=hint_reminder
|
||||
),
|
||||
llm_kwargs=self.llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt="",
|
||||
observe_window=[],
|
||||
console_slience=True
|
||||
)
|
||||
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) == 1:
|
||||
return matches[0].strip('python') # code block
|
||||
return None
|
||||
|
||||
code_block = get_code_block(result)
|
||||
if code_block is not None:
|
||||
code_block = self.sync_and_patch(original=code, revised=code_block)
|
||||
return code_block
|
||||
else:
|
||||
return code
|
||||
|
||||
def get_markdown_block_in_html(self, html):
|
||||
from bs4 import BeautifulSoup
|
||||
soup = BeautifulSoup(html, 'lxml')
|
||||
found_list = soup.find_all("div", class_="markdown-body")
|
||||
if found_list:
|
||||
res = found_list[0]
|
||||
return res.prettify()
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def sync_and_patch(self, original, revised):
|
||||
"""Ensure the number of pre-string empty lines in revised matches those in original."""
|
||||
|
||||
def count_leading_empty_lines(s, reverse=False):
|
||||
"""Count the number of leading empty lines in a string."""
|
||||
lines = s.split('\n')
|
||||
if reverse: lines = list(reversed(lines))
|
||||
count = 0
|
||||
for line in lines:
|
||||
if line.strip() == '':
|
||||
count += 1
|
||||
else:
|
||||
break
|
||||
return count
|
||||
|
||||
original_empty_lines = count_leading_empty_lines(original)
|
||||
revised_empty_lines = count_leading_empty_lines(revised)
|
||||
|
||||
if original_empty_lines > revised_empty_lines:
|
||||
additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
|
||||
revised = additional_lines + revised
|
||||
elif original_empty_lines < revised_empty_lines:
|
||||
lines = revised.split('\n')
|
||||
revised = '\n'.join(lines[revised_empty_lines - original_empty_lines:])
|
||||
|
||||
original_empty_lines = count_leading_empty_lines(original, reverse=True)
|
||||
revised_empty_lines = count_leading_empty_lines(revised, reverse=True)
|
||||
|
||||
if original_empty_lines > revised_empty_lines:
|
||||
additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
|
||||
revised = revised + additional_lines
|
||||
elif original_empty_lines < revised_empty_lines:
|
||||
lines = revised.split('\n')
|
||||
revised = '\n'.join(lines[:-(revised_empty_lines - original_empty_lines)])
|
||||
|
||||
return revised
|
||||
|
||||
def begin_comment_source_code(self, chatbot=None, history=None):
|
||||
# from toolbox import update_ui_lastest_msg
|
||||
assert self.path is not None
|
||||
assert '.py' in self.path # must be python source code
|
||||
# write_target = self.path + '.revised.py'
|
||||
|
||||
write_content = ""
|
||||
# with open(self.path + '.revised.py', 'w+', encoding='utf8') as f:
|
||||
while True:
|
||||
try:
|
||||
# yield from update_ui_lastest_msg(f"({self.file_basename}) 正在读取下一段代码片段:\n", chatbot=chatbot, history=history, delay=0)
|
||||
next_batch, line_no_start, line_no_end = self.get_next_batch()
|
||||
self.observe_window_update(f"正在处理{self.file_basename} - {line_no_start}/{len(self.full_context)}\n")
|
||||
# yield from update_ui_lastest_msg(f"({self.file_basename}) 处理代码片段:\n\n{next_batch}", chatbot=chatbot, history=history, delay=0)
|
||||
|
||||
hint = None
|
||||
MAX_ATTEMPT = 2
|
||||
for attempt in range(MAX_ATTEMPT):
|
||||
result = self.tag_code(next_batch, hint)
|
||||
try:
|
||||
successful, hint = self.verify_successful(next_batch, result)
|
||||
except Exception as e:
|
||||
logger.error('ignored exception:\n' + str(e))
|
||||
break
|
||||
if successful:
|
||||
break
|
||||
if attempt == MAX_ATTEMPT - 1:
|
||||
# cannot deal with this, give up
|
||||
result = next_batch
|
||||
break
|
||||
|
||||
# f.write(result)
|
||||
write_content += result
|
||||
except StopIteration:
|
||||
next_batch, line_no_start, line_no_end = [], -1, -1
|
||||
return None, write_content
|
||||
|
||||
def verify_successful(self, original, revised):
|
||||
""" Determine whether the revised code contains every line that already exists
|
||||
"""
|
||||
from crazy_functions.ast_fns.comment_remove import remove_python_comments
|
||||
original = remove_python_comments(original)
|
||||
original_lines = original.split('\n')
|
||||
revised_lines = revised.split('\n')
|
||||
|
||||
for l in original_lines:
|
||||
l = l.strip()
|
||||
if '\'' in l or '\"' in l: continue # ast sometimes toggle " to '
|
||||
found = False
|
||||
for lt in revised_lines:
|
||||
if l in lt:
|
||||
found = True
|
||||
break
|
||||
if not found:
|
||||
return False, l
|
||||
return True, None
|
||||
@@ -1,45 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<style>ADVANCED_CSS</style>
|
||||
<meta charset="UTF-8">
|
||||
<title>源文件对比</title>
|
||||
<style>
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
height: 100vh;
|
||||
margin: 0;
|
||||
}
|
||||
.container {
|
||||
display: flex;
|
||||
width: 95%;
|
||||
height: -webkit-fill-available;
|
||||
}
|
||||
.code-container {
|
||||
flex: 1;
|
||||
margin: 0px;
|
||||
padding: 0px;
|
||||
border: 1px solid #ccc;
|
||||
background-color: #f9f9f9;
|
||||
overflow: auto;
|
||||
}
|
||||
pre {
|
||||
white-space: pre-wrap;
|
||||
word-wrap: break-word;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<div class="code-container">
|
||||
REPLACE_CODE_FILE_LEFT
|
||||
</div>
|
||||
<div class="code-container">
|
||||
REPLACE_CODE_FILE_RIGHT
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,5 +1,4 @@
|
||||
import threading, time
|
||||
from loguru import logger
|
||||
|
||||
class WatchDog():
|
||||
def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
|
||||
@@ -9,12 +8,12 @@ class WatchDog():
|
||||
self.interval = interval
|
||||
self.msg = msg
|
||||
self.kill_dog = False
|
||||
|
||||
|
||||
def watch(self):
|
||||
while True:
|
||||
if self.kill_dog: break
|
||||
if time.time() - self.last_feed > self.timeout:
|
||||
if len(self.msg) > 0: logger.info(self.msg)
|
||||
if len(self.msg) > 0: print(self.msg)
|
||||
self.bark_fn()
|
||||
break
|
||||
time.sleep(self.interval)
|
||||
|
||||
@@ -1,54 +0,0 @@
|
||||
import token
|
||||
import tokenize
|
||||
import copy
|
||||
import io
|
||||
|
||||
|
||||
def remove_python_comments(input_source: str) -> str:
|
||||
source_flag = copy.copy(input_source)
|
||||
source = io.StringIO(input_source)
|
||||
ls = input_source.split('\n')
|
||||
prev_toktype = token.INDENT
|
||||
readline = source.readline
|
||||
|
||||
def get_char_index(lineno, col):
|
||||
# find the index of the char in the source code
|
||||
if lineno == 1:
|
||||
return len('\n'.join(ls[:(lineno-1)])) + col
|
||||
else:
|
||||
return len('\n'.join(ls[:(lineno-1)])) + col + 1
|
||||
|
||||
def replace_char_between(start_lineno, start_col, end_lineno, end_col, source, replace_char, ls):
|
||||
# replace char between start_lineno, start_col and end_lineno, end_col with replace_char, but keep '\n' and ' '
|
||||
b = get_char_index(start_lineno, start_col)
|
||||
e = get_char_index(end_lineno, end_col)
|
||||
for i in range(b, e):
|
||||
if source[i] == '\n':
|
||||
source = source[:i] + '\n' + source[i+1:]
|
||||
elif source[i] == ' ':
|
||||
source = source[:i] + ' ' + source[i+1:]
|
||||
else:
|
||||
source = source[:i] + replace_char + source[i+1:]
|
||||
return source
|
||||
|
||||
tokgen = tokenize.generate_tokens(readline)
|
||||
for toktype, ttext, (slineno, scol), (elineno, ecol), ltext in tokgen:
|
||||
if toktype == token.STRING and (prev_toktype == token.INDENT):
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
elif toktype == token.STRING and (prev_toktype == token.NEWLINE):
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
elif toktype == tokenize.COMMENT:
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
prev_toktype = toktype
|
||||
return source_flag
|
||||
|
||||
|
||||
# 示例使用
|
||||
if __name__ == "__main__":
|
||||
with open("source.py", "r", encoding="utf-8") as f:
|
||||
source_code = f.read()
|
||||
|
||||
cleaned_code = remove_python_comments(source_code)
|
||||
|
||||
with open("cleaned_source.py", "w", encoding="utf-8") as f:
|
||||
f.write(cleaned_code)
|
||||
141
crazy_functions/chatglm微调工具.py
Normal file
141
crazy_functions/chatglm微调工具.py
Normal file
@@ -0,0 +1,141 @@
|
||||
from toolbox import CatchException, update_ui, promote_file_to_downloadzone
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
import datetime, json
|
||||
|
||||
def fetch_items(list_of_items, batch_size):
|
||||
for i in range(0, len(list_of_items), batch_size):
|
||||
yield list_of_items[i:i + batch_size]
|
||||
|
||||
def string_to_options(arguments):
|
||||
import argparse
|
||||
import shlex
|
||||
|
||||
# Create an argparse.ArgumentParser instance
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# Add command-line arguments
|
||||
parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
|
||||
parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
|
||||
parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
|
||||
parser.add_argument("--batch", type=int, help="System prompt", default=50)
|
||||
parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
|
||||
parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
|
||||
parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
|
||||
parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
|
||||
parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
|
||||
|
||||
|
||||
|
||||
# Parse the arguments
|
||||
args = parser.parse_args(shlex.split(arguments))
|
||||
|
||||
return args
|
||||
|
||||
@CatchException
|
||||
def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
args = plugin_kwargs.get("advanced_arg", None)
|
||||
if args is None:
|
||||
chatbot.append(("没给定指令", "退出"))
|
||||
yield from update_ui(chatbot=chatbot, history=history); return
|
||||
else:
|
||||
arguments = string_to_options(arguments=args)
|
||||
|
||||
dat = []
|
||||
with open(txt, 'r', encoding='utf8') as f:
|
||||
for line in f.readlines():
|
||||
json_dat = json.loads(line)
|
||||
dat.append(json_dat["content"])
|
||||
|
||||
llm_kwargs['llm_model'] = arguments.llm_to_learn
|
||||
for batch in fetch_items(dat, arguments.batch):
|
||||
res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
|
||||
inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[] for _ in (batch)],
|
||||
sys_prompt_array=[arguments.system_prompt for _ in (batch)],
|
||||
max_workers=10 # OpenAI所允许的最大并行过载
|
||||
)
|
||||
|
||||
with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
|
||||
for b, r in zip(batch, res[1::2]):
|
||||
f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
|
||||
|
||||
promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
|
||||
return
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
import subprocess
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
args = plugin_kwargs.get("advanced_arg", None)
|
||||
if args is None:
|
||||
chatbot.append(("没给定指令", "退出"))
|
||||
yield from update_ui(chatbot=chatbot, history=history); return
|
||||
else:
|
||||
arguments = string_to_options(arguments=args)
|
||||
|
||||
|
||||
|
||||
pre_seq_len = arguments.pre_seq_len # 128
|
||||
learning_rate = arguments.learning_rate # 2e-2
|
||||
num_gpus = arguments.num_gpus # 1
|
||||
json_dataset = arguments.json_dataset # 't_code.json'
|
||||
ptuning_directory = arguments.ptuning_directory # '/home/hmp/ChatGLM2-6B/ptuning'
|
||||
|
||||
command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
|
||||
--do_train \
|
||||
--train_file AdvertiseGen/{json_dataset} \
|
||||
--validation_file AdvertiseGen/{json_dataset} \
|
||||
--preprocessing_num_workers 20 \
|
||||
--prompt_column content \
|
||||
--response_column summary \
|
||||
--overwrite_cache \
|
||||
--model_name_or_path THUDM/chatglm2-6b \
|
||||
--output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
|
||||
--overwrite_output_dir \
|
||||
--max_source_length 256 \
|
||||
--max_target_length 256 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 16 \
|
||||
--predict_with_generate \
|
||||
--max_steps 100 \
|
||||
--logging_steps 10 \
|
||||
--save_steps 20 \
|
||||
--learning_rate {learning_rate} \
|
||||
--pre_seq_len {pre_seq_len} \
|
||||
--quantization_bit 4"
|
||||
|
||||
process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
|
||||
try:
|
||||
process.communicate(timeout=3600*24)
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
return
|
||||
@@ -1,41 +1,27 @@
|
||||
import os
|
||||
import threading
|
||||
from loguru import logger
|
||||
from shared_utils.char_visual_effect import scolling_visual_effect
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
|
||||
import threading
|
||||
import os
|
||||
import logging
|
||||
|
||||
def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
|
||||
"""
|
||||
当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
|
||||
输入：
|
||||
- inputs 本次请求
|
||||
- history 历史上下文
|
||||
- max_token_limit 最大token限制
|
||||
输出:
|
||||
- inputs 本次请求（经过clip）
|
||||
- history 历史上下文（经过clip）
|
||||
"""
|
||||
def input_clipping(inputs, history, max_token_limit):
|
||||
import numpy as np
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
|
||||
|
||||
mode = 'input-and-history'
|
||||
# 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
|
||||
input_token_num = get_token_num(inputs)
|
||||
original_input_len = len(inputs)
|
||||
if input_token_num < max_token_limit//2:
|
||||
if input_token_num < max_token_limit//2:
|
||||
mode = 'only-history'
|
||||
max_token_limit = max_token_limit - input_token_num
|
||||
|
||||
everything = [inputs] if mode == 'input-and-history' else ['']
|
||||
everything.extend(history)
|
||||
full_token_num = n_token = get_token_num('\n'.join(everything))
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
everything_token = [get_token_num(e) for e in everything]
|
||||
everything_token_num = sum(everything_token)
|
||||
delta = max(everything_token) // 16 # 截断时的颗粒度
|
||||
|
||||
|
||||
while n_token > max_token_limit:
|
||||
where = np.argmax(everything_token)
|
||||
encoded = enc.encode(everything[where], disallowed_special=())
|
||||
@@ -46,29 +32,15 @@ def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
|
||||
|
||||
if mode == 'input-and-history':
|
||||
inputs = everything[0]
|
||||
full_token_num = everything_token_num
|
||||
else:
|
||||
full_token_num = everything_token_num + input_token_num
|
||||
|
||||
pass
|
||||
history = everything[1:]
|
||||
|
||||
flags = {
|
||||
"mode": mode,
|
||||
"original_input_token_num": input_token_num,
|
||||
"original_full_token_num": full_token_num,
|
||||
"original_input_len": original_input_len,
|
||||
"clipped_input_len": len(inputs),
|
||||
}
|
||||
|
||||
if not return_clip_flags:
|
||||
return inputs, history
|
||||
else:
|
||||
return inputs, history, flags
|
||||
return inputs, history
|
||||
|
||||
def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs, inputs_show_user, llm_kwargs,
|
||||
inputs, inputs_show_user, llm_kwargs,
|
||||
chatbot, history, sys_prompt, refresh_interval=0.2,
|
||||
handle_token_exceed=True,
|
||||
handle_token_exceed=True,
|
||||
retry_times_at_unknown_error=2,
|
||||
):
|
||||
"""
|
||||
@@ -105,7 +77,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
exceeded_cnt = 0
|
||||
while True:
|
||||
# watchdog error
|
||||
if len(mutable) >= 2 and (time.time()-mutable[1]) > watch_dog_patience:
|
||||
if len(mutable) >= 2 and (time.time()-mutable[1]) > watch_dog_patience:
|
||||
raise RuntimeError("检测到程序终止。")
|
||||
try:
|
||||
# 【第一种情况】：顺利完成
|
||||
@@ -133,7 +105,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
except:
|
||||
# 【第三种情况】：其他错误：重试几次
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
logger.error(tb_str)
|
||||
print(tb_str)
|
||||
mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
|
||||
if retry_op > 0:
|
||||
retry_op -= 1
|
||||
@@ -163,31 +135,18 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
|
||||
return final_result
|
||||
|
||||
def can_multi_process(llm) -> bool:
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
def default_condition(llm) -> bool:
|
||||
# legacy condition
|
||||
if llm.startswith('gpt-'): return True
|
||||
if llm.startswith('chatgpt-'): return True
|
||||
if llm.startswith('api2d-'): return True
|
||||
if llm.startswith('azure-'): return True
|
||||
if llm.startswith('spark'): return True
|
||||
if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
|
||||
return False
|
||||
|
||||
if llm in model_info:
|
||||
if 'can_multi_thread' in model_info[llm]:
|
||||
return model_info[llm]['can_multi_thread']
|
||||
else:
|
||||
return default_condition(llm)
|
||||
else:
|
||||
return default_condition(llm)
|
||||
def can_multi_process(llm):
|
||||
if llm.startswith('gpt-'): return True
|
||||
if llm.startswith('api2d-'): return True
|
||||
if llm.startswith('azure-'): return True
|
||||
if llm.startswith('spark'): return True
|
||||
if llm.startswith('zhipuai'): return True
|
||||
return False
|
||||
|
||||
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array, inputs_show_user_array, llm_kwargs,
|
||||
chatbot, history_array, sys_prompt_array,
|
||||
refresh_interval=0.2, max_workers=-1, scroller_max_len=75,
|
||||
inputs_array, inputs_show_user_array, llm_kwargs,
|
||||
chatbot, history_array, sys_prompt_array,
|
||||
refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
|
||||
handle_token_exceed=True, show_user_at_complete=False,
|
||||
retry_times_at_unknown_error=2,
|
||||
):
|
||||
@@ -230,7 +189,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
# 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
|
||||
if not can_multi_process(llm_kwargs['llm_model']):
|
||||
max_workers = 1
|
||||
|
||||
|
||||
executor = ThreadPoolExecutor(max_workers=max_workers)
|
||||
n_frag = len(inputs_array)
|
||||
# 用户反馈
|
||||
@@ -255,7 +214,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
try:
|
||||
# 【第一种情况】：顺利完成
|
||||
gpt_say = predict_no_ui_long_connection(
|
||||
inputs=inputs, llm_kwargs=llm_kwargs, history=history,
|
||||
inputs=inputs, llm_kwargs=llm_kwargs, history=history,
|
||||
sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
|
||||
)
|
||||
mutable[index][2] = "已成功"
|
||||
@@ -284,10 +243,10 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
# 【第三种情况】：其他错误
|
||||
if detect_timeout(): raise RuntimeError("检测到程序终止。")
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
logger.error(tb_str)
|
||||
print(tb_str)
|
||||
gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
|
||||
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
|
||||
if retry_op > 0:
|
||||
if retry_op > 0:
|
||||
retry_op -= 1
|
||||
wait = random.randint(5, 20)
|
||||
if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
|
||||
@@ -312,8 +271,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
|
||||
range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
|
||||
cnt = 0
|
||||
|
||||
|
||||
while True:
|
||||
# yield一次以刷新前端页面
|
||||
time.sleep(refresh_interval)
|
||||
@@ -326,11 +283,12 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
mutable[thread_index][1] = time.time()
|
||||
# 在前端打印些好玩的东西
|
||||
for thread_index, _ in enumerate(worker_done):
|
||||
print_something_really_funny = f"[ ...`{scolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
|
||||
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
|
||||
replace('\n', '').replace('`', '.').replace(' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
|
||||
observe_win.append(print_something_really_funny)
|
||||
# 在前端打印些好玩的东西
|
||||
stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
|
||||
if not done else f'`{mutable[thread_index][2]}`\n\n'
|
||||
stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
|
||||
if not done else f'`{mutable[thread_index][2]}`\n\n'
|
||||
for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
|
||||
# 在前端打印些好玩的东西
|
||||
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))]
|
||||
@@ -344,7 +302,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
for inputs_show_user, f in zip(inputs_show_user_array, futures):
|
||||
gpt_res = f.result()
|
||||
gpt_response_collection.extend([inputs_show_user, gpt_res])
|
||||
|
||||
|
||||
# 是否在结束时，在界面上显示结果
|
||||
if show_user_at_complete:
|
||||
for inputs_show_user, f in zip(inputs_show_user_array, futures):
|
||||
@@ -379,7 +337,7 @@ def read_and_clean_pdf_text(fp):
|
||||
import fitz, copy
|
||||
import re
|
||||
import numpy as np
|
||||
# from shared_utils.colorful import print亮黄, print亮绿
|
||||
from colorful import print亮黄, print亮绿
|
||||
fc = 0 # Index 0 文本
|
||||
fs = 1 # Index 1 字体
|
||||
fb = 2 # Index 2 框框
|
||||
@@ -394,7 +352,7 @@ def read_and_clean_pdf_text(fp):
|
||||
if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
|
||||
fsize_statiscs[wtf['size']] += len(wtf['text'])
|
||||
return max(fsize_statiscs, key=fsize_statiscs.get)
|
||||
|
||||
|
||||
def ffsize_same(a,b):
|
||||
"""
|
||||
提取字体大小是否近似相等
|
||||
@@ -430,7 +388,7 @@ def read_and_clean_pdf_text(fp):
|
||||
if index == 0:
|
||||
page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
|
||||
'- ', '') for t in text_areas['blocks'] if 'lines' in t]
|
||||
|
||||
|
||||
############################## <第 2 步，获取正文主字体> ##################################
|
||||
try:
|
||||
fsize_statiscs = {}
|
||||
@@ -446,7 +404,7 @@ def read_and_clean_pdf_text(fp):
|
||||
mega_sec = []
|
||||
sec = []
|
||||
for index, line in enumerate(meta_line):
|
||||
if index == 0:
|
||||
if index == 0:
|
||||
sec.append(line[fc])
|
||||
continue
|
||||
if REMOVE_FOOT_NOTE:
|
||||
@@ -543,12 +501,12 @@ def get_files_from_everything(txt, type): # type='.md'
|
||||
"""
|
||||
这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
|
||||
下面是对每个参数和返回值的说明：
|
||||
参数
|
||||
- txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。
|
||||
参数
|
||||
- txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。
|
||||
- type: 字符串，表示要搜索的文件类型。默认是.md。
|
||||
返回值
|
||||
- success: 布尔值，表示函数是否成功执行。
|
||||
- file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。
|
||||
返回值
|
||||
- success: 布尔值，表示函数是否成功执行。
|
||||
- file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。
|
||||
- project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
|
||||
该函数详细注释已添加，请确认是否满足您的需要。
|
||||
"""
|
||||
@@ -596,15 +554,15 @@ class nougat_interface():
|
||||
def nougat_with_timeout(self, command, cwd, timeout=3600):
|
||||
import subprocess
|
||||
from toolbox import ProxyNetworkActivate
|
||||
logger.info(f'正在执行命令 {command}')
|
||||
logging.info(f'正在执行命令 {command}')
|
||||
with ProxyNetworkActivate("Nougat_Download"):
|
||||
process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
|
||||
process = subprocess.Popen(command, shell=True, cwd=cwd, env=os.environ)
|
||||
try:
|
||||
stdout, stderr = process.communicate(timeout=timeout)
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
stdout, stderr = process.communicate()
|
||||
logger.error("Process timed out!")
|
||||
print("Process timed out!")
|
||||
return False
|
||||
return True
|
||||
|
||||
@@ -612,7 +570,7 @@ class nougat_interface():
|
||||
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...",
|
||||
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
|
||||
@@ -620,10 +578,9 @@ class nougat_interface():
|
||||
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）",
|
||||
yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
|
||||
self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
|
||||
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()
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
import os
|
||||
from textwrap import indent
|
||||
from loguru import logger
|
||||
|
||||
class FileNode:
|
||||
def __init__(self, name, build_manifest=False):
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.children = []
|
||||
self.is_leaf = False
|
||||
@@ -11,9 +10,7 @@ class FileNode:
|
||||
self.parenting_ship = []
|
||||
self.comment = ""
|
||||
self.comment_maxlen_show = 50
|
||||
self.build_manifest = build_manifest
|
||||
self.manifest = {}
|
||||
|
||||
|
||||
@staticmethod
|
||||
def add_linebreaks_at_spaces(string, interval=10):
|
||||
return '\n'.join(string[i:i+interval] for i in range(0, len(string), interval))
|
||||
@@ -32,7 +29,6 @@ class FileNode:
|
||||
level = 1
|
||||
if directory_names == "":
|
||||
new_node = FileNode(file_name)
|
||||
self.manifest[file_path] = new_node
|
||||
current_node.children.append(new_node)
|
||||
new_node.is_leaf = True
|
||||
new_node.comment = self.sanitize_comment(file_comment)
|
||||
@@ -54,14 +50,13 @@ class FileNode:
|
||||
new_node.level = level - 1
|
||||
current_node = new_node
|
||||
term = FileNode(file_name)
|
||||
self.manifest[file_path] = term
|
||||
term.level = level
|
||||
term.comment = self.sanitize_comment(file_comment)
|
||||
term.is_leaf = True
|
||||
current_node.children.append(term)
|
||||
|
||||
def print_files_recursively(self, level=0, code="R0"):
|
||||
logger.info(' '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
|
||||
print(' '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
|
||||
for j, child in enumerate(self.children):
|
||||
child.print_files_recursively(level=level+1, code=code+str(j))
|
||||
self.parenting_ship.extend(child.parenting_ship)
|
||||
@@ -124,4 +119,4 @@ if __name__ == "__main__":
|
||||
"用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器",
|
||||
"包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类",
|
||||
]
|
||||
logger.info(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
|
||||
print(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
|
||||
@@ -8,7 +8,7 @@ import random
|
||||
|
||||
class MiniGame_ASCII_Art(GptAcademicGameBaseState):
|
||||
def step(self, prompt, chatbot, history):
|
||||
if self.step_cnt == 0:
|
||||
if self.step_cnt == 0:
|
||||
chatbot.append(["我画你猜（动物）", "请稍等..."])
|
||||
else:
|
||||
if prompt.strip() == 'exit':
|
||||
|
||||
@@ -88,23 +88,23 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
self.story = []
|
||||
chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
|
||||
self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
|
||||
|
||||
|
||||
|
||||
|
||||
def generate_story_image(self, story_paragraph):
|
||||
try:
|
||||
from crazy_functions.AntFin import gen_image
|
||||
from crazy_functions.图片生成 import gen_image
|
||||
prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
|
||||
image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
|
||||
return f'<br/><div align="center"><img src="file={image_path}"></div>'
|
||||
except:
|
||||
return ''
|
||||
|
||||
|
||||
def step(self, prompt, chatbot, history):
|
||||
|
||||
|
||||
"""
|
||||
首先，处理游戏初始化等特殊情况
|
||||
"""
|
||||
if self.step_cnt == 0:
|
||||
if self.step_cnt == 0:
|
||||
self.begin_game_step_0(prompt, chatbot, history)
|
||||
self.lock_plugin(chatbot)
|
||||
self.cur_task = 'head_start'
|
||||
@@ -132,7 +132,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
inputs_ = prompts_hs.format(headstart=self.headstart)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, '故事开头', self.llm_kwargs,
|
||||
inputs_, '故事开头', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
@@ -147,7 +147,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
|
||||
history_ = []
|
||||
self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs,
|
||||
inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs,
|
||||
chatbot,
|
||||
history_,
|
||||
self.sys_prompt_
|
||||
@@ -166,7 +166,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
@@ -181,10 +181,10 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
|
||||
history_ = []
|
||||
self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_,
|
||||
'请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs,
|
||||
chatbot,
|
||||
history_,
|
||||
inputs_,
|
||||
'请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs,
|
||||
chatbot,
|
||||
history_,
|
||||
self.sys_prompt_
|
||||
)
|
||||
self.cur_task = 'user_choice'
|
||||
@@ -200,7 +200,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
# # 配图
|
||||
|
||||
@@ -5,7 +5,7 @@ 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:
|
||||
if len(matches) == 1:
|
||||
return "```" + matches[0] + "```" # code block
|
||||
raise RuntimeError("GPT is not generating proper code.")
|
||||
|
||||
@@ -13,10 +13,10 @@ def is_same_thing(a, b, llm_kwargs):
|
||||
from pydantic import BaseModel, Field
|
||||
class IsSameThing(BaseModel):
|
||||
is_same_thing: bool = Field(description="determine whether two objects are same thing.", default=False)
|
||||
|
||||
def run_gpt_fn(inputs, sys_prompt, history=[]):
|
||||
|
||||
def run_gpt_fn(inputs, sys_prompt, history=[]):
|
||||
return predict_no_ui_long_connection(
|
||||
inputs=inputs, llm_kwargs=llm_kwargs,
|
||||
inputs=inputs, llm_kwargs=llm_kwargs,
|
||||
history=history, sys_prompt=sys_prompt, observe_window=[]
|
||||
)
|
||||
|
||||
@@ -24,7 +24,7 @@ def is_same_thing(a, b, llm_kwargs):
|
||||
inputs_01 = "Identity whether the user input and the target is the same thing: \n target object: {a} \n user input object: {b} \n\n\n".format(a=a, b=b)
|
||||
inputs_01 += "\n\n\n Note that the user may describe the target object with a different language, e.g. cat and 猫 are the same thing."
|
||||
analyze_res_cot_01 = run_gpt_fn(inputs_01, "", [])
|
||||
|
||||
|
||||
inputs_02 = inputs_01 + gpt_json_io.format_instructions
|
||||
analyze_res = run_gpt_fn(inputs_02, "", [inputs_01, analyze_res_cot_01])
|
||||
|
||||
|
||||
@@ -41,11 +41,11 @@ def is_function_successfully_generated(fn_path, class_name, return_dict):
|
||||
# Now you can create an instance of the class
|
||||
instance = some_class()
|
||||
return_dict['success'] = True
|
||||
return
|
||||
return
|
||||
except:
|
||||
return_dict['traceback'] = trimmed_format_exc()
|
||||
return
|
||||
|
||||
|
||||
def subprocess_worker(code, file_path, return_dict):
|
||||
return_dict['result'] = None
|
||||
return_dict['success'] = False
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import platform
|
||||
import platform
|
||||
import pickle
|
||||
import multiprocessing
|
||||
|
||||
|
||||
@@ -24,8 +24,8 @@ class Actor(BaseModel):
|
||||
film_names: List[str] = Field(description="list of names of films they starred in")
|
||||
"""
|
||||
|
||||
import json, re
|
||||
from loguru import logger as logging
|
||||
import json, re, logging
|
||||
|
||||
|
||||
PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
|
||||
|
||||
@@ -62,8 +62,8 @@ class GptJsonIO():
|
||||
if "type" in reduced_schema:
|
||||
del reduced_schema["type"]
|
||||
# Ensure json in context is well-formed with double quotes.
|
||||
schema_str = json.dumps(reduced_schema)
|
||||
if self.example_instruction:
|
||||
schema_str = json.dumps(reduced_schema)
|
||||
return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
|
||||
else:
|
||||
return PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE.format(schema=schema_str)
|
||||
@@ -89,7 +89,7 @@ class GptJsonIO():
|
||||
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
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
|
||||
def structure_output(txt, prompt, err_msg, run_gpt_fn, pydantic_cls):
|
||||
gpt_json_io = GptJsonIO(pydantic_cls)
|
||||
analyze_res = run_gpt_fn(
|
||||
txt,
|
||||
sys_prompt=prompt + gpt_json_io.format_instructions
|
||||
)
|
||||
try:
|
||||
friend = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
except JsonStringError as e:
|
||||
return None, err_msg
|
||||
|
||||
err_msg = ""
|
||||
return friend, err_msg
|
||||
|
||||
|
||||
def select_tool(prompt, run_gpt_fn, pydantic_cls):
|
||||
pydantic_cls_instance, err_msg = structure_output(
|
||||
txt=prompt,
|
||||
prompt="根据提示, 分析应该调用哪个工具函数\n\n",
|
||||
err_msg=f"不能理解该联系人",
|
||||
run_gpt_fn=run_gpt_fn,
|
||||
pydantic_cls=pydantic_cls
|
||||
)
|
||||
return pydantic_cls_instance, err_msg
|
||||
@@ -1,17 +1,14 @@
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder, gen_time_str
|
||||
from toolbox import get_conf, promote_file_to_downloadzone
|
||||
from crazy_functions.latex_fns.latex_toolbox import PRESERVE, TRANSFORM
|
||||
from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||
from crazy_functions.latex_fns.latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
|
||||
from crazy_functions.latex_fns.latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
|
||||
from crazy_functions.latex_fns.latex_toolbox import find_title_and_abs
|
||||
from crazy_functions.latex_fns.latex_pickle_io import objdump, objload
|
||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
|
||||
from toolbox import get_conf, objdump, objload, promote_file_to_downloadzone
|
||||
from .latex_toolbox import PRESERVE, TRANSFORM
|
||||
from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||
from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
|
||||
from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
|
||||
from .latex_toolbox import find_title_and_abs
|
||||
|
||||
import os, shutil
|
||||
import re
|
||||
import numpy as np
|
||||
|
||||
pj = os.path.join
|
||||
|
||||
@@ -93,16 +90,16 @@ class LatexPaperSplit():
|
||||
"版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。" + \
|
||||
"项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
|
||||
# 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
|
||||
self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
|
||||
self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
|
||||
self.title = "unknown"
|
||||
self.abstract = "unknown"
|
||||
|
||||
def read_title_and_abstract(self, txt):
|
||||
try:
|
||||
title, abstract = find_title_and_abs(txt)
|
||||
if title is not None:
|
||||
if title is not None:
|
||||
self.title = title.replace('\n', ' ').replace('\\\\', ' ').replace(' ', '').replace(' ', '')
|
||||
if abstract is not None:
|
||||
if abstract is not None:
|
||||
self.abstract = abstract.replace('\n', ' ').replace('\\\\', ' ').replace(' ', '').replace(' ', '')
|
||||
except:
|
||||
pass
|
||||
@@ -114,7 +111,7 @@ class LatexPaperSplit():
|
||||
result_string = ""
|
||||
node_cnt = 0
|
||||
line_cnt = 0
|
||||
|
||||
|
||||
for node in self.nodes:
|
||||
if node.preserve:
|
||||
line_cnt += node.string.count('\n')
|
||||
@@ -147,7 +144,7 @@ class LatexPaperSplit():
|
||||
return result_string
|
||||
|
||||
|
||||
def split(self, txt, project_folder, opts):
|
||||
def split(self, txt, project_folder, opts):
|
||||
"""
|
||||
break down latex file to a linked list,
|
||||
each node use a preserve flag to indicate whether it should
|
||||
@@ -158,7 +155,7 @@ class LatexPaperSplit():
|
||||
manager = multiprocessing.Manager()
|
||||
return_dict = manager.dict()
|
||||
p = multiprocessing.Process(
|
||||
target=split_subprocess,
|
||||
target=split_subprocess,
|
||||
args=(txt, project_folder, return_dict, opts))
|
||||
p.start()
|
||||
p.join()
|
||||
@@ -220,13 +217,13 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
||||
from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
|
||||
|
||||
# <-------- 寻找主tex文件 ---------->
|
||||
# <-------- 寻找主tex文件 ---------->
|
||||
maintex = find_main_tex_file(file_manifest, mode)
|
||||
chatbot.append((f"定位主Latex文件", f'[Local Message] 分析结果：该项目的Latex主文件是{maintex}, 如果分析错误, 请立即终止程序, 删除或修改歧义文件, 然后重试。主程序即将开始, 请稍候。'))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
time.sleep(3)
|
||||
|
||||
# <-------- 读取Latex文件, 将多文件tex工程融合为一个巨型tex ---------->
|
||||
# <-------- 读取Latex文件, 将多文件tex工程融合为一个巨型tex ---------->
|
||||
main_tex_basename = os.path.basename(maintex)
|
||||
assert main_tex_basename.endswith('.tex')
|
||||
main_tex_basename_bare = main_tex_basename[:-4]
|
||||
@@ -243,13 +240,13 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
||||
with open(project_folder + '/merge.tex', 'w', encoding='utf-8', errors='replace') as f:
|
||||
f.write(merged_content)
|
||||
|
||||
# <-------- 精细切分latex文件 ---------->
|
||||
# <-------- 精细切分latex文件 ---------->
|
||||
chatbot.append((f"Latex文件融合完成", f'[Local Message] 正在精细切分latex文件，这需要一段时间计算，文档越长耗时越长，请耐心等待。'))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
lps = LatexPaperSplit()
|
||||
lps.read_title_and_abstract(merged_content)
|
||||
res = lps.split(merged_content, project_folder, opts) # 消耗时间的函数
|
||||
# <-------- 拆分过长的latex片段 ---------->
|
||||
# <-------- 拆分过长的latex片段 ---------->
|
||||
pfg = LatexPaperFileGroup()
|
||||
for index, r in enumerate(res):
|
||||
pfg.file_paths.append('segment-' + str(index))
|
||||
@@ -258,17 +255,17 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
# <-------- 根据需要切换prompt ---------->
|
||||
# <-------- 根据需要切换prompt ---------->
|
||||
inputs_array, sys_prompt_array = switch_prompt(pfg, mode)
|
||||
inputs_show_user_array = [f"{mode} {f}" for f in pfg.sp_file_tag]
|
||||
|
||||
if os.path.exists(pj(project_folder,'temp.pkl')):
|
||||
|
||||
# <-------- 【仅调试】如果存在调试缓存文件，则跳过GPT请求环节 ---------->
|
||||
# <-------- 【仅调试】如果存在调试缓存文件，则跳过GPT请求环节 ---------->
|
||||
pfg = objload(file=pj(project_folder,'temp.pkl'))
|
||||
|
||||
else:
|
||||
# <-------- gpt 多线程请求 ---------->
|
||||
# <-------- gpt 多线程请求 ---------->
|
||||
history_array = [[""] for _ in range(n_split)]
|
||||
# LATEX_EXPERIMENTAL, = get_conf('LATEX_EXPERIMENTAL')
|
||||
# if LATEX_EXPERIMENTAL:
|
||||
@@ -287,32 +284,32 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
||||
scroller_max_len = 40
|
||||
)
|
||||
|
||||
# <-------- 文本碎片重组为完整的tex片段 ---------->
|
||||
# <-------- 文本碎片重组为完整的tex片段 ---------->
|
||||
pfg.sp_file_result = []
|
||||
for i_say, gpt_say, orig_content in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], pfg.sp_file_contents):
|
||||
pfg.sp_file_result.append(gpt_say)
|
||||
pfg.merge_result()
|
||||
|
||||
# <-------- 临时存储用于调试 ---------->
|
||||
# <-------- 临时存储用于调试 ---------->
|
||||
pfg.get_token_num = None
|
||||
objdump(pfg, file=pj(project_folder,'temp.pkl'))
|
||||
|
||||
write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)
|
||||
|
||||
# <-------- 写出文件 ---------->
|
||||
# <-------- 写出文件 ---------->
|
||||
msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
|
||||
final_tex = lps.merge_result(pfg.file_result, mode, msg)
|
||||
objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
|
||||
|
||||
with open(project_folder + f'/merge_{mode}.tex', 'w', encoding='utf-8', errors='replace') as f:
|
||||
if mode != 'translate_zh' or "binary" in final_tex: f.write(final_tex)
|
||||
|
||||
|
||||
|
||||
# <-------- 整理结果, 退出 ---------->
|
||||
# <-------- 整理结果, 退出 ---------->
|
||||
chatbot.append((f"完成了吗？", 'GPT结果已输出, 即将编译PDF'))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------- 返回 ---------->
|
||||
# <-------- 返回 ---------->
|
||||
return project_folder + f'/merge_{mode}.tex'
|
||||
|
||||
|
||||
@@ -325,7 +322,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
|
||||
buggy_lines = [int(l) for l in buggy_lines]
|
||||
buggy_lines = sorted(buggy_lines)
|
||||
buggy_line = buggy_lines[0]-1
|
||||
logger.warning("reversing tex line that has errors", buggy_line)
|
||||
print("reversing tex line that has errors", buggy_line)
|
||||
|
||||
# 重组，逆转出错的段落
|
||||
if buggy_line not in fixed_line:
|
||||
@@ -339,7 +336,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
|
||||
|
||||
return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
|
||||
except:
|
||||
logger.error("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
|
||||
print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
|
||||
return False, -1, [-1]
|
||||
|
||||
|
||||
@@ -365,7 +362,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
|
||||
|
||||
|
||||
if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
|
||||
# 只有第二步成功，才能继续下面的步骤
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history) # 刷新Gradio前端界面
|
||||
@@ -382,7 +379,7 @@ 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
|
||||
logger.info( f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
|
||||
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', 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
|
||||
@@ -396,9 +393,9 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
|
||||
modified_pdf_success = os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf'))
|
||||
diff_pdf_success = os.path.exists(pj(work_folder, f'merge_diff.pdf'))
|
||||
results_ += f"原始PDF编译是否成功: {original_pdf_success};"
|
||||
results_ += f"转化PDF编译是否成功: {modified_pdf_success};"
|
||||
results_ += f"对比PDF编译是否成功: {diff_pdf_success};"
|
||||
results_ += f"原始PDF编译是否成功: {original_pdf_success};"
|
||||
results_ += f"转化PDF编译是否成功: {modified_pdf_success};"
|
||||
results_ += f"对比PDF编译是否成功: {diff_pdf_success};"
|
||||
yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
|
||||
|
||||
if diff_pdf_success:
|
||||
@@ -412,7 +409,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))
|
||||
promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
||||
# 将两个PDF拼接
|
||||
if original_pdf_success:
|
||||
if original_pdf_success:
|
||||
try:
|
||||
from .latex_toolbox import merge_pdfs
|
||||
concat_pdf = pj(work_folder_modified, f'comparison.pdf')
|
||||
@@ -421,14 +418,14 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
|
||||
promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
print(e)
|
||||
pass
|
||||
return True # 成功啦
|
||||
else:
|
||||
if n_fix>=max_try: break
|
||||
n_fix += 1
|
||||
can_retry, main_file_modified, buggy_lines = remove_buggy_lines(
|
||||
file_path=pj(work_folder_modified, f'{main_file_modified}.tex'),
|
||||
file_path=pj(work_folder_modified, f'{main_file_modified}.tex'),
|
||||
log_path=pj(work_folder_modified, f'{main_file_modified}.log'),
|
||||
tex_name=f'{main_file_modified}.tex',
|
||||
tex_name_pure=f'{main_file_modified}',
|
||||
@@ -448,14 +445,14 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
||||
import shutil
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
from toolbox import gen_time_str
|
||||
ch = construct_html()
|
||||
ch = construct_html()
|
||||
orig = ""
|
||||
trans = ""
|
||||
final = []
|
||||
for c,r in zip(sp_file_contents, sp_file_result):
|
||||
for c,r in zip(sp_file_contents, sp_file_result):
|
||||
final.append(c)
|
||||
final.append(r)
|
||||
for i, k in enumerate(final):
|
||||
for i, k in enumerate(final):
|
||||
if i%2==0:
|
||||
orig = k
|
||||
if i%2==1:
|
||||
@@ -467,71 +464,4 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
||||
promote_file_to_downloadzone(file=res, chatbot=chatbot)
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
logger.error('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
|
||||
def upload_to_gptac_cloud_if_user_allow(chatbot, arxiv_id):
|
||||
try:
|
||||
# 如果用户允许，我们将arxiv论文PDF上传到GPTAC学术云
|
||||
from toolbox import map_file_to_sha256
|
||||
# 检查是否顺利，如果没有生成预期的文件，则跳过
|
||||
is_result_good = False
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
if file_path.endswith('translate_zh.pdf'):
|
||||
is_result_good = True
|
||||
if not is_result_good:
|
||||
return
|
||||
# 上传文件
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
align_name = None
|
||||
# normalized name
|
||||
for name in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
if file_path.endswith(name): align_name = name
|
||||
# if match any align name
|
||||
if align_name:
|
||||
logger.info(f'Uploading to GPTAC cloud as the user has set `allow_cloud_io`: {file_path}')
|
||||
with open(file_path, 'rb') as f:
|
||||
import requests
|
||||
url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_upload'
|
||||
files = {'file': (align_name, f, 'application/octet-stream')}
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'file_hash': map_file_to_sha256(file_path),
|
||||
'language': 'zh',
|
||||
'trans_prompt': 'to_be_implemented',
|
||||
'llm_model': 'to_be_implemented',
|
||||
'llm_model_param': 'to_be_implemented',
|
||||
}
|
||||
resp = requests.post(url=url, files=files, data=data, timeout=30)
|
||||
logger.info(f'Uploading terminate ({resp.status_code})`: {file_path}')
|
||||
except:
|
||||
# 如果上传失败，不会中断程序，因为这是次要功能
|
||||
pass
|
||||
|
||||
def check_gptac_cloud(arxiv_id, chatbot):
|
||||
import requests
|
||||
success = False
|
||||
downloaded = []
|
||||
try:
|
||||
for pdf_target in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_exist'
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'name': pdf_target,
|
||||
}
|
||||
resp = requests.post(url=url, data=data)
|
||||
cache_hit_result = resp.text.strip('"')
|
||||
if cache_hit_result.startswith("http"):
|
||||
url = cache_hit_result
|
||||
logger.info(f'Downloading from GPTAC cloud: {url}')
|
||||
resp = requests.get(url=url, timeout=30)
|
||||
target = os.path.join(get_log_folder(plugin_name='gptac_cloud'), gen_time_str(), pdf_target)
|
||||
os.makedirs(os.path.dirname(target), exist_ok=True)
|
||||
with open(target, 'wb') as f:
|
||||
f.write(resp.content)
|
||||
new_path = promote_file_to_downloadzone(target, chatbot=chatbot)
|
||||
success = True
|
||||
downloaded.append(new_path)
|
||||
except:
|
||||
pass
|
||||
return success, downloaded
|
||||
print('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
import pickle
|
||||
|
||||
|
||||
class SafeUnpickler(pickle.Unpickler):
|
||||
|
||||
def get_safe_classes(self):
|
||||
from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
|
||||
from crazy_functions.latex_fns.latex_toolbox import LinkedListNode
|
||||
from numpy.core.multiarray import scalar
|
||||
from numpy import dtype
|
||||
# 定义允许的安全类
|
||||
safe_classes = {
|
||||
# 在这里添加其他安全的类
|
||||
'LatexPaperFileGroup': LatexPaperFileGroup,
|
||||
'LatexPaperSplit': LatexPaperSplit,
|
||||
'LinkedListNode': LinkedListNode,
|
||||
'scalar': scalar,
|
||||
'dtype': dtype,
|
||||
}
|
||||
return safe_classes
|
||||
|
||||
def find_class(self, module, name):
|
||||
# 只允许特定的类进行反序列化
|
||||
self.safe_classes = self.get_safe_classes()
|
||||
match_class_name = None
|
||||
for class_name in self.safe_classes.keys():
|
||||
if (class_name in f'{module}.{name}'):
|
||||
match_class_name = class_name
|
||||
if match_class_name is not None:
|
||||
return self.safe_classes[match_class_name]
|
||||
# 如果尝试加载未授权的类，则抛出异常
|
||||
raise pickle.UnpicklingError(f"Attempted to deserialize unauthorized class '{name}' from module '{module}'")
|
||||
|
||||
def objdump(obj, file="objdump.tmp"):
|
||||
|
||||
with open(file, "wb+") as f:
|
||||
pickle.dump(obj, f)
|
||||
return
|
||||
|
||||
|
||||
def objload(file="objdump.tmp"):
|
||||
import os
|
||||
|
||||
if not os.path.exists(file):
|
||||
return
|
||||
with open(file, "rb") as f:
|
||||
unpickler = SafeUnpickler(f)
|
||||
return unpickler.load()
|
||||
@@ -1,8 +1,6 @@
|
||||
import os
|
||||
import os, shutil
|
||||
import re
|
||||
import shutil
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
|
||||
PRESERVE = 0
|
||||
TRANSFORM = 1
|
||||
@@ -57,7 +55,7 @@ def post_process(root):
|
||||
str_stack.append("{")
|
||||
elif c == "}":
|
||||
if len(str_stack) == 1:
|
||||
logger.warning("fixing brace error")
|
||||
print("stack fix")
|
||||
return i
|
||||
str_stack.pop(-1)
|
||||
else:
|
||||
@@ -603,7 +601,7 @@ def compile_latex_with_timeout(command, cwd, timeout=60):
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
stdout, stderr = process.communicate()
|
||||
logger.error("Process timed out (compile_latex_with_timeout)!")
|
||||
print("Process timed out!")
|
||||
return False
|
||||
return True
|
||||
|
||||
@@ -644,216 +642,6 @@ def run_in_subprocess(func):
|
||||
|
||||
|
||||
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||
try:
|
||||
logger.info("Merging PDFs using _merge_pdfs_ng")
|
||||
_merge_pdfs_ng(pdf1_path, pdf2_path, output_path)
|
||||
except:
|
||||
logger.info("Merging PDFs using _merge_pdfs_legacy")
|
||||
_merge_pdfs_legacy(pdf1_path, pdf2_path, output_path)
|
||||
|
||||
|
||||
def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
|
||||
import PyPDF2 # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
|
||||
from PyPDF2.generic import NameObject, TextStringObject, ArrayObject, FloatObject, NumberObject
|
||||
|
||||
Percent = 1
|
||||
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
|
||||
# Open the first PDF file
|
||||
with open(pdf1_path, "rb") as pdf1_file:
|
||||
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
|
||||
# Open the second PDF file
|
||||
with open(pdf2_path, "rb") as pdf2_file:
|
||||
pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
|
||||
# Create a new PDF file to store the merged pages
|
||||
output_writer = PyPDF2.PdfFileWriter()
|
||||
# Determine the number of pages in each PDF file
|
||||
num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
|
||||
# Merge the pages from the two PDF files
|
||||
for page_num in range(num_pages):
|
||||
# Add the page from the first PDF file
|
||||
if page_num < pdf1_reader.numPages:
|
||||
page1 = pdf1_reader.getPage(page_num)
|
||||
else:
|
||||
page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
||||
# Add the page from the second PDF file
|
||||
if page_num < pdf2_reader.numPages:
|
||||
page2 = pdf2_reader.getPage(page_num)
|
||||
else:
|
||||
page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
||||
# Create a new empty page with double width
|
||||
new_page = PyPDF2.PageObject.createBlankPage(
|
||||
width=int(
|
||||
int(page1.mediaBox.getWidth())
|
||||
+ int(page2.mediaBox.getWidth()) * Percent
|
||||
),
|
||||
height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
|
||||
)
|
||||
new_page.mergeTranslatedPage(page1, 0, 0)
|
||||
new_page.mergeTranslatedPage(
|
||||
page2,
|
||||
int(
|
||||
int(page1.mediaBox.getWidth())
|
||||
- int(page2.mediaBox.getWidth()) * (1 - Percent)
|
||||
),
|
||||
0,
|
||||
)
|
||||
if "/Annots" in new_page:
|
||||
annotations = new_page["/Annots"]
|
||||
for i, annot in enumerate(annotations):
|
||||
annot_obj = annot.get_object()
|
||||
|
||||
# 检查注释类型是否是链接（/Link）
|
||||
if annot_obj.get("/Subtype") == "/Link":
|
||||
# 检查是否为内部链接跳转（/GoTo）或外部URI链接（/URI）
|
||||
action = annot_obj.get("/A")
|
||||
if action:
|
||||
|
||||
if "/S" in action and action["/S"] == "/GoTo":
|
||||
# 内部链接：跳转到文档中的某个页面
|
||||
dest = action.get("/D") # 目标页或目标位置
|
||||
# if dest and annot.idnum in page2_annot_id:
|
||||
# if dest in pdf2_reader.named_destinations:
|
||||
if dest and page2.annotations:
|
||||
if annot in page2.annotations:
|
||||
# 获取原始文件中跳转信息，包括跳转页面
|
||||
destination = pdf2_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf2_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
+ int(
|
||||
page1.mediaBox.getWidth()
|
||||
)
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
# 更新点击坐标
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(
|
||||
rect[0]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[1],
|
||||
FloatObject(
|
||||
rect[2]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
# if dest and annot.idnum in page1_annot_id:
|
||||
# if dest in pdf1_reader.named_destinations:
|
||||
if dest and page1.annotations:
|
||||
if annot in page1.annotations:
|
||||
# 获取原始文件中跳转信息，包括跳转页面
|
||||
destination = pdf1_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf1_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(rect[0]),
|
||||
rect[1],
|
||||
FloatObject(rect[2]),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
elif "/S" in action and action["/S"] == "/URI":
|
||||
# 外部链接：跳转到某个URI
|
||||
uri = action.get("/URI")
|
||||
output_writer.addPage(new_page)
|
||||
# Save the merged PDF file
|
||||
with open(output_path, "wb") as output_file:
|
||||
output_writer.write(output_file)
|
||||
|
||||
|
||||
def _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path):
|
||||
import PyPDF2 # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
|
||||
|
||||
Percent = 0.95
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import time, json, sys, struct
|
||||
import time, logging, json, sys, struct
|
||||
import numpy as np
|
||||
from loguru import logger as logging
|
||||
from scipy.io.wavfile import WAVE_FORMAT
|
||||
|
||||
def write_numpy_to_wave(filename, rate, data, add_header=False):
|
||||
@@ -86,8 +85,8 @@ def write_numpy_to_wave(filename, rate, data, add_header=False):
|
||||
|
||||
def is_speaker_speaking(vad, data, sample_rate):
|
||||
# Function to detect if the speaker is speaking
|
||||
# The WebRTC VAD only accepts 16-bit mono PCM audio,
|
||||
# sampled at 8000, 16000, 32000 or 48000 Hz.
|
||||
# The WebRTC VAD only accepts 16-bit mono PCM audio,
|
||||
# sampled at 8000, 16000, 32000 or 48000 Hz.
|
||||
# A frame must be either 10, 20, or 30 ms in duration:
|
||||
frame_duration = 30
|
||||
n_bit_each = int(sample_rate * frame_duration / 1000)*2 # x2 because audio is 16 bit (2 bytes)
|
||||
@@ -95,7 +94,7 @@ def is_speaker_speaking(vad, data, sample_rate):
|
||||
for t in range(len(data)):
|
||||
if t!=0 and t % n_bit_each == 0:
|
||||
res_list.append(vad.is_speech(data[t-n_bit_each:t], sample_rate))
|
||||
|
||||
|
||||
info = ''.join(['^' if r else '.' for r in res_list])
|
||||
info = info[:10]
|
||||
if any(res_list):
|
||||
@@ -107,14 +106,18 @@ def is_speaker_speaking(vad, data, sample_rate):
|
||||
class AliyunASR():
|
||||
|
||||
def test_on_sentence_begin(self, message, *args):
|
||||
# print("test_on_sentence_begin:{}".format(message))
|
||||
pass
|
||||
|
||||
def test_on_sentence_end(self, message, *args):
|
||||
# print("test_on_sentence_end:{}".format(message))
|
||||
message = json.loads(message)
|
||||
self.parsed_sentence = message['payload']['result']
|
||||
self.event_on_entence_end.set()
|
||||
# print(self.parsed_sentence)
|
||||
|
||||
def test_on_start(self, message, *args):
|
||||
# print("test_on_start:{}".format(message))
|
||||
pass
|
||||
|
||||
def test_on_error(self, message, *args):
|
||||
@@ -126,11 +129,13 @@ class AliyunASR():
|
||||
pass
|
||||
|
||||
def test_on_result_chg(self, message, *args):
|
||||
# print("test_on_chg:{}".format(message))
|
||||
message = json.loads(message)
|
||||
self.parsed_text = message['payload']['result']
|
||||
self.event_on_result_chg.set()
|
||||
|
||||
def test_on_completed(self, message, *args):
|
||||
# print("on_completed:args=>{} message=>{}".format(args, message))
|
||||
pass
|
||||
|
||||
def audio_convertion_thread(self, uuid):
|
||||
@@ -181,10 +186,10 @@ class AliyunASR():
|
||||
keep_alive_last_send_time = time.time()
|
||||
while not self.stop:
|
||||
# time.sleep(self.capture_interval)
|
||||
audio = rad.read(uuid.hex)
|
||||
audio = rad.read(uuid.hex)
|
||||
if audio is not None:
|
||||
# convert to pcm file
|
||||
temp_file = f'{temp_folder}/{uuid.hex}.pcm' #
|
||||
temp_file = f'{temp_folder}/{uuid.hex}.pcm' #
|
||||
dsdata = change_sample_rate(audio, rad.rate, NEW_SAMPLERATE) # 48000 --> 16000
|
||||
write_numpy_to_wave(temp_file, NEW_SAMPLERATE, dsdata)
|
||||
# read pcm binary
|
||||
@@ -243,14 +248,14 @@ class AliyunASR():
|
||||
|
||||
try:
|
||||
response = client.do_action_with_exception(request)
|
||||
logging.info(response)
|
||||
print(response)
|
||||
jss = json.loads(response)
|
||||
if 'Token' in jss and 'Id' in jss['Token']:
|
||||
token = jss['Token']['Id']
|
||||
expireTime = jss['Token']['ExpireTime']
|
||||
logging.info("token = " + token)
|
||||
logging.info("expireTime = " + str(expireTime))
|
||||
print("token = " + token)
|
||||
print("expireTime = " + str(expireTime))
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e)
|
||||
|
||||
return token
|
||||
|
||||
@@ -3,12 +3,12 @@ from scipy import interpolate
|
||||
|
||||
def Singleton(cls):
|
||||
_instance = {}
|
||||
|
||||
|
||||
def _singleton(*args, **kargs):
|
||||
if cls not in _instance:
|
||||
_instance[cls] = cls(*args, **kargs)
|
||||
return _instance[cls]
|
||||
|
||||
|
||||
return _singleton
|
||||
|
||||
|
||||
@@ -39,7 +39,7 @@ class RealtimeAudioDistribution():
|
||||
else:
|
||||
res = None
|
||||
return res
|
||||
|
||||
|
||||
def change_sample_rate(audio, old_sr, new_sr):
|
||||
duration = audio.shape[0] / old_sr
|
||||
|
||||
|
||||
@@ -1,39 +0,0 @@
|
||||
from toolbox import update_ui, get_conf, promote_file_to_downloadzone, update_ui_lastest_msg, generate_file_link
|
||||
from shared_utils.docker_as_service_api import stream_daas
|
||||
from shared_utils.docker_as_service_api import DockerServiceApiComModel
|
||||
|
||||
def download_video(video_id, only_audio, user_name, chatbot, history):
|
||||
from toolbox import get_log_folder
|
||||
chatbot.append([None, "Processing..."])
|
||||
yield from update_ui(chatbot, history)
|
||||
client_command = f'{video_id} --audio-only' if only_audio else video_id
|
||||
server_url = get_conf('DAAS_SERVER_URL')
|
||||
docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
|
||||
save_file_dir = get_log_folder(user_name, plugin_name='media_downloader')
|
||||
for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
|
||||
status_buf = ""
|
||||
status_buf += "DaaS message: \n\n"
|
||||
status_buf += output_manifest['server_message'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS standard error: \n\n"
|
||||
status_buf += output_manifest['server_std_err'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS standard output: \n\n"
|
||||
status_buf += output_manifest['server_std_out'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS file attach: \n\n"
|
||||
status_buf += str(output_manifest['server_file_attach'])
|
||||
yield from update_ui_lastest_msg(status_buf, chatbot, history)
|
||||
|
||||
return output_manifest['server_file_attach']
|
||||
|
||||
|
||||
def search_videos(keywords):
|
||||
from toolbox import get_log_folder
|
||||
client_command = keywords
|
||||
server_url = get_conf('DAAS_SERVER_URL').replace('stream', 'search')
|
||||
docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
|
||||
save_file_dir = get_log_folder("default_user", plugin_name='media_downloader')
|
||||
for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
|
||||
return output_manifest['server_message']
|
||||
|
||||
@@ -40,7 +40,7 @@ class GptAcademicState():
|
||||
|
||||
class GptAcademicGameBaseState():
|
||||
"""
|
||||
1. first init: __init__ ->
|
||||
1. first init: __init__ ->
|
||||
"""
|
||||
def init_game(self, chatbot, lock_plugin):
|
||||
self.plugin_name = None
|
||||
@@ -53,7 +53,7 @@ class GptAcademicGameBaseState():
|
||||
raise ValueError("callback_fn is None")
|
||||
chatbot._cookies['lock_plugin'] = self.callback_fn
|
||||
self.dump_state(chatbot)
|
||||
|
||||
|
||||
def get_plugin_name(self):
|
||||
if self.plugin_name is None:
|
||||
raise ValueError("plugin_name is None")
|
||||
@@ -71,7 +71,7 @@ class GptAcademicGameBaseState():
|
||||
state = chatbot._cookies.get(f'plugin_state/{plugin_name}', None)
|
||||
if state is not None:
|
||||
state = pickle.loads(state)
|
||||
else:
|
||||
else:
|
||||
state = cls()
|
||||
state.init_game(chatbot, lock_plugin)
|
||||
state.plugin_name = plugin_name
|
||||
@@ -79,7 +79,7 @@ class GptAcademicGameBaseState():
|
||||
state.chatbot = chatbot
|
||||
state.callback_fn = callback_fn
|
||||
return state
|
||||
|
||||
|
||||
def continue_game(self, prompt, chatbot, history):
|
||||
# 游戏主体
|
||||
yield from self.step(prompt, chatbot, history)
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
|
||||
from loguru import logger
|
||||
|
||||
def force_breakdown(txt, limit, get_token_fn):
|
||||
""" 当无法用标点、空行分割时，我们用最暴力的方法切割
|
||||
@@ -36,7 +35,7 @@ def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=F
|
||||
remain_txt_to_cut_storage = ""
|
||||
# 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
|
||||
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
|
||||
|
||||
|
||||
while True:
|
||||
if get_token_fn(remain_txt_to_cut) <= limit:
|
||||
# 如果剩余文本的token数小于限制，那么就不用切了
|
||||
@@ -77,7 +76,7 @@ def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=F
|
||||
remain_txt_to_cut = post
|
||||
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
|
||||
process = fin_len/total_len
|
||||
logger.info(f'正在文本切分 {int(process*100)}%')
|
||||
print(f'正在文本切分 {int(process*100)}%')
|
||||
if len(remain_txt_to_cut.strip()) == 0:
|
||||
break
|
||||
return res
|
||||
@@ -120,7 +119,7 @@ if __name__ == '__main__':
|
||||
for i in range(5):
|
||||
file_content += file_content
|
||||
|
||||
logger.info(len(file_content))
|
||||
print(len(file_content))
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ from toolbox import promote_file_to_downloadzone
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from toolbox import get_conf
|
||||
from toolbox import ProxyNetworkActivate
|
||||
from shared_utils.colorful import *
|
||||
from colorful import *
|
||||
import requests
|
||||
import random
|
||||
import copy
|
||||
@@ -64,15 +64,15 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
|
||||
# 再做一个小修改：重新修改当前part的标题，默认用英文的
|
||||
cur_value += value
|
||||
translated_res_array.append(cur_value)
|
||||
res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + translated_res_array,
|
||||
file_basename = f"{gen_time_str()}-translated_only.md",
|
||||
res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + translated_res_array,
|
||||
file_basename = f"{gen_time_str()}-translated_only.md",
|
||||
file_fullname = None,
|
||||
auto_caption = False)
|
||||
promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
|
||||
generated_conclusion_files.append(res_path)
|
||||
return res_path
|
||||
|
||||
def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs={}):
|
||||
def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
@@ -138,17 +138,17 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
|
||||
chatbot=chatbot,
|
||||
history_array=[meta for _ in inputs_array],
|
||||
sys_prompt_array=[
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "") for _ in inputs_array],
|
||||
"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],
|
||||
)
|
||||
# -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
|
||||
produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)
|
||||
|
||||
# -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-=
|
||||
ch = construct_html()
|
||||
ch = construct_html()
|
||||
orig = ""
|
||||
trans = ""
|
||||
gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
|
||||
for i,k in enumerate(gpt_response_collection_html):
|
||||
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:
|
||||
@@ -159,7 +159,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
|
||||
|
||||
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):
|
||||
for i, k in enumerate(final):
|
||||
if i%2==0:
|
||||
orig = k
|
||||
if i%2==1:
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
import os
|
||||
from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
|
||||
from crazy_functions.pdf_fns.parse_pdf import parse_pdf, translate_pdf
|
||||
|
||||
def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
|
||||
import copy, json
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
generated_conclusion_files = []
|
||||
generated_html_files = []
|
||||
DST_LANG = "中文"
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
for index, fp in enumerate(file_manifest):
|
||||
chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
article_dict = parse_pdf(fp, grobid_url)
|
||||
grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
|
||||
with open(grobid_json_res, 'w+', encoding='utf8') as f:
|
||||
f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
|
||||
promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
|
||||
if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
|
||||
yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs=plugin_kwargs)
|
||||
chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
@@ -1,250 +0,0 @@
|
||||
from toolbox import get_log_folder, gen_time_str, get_conf
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import promote_file_to_downloadzone, extract_archive
|
||||
from toolbox import generate_file_link, zip_folder
|
||||
from crazy_functions.crazy_utils import get_files_from_everything
|
||||
from shared_utils.colorful import *
|
||||
from loguru import logger
|
||||
import os
|
||||
import time
|
||||
|
||||
def refresh_key(doc2x_api_key):
|
||||
import requests, json
|
||||
url = "https://api.doc2x.noedgeai.com/api/token/refresh"
|
||||
res = requests.post(
|
||||
url,
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key}
|
||||
)
|
||||
res_json = []
|
||||
if res.status_code == 200:
|
||||
decoded = res.content.decode("utf-8")
|
||||
res_json = json.loads(decoded)
|
||||
doc2x_api_key = res_json['data']['token']
|
||||
else:
|
||||
raise RuntimeError(format("[ERROR] status code: %d, body: %s" % (res.status_code, res.text)))
|
||||
return doc2x_api_key
|
||||
|
||||
|
||||
|
||||
def 解析PDF_DOC2X_转Latex(pdf_file_path):
|
||||
zip_file_path, unzipped_folder = 解析PDF_DOC2X(pdf_file_path, format='tex')
|
||||
return unzipped_folder
|
||||
|
||||
|
||||
def 解析PDF_DOC2X(pdf_file_path, format='tex'):
|
||||
"""
|
||||
format: 'tex', 'md', 'docx'
|
||||
"""
|
||||
import requests, json, os
|
||||
DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
|
||||
latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
|
||||
markdown_dir = get_log_folder(plugin_name="pdf_ocr")
|
||||
doc2x_api_key = DOC2X_API_KEY
|
||||
|
||||
|
||||
# < ------ 第1步：上传 ------ >
|
||||
logger.info("Doc2x 第1步：上传")
|
||||
with open(pdf_file_path, 'rb') as file:
|
||||
res = requests.post(
|
||||
"https://v2.doc2x.noedgeai.com/api/v2/parse/pdf",
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
data=file
|
||||
)
|
||||
# res_json = []
|
||||
if res.status_code == 200:
|
||||
res_json = res.json()
|
||||
else:
|
||||
raise RuntimeError(f"Doc2x return an error: {res.json()}")
|
||||
uuid = res_json['data']['uid']
|
||||
|
||||
# < ------ 第2步：轮询等待 ------ >
|
||||
logger.info("Doc2x 第2步：轮询等待")
|
||||
params = {'uid': uuid}
|
||||
while True:
|
||||
res = requests.get(
|
||||
'https://v2.doc2x.noedgeai.com/api/v2/parse/status',
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
params=params
|
||||
)
|
||||
res_json = res.json()
|
||||
if res_json['data']['status'] == "success":
|
||||
break
|
||||
elif res_json['data']['status'] == "processing":
|
||||
time.sleep(3)
|
||||
logger.info(f"Doc2x is processing at {res_json['data']['progress']}%")
|
||||
elif res_json['data']['status'] == "failed":
|
||||
raise RuntimeError(f"Doc2x return an error: {res_json}")
|
||||
|
||||
|
||||
# < ------ 第3步：提交转化 ------ >
|
||||
logger.info("Doc2x 第3步：提交转化")
|
||||
data = {
|
||||
"uid": uuid,
|
||||
"to": format,
|
||||
"formula_mode": "dollar",
|
||||
"filename": "output"
|
||||
}
|
||||
res = requests.post(
|
||||
'https://v2.doc2x.noedgeai.com/api/v2/convert/parse',
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
json=data
|
||||
)
|
||||
if res.status_code == 200:
|
||||
res_json = res.json()
|
||||
else:
|
||||
raise RuntimeError(f"Doc2x return an error: {res.json()}")
|
||||
|
||||
|
||||
# < ------ 第4步：等待结果 ------ >
|
||||
logger.info("Doc2x 第4步：等待结果")
|
||||
params = {'uid': uuid}
|
||||
while True:
|
||||
res = requests.get(
|
||||
'https://v2.doc2x.noedgeai.com/api/v2/convert/parse/result',
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
params=params
|
||||
)
|
||||
res_json = res.json()
|
||||
if res_json['data']['status'] == "success":
|
||||
break
|
||||
elif res_json['data']['status'] == "processing":
|
||||
time.sleep(3)
|
||||
logger.info(f"Doc2x still processing")
|
||||
elif res_json['data']['status'] == "failed":
|
||||
raise RuntimeError(f"Doc2x return an error: {res_json}")
|
||||
|
||||
|
||||
# < ------ 第5步：最后的处理 ------ >
|
||||
logger.info("Doc2x 第5步：最后的处理")
|
||||
|
||||
if format=='tex':
|
||||
target_path = latex_dir
|
||||
if format=='md':
|
||||
target_path = markdown_dir
|
||||
os.makedirs(target_path, exist_ok=True)
|
||||
|
||||
max_attempt = 3
|
||||
# < ------ 下载 ------ >
|
||||
for attempt in range(max_attempt):
|
||||
try:
|
||||
result_url = res_json['data']['url']
|
||||
res = requests.get(result_url)
|
||||
zip_path = os.path.join(target_path, gen_time_str() + '.zip')
|
||||
unzip_path = os.path.join(target_path, gen_time_str())
|
||||
if res.status_code == 200:
|
||||
with open(zip_path, "wb") as f: f.write(res.content)
|
||||
else:
|
||||
raise RuntimeError(f"Doc2x return an error: {res.json()}")
|
||||
except Exception as e:
|
||||
if attempt < max_attempt - 1:
|
||||
logger.error(f"Failed to download latex file, retrying... {e}")
|
||||
time.sleep(3)
|
||||
continue
|
||||
else:
|
||||
raise e
|
||||
|
||||
# < ------ 解压 ------ >
|
||||
import zipfile
|
||||
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
|
||||
zip_ref.extractall(unzip_path)
|
||||
return zip_path, unzip_path
|
||||
|
||||
|
||||
def 解析PDF_DOC2X_单文件(fp, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request):
|
||||
|
||||
def pdf2markdown(filepath):
|
||||
chatbot.append((None, f"Doc2x 解析中"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format='md')
|
||||
|
||||
promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
|
||||
chatbot.append((None, f"完成解析 {md_zip_path} ..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return md_zip_path
|
||||
|
||||
def deliver_to_markdown_plugin(md_zip_path, user_request):
|
||||
from crazy_functions.Markdown_Translate import Markdown英译中
|
||||
import shutil, re
|
||||
|
||||
time_tag = gen_time_str()
|
||||
target_path_base = get_log_folder(chatbot.get_user())
|
||||
file_origin_name = os.path.basename(md_zip_path)
|
||||
this_file_path = os.path.join(target_path_base, file_origin_name)
|
||||
os.makedirs(target_path_base, exist_ok=True)
|
||||
shutil.copyfile(md_zip_path, this_file_path)
|
||||
ex_folder = this_file_path + ".extract"
|
||||
extract_archive(
|
||||
file_path=this_file_path, dest_dir=ex_folder
|
||||
)
|
||||
|
||||
# edit markdown files
|
||||
success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md')
|
||||
for generated_fp in file_manifest:
|
||||
# 修正一些公式问题
|
||||
with open(generated_fp, 'r', encoding='utf8') as f:
|
||||
content = f.read()
|
||||
# 将公式中的\[ \]替换成$$
|
||||
content = content.replace(r'\[', r'$$').replace(r'\]', r'$$')
|
||||
# 将公式中的\( \)替换成$
|
||||
content = content.replace(r'\(', r'$').replace(r'\)', r'$')
|
||||
content = content.replace('```markdown', '\n').replace('```', '\n')
|
||||
with open(generated_fp, 'w', encoding='utf8') as f:
|
||||
f.write(content)
|
||||
promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 生成在线预览html
|
||||
file_name = '在线预览翻译（原文）' + gen_time_str() + '.html'
|
||||
preview_fp = os.path.join(ex_folder, file_name)
|
||||
from shared_utils.advanced_markdown_format import markdown_convertion_for_file
|
||||
with open(generated_fp, "r", encoding="utf-8") as f:
|
||||
md = f.read()
|
||||
# # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
|
||||
# md = re.sub(r'^<table>', r'.<table>', md, flags=re.MULTILINE)
|
||||
html = markdown_convertion_for_file(md)
|
||||
with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
|
||||
chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
|
||||
promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
|
||||
|
||||
|
||||
|
||||
chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
|
||||
plugin_kwargs['markdown_expected_output_dir'] = ex_folder
|
||||
|
||||
translated_f_name = 'translated_markdown.md'
|
||||
generated_fp = plugin_kwargs['markdown_expected_output_path'] = os.path.join(ex_folder, translated_f_name)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
yield from Markdown英译中(ex_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
if os.path.exists(generated_fp):
|
||||
# 修正一些公式问题
|
||||
with open(generated_fp, 'r', encoding='utf8') as f: content = f.read()
|
||||
content = content.replace('```markdown', '\n').replace('```', '\n')
|
||||
# Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
|
||||
# content = re.sub(r'^<table>', r'.<table>', content, flags=re.MULTILINE)
|
||||
with open(generated_fp, 'w', encoding='utf8') as f: f.write(content)
|
||||
# 生成在线预览html
|
||||
file_name = '在线预览翻译' + gen_time_str() + '.html'
|
||||
preview_fp = os.path.join(ex_folder, file_name)
|
||||
from shared_utils.advanced_markdown_format import markdown_convertion_for_file
|
||||
with open(generated_fp, "r", encoding="utf-8") as f:
|
||||
md = f.read()
|
||||
html = markdown_convertion_for_file(md)
|
||||
with open(preview_fp, "w", encoding="utf-8") as f: f.write(html)
|
||||
promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
|
||||
# 生成包含图片的压缩包
|
||||
dest_folder = get_log_folder(chatbot.get_user())
|
||||
zip_name = '翻译后的带图文档.zip'
|
||||
zip_folder(source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name)
|
||||
zip_fp = os.path.join(dest_folder, zip_name)
|
||||
promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
md_zip_path = yield from pdf2markdown(fp)
|
||||
yield from deliver_to_markdown_plugin(md_zip_path, user_request)
|
||||
|
||||
def 解析PDF_基于DOC2X(file_manifest, *args):
|
||||
for index, fp in enumerate(file_manifest):
|
||||
yield from 解析PDF_DOC2X_单文件(fp, *args)
|
||||
return
|
||||
|
||||
|
||||
@@ -1,85 +0,0 @@
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
|
||||
import os
|
||||
import re
|
||||
def extract_text_from_files(txt, chatbot, history):
|
||||
"""
|
||||
查找pdf/md/word并获取文本内容并返回状态以及文本
|
||||
|
||||
输入参数 Args:
|
||||
chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
|
||||
history (list): List of chat history （历史，对话历史列表）
|
||||
|
||||
输出 Returns:
|
||||
文件是否存在(bool)
|
||||
final_result(list):文本内容
|
||||
page_one(list):第一页内容/摘要
|
||||
file_manifest(list):文件路径
|
||||
excption(string):需要用户手动处理的信息,如没出错则保持为空
|
||||
"""
|
||||
|
||||
final_result = []
|
||||
page_one = []
|
||||
file_manifest = []
|
||||
excption = ""
|
||||
|
||||
if txt == "":
|
||||
final_result.append(txt)
|
||||
return False, final_result, page_one, file_manifest, excption #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
#查找输入区内容中的文件
|
||||
file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
|
||||
file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
|
||||
file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx')
|
||||
file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')
|
||||
|
||||
if file_doc:
|
||||
excption = "word"
|
||||
return False, final_result, page_one, file_manifest, excption
|
||||
|
||||
file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
|
||||
if file_num == 0:
|
||||
final_result.append(txt)
|
||||
return False, final_result, page_one, file_manifest, excption #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
if file_pdf:
|
||||
try: # 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
import fitz
|
||||
except:
|
||||
excption = "pdf"
|
||||
return False, final_result, page_one, file_manifest, excption
|
||||
for index, fp in enumerate(pdf_manifest):
|
||||
file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
final_result.append(file_content)
|
||||
page_one.append(pdf_one)
|
||||
file_manifest.append(os.path.relpath(fp, folder_pdf))
|
||||
|
||||
if file_md:
|
||||
for index, fp in enumerate(md_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE) #接下来提取md中的一级/二级标题作为摘要
|
||||
if len(headers) > 0:
|
||||
page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
|
||||
else:
|
||||
page_one.append("")
|
||||
final_result.append(file_content)
|
||||
file_manifest.append(os.path.relpath(fp, folder_md))
|
||||
|
||||
if file_word:
|
||||
try: # 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
from docx import Document
|
||||
except:
|
||||
excption = "word_pip"
|
||||
return False, final_result, page_one, file_manifest, excption
|
||||
for index, fp in enumerate(word_manifest):
|
||||
doc = Document(fp)
|
||||
file_content = '\n'.join([p.text for p in doc.paragraphs])
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
page_one.append(file_content[:200])
|
||||
final_result.append(file_content)
|
||||
file_manifest.append(os.path.relpath(fp, folder_word))
|
||||
|
||||
return True, final_result, page_one, file_manifest, excption
|
||||
@@ -1,73 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html xmlns="http://www.w3.org/1999/xhtml">
|
||||
|
||||
<head>
|
||||
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
|
||||
<title>GPT-Academic 翻译报告书</title>
|
||||
<style>
|
||||
.centered-a {
|
||||
color: red;
|
||||
text-align: center;
|
||||
margin-bottom: 2%;
|
||||
font-size: 1.5em;
|
||||
}
|
||||
.centered-b {
|
||||
color: red;
|
||||
text-align: center;
|
||||
margin-top: 10%;
|
||||
margin-bottom: 20%;
|
||||
font-size: 1.5em;
|
||||
}
|
||||
.centered-c {
|
||||
color: rgba(255, 0, 0, 0);
|
||||
text-align: center;
|
||||
margin-top: 2%;
|
||||
margin-bottom: 20%;
|
||||
font-size: 7em;
|
||||
}
|
||||
</style>
|
||||
<script>
|
||||
// Configure MathJax settings
|
||||
MathJax = {
|
||||
tex: {
|
||||
inlineMath: [
|
||||
['$', '$'],
|
||||
['\(', '\)']
|
||||
]
|
||||
}
|
||||
}
|
||||
addEventListener('zero-md-rendered', () => {MathJax.typeset(); console.log('MathJax typeset!');})
|
||||
</script>
|
||||
<!-- Load MathJax library -->
|
||||
<script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
|
||||
<script
|
||||
type="module"
|
||||
src="https://cdn.jsdelivr.net/gh/zerodevx/zero-md@2/dist/zero-md.min.js"
|
||||
></script>
|
||||
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="test_temp1" style="width:10%; height: 500px; float:left;">
|
||||
|
||||
</div>
|
||||
<div class="test_temp2" style="width:80%; height: 500px; float:left;">
|
||||
<!-- Simply set the `src` attribute to your MD file and win -->
|
||||
<div class="centered-a">
|
||||
请按Ctrl+S保存此页面，否则该页面可能在几分钟后失效。
|
||||
</div>
|
||||
<zero-md src="translated_markdown.md" no-shadow>
|
||||
</zero-md>
|
||||
<div class="centered-b">
|
||||
本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
|
||||
</div>
|
||||
<div class="centered-c">
|
||||
本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
|
||||
</div>
|
||||
</div>
|
||||
<div class="test_temp3" style="width:10%; height: 500px; float:left;">
|
||||
</div>
|
||||
|
||||
</body>
|
||||
|
||||
</html>
|
||||
@@ -1,52 +0,0 @@
|
||||
import os, json, base64
|
||||
from pydantic import BaseModel, Field
|
||||
from textwrap import dedent
|
||||
from typing import List
|
||||
|
||||
class ArgProperty(BaseModel): # PLUGIN_ARG_MENU
|
||||
title: str = Field(description="The title", default="")
|
||||
description: str = Field(description="The description", default="")
|
||||
default_value: str = Field(description="The default value", default="")
|
||||
type: str = Field(description="The type", default="") # currently we support ['string', 'dropdown']
|
||||
options: List[str] = Field(default=[], description="List of options available for the argument") # only used when type is 'dropdown'
|
||||
|
||||
class GptAcademicPluginTemplate():
|
||||
def __init__(self):
|
||||
# please note that `execute` method may run in different threads,
|
||||
# thus you should not store any state in the plugin instance,
|
||||
# which may be accessed by multiple threads
|
||||
pass
|
||||
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
An example as below:
|
||||
```
|
||||
def define_arg_selection_menu(self):
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="main input", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
"advanced_arg":
|
||||
ArgProperty(title="advanced arguments", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
"additional_arg_01":
|
||||
ArgProperty(title="additional", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
```
|
||||
"""
|
||||
raise NotImplementedError("You need to implement this method in your plugin class")
|
||||
|
||||
|
||||
def get_js_code_for_generating_menu(self, btnName):
|
||||
define_arg_selection = self.define_arg_selection_menu()
|
||||
|
||||
if len(define_arg_selection.keys()) > 8:
|
||||
raise ValueError("You can only have up to 8 arguments in the define_arg_selection")
|
||||
# if "main_input" not in define_arg_selection:
|
||||
# raise ValueError("You must have a 'main_input' in the define_arg_selection")
|
||||
|
||||
DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
|
||||
return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
raise NotImplementedError("You need to implement this method in your plugin class")
|
||||
@@ -1,87 +0,0 @@
|
||||
SearchOptimizerPrompt="""作为一个网页搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高网页检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于 Nginx 的介绍和在Ubuntu上的使用等。
|
||||
"
|
||||
原问题: 怎么下载
|
||||
检索词: ["Nginx 下载","Ubuntu Nginx","Ubuntu安装Nginx"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于 Nginx 的介绍和使用等。
|
||||
Q: 报错 "no connection"
|
||||
A: 报错"no connection"可能是因为……
|
||||
"
|
||||
原问题: 怎么解决
|
||||
检索词: ["Nginx报错"no connection" 解决","Nginx'no connection'报错 原因","Nginx提示'no connection'"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
|
||||
"
|
||||
原问题: 你知道 Python 么？
|
||||
检索词: ["Python","Python 使用教程。","Python 特点和优势"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 列出Java的三种特点？
|
||||
A: 1. Java 是一种编译型语言。
|
||||
2. Java 是一种面向对象的编程语言。
|
||||
3. Java 是一种跨平台的编程语言。
|
||||
"
|
||||
原问题: 介绍下第2点。
|
||||
检索词: ["Java 面向对象特点","Java 面向对象编程优势。","Java 面向对象编程"]
|
||||
----------------
|
||||
现在有历史记录:
|
||||
"
|
||||
{history}
|
||||
"
|
||||
有其原问题: {query}
|
||||
直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
|
||||
"""
|
||||
|
||||
SearchAcademicOptimizerPrompt="""作为一个学术论文搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高学术论文检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于深度学习的介绍和在图像识别中的应用等。
|
||||
"
|
||||
原问题: 怎么下载相关论文
|
||||
检索词: ["深度学习 图像识别 论文下载","图像识别 深度学习 研究论文","深度学习 图像识别 论文资源","Deep Learning Image Recognition Paper Download","Image Recognition Deep Learning Research Paper"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于深度学习的介绍和应用等。
|
||||
Q: 报错 "模型不收敛"
|
||||
A: 报错"模型不收敛"可能是因为……
|
||||
"
|
||||
原问题: 怎么解决
|
||||
检索词: ["深度学习 模型不收敛 解决方案 论文","深度学习 模型不收敛 原因 研究","深度学习 模型不收敛 论文","Deep Learning Model Convergence Issue Solution Paper","Deep Learning Model Convergence Problem Research"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
|
||||
"
|
||||
原问题: 你知道 GAN 么？
|
||||
检索词: ["生成对抗网络 论文","GAN 使用教程 论文","GAN 特点和优势 研究","Generative Adversarial Network Paper","GAN Usage Tutorial Paper"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 列出机器学习的三种应用？
|
||||
A: 1. 机器学习在图像识别中的应用。
|
||||
2. 机器学习在自然语言处理中的应用。
|
||||
3. 机器学习在推荐系统中的应用。
|
||||
"
|
||||
原问题: 介绍下第2点。
|
||||
检索词: ["机器学习 自然语言处理 应用 论文","机器学习 自然语言处理 研究","机器学习 NLP 应用 论文","Machine Learning Natural Language Processing Application Paper","Machine Learning NLP Research"]
|
||||
----------------
|
||||
现在有历史记录:
|
||||
"
|
||||
{history}
|
||||
"
|
||||
有其原问题: {query}
|
||||
直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
|
||||
"""
|
||||
@@ -1,138 +0,0 @@
|
||||
import atexit
|
||||
from loguru import logger
|
||||
from typing import List
|
||||
|
||||
from llama_index.core import Document
|
||||
from llama_index.core.ingestion import run_transformations
|
||||
from llama_index.core.schema import TextNode
|
||||
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
|
||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||
Now, you have context information as below:
|
||||
---------------------
|
||||
{context_str}
|
||||
---------------------
|
||||
Answer the user request below (use the context information if necessary, otherwise you can ignore them):
|
||||
---------------------
|
||||
{query_str}
|
||||
"""
|
||||
|
||||
QUESTION_ANSWER_RECORD = """\
|
||||
{{
|
||||
"type": "This is a previous conversation with the user",
|
||||
"question": "{question}",
|
||||
"answer": "{answer}",
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
class SaveLoad():
|
||||
|
||||
def does_checkpoint_exist(self, checkpoint_dir=None):
|
||||
import os, glob
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if not os.path.exists(checkpoint_dir): return False
|
||||
if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
|
||||
return True
|
||||
|
||||
def save_to_checkpoint(self, checkpoint_dir=None):
|
||||
logger.info(f'saving vector store to: {checkpoint_dir}')
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
|
||||
|
||||
def load_from_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
|
||||
logger.info('loading checkpoint from disk')
|
||||
from llama_index.core import StorageContext, load_index_from_storage
|
||||
storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
|
||||
self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
|
||||
return self.vs_index
|
||||
else:
|
||||
return self.create_new_vs()
|
||||
|
||||
def create_new_vs(self):
|
||||
return GptacVectorStoreIndex.default_vector_store(embed_model=self.embed_model)
|
||||
|
||||
def purge(self):
|
||||
import shutil
|
||||
shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
|
||||
self.vs_index = self.create_new_vs(self.checkpoint_dir)
|
||||
|
||||
|
||||
class LlamaIndexRagWorker(SaveLoad):
|
||||
def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
|
||||
self.debug_mode = True
|
||||
self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
|
||||
self.user_name = user_name
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
if auto_load_checkpoint:
|
||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.vs_index = self.create_new_vs()
|
||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||
|
||||
def assign_embedding_model(self):
|
||||
pass
|
||||
|
||||
def inspect_vector_store(self):
|
||||
# This function is for debugging
|
||||
self.vs_index.storage_context.index_store.to_dict()
|
||||
docstore = self.vs_index.storage_context.docstore.docs
|
||||
vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
|
||||
logger.info('\n++ --------inspect_vector_store begin--------')
|
||||
logger.info(vector_store_preview)
|
||||
logger.info('oo --------inspect_vector_store end--------')
|
||||
return vector_store_preview
|
||||
|
||||
def add_documents_to_vector_store(self, document_list: List[Document]):
|
||||
"""
|
||||
Adds a list of Document objects to the vector store after processing.
|
||||
"""
|
||||
documents = document_list
|
||||
documents_nodes = run_transformations(
|
||||
documents, # type: ignore
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def add_text_to_vector_store(self, text: str):
|
||||
node = TextNode(text=text)
|
||||
documents_nodes = run_transformations(
|
||||
[node],
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def remember_qa(self, question, answer):
|
||||
formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
|
||||
self.add_text_to_vector_store(formatted_str)
|
||||
|
||||
def retrieve_from_store_with_query(self, query):
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
retriever = self.vs_index.as_retriever()
|
||||
return retriever.retrieve(query)
|
||||
|
||||
def build_prompt(self, query, nodes):
|
||||
context_str = self.generate_node_array_preview(nodes)
|
||||
return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
|
||||
|
||||
def generate_node_array_preview(self, nodes):
|
||||
buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
|
||||
if self.debug_mode: logger.info(buf)
|
||||
return buf
|
||||
|
||||
def purge_vector_store(self):
|
||||
"""
|
||||
Purges the current vector store and creates a new one.
|
||||
"""
|
||||
self.purge()
|
||||
@@ -1,108 +0,0 @@
|
||||
import llama_index
|
||||
import os
|
||||
import atexit
|
||||
from typing import List
|
||||
from loguru import logger
|
||||
from llama_index.core import Document
|
||||
from llama_index.core.schema import TextNode
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
from shared_utils.connect_void_terminal import get_chat_default_kwargs
|
||||
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from llama_index.core.ingestion import run_transformations
|
||||
from llama_index.core import PromptTemplate
|
||||
from llama_index.core.response_synthesizers import TreeSummarize
|
||||
from llama_index.core import StorageContext
|
||||
from llama_index.vector_stores.milvus import MilvusVectorStore
|
||||
from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
|
||||
|
||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||
Now, you have context information as below:
|
||||
---------------------
|
||||
{context_str}
|
||||
---------------------
|
||||
Answer the user request below (use the context information if necessary, otherwise you can ignore them):
|
||||
---------------------
|
||||
{query_str}
|
||||
"""
|
||||
|
||||
QUESTION_ANSWER_RECORD = """\
|
||||
{{
|
||||
"type": "This is a previous conversation with the user",
|
||||
"question": "{question}",
|
||||
"answer": "{answer}",
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
class MilvusSaveLoad():
|
||||
|
||||
def does_checkpoint_exist(self, checkpoint_dir=None):
|
||||
import os, glob
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if not os.path.exists(checkpoint_dir): return False
|
||||
if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
|
||||
return True
|
||||
|
||||
def save_to_checkpoint(self, checkpoint_dir=None):
|
||||
logger.info(f'saving vector store to: {checkpoint_dir}')
|
||||
# if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
# self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
|
||||
|
||||
def load_from_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
|
||||
logger.info('loading checkpoint from disk')
|
||||
from llama_index.core import StorageContext, load_index_from_storage
|
||||
storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
|
||||
try:
|
||||
self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
|
||||
return self.vs_index
|
||||
except:
|
||||
return self.create_new_vs(checkpoint_dir)
|
||||
else:
|
||||
return self.create_new_vs(checkpoint_dir)
|
||||
|
||||
def create_new_vs(self, checkpoint_dir, overwrite=False):
|
||||
vector_store = MilvusVectorStore(
|
||||
uri=os.path.join(checkpoint_dir, "milvus_demo.db"),
|
||||
dim=self.embed_model.embedding_dimension(),
|
||||
overwrite=overwrite
|
||||
)
|
||||
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
||||
index = GptacVectorStoreIndex.default_vector_store(storage_context=storage_context, embed_model=self.embed_model)
|
||||
return index
|
||||
|
||||
def purge(self):
|
||||
self.vs_index = self.create_new_vs(self.checkpoint_dir, overwrite=True)
|
||||
|
||||
class MilvusRagWorker(MilvusSaveLoad, LlamaIndexRagWorker):
|
||||
|
||||
def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
|
||||
self.debug_mode = True
|
||||
self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
|
||||
self.user_name = user_name
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
if auto_load_checkpoint:
|
||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.vs_index = self.create_new_vs(checkpoint_dir)
|
||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||
|
||||
def inspect_vector_store(self):
|
||||
# This function is for debugging
|
||||
try:
|
||||
self.vs_index.storage_context.index_store.to_dict()
|
||||
docstore = self.vs_index.storage_context.docstore.docs
|
||||
if not docstore.items():
|
||||
raise ValueError("cannot inspect")
|
||||
vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
|
||||
except:
|
||||
dummy_retrieve_res: List["NodeWithScore"] = self.vs_index.as_retriever().retrieve(' ')
|
||||
vector_store_preview = "\n".join(
|
||||
[f"{node.id_} | {node.text}" for node in dummy_retrieve_res]
|
||||
)
|
||||
logger.info('\n++ --------inspect_vector_store begin--------')
|
||||
logger.info(vector_store_preview)
|
||||
logger.info('oo --------inspect_vector_store end--------')
|
||||
return vector_store_preview
|
||||
@@ -1,22 +0,0 @@
|
||||
import os
|
||||
from llama_index.core import SimpleDirectoryReader
|
||||
|
||||
supports_format = ['.csv', '.docx', '.epub', '.ipynb', '.mbox', '.md', '.pdf', '.txt', '.ppt',
|
||||
'.pptm', '.pptx']
|
||||
|
||||
|
||||
# 修改后的 extract_text 函数，结合 SimpleDirectoryReader 和自定义解析逻辑
|
||||
def extract_text(file_path):
|
||||
_, ext = os.path.splitext(file_path.lower())
|
||||
|
||||
# 使用 SimpleDirectoryReader 处理它支持的文件格式
|
||||
if ext in supports_format:
|
||||
try:
|
||||
reader = SimpleDirectoryReader(input_files=[file_path])
|
||||
documents = reader.load_data()
|
||||
if len(documents) > 0:
|
||||
return documents[0].text
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return None
|
||||
@@ -1,58 +0,0 @@
|
||||
from llama_index.core import VectorStoreIndex
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from llama_index.core.callbacks.base import CallbackManager
|
||||
from llama_index.core.schema import TransformComponent
|
||||
from llama_index.core.service_context import ServiceContext
|
||||
from llama_index.core.settings import (
|
||||
Settings,
|
||||
callback_manager_from_settings_or_context,
|
||||
transformations_from_settings_or_context,
|
||||
)
|
||||
from llama_index.core.storage.storage_context import StorageContext
|
||||
|
||||
|
||||
class GptacVectorStoreIndex(VectorStoreIndex):
|
||||
|
||||
@classmethod
|
||||
def default_vector_store(
|
||||
cls,
|
||||
storage_context: Optional[StorageContext] = None,
|
||||
show_progress: bool = False,
|
||||
callback_manager: Optional[CallbackManager] = None,
|
||||
transformations: Optional[List[TransformComponent]] = None,
|
||||
# deprecated
|
||||
service_context: Optional[ServiceContext] = None,
|
||||
embed_model = None,
|
||||
**kwargs: Any,
|
||||
):
|
||||
"""Create index from documents.
|
||||
|
||||
Args:
|
||||
documents (Optional[Sequence[BaseDocument]]): List of documents to
|
||||
build the index from.
|
||||
|
||||
"""
|
||||
storage_context = storage_context or StorageContext.from_defaults()
|
||||
docstore = storage_context.docstore
|
||||
callback_manager = (
|
||||
callback_manager
|
||||
or callback_manager_from_settings_or_context(Settings, service_context)
|
||||
)
|
||||
transformations = transformations or transformations_from_settings_or_context(
|
||||
Settings, service_context
|
||||
)
|
||||
|
||||
with callback_manager.as_trace("index_construction"):
|
||||
|
||||
return cls(
|
||||
nodes=[],
|
||||
storage_context=storage_context,
|
||||
callback_manager=callback_manager,
|
||||
show_progress=show_progress,
|
||||
transformations=transformations,
|
||||
service_context=service_context,
|
||||
embed_model=embed_model,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -1,17 +1,16 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
import threading
|
||||
from toolbox import Singleton
|
||||
import os
|
||||
import shutil
|
||||
import os
|
||||
import uuid
|
||||
import tqdm
|
||||
import shutil
|
||||
import threading
|
||||
import numpy as np
|
||||
from toolbox import Singleton
|
||||
from loguru import logger
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain.docstore.document import Document
|
||||
from typing import List, Tuple
|
||||
import numpy as np
|
||||
from crazy_functions.vector_fns.general_file_loader import load_file
|
||||
|
||||
embedding_model_dict = {
|
||||
@@ -29,7 +28,7 @@ EMBEDDING_DEVICE = "cpu"
|
||||
|
||||
# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
|
||||
PROMPT_TEMPLATE = """已知信息：
|
||||
{context}
|
||||
{context}
|
||||
|
||||
根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
|
||||
|
||||
@@ -59,7 +58,7 @@ OPEN_CROSS_DOMAIN = False
|
||||
def similarity_search_with_score_by_vector(
|
||||
self, embedding: List[float], k: int = 4
|
||||
) -> List[Tuple[Document, float]]:
|
||||
|
||||
|
||||
def seperate_list(ls: List[int]) -> List[List[int]]:
|
||||
lists = []
|
||||
ls1 = [ls[0]]
|
||||
@@ -151,17 +150,17 @@ class LocalDocQA:
|
||||
failed_files = []
|
||||
if isinstance(filepath, str):
|
||||
if not os.path.exists(filepath):
|
||||
logger.error("路径不存在")
|
||||
print("路径不存在")
|
||||
return None
|
||||
elif os.path.isfile(filepath):
|
||||
file = os.path.split(filepath)[-1]
|
||||
try:
|
||||
docs = load_file(filepath, SENTENCE_SIZE)
|
||||
logger.info(f"{file} 已成功加载")
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(filepath)
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
logger.error(f"{file} 未能成功加载")
|
||||
print(e)
|
||||
print(f"{file} 未能成功加载")
|
||||
return None
|
||||
elif os.path.isdir(filepath):
|
||||
docs = []
|
||||
@@ -171,23 +170,23 @@ class LocalDocQA:
|
||||
docs += load_file(fullfilepath, SENTENCE_SIZE)
|
||||
loaded_files.append(fullfilepath)
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
print(e)
|
||||
failed_files.append(file)
|
||||
|
||||
if len(failed_files) > 0:
|
||||
logger.error("以下文件未能成功加载：")
|
||||
print("以下文件未能成功加载：")
|
||||
for file in failed_files:
|
||||
logger.error(f"{file}\n")
|
||||
print(f"{file}\n")
|
||||
|
||||
else:
|
||||
docs = []
|
||||
for file in filepath:
|
||||
docs += load_file(file, SENTENCE_SIZE)
|
||||
logger.info(f"{file} 已成功加载")
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(file)
|
||||
|
||||
if len(docs) > 0:
|
||||
logger.info("文件加载完毕，正在生成向量库")
|
||||
print("文件加载完毕，正在生成向量库")
|
||||
if vs_path and os.path.isdir(vs_path):
|
||||
try:
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
@@ -201,7 +200,7 @@ class LocalDocQA:
|
||||
return vs_path, loaded_files
|
||||
else:
|
||||
raise RuntimeError("文件加载失败，请检查文件格式是否正确")
|
||||
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
ds = self.vector_store.docstore
|
||||
return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
|
||||
@@ -234,7 +233,7 @@ class LocalDocQA:
|
||||
prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
|
||||
prompt += "\n\n---\n\n"
|
||||
prompt = prompt.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
# logger.info(prompt)
|
||||
# print(prompt)
|
||||
response = {"query": query, "source_documents": related_docs_with_score}
|
||||
return response, prompt
|
||||
|
||||
@@ -263,7 +262,7 @@ def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_co
|
||||
else:
|
||||
pass
|
||||
# file_status = "文件未成功加载，请重新上传文件"
|
||||
# logger.info(file_status)
|
||||
# print(file_status)
|
||||
return local_doc_qa, vs_path
|
||||
|
||||
@Singleton
|
||||
@@ -279,7 +278,7 @@ class knowledge_archive_interface():
|
||||
if self.text2vec_large_chinese is None:
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
from toolbox import ProxyNetworkActivate
|
||||
logger.info('Checking Text2vec ...')
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
@@ -291,10 +290,10 @@ class knowledge_archive_interface():
|
||||
self.threadLock.acquire()
|
||||
# import uuid
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=file_manifest,
|
||||
files=file_manifest,
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
@@ -305,7 +304,7 @@ class knowledge_archive_interface():
|
||||
|
||||
def get_current_archive_id(self):
|
||||
return self.current_id
|
||||
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
return self.qa_handle.get_loaded_file(vs_path)
|
||||
|
||||
@@ -313,10 +312,10 @@ class knowledge_archive_interface():
|
||||
self.threadLock.acquire()
|
||||
if not self.current_id == id:
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=[],
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
@@ -330,7 +329,7 @@ class knowledge_archive_interface():
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
|
||||
@@ -10,7 +10,7 @@ def read_avail_plugin_enum():
|
||||
from crazy_functional import get_crazy_functions
|
||||
plugin_arr = get_crazy_functions()
|
||||
# remove plugins with out explaination
|
||||
plugin_arr = {k:v for k, v in plugin_arr.items() if ('Info' in v) and ('Function' in v)}
|
||||
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)}
|
||||
@@ -35,9 +35,9 @@ 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."
|
||||
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):
|
||||
@@ -82,7 +82,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
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 = ""
|
||||
@@ -99,7 +99,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
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
|
||||
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)
|
||||
|
||||
@@ -10,7 +10,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
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。",
|
||||
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
|
||||
@@ -35,7 +35,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
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)
|
||||
@@ -45,11 +45,11 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
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}",
|
||||
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",
|
||||
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
|
||||
)
|
||||
|
||||
@@ -69,7 +69,7 @@ def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history
|
||||
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。",
|
||||
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
|
||||
|
||||
@@ -6,7 +6,7 @@ class VoidTerminalState():
|
||||
|
||||
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)
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
191
crazy_functions/下载arxiv论文翻译摘要.py
Normal file
191
crazy_functions/下载arxiv论文翻译摘要.py
Normal file
@@ -0,0 +1,191 @@
|
||||
from toolbox import update_ui, get_log_folder
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, get_conf
|
||||
import re, requests, unicodedata, os
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
def download_arxiv_(url_pdf):
|
||||
if 'arxiv.org' not in url_pdf:
|
||||
if ('.' in url_pdf) and ('/' not in url_pdf):
|
||||
new_url = 'https://arxiv.org/abs/'+url_pdf
|
||||
print('下载编号：', url_pdf, '自动定位：', new_url)
|
||||
# download_arxiv_(new_url)
|
||||
return download_arxiv_(new_url)
|
||||
else:
|
||||
print('不能识别的URL！')
|
||||
return None
|
||||
if 'abs' in url_pdf:
|
||||
url_pdf = url_pdf.replace('abs', 'pdf')
|
||||
url_pdf = url_pdf + '.pdf'
|
||||
|
||||
url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
|
||||
title, other_info = get_name(_url_=url_abs)
|
||||
|
||||
paper_id = title.split()[0] # '[1712.00559]'
|
||||
if '2' in other_info['year']:
|
||||
title = other_info['year'] + ' ' + title
|
||||
|
||||
known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
|
||||
for k in known_conf:
|
||||
if k in other_info['comment']:
|
||||
title = k + ' ' + title
|
||||
|
||||
download_dir = get_log_folder(plugin_name='arxiv')
|
||||
os.makedirs(download_dir, exist_ok=True)
|
||||
|
||||
title_str = title.replace('?', '？')\
|
||||
.replace(':', '：')\
|
||||
.replace('\"', '“')\
|
||||
.replace('\n', '')\
|
||||
.replace(' ', ' ')\
|
||||
.replace(' ', ' ')
|
||||
|
||||
requests_pdf_url = url_pdf
|
||||
file_path = download_dir+title_str
|
||||
|
||||
print('下载中')
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(requests_pdf_url, proxies=proxies)
|
||||
with open(file_path, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
print('下载完成')
|
||||
|
||||
# print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
|
||||
# subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
|
||||
|
||||
x = "%s %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
|
||||
x = x.replace('?', '？')\
|
||||
.replace(':', '：')\
|
||||
.replace('\"', '“')\
|
||||
.replace('\n', '')\
|
||||
.replace(' ', ' ')\
|
||||
.replace(' ', ' ')
|
||||
return file_path, other_info
|
||||
|
||||
|
||||
def get_name(_url_):
|
||||
import os
|
||||
from bs4 import BeautifulSoup
|
||||
print('正在获取文献名！')
|
||||
print(_url_)
|
||||
|
||||
# arxiv_recall = {}
|
||||
# if os.path.exists('./arxiv_recall.pkl'):
|
||||
# with open('./arxiv_recall.pkl', 'rb') as f:
|
||||
# arxiv_recall = pickle.load(f)
|
||||
|
||||
# if _url_ in arxiv_recall:
|
||||
# print('在缓存中')
|
||||
# return arxiv_recall[_url_]
|
||||
|
||||
proxies = get_conf('proxies')
|
||||
res = requests.get(_url_, proxies=proxies)
|
||||
|
||||
bs = BeautifulSoup(res.text, 'html.parser')
|
||||
other_details = {}
|
||||
|
||||
# get year
|
||||
try:
|
||||
year = bs.find_all(class_='dateline')[0].text
|
||||
year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
|
||||
other_details['year'] = year
|
||||
abstract = bs.find_all(class_='abstract mathjax')[0].text
|
||||
other_details['abstract'] = abstract
|
||||
except:
|
||||
other_details['year'] = ''
|
||||
print('年份获取失败')
|
||||
|
||||
# get author
|
||||
try:
|
||||
authors = bs.find_all(class_='authors')[0].text
|
||||
authors = authors.split('Authors:')[1]
|
||||
other_details['authors'] = authors
|
||||
except:
|
||||
other_details['authors'] = ''
|
||||
print('authors获取失败')
|
||||
|
||||
# get comment
|
||||
try:
|
||||
comment = bs.find_all(class_='metatable')[0].text
|
||||
real_comment = None
|
||||
for item in comment.replace('\n', ' ').split(' '):
|
||||
if 'Comments' in item:
|
||||
real_comment = item
|
||||
if real_comment is not None:
|
||||
other_details['comment'] = real_comment
|
||||
else:
|
||||
other_details['comment'] = ''
|
||||
except:
|
||||
other_details['comment'] = ''
|
||||
print('年份获取失败')
|
||||
|
||||
title_str = BeautifulSoup(
|
||||
res.text, 'html.parser').find('title').contents[0]
|
||||
print('获取成功：', title_str)
|
||||
# arxiv_recall[_url_] = (title_str+'.pdf', other_details)
|
||||
# with open('./arxiv_recall.pkl', 'wb') as f:
|
||||
# pickle.dump(arxiv_recall, f)
|
||||
|
||||
return title_str+'.pdf', other_details
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
|
||||
import glob
|
||||
import os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import bs4
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 提取摘要，下载PDF文档
|
||||
try:
|
||||
pdf_path, info = download_arxiv_(txt)
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"下载pdf文件未成功")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 翻译摘要等
|
||||
i_say = f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
|
||||
i_say_show_user = f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
# 单线，获取文章meta信息
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot, history=[],
|
||||
sys_prompt="Your job is to collect information from materials and translate to Chinese。",
|
||||
)
|
||||
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
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
|
||||
|
||||
40
crazy_functions/互动小游戏.py
Normal file
40
crazy_functions/互动小游戏.py
Normal file
@@ -0,0 +1,40 @@
|
||||
from toolbox import CatchException, update_ui, update_ui_lastest_msg
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
|
||||
|
||||
@CatchException
|
||||
def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
from crazy_functions.game_fns.game_interactive_story import MiniGame_ResumeStory
|
||||
# 清空历史
|
||||
history = []
|
||||
# 选择游戏
|
||||
cls = MiniGame_ResumeStory
|
||||
# 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
|
||||
state = cls.sync_state(chatbot,
|
||||
llm_kwargs,
|
||||
cls,
|
||||
plugin_name='MiniGame_ResumeStory',
|
||||
callback_fn='crazy_functions.互动小游戏->随机小游戏',
|
||||
lock_plugin=True
|
||||
)
|
||||
yield from state.continue_game(prompt, chatbot, history)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
from crazy_functions.game_fns.game_ascii_art import MiniGame_ASCII_Art
|
||||
# 清空历史
|
||||
history = []
|
||||
# 选择游戏
|
||||
cls = MiniGame_ASCII_Art
|
||||
# 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
|
||||
state = cls.sync_state(chatbot,
|
||||
llm_kwargs,
|
||||
cls,
|
||||
plugin_name='MiniGame_ASCII_Art',
|
||||
callback_fn='crazy_functions.互动小游戏->随机小游戏1',
|
||||
lock_plugin=True
|
||||
)
|
||||
yield from state.continue_game(prompt, chatbot, history)
|
||||
63
crazy_functions/交互功能函数模板.py
Normal file
63
crazy_functions/交互功能函数模板.py
Normal file
@@ -0,0 +1,63 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
|
||||
@CatchException
|
||||
def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数, 如温度和top_p等, 一般原样传递下去就行
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
|
||||
|
||||
if state is None:
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数' # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数
|
||||
chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword' # 赋予插件状态
|
||||
|
||||
chatbot.append(("第一次调用：", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if state == 'wait_user_keyword':
|
||||
chatbot._cookies['lock_plugin'] = None # 解除插件锁定，避免遗忘导致死锁
|
||||
chatbot._cookies['plugin_state_0001'] = None # 解除插件状态，避免遗忘导致死锁
|
||||
|
||||
# 解除插件锁定
|
||||
chatbot.append((f"获取关键词：{txt}", ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
page_return = get_image_page_by_keyword(txt)
|
||||
inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=inputs, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="When you want to show an image, use markdown format. e.g. . If there are no image url provided, answer 'no image url provided'"
|
||||
)
|
||||
chatbot[-1] = [chatbot[-1][0], gpt_say]
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------------
|
||||
|
||||
def get_image_page_by_keyword(keyword):
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
|
||||
res = "image urls: \n"
|
||||
for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
|
||||
try:
|
||||
res += image_element["data-src"]
|
||||
res += "\n"
|
||||
except:
|
||||
pass
|
||||
return res
|
||||
252
crazy_functions/函数动态生成.py
Normal file
252
crazy_functions/函数动态生成.py
Normal file
@@ -0,0 +1,252 @@
|
||||
# 本源代码中, ⭐ = 关键步骤
|
||||
"""
|
||||
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
|
||||
|
||||
Testing:
|
||||
- Crop the image, keeping the bottom half.
|
||||
- Swap the blue channel and red channel of the image.
|
||||
- Convert the image to grayscale.
|
||||
- Convert the CSV file to an Excel spreadsheet.
|
||||
"""
|
||||
|
||||
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
|
||||
from .crazy_utils import input_clipping, try_install_deps
|
||||
from crazy_functions.gen_fns.gen_fns_shared import is_function_successfully_generated
|
||||
from crazy_functions.gen_fns.gen_fns_shared import get_class_name
|
||||
from crazy_functions.gen_fns.gen_fns_shared import subprocess_worker
|
||||
from crazy_functions.gen_fns.gen_fns_shared import try_make_module
|
||||
import os
|
||||
import time
|
||||
import glob
|
||||
import multiprocessing
|
||||
|
||||
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 world-class 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. You need to replace `...` with valid packages, do not give `...` in your answer!"
|
||||
)
|
||||
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 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 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 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
|
||||
# 清空历史
|
||||
history = []
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append(["正在启动: 插件动态生成插件", "插件动态生成, 执行开始, 作者Binary-Husky."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# ⭐ 文件上传区是否有东西
|
||||
# 1. 如果有文件: 作为函数参数
|
||||
# 2. 如果没有文件：需要用GPT提取参数 （太懒了，以后再写，虚空终端已经实现了类似的代码）
|
||||
file_list = []
|
||||
if get_plugin_arg(plugin_kwargs, key="file_path_arg", default=False):
|
||||
file_path = get_plugin_arg(plugin_kwargs, key="file_path_arg", default=None)
|
||||
file_list.append(file_path)
|
||||
yield from update_ui_lastest_msg(f"当前文件: {file_path}", chatbot, history, 1)
|
||||
elif have_any_recent_upload_files(chatbot):
|
||||
file_dir = get_recent_file_prompt_support(chatbot)
|
||||
file_list = glob.glob(os.path.join(file_dir, '**/*'), recursive=True)
|
||||
yield from update_ui_lastest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
|
||||
else:
|
||||
chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
|
||||
yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
return # 2. 如果没有文件
|
||||
if len(file_list) == 0:
|
||||
chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
|
||||
yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
return # 2. 如果没有文件
|
||||
|
||||
# 读取文件
|
||||
file_type = file_list[0].split('.')[-1]
|
||||
|
||||
# 粗心检查
|
||||
if is_the_upload_folder(txt):
|
||||
yield from update_ui_lastest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
|
||||
return
|
||||
|
||||
# 开始干正事
|
||||
MAX_TRY = 3
|
||||
for j in range(MAX_TRY): # 最多重试5次
|
||||
traceback = ""
|
||||
try:
|
||||
# ⭐ 开始啦 ！
|
||||
code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
|
||||
yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
|
||||
chatbot.append(["代码生成阶段结束", ""])
|
||||
yield from update_ui_lastest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
|
||||
# ⭐ 分离代码块
|
||||
code = get_code_block(code)
|
||||
# ⭐ 检查模块
|
||||
ok, traceback = try_make_module(code, chatbot)
|
||||
# 搞定代码生成
|
||||
if ok: break
|
||||
except Exception as e:
|
||||
if not traceback: traceback = trimmed_format_exc()
|
||||
# 处理异常
|
||||
if not traceback: traceback = trimmed_format_exc()
|
||||
yield from update_ui_lastest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
|
||||
|
||||
# 代码生成结束, 开始执行
|
||||
TIME_LIMIT = 15
|
||||
yield from update_ui_lastest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
|
||||
manager = multiprocessing.Manager()
|
||||
return_dict = manager.dict()
|
||||
|
||||
# ⭐ 到最后一步了，开始逐个文件进行处理
|
||||
for file_path in file_list:
|
||||
if os.path.exists(file_path):
|
||||
chatbot.append([f"正在处理文件: {file_path}", f"请稍等..."])
|
||||
chatbot = for_immediate_show_off_when_possible(file_type, file_path, chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
else:
|
||||
continue
|
||||
|
||||
# ⭐⭐⭐ subprocess_worker ⭐⭐⭐
|
||||
p = multiprocessing.Process(target=subprocess_worker, args=(code, file_path, return_dict))
|
||||
# ⭐ 开始执行，时间限制TIME_LIMIT
|
||||
p.start(); p.join(timeout=TIME_LIMIT)
|
||||
if p.is_alive(): p.terminate(); p.join()
|
||||
p.close()
|
||||
res = return_dict['result']
|
||||
success = return_dict['success']
|
||||
traceback = return_dict['traceback']
|
||||
if not success:
|
||||
if not traceback: traceback = trimmed_format_exc()
|
||||
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
|
||||
|
||||
31
crazy_functions/命令行助手.py
Normal file
31
crazy_functions/命令行助手.py
Normal file
@@ -0,0 +1,31 @@
|
||||
from toolbox import CatchException, update_ui, gen_time_str
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import input_clipping
|
||||
import copy, json
|
||||
|
||||
@CatchException
|
||||
def 命令行助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
# 清空历史, 以免输入溢出
|
||||
history = []
|
||||
|
||||
# 输入
|
||||
i_say = "请写bash命令实现以下功能：" + txt
|
||||
# 开始
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你是一个Linux大师级用户。注意，当我要求你写bash命令时，尽可能地仅用一行命令解决我的要求。"
|
||||
)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
|
||||
276
crazy_functions/图片生成.py
Normal file
276
crazy_functions/图片生成.py
Normal file
@@ -0,0 +1,276 @@
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
|
||||
|
||||
|
||||
def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", quality=None, style=None):
|
||||
import requests, json, time, os
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
proxies = get_conf('proxies')
|
||||
# Set up OpenAI API key and model
|
||||
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
|
||||
chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||||
# 'https://api.openai.com/v1/chat/completions'
|
||||
img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
|
||||
# # Generate the image
|
||||
url = img_endpoint
|
||||
headers = {
|
||||
'Authorization': f"Bearer {api_key}",
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
data = {
|
||||
'prompt': prompt,
|
||||
'n': 1,
|
||||
'size': resolution,
|
||||
'model': model,
|
||||
'response_format': 'url'
|
||||
}
|
||||
if quality is not None:
|
||||
data['quality'] = quality
|
||||
if style is not None:
|
||||
data['style'] = style
|
||||
response = requests.post(url, headers=headers, json=data, proxies=proxies)
|
||||
print(response.content)
|
||||
try:
|
||||
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
|
||||
except:
|
||||
raise RuntimeError(response.content.decode())
|
||||
# 文件保存到本地
|
||||
r = requests.get(image_url, proxies=proxies)
|
||||
file_path = 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)
|
||||
|
||||
|
||||
return image_url, file_path+file_name
|
||||
|
||||
|
||||
def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="dall-e-2"):
|
||||
import requests, json, time, os
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
proxies = get_conf('proxies')
|
||||
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
|
||||
chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||||
# 'https://api.openai.com/v1/chat/completions'
|
||||
img_endpoint = chat_endpoint.replace('chat/completions','images/edits')
|
||||
# # Generate the image
|
||||
url = img_endpoint
|
||||
n = 1
|
||||
headers = {
|
||||
'Authorization': f"Bearer {api_key}",
|
||||
}
|
||||
make_transparent(image_path, image_path+'.tsp.png')
|
||||
make_square_image(image_path+'.tsp.png', image_path+'.tspsq.png')
|
||||
resize_image(image_path+'.tspsq.png', image_path+'.ready.png', max_size=1024)
|
||||
image_path = image_path+'.ready.png'
|
||||
with open(image_path, 'rb') as f:
|
||||
file_content = f.read()
|
||||
files = {
|
||||
'image': (os.path.basename(image_path), file_content),
|
||||
# 'mask': ('mask.png', open('mask.png', 'rb'))
|
||||
'prompt': (None, prompt),
|
||||
"n": (None, str(n)),
|
||||
'size': (None, resolution),
|
||||
}
|
||||
|
||||
response = requests.post(url, headers=headers, files=files, proxies=proxies)
|
||||
print(response.content)
|
||||
try:
|
||||
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
|
||||
except:
|
||||
raise RuntimeError(response.content.decode())
|
||||
# 文件保存到本地
|
||||
r = requests.get(image_url, proxies=proxies)
|
||||
file_path = 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)
|
||||
|
||||
|
||||
return image_url, file_path+file_name
|
||||
|
||||
|
||||
@CatchException
|
||||
def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
if prompt.strip() == "":
|
||||
chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
return
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
resolution = plugin_kwargs.get("advanced_arg", '1024x1024')
|
||||
image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
|
||||
chatbot.append([prompt,
|
||||
f'图像中转网址: <br/>`{image_url}`<br/>'+
|
||||
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
|
||||
f'本地文件地址: <br/>`{image_path}`<br/>'+
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
|
||||
|
||||
@CatchException
|
||||
def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
if prompt.strip() == "":
|
||||
chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
return
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
resolution_arg = plugin_kwargs.get("advanced_arg", '1024x1024-standard-vivid').lower()
|
||||
parts = resolution_arg.split('-')
|
||||
resolution = parts[0] # 解析分辨率
|
||||
quality = 'standard' # 质量与风格默认值
|
||||
style = 'vivid'
|
||||
# 遍历检查是否有额外参数
|
||||
for part in parts[1:]:
|
||||
if part in ['hd', 'standard']:
|
||||
quality = part
|
||||
elif part in ['vivid', 'natural']:
|
||||
style = part
|
||||
image_url, image_path = gen_image(llm_kwargs, prompt, resolution, model="dall-e-3", quality=quality, style=style)
|
||||
chatbot.append([prompt,
|
||||
f'图像中转网址: <br/>`{image_url}`<br/>'+
|
||||
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
|
||||
f'本地文件地址: <br/>`{image_path}`<br/>'+
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
|
||||
|
||||
class ImageEditState(GptAcademicState):
|
||||
# 尚未完成
|
||||
def get_image_file(self, x):
|
||||
import os, glob
|
||||
if len(x) == 0: return False, None
|
||||
if not os.path.exists(x): return False, None
|
||||
if x.endswith('.png'): return True, x
|
||||
file_manifest = [f for f in glob.glob(f'{x}/**/*.png', recursive=True)]
|
||||
confirm = (len(file_manifest) >= 1 and file_manifest[0].endswith('.png') and os.path.exists(file_manifest[0]))
|
||||
file = None if not confirm else file_manifest[0]
|
||||
return confirm, file
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.图片生成->图片修改_DALLE2'
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def unlock_plugin(self, chatbot):
|
||||
self.reset()
|
||||
chatbot._cookies['lock_plugin'] = None
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def get_resolution(self, x):
|
||||
return (x in ['256x256', '512x512', '1024x1024']), x
|
||||
|
||||
def get_prompt(self, x):
|
||||
confirm = (len(x)>=5) and (not self.get_resolution(x)[0]) and (not self.get_image_file(x)[0])
|
||||
return confirm, x
|
||||
|
||||
def reset(self):
|
||||
self.req = [
|
||||
{'value':None, 'description': '请先上传图像（必须是.png格式）, 然后再次点击本插件', 'verify_fn': self.get_image_file},
|
||||
{'value':None, 'description': '请输入分辨率,可选：256x256, 512x512 或 1024x1024, 然后再次点击本插件', 'verify_fn': self.get_resolution},
|
||||
{'value':None, 'description': '请输入修改需求,建议您使用英文提示词, 然后再次点击本插件', 'verify_fn': self.get_prompt},
|
||||
]
|
||||
self.info = ""
|
||||
|
||||
def feed(self, prompt, chatbot):
|
||||
for r in self.req:
|
||||
if r['value'] is None:
|
||||
confirm, res = r['verify_fn'](prompt)
|
||||
if confirm:
|
||||
r['value'] = res
|
||||
self.dump_state(chatbot)
|
||||
break
|
||||
return self
|
||||
|
||||
def next_req(self):
|
||||
for r in self.req:
|
||||
if r['value'] is None:
|
||||
return r['description']
|
||||
return "已经收集到所有信息"
|
||||
|
||||
def already_obtained_all_materials(self):
|
||||
return all([x['value'] is not None for x in self.req])
|
||||
|
||||
@CatchException
|
||||
def 图片修改_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 尚未完成
|
||||
history = [] # 清空历史
|
||||
state = ImageEditState.get_state(chatbot, ImageEditState)
|
||||
state = state.feed(prompt, chatbot)
|
||||
state.lock_plugin(chatbot)
|
||||
if not state.already_obtained_all_materials():
|
||||
chatbot.append(["图片修改\n\n1. 上传图片（图片中需要修改的位置用橡皮擦擦除为纯白色，即RGB=255,255,255）\n2. 输入分辨率 \n3. 输入修改需求", state.next_req()])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
image_path = state.req[0]['value']
|
||||
resolution = state.req[1]['value']
|
||||
prompt = state.req[2]['value']
|
||||
chatbot.append(["图片修改, 执行中", f"图片:`{image_path}`<br/>分辨率:`{resolution}`<br/>修改需求:`{prompt}`"])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
image_url, image_path = edit_image(llm_kwargs, prompt, image_path, resolution)
|
||||
chatbot.append([prompt,
|
||||
f'图像中转网址: <br/>`{image_url}`<br/>'+
|
||||
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
|
||||
f'本地文件地址: <br/>`{image_path}`<br/>'+
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
state.unlock_plugin(chatbot)
|
||||
|
||||
def make_transparent(input_image_path, output_image_path):
|
||||
from PIL import Image
|
||||
image = Image.open(input_image_path)
|
||||
image = image.convert("RGBA")
|
||||
data = image.getdata()
|
||||
new_data = []
|
||||
for item in data:
|
||||
if item[0] == 255 and item[1] == 255 and item[2] == 255:
|
||||
new_data.append((255, 255, 255, 0))
|
||||
else:
|
||||
new_data.append(item)
|
||||
image.putdata(new_data)
|
||||
image.save(output_image_path, "PNG")
|
||||
|
||||
def resize_image(input_path, output_path, max_size=1024):
|
||||
from PIL import Image
|
||||
with Image.open(input_path) as img:
|
||||
width, height = img.size
|
||||
if width > max_size or height > max_size:
|
||||
if width >= height:
|
||||
new_width = max_size
|
||||
new_height = int((max_size / width) * height)
|
||||
else:
|
||||
new_height = max_size
|
||||
new_width = int((max_size / height) * width)
|
||||
|
||||
resized_img = img.resize(size=(new_width, new_height))
|
||||
resized_img.save(output_path)
|
||||
else:
|
||||
img.save(output_path)
|
||||
|
||||
def make_square_image(input_path, output_path):
|
||||
from PIL import Image
|
||||
with Image.open(input_path) as img:
|
||||
width, height = img.size
|
||||
size = max(width, height)
|
||||
new_img = Image.new("RGBA", (size, size), color="black")
|
||||
new_img.paste(img, ((size - width) // 2, (size - height) // 2))
|
||||
new_img.save(output_path)
|
||||
101
crazy_functions/多智能体.py
Normal file
101
crazy_functions/多智能体.py
Normal file
@@ -0,0 +1,101 @@
|
||||
# 本源代码中, ⭐ = 关键步骤
|
||||
"""
|
||||
测试：
|
||||
- show me the solution of $x^2=cos(x)$, solve this problem with figure, and plot and save image to t.jpg
|
||||
|
||||
"""
|
||||
|
||||
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
|
||||
from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
|
||||
from crazy_functions.crazy_utils import input_clipping, try_install_deps
|
||||
from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
|
||||
from crazy_functions.agent_fns.auto_agent import AutoGenMath
|
||||
import time
|
||||
|
||||
def remove_model_prefix(llm):
|
||||
if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
|
||||
if llm.startswith('azure-'): llm = llm.replace('azure-', '')
|
||||
return llm
|
||||
|
||||
|
||||
@CatchException
|
||||
def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
# 检查当前的模型是否符合要求
|
||||
supported_llms = [
|
||||
"gpt-3.5-turbo-16k",
|
||||
'gpt-3.5-turbo-1106',
|
||||
"gpt-4",
|
||||
"gpt-4-32k",
|
||||
'gpt-4-1106-preview',
|
||||
"azure-gpt-3.5-turbo-16k",
|
||||
"azure-gpt-3.5-16k",
|
||||
"azure-gpt-4",
|
||||
"azure-gpt-4-32k",
|
||||
]
|
||||
from request_llms.bridge_all import model_info
|
||||
if model_info[llm_kwargs['llm_model']]["max_token"] < 8000: # 至少是8k上下文的模型
|
||||
chatbot.append([f"处理任务: {txt}", f"当前插件只支持{str(supported_llms)}, 当前模型{llm_kwargs['llm_model']}的最大上下文长度太短, 不能支撑AutoGen运行。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if model_info[llm_kwargs['llm_model']]["endpoint"] is not None: # 如果不是本地模型，加载API_KEY
|
||||
llm_kwargs['api_key'] = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import autogen
|
||||
if get_conf("AUTOGEN_USE_DOCKER"):
|
||||
import docker
|
||||
except:
|
||||
chatbot.append([ f"处理任务: {txt}",
|
||||
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 pyautogen docker```。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import autogen
|
||||
import glob, os, time, subprocess
|
||||
if get_conf("AUTOGEN_USE_DOCKER"):
|
||||
subprocess.Popen(["docker", "--version"])
|
||||
except:
|
||||
chatbot.append([f"处理任务: {txt}", f"缺少docker运行环境！"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 解锁插件
|
||||
chatbot.get_cookies()['lock_plugin'] = None
|
||||
persistent_class_multi_user_manager = GradioMultiuserManagerForPersistentClasses()
|
||||
user_uuid = chatbot.get_cookies().get('uuid')
|
||||
persistent_key = f"{user_uuid}->多智能体终端"
|
||||
if persistent_class_multi_user_manager.already_alive(persistent_key):
|
||||
# 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
|
||||
print('[debug] feed new user input')
|
||||
executor = persistent_class_multi_user_manager.get(persistent_key)
|
||||
exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
|
||||
else:
|
||||
# 运行多智能体终端 (首次)
|
||||
print('[debug] create new executor instance')
|
||||
history = []
|
||||
chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
executor = AutoGenMath(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
persistent_class_multi_user_manager.set(persistent_key, executor)
|
||||
exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="create")
|
||||
|
||||
if exit_reason == "wait_feedback":
|
||||
# 当用户点击了“等待反馈”按钮时，将executor存储到cookie中，等待用户的再次调用
|
||||
executor.chatbot.get_cookies()['lock_plugin'] = 'crazy_functions.多智能体->多智能体终端'
|
||||
else:
|
||||
executor.chatbot.get_cookies()['lock_plugin'] = None
|
||||
yield from update_ui(chatbot=executor.chatbot, history=executor.history) # 更新状态
|
||||
152
crazy_functions/对话历史存档.py
Normal file
152
crazy_functions/对话历史存档.py
Normal file
@@ -0,0 +1,152 @@
|
||||
from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
|
||||
import re
|
||||
|
||||
f_prefix = 'GPT-Academic对话存档'
|
||||
|
||||
def write_chat_to_file(chatbot, history=None, file_name=None):
|
||||
"""
|
||||
将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
if file_name is None:
|
||||
file_name = f_prefix + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
|
||||
fp = os.path.join(get_log_folder(get_user(chatbot), plugin_name='chat_history'), 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):
|
||||
for j, content in enumerate(contents):
|
||||
try: # 这个bug没找到触发条件，暂时先这样顶一下
|
||||
if type(content) != str: content = str(content)
|
||||
except:
|
||||
continue
|
||||
f.write(content)
|
||||
if j == 0:
|
||||
f.write('<hr style="border-top: dotted 3px #ccc;">')
|
||||
f.write('<hr color="red"> \n\n')
|
||||
f.write('<hr color="blue"> \n\n raw chat context:\n')
|
||||
f.write('<code>')
|
||||
for h in history:
|
||||
f.write("\n>>>" + h)
|
||||
f.write('</code>')
|
||||
promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
|
||||
return '对话历史写入：' + fp
|
||||
|
||||
def gen_file_preview(file_name):
|
||||
try:
|
||||
with open(file_name, 'r', encoding='utf8') as f:
|
||||
file_content = f.read()
|
||||
# pattern to match the text between <head> and </head>
|
||||
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
|
||||
file_content = re.sub(pattern, '', file_content)
|
||||
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
|
||||
history = history.strip('<code>')
|
||||
history = history.strip('</code>')
|
||||
history = history.split("\n>>>")
|
||||
return list(filter(lambda x:x!="", history))[0][:100]
|
||||
except:
|
||||
return ""
|
||||
|
||||
def read_file_to_chat(chatbot, history, file_name):
|
||||
with open(file_name, 'r', encoding='utf8') as f:
|
||||
file_content = f.read()
|
||||
# pattern to match the text between <head> and </head>
|
||||
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
|
||||
file_content = re.sub(pattern, '', file_content)
|
||||
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
|
||||
history = history.strip('<code>')
|
||||
history = history.strip('</code>')
|
||||
history = history.split("\n>>>")
|
||||
history = list(filter(lambda x:x!="", history))
|
||||
html = html.split('<hr color="red"> \n\n')
|
||||
html = list(filter(lambda x:x!="", html))
|
||||
chatbot.clear()
|
||||
for i, h in enumerate(html):
|
||||
i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
|
||||
chatbot.append([i_say, gpt_say])
|
||||
chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
|
||||
return chatbot, history
|
||||
|
||||
@CatchException
|
||||
def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
|
||||
chatbot.append(("保存当前对话",
|
||||
f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
def hide_cwd(str):
|
||||
import os
|
||||
current_path = os.getcwd()
|
||||
replace_path = "."
|
||||
return str.replace(current_path, replace_path)
|
||||
|
||||
@CatchException
|
||||
def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
from .crazy_utils import get_files_from_everything
|
||||
success, file_manifest, _ = get_files_from_everything(txt, type='.html')
|
||||
|
||||
if not success:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
import glob
|
||||
local_history = "<br/>".join([
|
||||
"`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`"
|
||||
for f in glob.glob(
|
||||
f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html',
|
||||
recursive=True
|
||||
)])
|
||||
chatbot.append([f"正在查找对话历史文件（html格式）: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
try:
|
||||
chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
except:
|
||||
chatbot.append([f"载入对话历史文件", f"对话历史文件损坏！"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
@CatchException
|
||||
def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
|
||||
import glob, os
|
||||
local_history = "<br/>".join([
|
||||
"`"+hide_cwd(f)+"`"
|
||||
for f in glob.glob(
|
||||
f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html', recursive=True
|
||||
)])
|
||||
for f in glob.glob(f'{get_log_folder(get_user(chatbot), plugin_name="chat_history")}/**/{f_prefix}*.html', recursive=True):
|
||||
os.remove(f)
|
||||
chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
127
crazy_functions/总结word文档.py
Normal file
127
crazy_functions/总结word文档.py
Normal file
@@ -0,0 +1,127 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
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docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, os
|
||||
# pip install python-docx 用于docx格式，跨平台
|
||||
# pip install pywin32 用于doc格式，仅支持Win平台
|
||||
for index, fp in enumerate(file_manifest):
|
||||
if fp.split(".")[-1] == "docx":
|
||||
from docx import Document
|
||||
doc = Document(fp)
|
||||
file_content = "\n".join([para.text for para in doc.paragraphs])
|
||||
else:
|
||||
try:
|
||||
import win32com.client
|
||||
word = win32com.client.Dispatch("Word.Application")
|
||||
word.visible = False
|
||||
# 打开文件
|
||||
doc = word.Documents.Open(os.getcwd() + '/' + fp)
|
||||
# file_content = doc.Content.Text
|
||||
doc = word.ActiveDocument
|
||||
file_content = doc.Range().Text
|
||||
doc.Close()
|
||||
word.Quit()
|
||||
except:
|
||||
raise RuntimeError('请先将.doc文档转换为.docx文档。')
|
||||
|
||||
# private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
from request_llms.bridge_all import model_info
|
||||
max_token = model_info[llm_kwargs['llm_model']]['max_token']
|
||||
TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
this_paper_history = []
|
||||
for i, paper_frag in enumerate(paper_fragments):
|
||||
i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
|
||||
i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=[],
|
||||
sys_prompt="总结文章。"
|
||||
)
|
||||
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.extend([i_say_show_user,gpt_say])
|
||||
this_paper_history.extend([i_say_show_user,gpt_say])
|
||||
|
||||
# 已经对该文章的所有片段总结完毕，如果文章被切分了，
|
||||
if len(paper_fragments) > 1:
|
||||
i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=this_paper_history,
|
||||
sys_prompt="总结文章。"
|
||||
)
|
||||
|
||||
history.extend([i_say,gpt_say])
|
||||
this_paper_history.extend([i_say,gpt_say])
|
||||
|
||||
res = write_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_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
|
||||
|
||||
|
||||
@CatchException
|
||||
def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
import glob, os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
from docx import Document
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 搜索需要处理的文件清单
|
||||
if txt.endswith('.docx') or txt.endswith('.doc'):
|
||||
file_manifest = [txt]
|
||||
else:
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
186
crazy_functions/总结音视频.py
Normal file
186
crazy_functions/总结音视频.py
Normal file
@@ -0,0 +1,186 @@
|
||||
from toolbox import CatchException, report_exception, 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):
|
||||
"""
|
||||
根据给定的切割时长将音频文件切割成多个片段。
|
||||
|
||||
Args:
|
||||
filename (str): 需要被切割的音频文件名。
|
||||
split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
|
||||
|
||||
Returns:
|
||||
filelist (list): 一个包含所有切割音频片段文件路径的列表。
|
||||
|
||||
"""
|
||||
from moviepy.editor import AudioFileClip
|
||||
import os
|
||||
os.makedirs(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)
|
||||
|
||||
# 计算文件总时长和切割点
|
||||
total_duration = audio.duration
|
||||
split_points = list(range(0, int(total_duration), split_duration))
|
||||
split_points.append(int(total_duration))
|
||||
filelist = []
|
||||
|
||||
# 切割音频文件
|
||||
for i in range(len(split_points) - 1):
|
||||
start_time = split_points[i]
|
||||
end_time = split_points[i + 1]
|
||||
split_audio = audio.subclip(start_time, end_time)
|
||||
split_audio.write_audiofile(f"{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
|
||||
|
||||
def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
|
||||
import os, requests
|
||||
from moviepy.editor import AudioFileClip
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
# 设置OpenAI密钥和模型
|
||||
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
|
||||
chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
|
||||
|
||||
whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
|
||||
url = whisper_endpoint
|
||||
headers = {
|
||||
'Authorization': f"Bearer {api_key}"
|
||||
}
|
||||
|
||||
os.makedirs(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
|
||||
ext = os.path.splitext(fp)[1]
|
||||
# 提取视频中的音频
|
||||
if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
|
||||
audio_clip = AudioFileClip(fp)
|
||||
audio_clip.write_audiofile(f"{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):
|
||||
with open(i, 'rb') as f:
|
||||
file_content = f.read() # 读取文件内容到内存
|
||||
files = {
|
||||
'file': (os.path.basename(i), file_content),
|
||||
}
|
||||
data = {
|
||||
"model": "whisper-1",
|
||||
"prompt": parse_prompt,
|
||||
'response_format': "text"
|
||||
}
|
||||
|
||||
chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
proxies = get_conf('proxies')
|
||||
response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
|
||||
|
||||
chatbot.append(["音频解析结果", response])
|
||||
history.extend(["音频解析结果", response])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
|
||||
i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=[],
|
||||
sys_prompt=f"总结音频。音频文件名{fp}"
|
||||
)
|
||||
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.extend([i_say_show_user, gpt_say])
|
||||
audio_history.extend([i_say_show_user, gpt_say])
|
||||
|
||||
# 已经对该文章的所有片段总结完毕，如果文章被切分了
|
||||
result = "".join(audio_history)
|
||||
if len(audio_history) > 1:
|
||||
i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
|
||||
i_say_show_user = f'第{index + 1}段音频的主要内容：'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=audio_history,
|
||||
sys_prompt="总结文章。"
|
||||
)
|
||||
history.extend([i_say, gpt_say])
|
||||
audio_history.extend([i_say, gpt_say])
|
||||
|
||||
res = write_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(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)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
|
||||
import glob, os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
try:
|
||||
from moviepy.editor import AudioFileClip
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 搜索需要处理的文件清单
|
||||
extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
|
||||
|
||||
if txt.endswith(tuple(extensions)):
|
||||
file_manifest = [txt]
|
||||
else:
|
||||
file_manifest = []
|
||||
for extension in extensions:
|
||||
file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
|
||||
yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
|
||||
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
261
crazy_functions/批量Markdown翻译.py
Normal file
261
crazy_functions/批量Markdown翻译.py
Normal file
@@ -0,0 +1,261 @@
|
||||
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_exception, get_log_folder
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
fast_debug = False
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
self.file_paths = []
|
||||
self.file_contents = []
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
self.get_token_num = get_token_num
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
for index, file_content in enumerate(self.file_contents):
|
||||
if self.get_token_num(file_content) < max_token_limit:
|
||||
self.sp_file_contents.append(file_content)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
|
||||
logging.info('Segmentation: done')
|
||||
|
||||
def merge_result(self):
|
||||
self.file_result = ["" for _ in range(len(self.file_paths))]
|
||||
for r, k in zip(self.sp_file_result, self.sp_file_index):
|
||||
self.file_result[k] += r
|
||||
|
||||
def write_result(self, language):
|
||||
manifest = []
|
||||
for path, res in zip(self.file_paths, self.file_result):
|
||||
dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')
|
||||
with open(dst_file, 'w', encoding='utf8') as f:
|
||||
manifest.append(dst_file)
|
||||
f.write(res)
|
||||
return manifest
|
||||
|
||||
def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
# <-------- 读取Markdown文件，删除其中的所有注释 ---------->
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
# 记录删除注释后的文本
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(file_content)
|
||||
|
||||
# <-------- 拆分过长的Markdown文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1500)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
# <-------- 多线程翻译开始 ---------->
|
||||
if language == 'en->zh':
|
||||
inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
elif language == 'zh->en':
|
||||
inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
else:
|
||||
inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
inputs_show_user_array=inputs_show_user_array,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[""] for _ in range(n_split)],
|
||||
sys_prompt_array=sys_prompt_array,
|
||||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||||
scroller_max_len = 80
|
||||
)
|
||||
try:
|
||||
pfg.sp_file_result = []
|
||||
for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
|
||||
pfg.sp_file_result.append(gpt_say)
|
||||
pfg.merge_result()
|
||||
pfg.write_result(language)
|
||||
except:
|
||||
logging.error(trimmed_format_exc())
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
create_report_file_name = gen_time_str() + f"-chatgpt.md"
|
||||
res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
|
||||
promote_file_to_downloadzone(res, chatbot=chatbot)
|
||||
history = gpt_response_collection
|
||||
chatbot.append((f"{fp}完成了吗？", res))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
def get_files_from_everything(txt, preference=''):
|
||||
if txt == "": return False, None, None
|
||||
success = True
|
||||
if txt.startswith('http'):
|
||||
import requests
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
# 网络的远程文件
|
||||
if preference == 'Github':
|
||||
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'
|
||||
response = requests.get(url, proxies=proxies)
|
||||
txt = response.json()['download_url']
|
||||
else:
|
||||
txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
|
||||
txt = txt.replace("/blob/", "/")
|
||||
|
||||
r = requests.get(txt, proxies=proxies)
|
||||
download_local = f'{get_log_folder(plugin_name="批量Markdown翻译")}/raw-readme-{gen_time_str()}.md'
|
||||
project_folder = f'{get_log_folder(plugin_name="批量Markdown翻译")}'
|
||||
with open(download_local, 'wb+') as f: f.write(r.content)
|
||||
file_manifest = [download_local]
|
||||
elif txt.endswith('.md'):
|
||||
# 直接给定文件
|
||||
file_manifest = [txt]
|
||||
project_folder = os.path.dirname(txt)
|
||||
elif os.path.exists(txt):
|
||||
# 本地路径，递归搜索
|
||||
project_folder = txt
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||||
else:
|
||||
project_folder = None
|
||||
file_manifest = []
|
||||
success = False
|
||||
|
||||
return success, file_manifest, project_folder
|
||||
|
||||
|
||||
@CatchException
|
||||
def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
|
||||
|
||||
if not success:
|
||||
# 什么都没有
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt)
|
||||
if not success:
|
||||
# 什么都没有
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
|
||||
|
||||
|
||||
@CatchException
|
||||
def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt)
|
||||
if not success:
|
||||
# 什么都没有
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
language = plugin_kwargs.get("advanced_arg", 'Chinese')
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)
|
||||
145
crazy_functions/批量总结PDF文档.py
Normal file
145
crazy_functions/批量总结PDF文档.py
Normal file
@@ -0,0 +1,145 @@
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
|
||||
from toolbox import CatchException, report_exception
|
||||
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
|
||||
|
||||
|
||||
|
||||
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
file_write_buffer = []
|
||||
for file_name in file_manifest:
|
||||
print('begin analysis on:', file_name)
|
||||
############################## <第 0 步，切割PDF> ##################################
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
# 的长度必须小于 2500 个 Token
|
||||
file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
|
||||
final_results = []
|
||||
final_results.append(paper_meta)
|
||||
|
||||
############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
|
||||
iteration_results = []
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096 * 0.7
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extract the main idea of this section with Chinese." # 提示
|
||||
)
|
||||
iteration_results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
|
||||
############################## <第 3 步，整理history，提取总结> ##################################
|
||||
final_results.extend(iteration_results)
|
||||
final_results.append(f'Please conclude this paper discussed above。')
|
||||
# This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
|
||||
NUM_OF_WORD = 1000
|
||||
i_say = """
|
||||
1. Mark the title of the paper (with Chinese translation)
|
||||
2. list all the authors' names (use English)
|
||||
3. mark the first author's affiliation (output Chinese translation only)
|
||||
4. mark the keywords of this article (use English)
|
||||
5. link to the paper, Github code link (if available, fill in Github:None if not)
|
||||
6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)
|
||||
- (1):What is the research background of this article?
|
||||
- (2):What are the past methods? What are the problems with them? Is the approach well motivated?
|
||||
- (3):What is the research methodology proposed in this paper?
|
||||
- (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?
|
||||
Follow the format of the output that follows:
|
||||
1. Title: xxx\n\n
|
||||
2. Authors: xxx\n\n
|
||||
3. Affiliation: xxx\n\n
|
||||
4. Keywords: xxx\n\n
|
||||
5. Urls: xxx or xxx , xxx \n\n
|
||||
6. Summary: \n\n
|
||||
- (1):xxx;\n
|
||||
- (2):xxx;\n
|
||||
- (3):xxx;\n
|
||||
- (4):xxx.\n\n
|
||||
Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible,
|
||||
do not have too much repetitive information, numerical values using the original numbers.
|
||||
"""
|
||||
# This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py
|
||||
file_write_buffer.extend(final_results)
|
||||
i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user='开始最终总结',
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results,
|
||||
sys_prompt= f"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters"
|
||||
)
|
||||
final_results.append(gpt_say)
|
||||
file_write_buffer.extend([i_say, gpt_say])
|
||||
############################## <第 4 步，设置一个token上限> ##################################
|
||||
_, final_results = input_clipping("", final_results, max_token_limit=3200)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
|
||||
|
||||
res = write_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
|
||||
|
||||
|
||||
@CatchException
|
||||
def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
import glob, os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import fitz
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 搜索需要处理的文件清单
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
162
crazy_functions/批量总结PDF文档pdfminer.py
Normal file
162
crazy_functions/批量总结PDF文档pdfminer.py
Normal file
@@ -0,0 +1,162 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
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
|
||||
|
||||
def readPdf(pdfPath):
|
||||
"""
|
||||
读取pdf文件，返回文本内容
|
||||
"""
|
||||
import pdfminer
|
||||
from pdfminer.pdfparser import PDFParser
|
||||
from pdfminer.pdfdocument import PDFDocument
|
||||
from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
|
||||
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
|
||||
from pdfminer.pdfdevice import PDFDevice
|
||||
from pdfminer.layout import LAParams
|
||||
from pdfminer.converter import PDFPageAggregator
|
||||
|
||||
fp = open(pdfPath, 'rb')
|
||||
|
||||
# Create a PDF parser object associated with the file object
|
||||
parser = PDFParser(fp)
|
||||
|
||||
# Create a PDF document object that stores the document structure.
|
||||
# Password for initialization as 2nd parameter
|
||||
document = PDFDocument(parser)
|
||||
# Check if the document allows text extraction. If not, abort.
|
||||
if not document.is_extractable:
|
||||
raise PDFTextExtractionNotAllowed
|
||||
|
||||
# Create a PDF resource manager object that stores shared resources.
|
||||
rsrcmgr = PDFResourceManager()
|
||||
|
||||
# Create a PDF device object.
|
||||
# device = PDFDevice(rsrcmgr)
|
||||
|
||||
# BEGIN LAYOUT ANALYSIS.
|
||||
# Set parameters for analysis.
|
||||
laparams = LAParams(
|
||||
char_margin=10.0,
|
||||
line_margin=0.2,
|
||||
boxes_flow=0.2,
|
||||
all_texts=False,
|
||||
)
|
||||
# Create a PDF page aggregator object.
|
||||
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
|
||||
# Create a PDF interpreter object.
|
||||
interpreter = PDFPageInterpreter(rsrcmgr, device)
|
||||
|
||||
# loop over all pages in the document
|
||||
outTextList = []
|
||||
for page in PDFPage.create_pages(document):
|
||||
# read the page into a layout object
|
||||
interpreter.process_page(page)
|
||||
layout = device.get_result()
|
||||
for obj in layout._objs:
|
||||
if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
|
||||
# print(obj.get_text())
|
||||
outTextList.append(obj.get_text())
|
||||
|
||||
return outTextList
|
||||
|
||||
|
||||
def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os
|
||||
from bs4 import BeautifulSoup
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
if ".tex" in fp:
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
if ".pdf" in fp.lower():
|
||||
file_content = readPdf(fp)
|
||||
file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
|
||||
|
||||
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=[],
|
||||
sys_prompt="总结文章。"
|
||||
) # 带超时倒计时
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
if not fast_debug: time.sleep(2)
|
||||
|
||||
all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
|
||||
i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
|
||||
chatbot.append((i_say, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt="总结文章。"
|
||||
) # 带超时倒计时
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_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
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import pdfminer, bs4
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
125
crazy_functions/批量翻译PDF文档_NOUGAT.py
Normal file
125
crazy_functions/批量翻译PDF文档_NOUGAT.py
Normal file
@@ -0,0 +1,125 @@
|
||||
from toolbox import CatchException, report_exception, get_log_folder, 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, 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, translate_pdf
|
||||
from colorful import *
|
||||
import copy
|
||||
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, user_request):
|
||||
|
||||
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
|
||||
|
||||
# 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')
|
||||
if len(file_manifest) > 0:
|
||||
# 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_exception(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
|
||||
success_mmd, file_manifest_mmd, _ = get_files_from_everything(txt, type='.mmd')
|
||||
success = success or success_mmd
|
||||
file_manifest += file_manifest_mmd
|
||||
chatbot.append(["文件列表：", ", ".join([e.split('/')[-1] for e in file_manifest])]);
|
||||
yield from update_ui( chatbot=chatbot, history=history)
|
||||
# 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_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {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 = 1024
|
||||
generated_conclusion_files = []
|
||||
generated_html_files = []
|
||||
DST_LANG = "中文"
|
||||
from crazy_functions.crazy_utils import nougat_interface
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
nougat_handle = nougat_interface()
|
||||
for index, fp in enumerate(file_manifest):
|
||||
if fp.endswith('pdf'):
|
||||
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)
|
||||
promote_file_to_downloadzone(fpp, rename_file=os.path.basename(fpp)+'.nougat.mmd', chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append(["当前论文无需解析：", fp]); yield from update_ui( chatbot=chatbot, history=history)
|
||||
fpp = fp
|
||||
with open(fpp, 'r', encoding='utf8') as f:
|
||||
article_content = f.readlines()
|
||||
article_dict = markdown_to_dict(article_content)
|
||||
logging.info(article_dict)
|
||||
yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
|
||||
|
||||
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
|
||||
|
||||
|
||||
@@ -1,16 +1,83 @@
|
||||
from toolbox import get_log_folder
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
from shared_utils.colorful import *
|
||||
from loguru import logger
|
||||
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, translate_pdf
|
||||
from colorful import *
|
||||
import os
|
||||
|
||||
def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
|
||||
@CatchException
|
||||
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
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:
|
||||
check_packages(["fitz", "tiktoken", "scipdf"])
|
||||
except:
|
||||
report_exception(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 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 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_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
grobid_url = get_avail_grobid_url()
|
||||
if grobid_url is not None:
|
||||
yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
|
||||
else:
|
||||
yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
|
||||
yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
|
||||
import copy, json
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
generated_conclusion_files = []
|
||||
generated_html_files = []
|
||||
DST_LANG = "中文"
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
for index, fp in enumerate(file_manifest):
|
||||
chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
article_dict = parse_pdf(fp, grobid_url)
|
||||
grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
|
||||
with open(grobid_json_res, 'w+', encoding='utf8') as f:
|
||||
f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
|
||||
promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
|
||||
|
||||
if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
|
||||
yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
|
||||
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):
|
||||
"""
|
||||
注意：此函数已经弃用！！新函数位于：crazy_functions/pdf_fns/parse_pdf.py
|
||||
此函数已经弃用
|
||||
"""
|
||||
import copy
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
@@ -30,7 +97,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
|
||||
# 单线，获取文章meta信息
|
||||
paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
|
||||
@@ -49,13 +116,12 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
chatbot=chatbot,
|
||||
history_array=[[paper_meta] for _ in paper_fragments],
|
||||
sys_prompt_array=[
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "")
|
||||
for _ in paper_fragments],
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
|
||||
# max_workers=5 # OpenAI所允许的最大并行过载
|
||||
)
|
||||
gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
|
||||
# 整理报告的格式
|
||||
for i,k in enumerate(gpt_response_collection_md):
|
||||
for i,k in enumerate(gpt_response_collection_md):
|
||||
if i%2==0:
|
||||
gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')} \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
|
||||
else:
|
||||
@@ -73,18 +139,18 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
|
||||
# write html
|
||||
try:
|
||||
ch = construct_html()
|
||||
ch = construct_html()
|
||||
orig = ""
|
||||
trans = ""
|
||||
gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
|
||||
for i,k in enumerate(gpt_response_collection_html):
|
||||
for i,k in enumerate(gpt_response_collection_html):
|
||||
if i%2==0:
|
||||
gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
|
||||
else:
|
||||
gpt_response_collection_html[i] = gpt_response_collection_html[i]
|
||||
final = ["论文概况", paper_meta_info.replace('# ', '### '), "二、论文翻译", ""]
|
||||
final.extend(gpt_response_collection_html)
|
||||
for i, k in enumerate(final):
|
||||
for i, k in enumerate(final):
|
||||
if i%2==0:
|
||||
orig = k
|
||||
if i%2==1:
|
||||
@@ -94,7 +160,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
generated_html_files.append(ch.save_file(create_report_file_name))
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
logger.error('writing html result failed:', trimmed_format_exc())
|
||||
print('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
# 准备文件的下载
|
||||
for pdf_path in generated_conclusion_files:
|
||||
191
crazy_functions/数学动画生成manim.py
Normal file
191
crazy_functions/数学动画生成manim.py
Normal file
@@ -0,0 +1,191 @@
|
||||
import os
|
||||
from toolbox import CatchException, update_ui, gen_time_str, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
|
||||
def inspect_dependency(chatbot, history):
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import manim
|
||||
return True
|
||||
except:
|
||||
chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install manim manimgl```"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return False
|
||||
|
||||
def eval_manim(code):
|
||||
import subprocess, sys, os, shutil
|
||||
|
||||
with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f:
|
||||
f.write(code)
|
||||
|
||||
def get_class_name(class_string):
|
||||
import re
|
||||
# Use regex to extract the class name
|
||||
class_name = re.search(r'class (\w+)\(', class_string).group(1)
|
||||
return class_name
|
||||
|
||||
class_name = get_class_name(code)
|
||||
|
||||
try:
|
||||
time_str = gen_time_str()
|
||||
subprocess.check_output([sys.executable, '-c', f"from gpt_log.MyAnimation import {class_name}; {class_name}().render()"])
|
||||
shutil.move(f'media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{time_str}.mp4')
|
||||
return f'gpt_log/{time_str}.mp4'
|
||||
except subprocess.CalledProcessError as e:
|
||||
output = e.output.decode()
|
||||
print(f"Command returned non-zero exit status {e.returncode}: {output}.")
|
||||
return f"Evaluating python script failed: {e.output}."
|
||||
except:
|
||||
print('generating mp4 failed')
|
||||
return "Generating mp4 failed."
|
||||
|
||||
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) != 1:
|
||||
raise RuntimeError("GPT is not generating proper code.")
|
||||
return matches[0].strip('python') # code block
|
||||
|
||||
@CatchException
|
||||
def 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..."
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖, 如果缺少依赖, 则给出安装建议
|
||||
dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
|
||||
if not dep_ok: return
|
||||
|
||||
# 输入
|
||||
i_say = f'Generate a animation to show: ' + txt
|
||||
demo = ["Here is some examples of manim", examples_of_manim()]
|
||||
_, demo = input_clipping(inputs="", history=demo, max_token_limit=2560)
|
||||
# 开始
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo,
|
||||
sys_prompt=
|
||||
r"Write a animation script with 3blue1brown's manim. "+
|
||||
r"Please begin with `from manim import *`. " +
|
||||
r"Answer me with a code block wrapped by ```."
|
||||
)
|
||||
chatbot.append(["开始生成动画", "..."])
|
||||
history.extend([i_say, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
# 将代码转为动画
|
||||
code = get_code_block(gpt_say)
|
||||
res = eval_manim(code)
|
||||
|
||||
chatbot.append(("生成的视频文件路径", res))
|
||||
if os.path.exists(res):
|
||||
promote_file_to_downloadzone(res, chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
# 在这里放一些网上搜集的demo，辅助gpt生成代码
|
||||
def examples_of_manim():
|
||||
return r"""
|
||||
|
||||
|
||||
```
|
||||
|
||||
class MovingGroupToDestination(Scene):
|
||||
def construct(self):
|
||||
group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4)
|
||||
dest = Dot([4, 3, 0], color=YELLOW)
|
||||
self.add(group, dest)
|
||||
self.play(group.animate.shift(dest.get_center() - group[2].get_center()))
|
||||
self.wait(0.5)
|
||||
|
||||
```
|
||||
|
||||
|
||||
```
|
||||
|
||||
class LatexWithMovingFramebox(Scene):
|
||||
def construct(self):
|
||||
text=MathTex(
|
||||
"\\frac{d}{dx}f(x)g(x)=","f(x)\\frac{d}{dx}g(x)","+",
|
||||
"g(x)\\frac{d}{dx}f(x)"
|
||||
)
|
||||
self.play(Write(text))
|
||||
framebox1 = SurroundingRectangle(text[1], buff = .1)
|
||||
framebox2 = SurroundingRectangle(text[3], buff = .1)
|
||||
self.play(
|
||||
Create(framebox1),
|
||||
)
|
||||
self.wait()
|
||||
self.play(
|
||||
ReplacementTransform(framebox1,framebox2),
|
||||
)
|
||||
self.wait()
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
```
|
||||
|
||||
class PointWithTrace(Scene):
|
||||
def construct(self):
|
||||
path = VMobject()
|
||||
dot = Dot()
|
||||
path.set_points_as_corners([dot.get_center(), dot.get_center()])
|
||||
def update_path(path):
|
||||
previous_path = path.copy()
|
||||
previous_path.add_points_as_corners([dot.get_center()])
|
||||
path.become(previous_path)
|
||||
path.add_updater(update_path)
|
||||
self.add(path, dot)
|
||||
self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))
|
||||
self.wait()
|
||||
self.play(dot.animate.shift(UP))
|
||||
self.play(dot.animate.shift(LEFT))
|
||||
self.wait()
|
||||
|
||||
```
|
||||
|
||||
```
|
||||
|
||||
# do not use get_graph, this funciton is deprecated
|
||||
|
||||
class ExampleFunctionGraph(Scene):
|
||||
def construct(self):
|
||||
cos_func = FunctionGraph(
|
||||
lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t),
|
||||
color=RED,
|
||||
)
|
||||
|
||||
sin_func_1 = FunctionGraph(
|
||||
lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
|
||||
color=BLUE,
|
||||
)
|
||||
|
||||
sin_func_2 = FunctionGraph(
|
||||
lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
|
||||
x_range=[-4, 4],
|
||||
color=GREEN,
|
||||
).move_to([0, 1, 0])
|
||||
|
||||
self.add(cos_func, sin_func_1, sin_func_2)
|
||||
|
||||
```
|
||||
"""
|
||||
109
crazy_functions/理解PDF文档内容.py
Normal file
109
crazy_functions/理解PDF文档内容.py
Normal file
@@ -0,0 +1,109 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
fast_debug = False
|
||||
|
||||
|
||||
def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import tiktoken
|
||||
print('begin analysis on:', file_name)
|
||||
|
||||
############################## <第 0 步，切割PDF> ##################################
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
# 的长度必须小于 2500 个 Token
|
||||
file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
|
||||
final_results = []
|
||||
final_results.append(paper_meta)
|
||||
|
||||
############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
|
||||
iteration_results = []
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]} ...."
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extract the main idea of this section, answer me with Chinese." # 提示
|
||||
)
|
||||
iteration_results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
|
||||
############################## <第 3 步，整理history> ##################################
|
||||
final_results.extend(iteration_results)
|
||||
final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
|
||||
# 接下来两句话只显示在界面上，不起实际作用
|
||||
i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
|
||||
############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
|
||||
from .crazy_utils import input_clipping
|
||||
_, final_results = input_clipping("", final_results, max_token_limit=3200)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
|
||||
|
||||
|
||||
@CatchException
|
||||
def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
import glob, os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import fitz
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "":
|
||||
txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 搜索需要处理的文件清单
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
txt = file_manifest[0]
|
||||
# 开始正式执行任务
|
||||
yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
56
crazy_functions/生成函数注释.py
Normal file
56
crazy_functions/生成函数注释.py
Normal file
@@ -0,0 +1,56 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
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 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, os
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
|
||||
i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
|
||||
i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt) # 带超时倒计时
|
||||
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
if not fast_debug: time.sleep(2)
|
||||
|
||||
if not fast_debug:
|
||||
res = write_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
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
302
crazy_functions/生成多种Mermaid图表.py
Normal file
302
crazy_functions/生成多种Mermaid图表.py
Normal file
@@ -0,0 +1,302 @@
|
||||
from toolbox import CatchException, update_ui, report_exception
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
import datetime
|
||||
|
||||
#以下是每类图表的PROMPT
|
||||
SELECT_PROMPT = """
|
||||
“{subject}”
|
||||
=============
|
||||
以上是从文章中提取的摘要,将会使用这些摘要绘制图表。请你选择一个合适的图表类型:
|
||||
1 流程图
|
||||
2 序列图
|
||||
3 类图
|
||||
4 饼图
|
||||
5 甘特图
|
||||
6 状态图
|
||||
7 实体关系图
|
||||
8 象限提示图
|
||||
不需要解释原因，仅需要输出单个不带任何标点符号的数字。
|
||||
"""
|
||||
#没有思维导图!!!测试发现模型始终会优先选择思维导图
|
||||
#流程图
|
||||
PROMPT_1 = """
|
||||
请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
graph TD
|
||||
P(编程) --> L1(Python)
|
||||
P(编程) --> L2(C)
|
||||
P(编程) --> L3(C++)
|
||||
P(编程) --> L4(Javascipt)
|
||||
P(编程) --> L5(PHP)
|
||||
```
|
||||
"""
|
||||
#序列图
|
||||
PROMPT_2 = """
|
||||
请你给出围绕“{subject}”的序列图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant A as 用户
|
||||
participant B as 系统
|
||||
A->>B: 登录请求
|
||||
B->>A: 登录成功
|
||||
A->>B: 获取数据
|
||||
B->>A: 返回数据
|
||||
```
|
||||
"""
|
||||
#类图
|
||||
PROMPT_3 = """
|
||||
请你给出围绕“{subject}”的类图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
classDiagram
|
||||
Class01 <|-- AveryLongClass : Cool
|
||||
Class03 *-- Class04
|
||||
Class05 o-- Class06
|
||||
Class07 .. Class08
|
||||
Class09 --> C2 : Where am i?
|
||||
Class09 --* C3
|
||||
Class09 --|> Class07
|
||||
Class07 : equals()
|
||||
Class07 : Object[] elementData
|
||||
Class01 : size()
|
||||
Class01 : int chimp
|
||||
Class01 : int gorilla
|
||||
Class08 <--> C2: Cool label
|
||||
```
|
||||
"""
|
||||
#饼图
|
||||
PROMPT_4 = """
|
||||
请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
pie title Pets adopted by volunteers
|
||||
"狗" : 386
|
||||
"猫" : 85
|
||||
"兔子" : 15
|
||||
```
|
||||
"""
|
||||
#甘特图
|
||||
PROMPT_5 = """
|
||||
请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
gantt
|
||||
title 项目开发流程
|
||||
dateFormat YYYY-MM-DD
|
||||
section 设计
|
||||
需求分析 :done, des1, 2024-01-06,2024-01-08
|
||||
原型设计 :active, des2, 2024-01-09, 3d
|
||||
UI设计 : des3, after des2, 5d
|
||||
section 开发
|
||||
前端开发 :2024-01-20, 10d
|
||||
后端开发 :2024-01-20, 10d
|
||||
```
|
||||
"""
|
||||
#状态图
|
||||
PROMPT_6 = """
|
||||
请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
stateDiagram-v2
|
||||
[*] --> Still
|
||||
Still --> [*]
|
||||
Still --> Moving
|
||||
Moving --> Still
|
||||
Moving --> Crash
|
||||
Crash --> [*]
|
||||
```
|
||||
"""
|
||||
#实体关系图
|
||||
PROMPT_7 = """
|
||||
请你给出围绕“{subject}”的实体关系图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
erDiagram
|
||||
CUSTOMER ||--o{ ORDER : places
|
||||
ORDER ||--|{ LINE-ITEM : contains
|
||||
CUSTOMER {
|
||||
string name
|
||||
string id
|
||||
}
|
||||
ORDER {
|
||||
string orderNumber
|
||||
date orderDate
|
||||
string customerID
|
||||
}
|
||||
LINE-ITEM {
|
||||
number quantity
|
||||
string productID
|
||||
}
|
||||
```
|
||||
"""
|
||||
#象限提示图
|
||||
PROMPT_8 = """
|
||||
请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
graph LR
|
||||
A[Hard skill] --> B(Programming)
|
||||
A[Hard skill] --> C(Design)
|
||||
D[Soft skill] --> E(Coordination)
|
||||
D[Soft skill] --> F(Communication)
|
||||
```
|
||||
"""
|
||||
#思维导图
|
||||
PROMPT_9 = """
|
||||
{subject}
|
||||
==========
|
||||
请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
mindmap
|
||||
root((mindmap))
|
||||
Origins
|
||||
Long history
|
||||
::icon(fa fa-book)
|
||||
Popularisation
|
||||
British popular psychology author Tony Buzan
|
||||
Research
|
||||
On effectiveness<br/>and features
|
||||
On Automatic creation
|
||||
Uses
|
||||
Creative techniques
|
||||
Strategic planning
|
||||
Argument mapping
|
||||
Tools
|
||||
Pen and paper
|
||||
Mermaid
|
||||
```
|
||||
"""
|
||||
|
||||
def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
|
||||
############################## <第 0 步，切割输入> ##################################
|
||||
# 借用PDF切割中的函数对文本进行切割
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
txt = str(history).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你从历史记录或文件中提取摘要。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history) # 更新UI
|
||||
results = []
|
||||
MAX_WORD_TOTAL = 4096
|
||||
n_txt = len(txt)
|
||||
last_iteration_result = "从以下文本中提取摘要。"
|
||||
if n_txt >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_txt):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i][:200]} ...."
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main content of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese." # 提示
|
||||
)
|
||||
results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
gpt_say = plugin_kwargs.get("advanced_arg", "") #将图表类型参数赋值为插件参数
|
||||
results_txt = '\n'.join(results) #合并摘要
|
||||
if gpt_say not in ['1','2','3','4','5','6','7','8','9']: #如插件参数不正确则使用对话模型判断
|
||||
i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
i_say = SELECT_PROMPT.format(subject=results_txt)
|
||||
i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。'
|
||||
for i in range(3):
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=""
|
||||
)
|
||||
if gpt_say in ['1','2','3','4','5','6','7','8','9']: #判断返回是否正确
|
||||
break
|
||||
if gpt_say not in ['1','2','3','4','5','6','7','8','9']:
|
||||
gpt_say = '1'
|
||||
############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
|
||||
if gpt_say == '1':
|
||||
i_say = PROMPT_1.format(subject=results_txt)
|
||||
elif gpt_say == '2':
|
||||
i_say = PROMPT_2.format(subject=results_txt)
|
||||
elif gpt_say == '3':
|
||||
i_say = PROMPT_3.format(subject=results_txt)
|
||||
elif gpt_say == '4':
|
||||
i_say = PROMPT_4.format(subject=results_txt)
|
||||
elif gpt_say == '5':
|
||||
i_say = PROMPT_5.format(subject=results_txt)
|
||||
elif gpt_say == '6':
|
||||
i_say = PROMPT_6.format(subject=results_txt)
|
||||
elif gpt_say == '7':
|
||||
i_say = PROMPT_7.replace("{subject}", results_txt) #由于实体关系图用到了{}符号
|
||||
elif gpt_say == '8':
|
||||
i_say = PROMPT_8.format(subject=results_txt)
|
||||
elif gpt_say == '9':
|
||||
i_say = PROMPT_9.format(subject=results_txt)
|
||||
i_say_show_user = f'请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你精通使用mermaid语法来绘制图表,首先确保语法正确,其次避免在mermaid语法中使用不允许的字符,此外也应当分考虑图表的可读性。"
|
||||
)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
def 输入区文件处理(txt):
|
||||
if txt == "": return False, txt
|
||||
success = True
|
||||
import glob
|
||||
from .crazy_utils import get_files_from_everything
|
||||
file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
|
||||
file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
|
||||
if len(pdf_manifest) == 0 and len(md_manifest) == 0:
|
||||
return False, txt #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
final_result = ""
|
||||
if file_pdf:
|
||||
for index, fp in enumerate(pdf_manifest):
|
||||
file_content, page_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
final_result += "\n" + file_content
|
||||
if file_md:
|
||||
for index, fp in enumerate(md_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
final_result += "\n" + file_content
|
||||
return True, final_result
|
||||
|
||||
@CatchException
|
||||
def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
import os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"根据当前聊天历史或文件中(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
|
||||
\n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import fitz
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if os.path.exists(txt): #如输入区无内容则直接解析历史记录
|
||||
file_exist, txt = 输入区文件处理(txt)
|
||||
else:
|
||||
file_exist = False
|
||||
|
||||
if file_exist : history = [] #如输入区内容为文件则清空历史记录
|
||||
history.append(txt) #将解析后的txt传递加入到历史中
|
||||
|
||||
yield from 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs)
|
||||
117
crazy_functions/知识库问答.py
Normal file
117
crazy_functions/知识库问答.py
Normal file
@@ -0,0 +1,117 @@
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
|
||||
|
||||
install_msg ="""
|
||||
|
||||
1. python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
2. python -m pip install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
|
||||
3. python -m pip install unstructured[all-docs] --upgrade
|
||||
|
||||
4. python -c 'import nltk; nltk.download("punkt")'
|
||||
"""
|
||||
|
||||
@CatchException
|
||||
def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
|
||||
# < --------------------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
|
||||
try:
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
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'], 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 = []
|
||||
spl = ["txt", "doc", "docx", "email", "epub", "html", "json", "md", "msg", "pdf", "ppt", "pptx", "rtf"]
|
||||
for sp in spl:
|
||||
_, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')
|
||||
file_manifest += file_manifest_tmp
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
chatbot.append(["没有找到任何可读取文件", "当前支持的格式包括: txt, md, docx, pptx, pdf, json等"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
chatbot.append(['<br/>'.join(file_manifest), "正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
|
||||
# < -------------------构建知识库--------------- >
|
||||
chatbot.append(['<br/>'.join(file_manifest), "正在构建知识库..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
print('Establishing knowledge archive ...')
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
kai = knowledge_archive_interface()
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
kai.feed_archive(file_manifest=file_manifest, vs_path=vs_path, id=kai_id)
|
||||
kai_files = kai.get_loaded_file(vs_path=vs_path)
|
||||
kai_files = '<br/>'.join(kai_files)
|
||||
# chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
|
||||
# yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()
|
||||
# chatbot._cookies['lock_plugin'] = 'crazy_functions.知识库文件注入->读取知识库作答'
|
||||
# chatbot.append(['完成', "“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。"])
|
||||
chatbot.append(['构建完成', f"当前知识库内的有效文件：\n\n---\n\n{kai_files}\n\n---\n\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
@CatchException
|
||||
def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request=-1):
|
||||
# resolve deps
|
||||
try:
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
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'], 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 ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
kai_id = plugin_kwargs.get("advanced_arg", 'default')
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
resp, prompt = kai.answer_with_archive_by_id(txt, kai_id, vs_path)
|
||||
|
||||
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,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=system_prompt
|
||||
)
|
||||
history.extend((prompt, gpt_say))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
106
crazy_functions/联网的ChatGPT.py
Normal file
106
crazy_functions/联网的ChatGPT.py
Normal file
@@ -0,0 +1,106 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
def google(query, proxies):
|
||||
query = query # 在此处替换您要搜索的关键词
|
||||
url = f"https://www.google.com/search?q={query}"
|
||||
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
|
||||
response = requests.get(url, headers=headers, proxies=proxies)
|
||||
soup = BeautifulSoup(response.content, 'html.parser')
|
||||
results = []
|
||||
for g in soup.find_all('div', class_='g'):
|
||||
anchors = g.find_all('a')
|
||||
if anchors:
|
||||
link = anchors[0]['href']
|
||||
if link.startswith('/url?q='):
|
||||
link = link[7:]
|
||||
if not link.startswith('http'):
|
||||
continue
|
||||
title = g.find('h3').text
|
||||
item = {'title': title, 'link': link}
|
||||
results.append(item)
|
||||
|
||||
for r in results:
|
||||
print(r['link'])
|
||||
return results
|
||||
|
||||
def scrape_text(url, proxies) -> str:
|
||||
"""Scrape text from a webpage
|
||||
|
||||
Args:
|
||||
url (str): The URL to scrape text from
|
||||
|
||||
Returns:
|
||||
str: The scraped text
|
||||
"""
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
|
||||
'Content-Type': 'text/plain',
|
||||
}
|
||||
try:
|
||||
response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
|
||||
if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
|
||||
except:
|
||||
return "无法连接到该网页"
|
||||
soup = BeautifulSoup(response.text, "html.parser")
|
||||
for script in soup(["script", "style"]):
|
||||
script.extract()
|
||||
text = soup.get_text()
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
text = "\n".join(chunk for chunk in chunks if chunk)
|
||||
return text
|
||||
|
||||
@CatchException
|
||||
def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((f"请结合互联网信息回答以下问题：{txt}",
|
||||
"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# ------------- < 第1步：爬取搜索引擎的结果 > -------------
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
urls = google(txt, proxies)
|
||||
history = []
|
||||
if len(urls) == 0:
|
||||
chatbot.append((f"结论：{txt}",
|
||||
"[Local Message] 受到google限制，无法从google获取信息！"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
return
|
||||
# ------------- < 第2步：依次访问网页 > -------------
|
||||
max_search_result = 5 # 最多收纳多少个网页的结果
|
||||
for index, url in enumerate(urls[:max_search_result]):
|
||||
res = scrape_text(url['link'], proxies)
|
||||
history.extend([f"第{index}份搜索结果：", res])
|
||||
chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# ------------- < 第3步：ChatGPT综合 > -------------
|
||||
i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
|
||||
i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
|
||||
inputs=i_say,
|
||||
history=history,
|
||||
max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
|
||||
)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
106
crazy_functions/联网的ChatGPT_bing版.py
Normal file
106
crazy_functions/联网的ChatGPT_bing版.py
Normal file
@@ -0,0 +1,106 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
|
||||
def bing_search(query, proxies=None):
|
||||
query = query
|
||||
url = f"https://cn.bing.com/search?q={query}"
|
||||
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
|
||||
response = requests.get(url, headers=headers, proxies=proxies)
|
||||
soup = BeautifulSoup(response.content, 'html.parser')
|
||||
results = []
|
||||
for g in soup.find_all('li', class_='b_algo'):
|
||||
anchors = g.find_all('a')
|
||||
if anchors:
|
||||
link = anchors[0]['href']
|
||||
if not link.startswith('http'):
|
||||
continue
|
||||
title = g.find('h2').text
|
||||
item = {'title': title, 'link': link}
|
||||
results.append(item)
|
||||
|
||||
for r in results:
|
||||
print(r['link'])
|
||||
return results
|
||||
|
||||
|
||||
def scrape_text(url, proxies) -> str:
|
||||
"""Scrape text from a webpage
|
||||
|
||||
Args:
|
||||
url (str): The URL to scrape text from
|
||||
|
||||
Returns:
|
||||
str: The scraped text
|
||||
"""
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
|
||||
'Content-Type': 'text/plain',
|
||||
}
|
||||
try:
|
||||
response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
|
||||
if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
|
||||
except:
|
||||
return "无法连接到该网页"
|
||||
soup = BeautifulSoup(response.text, "html.parser")
|
||||
for script in soup(["script", "style"]):
|
||||
script.extract()
|
||||
text = soup.get_text()
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
text = "\n".join(chunk for chunk in chunks if chunk)
|
||||
return text
|
||||
|
||||
@CatchException
|
||||
def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((f"请结合互联网信息回答以下问题：{txt}",
|
||||
"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# ------------- < 第1步：爬取搜索引擎的结果 > -------------
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
urls = bing_search(txt, proxies)
|
||||
history = []
|
||||
if len(urls) == 0:
|
||||
chatbot.append((f"结论：{txt}",
|
||||
"[Local Message] 受到bing限制，无法从bing获取信息！"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
return
|
||||
# ------------- < 第2步：依次访问网页 > -------------
|
||||
max_search_result = 8 # 最多收纳多少个网页的结果
|
||||
for index, url in enumerate(urls[:max_search_result]):
|
||||
res = scrape_text(url['link'], proxies)
|
||||
history.extend([f"第{index}份搜索结果：", res])
|
||||
chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# ------------- < 第3步：ChatGPT综合 > -------------
|
||||
i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
|
||||
i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
|
||||
inputs=i_say,
|
||||
history=history,
|
||||
max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
|
||||
)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
180
crazy_functions/虚空终端.py
Normal file
180
crazy_functions/虚空终端.py
Normal file
@@ -0,0 +1,180 @@
|
||||
"""
|
||||
Explanation of the Void Terminal Plugin:
|
||||
|
||||
Please describe in natural language what you want to do.
|
||||
|
||||
1. You can open the plugin's dropdown menu to explore various capabilities of this project, and then describe your needs in natural language, for example:
|
||||
- "Please call the plugin to translate a PDF paper for me. I just uploaded the paper to the upload area."
|
||||
- "Please use the plugin to translate a PDF paper, with the address being https://www.nature.com/articles/s41586-019-1724-z.pdf."
|
||||
- "Generate an image with blooming flowers and lush green grass using the plugin."
|
||||
- "Translate the README using the plugin. The GitHub URL is https://github.com/facebookresearch/co-tracker."
|
||||
- "Translate an Arxiv paper for me. The Arxiv ID is 1812.10695. Remember to use the plugin and don't do it manually!"
|
||||
- "I don't like the current interface color. Modify the configuration and change the theme to THEME="High-Contrast"."
|
||||
- "Could you please explain the structure of the Transformer network?"
|
||||
|
||||
2. If you use keywords like "call the plugin xxx", "modify the configuration xxx", "please", etc., your intention can be recognized more accurately.
|
||||
|
||||
3. Your intention can be recognized more accurately when using powerful models like GPT4. This plugin is relatively new, so please feel free to provide feedback on GitHub.
|
||||
|
||||
4. Now, if you need to process a file, please upload the file (drag the file to the file upload area) or describe the path to the file.
|
||||
|
||||
5. If you don't need to upload a file, you can simply repeat your command again.
|
||||
"""
|
||||
explain_msg = """
|
||||
## 虚空终端插件说明:
|
||||
|
||||
1. 请用**自然语言**描述您需要做什么。例如：
|
||||
- 「请调用插件，为我翻译PDF论文，论文我刚刚放到上传区了」
|
||||
- 「请调用插件翻译PDF论文，地址为https://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_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
from crazy_functions.vt_fns.vt_state import VoidTerminalState
|
||||
from crazy_functions.vt_fns.vt_modify_config import modify_configuration_hot
|
||||
from crazy_functions.vt_fns.vt_modify_config import modify_configuration_reboot
|
||||
from crazy_functions.vt_fns.vt_call_plugin import execute_plugin
|
||||
|
||||
class UserIntention(BaseModel):
|
||||
user_prompt: str = Field(description="the content of user input", default="")
|
||||
intention_type: str = Field(description="the type of user intention, choose from ['ModifyConfiguration', 'ExecutePlugin', 'Chat']", default="ExecutePlugin")
|
||||
user_provide_file: bool = Field(description="whether the user provides a path to a file", default=False)
|
||||
user_provide_url: bool = Field(description="whether the user provides a url", default=False)
|
||||
|
||||
|
||||
def chat(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=system_prompt
|
||||
)
|
||||
chatbot[-1] = [txt, gpt_say]
|
||||
history.extend([txt, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
pass
|
||||
|
||||
|
||||
explain_intention_to_user = {
|
||||
'Chat': "聊天对话",
|
||||
'ExecutePlugin': "调用插件",
|
||||
'ModifyConfiguration': "修改配置",
|
||||
}
|
||||
|
||||
|
||||
def analyze_intention_with_simple_rules(txt):
|
||||
user_intention = UserIntention()
|
||||
user_intention.user_prompt = txt
|
||||
is_certain = False
|
||||
|
||||
if '请问' in txt:
|
||||
is_certain = True
|
||||
user_intention.intention_type = 'Chat'
|
||||
|
||||
if '用插件' in txt:
|
||||
is_certain = True
|
||||
user_intention.intention_type = 'ExecutePlugin'
|
||||
|
||||
if '修改配置' in txt:
|
||||
is_certain = True
|
||||
user_intention.intention_type = 'ModifyConfiguration'
|
||||
|
||||
return is_certain, user_intention
|
||||
|
||||
|
||||
@CatchException
|
||||
def 虚空终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
disable_auto_promotion(chatbot=chatbot)
|
||||
# 获取当前虚空终端状态
|
||||
state = VoidTerminalState.get_state(chatbot)
|
||||
appendix_msg = ""
|
||||
|
||||
# 用简单的关键词检测用户意图
|
||||
is_certain, _ = analyze_intention_with_simple_rules(txt)
|
||||
if 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, user_request)
|
||||
return
|
||||
else:
|
||||
# 如果意图模糊，提示
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
|
||||
state.lock_plugin(chatbot=chatbot)
|
||||
chatbot.append(("虚空终端状态:", explain_msg+appendix_msg))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
|
||||
|
||||
def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
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
|
||||
|
||||
140
crazy_functions/解析JupyterNotebook.py
Normal file
140
crazy_functions/解析JupyterNotebook.py
Normal file
@@ -0,0 +1,140 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
fast_debug = True
|
||||
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
self.file_paths = []
|
||||
self.file_contents = []
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
for index, file_content in enumerate(self.file_contents):
|
||||
if self.get_token_num(file_content) < max_token_limit:
|
||||
self.sp_file_contents.append(file_content)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(
|
||||
self.file_paths[index] + f".part-{j}.txt")
|
||||
|
||||
|
||||
|
||||
def parseNotebook(filename, enable_markdown=1):
|
||||
import json
|
||||
|
||||
CodeBlocks = []
|
||||
with open(filename, 'r', encoding='utf-8', errors='replace') as f:
|
||||
notebook = json.load(f)
|
||||
for cell in notebook['cells']:
|
||||
if cell['cell_type'] == 'code' and cell['source']:
|
||||
# remove blank lines
|
||||
cell['source'] = [line for line in cell['source'] if line.strip()
|
||||
!= '']
|
||||
CodeBlocks.append("".join(cell['source']))
|
||||
elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
|
||||
cell['source'] = [line for line in cell['source'] if line.strip()
|
||||
!= '']
|
||||
CodeBlocks.append("Markdown:"+"".join(cell['source']))
|
||||
|
||||
Code = ""
|
||||
for idx, code in enumerate(CodeBlocks):
|
||||
Code += f"This is {idx+1}th code block: \n"
|
||||
Code += code+"\n"
|
||||
|
||||
return Code
|
||||
|
||||
|
||||
def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
enable_markdown = plugin_kwargs.get("advanced_arg", "1")
|
||||
try:
|
||||
enable_markdown = int(enable_markdown)
|
||||
except ValueError:
|
||||
enable_markdown = 1
|
||||
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
for fp in file_manifest:
|
||||
file_content = parseNotebook(fp, enable_markdown=enable_markdown)
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(file_content)
|
||||
|
||||
# <-------- 拆分过长的IPynb文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
|
||||
r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
|
||||
r"Start a new line for a block and block num use Chinese." +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional programmer."] * n_split
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
inputs_show_user_array=inputs_show_user_array,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[""] for _ in range(n_split)],
|
||||
sys_prompt_array=sys_prompt_array,
|
||||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||||
scroller_max_len=80
|
||||
)
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
block_result = " \n".join(gpt_response_collection)
|
||||
chatbot.append(("解析的结果如下", block_result))
|
||||
history.extend(["解析的结果如下", block_result])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------- 写入文件，退出 ---------->
|
||||
res = write_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
|
||||
|
||||
@CatchException
|
||||
def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对IPynb文件进行解析。Contributor: codycjy."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
history = [] # 清空历史
|
||||
import glob
|
||||
import os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "":
|
||||
txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if txt.endswith('.ipynb'):
|
||||
file_manifest = [txt]
|
||||
else:
|
||||
file_manifest = [f for f in glob.glob(
|
||||
f'{project_folder}/**/*.ipynb', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )
|
||||
375
crazy_functions/解析项目源代码.py
Normal file
375
crazy_functions/解析项目源代码.py
Normal file
@@ -0,0 +1,375 @@
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
|
||||
from toolbox import CatchException, report_exception, 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
|
||||
disable_auto_promotion(chatbot=chatbot)
|
||||
|
||||
summary_batch_isolation = True
|
||||
inputs_array = []
|
||||
inputs_show_user_array = []
|
||||
history_array = []
|
||||
sys_prompt_array = []
|
||||
report_part_1 = []
|
||||
|
||||
assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
|
||||
############################## <第一步，逐个文件分析，多线程> ##################################
|
||||
for index, fp in enumerate(file_manifest):
|
||||
# 读取文件
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {fp}'
|
||||
# 装载请求内容
|
||||
inputs_array.append(i_say)
|
||||
inputs_show_user_array.append(i_say_show_user)
|
||||
history_array.append([])
|
||||
sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
|
||||
|
||||
# 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array = inputs_array,
|
||||
inputs_show_user_array = inputs_show_user_array,
|
||||
history_array = history_array,
|
||||
sys_prompt_array = sys_prompt_array,
|
||||
llm_kwargs = llm_kwargs,
|
||||
chatbot = chatbot,
|
||||
show_user_at_complete = True
|
||||
)
|
||||
|
||||
# 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
|
||||
report_part_1 = copy.deepcopy(gpt_response_collection)
|
||||
history_to_return = report_part_1
|
||||
res = write_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
|
||||
|
||||
############################## <第二步，综合，单线程，分组+迭代处理> ##################################
|
||||
batchsize = 16 # 10个文件为一组
|
||||
report_part_2 = []
|
||||
previous_iteration_files = []
|
||||
last_iteration_result = ""
|
||||
while True:
|
||||
if len(file_manifest) == 0: break
|
||||
this_iteration_file_manifest = file_manifest[:batchsize]
|
||||
this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
|
||||
file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
|
||||
# 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
|
||||
for index, content in enumerate(this_iteration_gpt_response_collection):
|
||||
if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
|
||||
this_iteration_files = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
|
||||
previous_iteration_files.extend(this_iteration_files)
|
||||
previous_iteration_files_string = ', '.join(previous_iteration_files)
|
||||
current_iteration_focus = ', '.join(this_iteration_files)
|
||||
if summary_batch_isolation: focus = current_iteration_focus
|
||||
else: focus = previous_iteration_files_string
|
||||
i_say = f'用一张Markdown表格简要描述以下文件的功能：{focus}。根据以上分析，用一句话概括程序的整体功能。'
|
||||
if last_iteration_result != "":
|
||||
sys_prompt_additional = "已知某些代码的局部作用是:" + last_iteration_result + "\n请继续分析其他源代码，从而更全面地理解项目的整体功能。"
|
||||
else:
|
||||
sys_prompt_additional = ""
|
||||
inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
|
||||
this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
|
||||
this_iteration_history.append(last_iteration_result)
|
||||
# 裁剪input
|
||||
inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)
|
||||
result = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
|
||||
history=this_iteration_history_feed, # 迭代之前的分析
|
||||
sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
|
||||
|
||||
diagram_code = make_diagram(this_iteration_files, result, this_iteration_history_feed)
|
||||
summary = "请用一句话概括这些文件的整体功能。\n\n" + diagram_code
|
||||
summary_result = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=summary,
|
||||
inputs_show_user=summary,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=[i_say, result], # 迭代之前的分析
|
||||
sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
|
||||
|
||||
report_part_2.extend([i_say, result])
|
||||
last_iteration_result = summary_result
|
||||
file_manifest = file_manifest[batchsize:]
|
||||
gpt_response_collection = gpt_response_collection[batchsize*2:]
|
||||
|
||||
############################## <END> ##################################
|
||||
history_to_return.extend(report_part_2)
|
||||
res = write_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
|
||||
|
||||
def make_diagram(this_iteration_files, result, this_iteration_history_feed):
|
||||
from crazy_functions.diagram_fns.file_tree import build_file_tree_mermaid_diagram
|
||||
return build_file_tree_mermaid_diagram(this_iteration_history_feed[0::2], this_iteration_history_feed[1::2], "项目示意图")
|
||||
|
||||
@CatchException
|
||||
def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob
|
||||
file_manifest = [f for f in glob.glob('./*.py')] + \
|
||||
[f for f in glob.glob('./*/*.py')]
|
||||
project_folder = './'
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个Matlab项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.m', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到任何`.m`源文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.vue', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.less', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.sass', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.wxml', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.wxss', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
@CatchException
|
||||
def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \
|
||||
[f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
txt_pattern = plugin_kwargs.get("advanced_arg")
|
||||
txt_pattern = txt_pattern.replace("，", ",")
|
||||
# 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
|
||||
pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
|
||||
if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
|
||||
# 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
|
||||
pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
|
||||
pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
|
||||
# 将要忽略匹配的文件名(例如: ^README.md)
|
||||
pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
|
||||
# 生成正则表达式
|
||||
pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
|
||||
pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
|
||||
|
||||
history.clear()
|
||||
import glob, os, re
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
# 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):
|
||||
extract_folder_path = maybe_dir[0]
|
||||
else:
|
||||
extract_folder_path = project_folder
|
||||
# 按输入的匹配模式寻找上传的非压缩文件和已解压的文件
|
||||
file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
|
||||
os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
63
crazy_functions/询问多个大语言模型.py
Normal file
63
crazy_functions/询问多个大语言模型.py
Normal file
@@ -0,0 +1,63 @@
|
||||
from toolbox import CatchException, update_ui, get_conf
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import datetime
|
||||
@CatchException
|
||||
def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
MULTI_QUERY_LLM_MODELS = get_conf('MULTI_QUERY_LLM_MODELS')
|
||||
chatbot.append((txt, "正在同时咨询" + MULTI_QUERY_LLM_MODELS))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
|
||||
llm_kwargs['llm_model'] = MULTI_QUERY_LLM_MODELS # 支持任意数量的llm接口，用&符号分隔
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
@CatchException
|
||||
def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
|
||||
llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
|
||||
|
||||
chatbot.append((txt, f"正在同时咨询{llm_kwargs['llm_model']}"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
192
crazy_functions/语音助手.py
Normal file
192
crazy_functions/语音助手.py
Normal file
@@ -0,0 +1,192 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, get_conf, markdown_convertion
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.agent_fns.watchdog import WatchDog
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
import threading, time
|
||||
import numpy as np
|
||||
from .live_audio.aliyunASR import AliyunASR
|
||||
import json
|
||||
import re
|
||||
|
||||
|
||||
def chatbot2history(chatbot):
|
||||
history = []
|
||||
for c in chatbot:
|
||||
for q in c:
|
||||
if q in ["[ 请讲话 ]", "[ 等待GPT响应 ]", "[ 正在等您说完问题 ]"]:
|
||||
continue
|
||||
elif q.startswith("[ 正在等您说完问题 ]"):
|
||||
continue
|
||||
else:
|
||||
history.append(q.strip('<div class="markdown-body">').strip('</div>').strip('<p>').strip('</p>'))
|
||||
return history
|
||||
|
||||
def visualize_audio(chatbot, audio_shape):
|
||||
if len(chatbot) == 0: chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
|
||||
chatbot[-1] = list(chatbot[-1])
|
||||
p1 = '「'
|
||||
p2 = '」'
|
||||
chatbot[-1][-1] = re.sub(p1+r'(.*)'+p2, '', chatbot[-1][-1])
|
||||
chatbot[-1][-1] += (p1+f"`{audio_shape}`"+p2)
|
||||
|
||||
class AsyncGptTask():
|
||||
def __init__(self) -> None:
|
||||
self.observe_future = []
|
||||
self.observe_future_chatbot_index = []
|
||||
|
||||
def gpt_thread_worker(self, i_say, llm_kwargs, history, sys_prompt, observe_window, index):
|
||||
try:
|
||||
MAX_TOKEN_ALLO = 2560
|
||||
i_say, history = input_clipping(i_say, history, max_token_limit=MAX_TOKEN_ALLO)
|
||||
gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt,
|
||||
observe_window=observe_window[index], console_slience=True)
|
||||
except ConnectionAbortedError as token_exceed_err:
|
||||
print('至少一个线程任务Token溢出而失败', e)
|
||||
except Exception as e:
|
||||
print('至少一个线程任务意外失败', e)
|
||||
|
||||
def add_async_gpt_task(self, i_say, chatbot_index, llm_kwargs, history, system_prompt):
|
||||
self.observe_future.append([""])
|
||||
self.observe_future_chatbot_index.append(chatbot_index)
|
||||
cur_index = len(self.observe_future)-1
|
||||
th_new = threading.Thread(target=self.gpt_thread_worker, args=(i_say, llm_kwargs, history, system_prompt, self.observe_future, cur_index))
|
||||
th_new.daemon = True
|
||||
th_new.start()
|
||||
|
||||
def update_chatbot(self, chatbot):
|
||||
for of, ofci in zip(self.observe_future, self.observe_future_chatbot_index):
|
||||
try:
|
||||
chatbot[ofci] = list(chatbot[ofci])
|
||||
chatbot[ofci][1] = markdown_convertion(of[0])
|
||||
except:
|
||||
self.observe_future = []
|
||||
self.observe_future_chatbot_index = []
|
||||
return chatbot
|
||||
|
||||
class InterviewAssistant(AliyunASR):
|
||||
def __init__(self):
|
||||
self.capture_interval = 0.5 # second
|
||||
self.stop = False
|
||||
self.parsed_text = "" # 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.audio_shape = "" # 音频的可视化表现, 由 audio_convertion_thread() 写入
|
||||
self.event_on_result_chg = threading.Event()
|
||||
self.event_on_entence_end = threading.Event()
|
||||
self.event_on_commit_question = threading.Event()
|
||||
|
||||
def __del__(self):
|
||||
self.stop = True
|
||||
self.stop_msg = ""
|
||||
self.commit_wd.kill_dog = True
|
||||
self.plugin_wd.kill_dog = True
|
||||
|
||||
def init(self, chatbot):
|
||||
# 初始化音频采集线程
|
||||
self.captured_audio = np.array([])
|
||||
self.keep_latest_n_second = 10
|
||||
self.commit_after_pause_n_second = 2.0
|
||||
self.ready_audio_flagment = None
|
||||
self.stop = False
|
||||
self.plugin_wd = WatchDog(timeout=5, bark_fn=self.__del__, msg="程序终止")
|
||||
self.aut = threading.Thread(target=self.audio_convertion_thread, args=(chatbot._cookies['uuid'],))
|
||||
self.aut.daemon = True
|
||||
self.aut.start()
|
||||
# th2 = threading.Thread(target=self.audio2txt_thread, args=(chatbot._cookies['uuid'],))
|
||||
# th2.daemon = True
|
||||
# th2.start()
|
||||
|
||||
def no_audio_for_a_while(self):
|
||||
if len(self.buffered_sentence) < 7: # 如果一句话小于7个字，暂不提交
|
||||
self.commit_wd.begin_watch()
|
||||
else:
|
||||
self.event_on_commit_question.set()
|
||||
|
||||
def begin(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
# main plugin function
|
||||
self.init(chatbot)
|
||||
chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
self.plugin_wd.begin_watch()
|
||||
self.agt = AsyncGptTask()
|
||||
self.commit_wd = WatchDog(timeout=self.commit_after_pause_n_second, bark_fn=self.no_audio_for_a_while, interval=0.2)
|
||||
self.commit_wd.begin_watch()
|
||||
|
||||
while not self.stop:
|
||||
self.event_on_result_chg.wait(timeout=0.25) # run once every 0.25 second
|
||||
chatbot = self.agt.update_chatbot(chatbot) # 将子线程的gpt结果写入chatbot
|
||||
history = chatbot2history(chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
self.plugin_wd.feed()
|
||||
|
||||
if self.event_on_result_chg.is_set():
|
||||
# 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
|
||||
history = chatbot2history(chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
self.commit_wd.feed()
|
||||
|
||||
if self.event_on_entence_end.is_set():
|
||||
# called when a sentence has ended
|
||||
self.event_on_entence_end.clear()
|
||||
self.parsed_text = self.parsed_sentence
|
||||
self.buffered_sentence += self.parsed_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
|
||||
self.event_on_commit_question.clear()
|
||||
if len(self.buffered_sentence) == 0: raise RuntimeError
|
||||
|
||||
self.commit_wd.begin_watch()
|
||||
chatbot[-1] = list(chatbot[-1])
|
||||
chatbot[-1] = [self.buffered_sentence, "[ 等待GPT响应 ]"]
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# add gpt task 创建子线程请求gpt，避免线程阻塞
|
||||
history = chatbot2history(chatbot)
|
||||
self.agt.add_async_gpt_task(self.buffered_sentence, len(chatbot)-1, llm_kwargs, history, system_prompt)
|
||||
|
||||
self.buffered_sentence = ""
|
||||
chatbot.append(["[ 请讲话 ]", "[ 正在等您说完问题 ]"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not self.event_on_result_chg.is_set() and not self.event_on_entence_end.is_set() and not self.event_on_commit_question.is_set():
|
||||
visualize_audio(chatbot, self.audio_shape)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if len(self.stop_msg) != 0:
|
||||
raise RuntimeError(self.stop_msg)
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# pip install -U openai-whisper
|
||||
chatbot.append(["对话助手函数插件：使用时，双手离开鼠标键盘吧", "音频助手, 正在听您讲话（点击“停止”键可终止程序）..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import nls
|
||||
from scipy import io
|
||||
except:
|
||||
chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
APPKEY = get_conf('ALIYUN_APPKEY')
|
||||
if APPKEY == "":
|
||||
chatbot.append(["导入依赖失败", "没有阿里云语音识别APPKEY和TOKEN, 详情见https://help.aliyun.com/document_detail/450255.html"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
ia = InterviewAssistant()
|
||||
yield from ia.begin(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
64
crazy_functions/读文章写摘要.py
Normal file
64
crazy_functions/读文章写摘要.py
Normal file
@@ -0,0 +1,64 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
|
||||
def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
|
||||
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
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
|
||||
|
||||
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_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
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
|
||||
# [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
185
crazy_functions/谷歌检索小助手.py
Normal file
185
crazy_functions/谷歌检索小助手.py
Normal file
@@ -0,0 +1,185 @@
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from toolbox import CatchException, report_exception, 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 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/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'
|
||||
}
|
||||
try:
|
||||
session.proxies.update(proxies)
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议：检查USE_PROXY选项是否修改。",
|
||||
b=f"尝试直接连接")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
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"):
|
||||
title = result.a.text.replace('\n', ' ').replace(' ', ' ')
|
||||
author = result.select_one(".gs_a").text
|
||||
try:
|
||||
citation = result.select_one(".gs_fl > a[href*='cites']").text # 引用次数是链接中的文本，直接取出来
|
||||
except:
|
||||
citation = 'cited by 0'
|
||||
abstract = result.select_one(".gs_rs").text.strip() # 摘要在 .gs_rs 中的文本，需要清除首尾空格
|
||||
|
||||
# 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())
|
||||
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
|
||||
|
||||
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,
|
||||
})
|
||||
|
||||
chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
|
||||
return profile
|
||||
|
||||
@CatchException
|
||||
def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
disable_auto_promotion(chatbot=chatbot)
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import arxiv
|
||||
import math
|
||||
from bs4 import BeautifulSoup
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
|
||||
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:
|
||||
i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
|
||||
"1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
|
||||
f"以下是信息源：{str(meta_paper_info_list[:batchsize])}"
|
||||
|
||||
inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
|
||||
)
|
||||
|
||||
history.extend([ f"第{batch+1}批", gpt_say ])
|
||||
meta_paper_info_list = meta_paper_info_list[batchsize:]
|
||||
|
||||
chatbot.append(["状态？",
|
||||
"已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
|
||||
msg = '正常'
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
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
|
||||
54
crazy_functions/辅助功能.py
Normal file
54
crazy_functions/辅助功能.py
Normal file
@@ -0,0 +1,54 @@
|
||||
# encoding: utf-8
|
||||
# @Time : 2023/4/19
|
||||
# @Author : Spike
|
||||
# @Descr :
|
||||
from toolbox import update_ui, get_conf, get_user
|
||||
from toolbox import CatchException
|
||||
from toolbox import default_user_name
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import shutil
|
||||
import os
|
||||
|
||||
|
||||
@CatchException
|
||||
def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
if txt:
|
||||
show_say = txt
|
||||
prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
|
||||
else:
|
||||
prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
|
||||
show_say = '分析上述回答，再列出用户可能提出的三个问题。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=prompt,
|
||||
inputs_show_user=show_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt=system_prompt
|
||||
)
|
||||
chatbot[-1] = (show_say, gpt_say)
|
||||
history.extend([show_say, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
@CatchException
|
||||
def 清除缓存(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
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
|
||||
|
||||
def _get_log_folder(user=default_user_name):
|
||||
PATH_LOGGING = get_conf('PATH_LOGGING')
|
||||
_dir = os.path.join(PATH_LOGGING, user)
|
||||
if not os.path.exists(_dir): os.makedirs(_dir)
|
||||
return _dir
|
||||
|
||||
def _get_upload_folder(user=default_user_name):
|
||||
PATH_PRIVATE_UPLOAD = get_conf('PATH_PRIVATE_UPLOAD')
|
||||
_dir = os.path.join(PATH_PRIVATE_UPLOAD, user)
|
||||
return _dir
|
||||
|
||||
shutil.rmtree(_get_log_folder(get_user(chatbot)), ignore_errors=True)
|
||||
shutil.rmtree(_get_upload_folder(get_user(chatbot)), 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
|
||||
99
crazy_functions/高级功能函数模板.py
Normal file
99
crazy_functions/高级功能函数模板.py
Normal file
@@ -0,0 +1,99 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import datetime
|
||||
|
||||
高阶功能模板函数示意图 = f"""
|
||||
```mermaid
|
||||
flowchart TD
|
||||
%% <gpt_academic_hide_mermaid_code> 一个特殊标记，用于在生成mermaid图表时隐藏代码块
|
||||
subgraph 函数调用["函数调用过程"]
|
||||
AA["输入栏用户输入的文本(txt)"] --> BB["gpt模型参数(llm_kwargs)"]
|
||||
BB --> CC["插件模型参数(plugin_kwargs)"]
|
||||
CC --> DD["对话显示框的句柄(chatbot)"]
|
||||
DD --> EE["对话历史(history)"]
|
||||
EE --> FF["系统提示词(system_prompt)"]
|
||||
FF --> GG["当前用户信息(web_port)"]
|
||||
|
||||
A["开始(查询5天历史事件)"]
|
||||
A --> B["获取当前月份和日期"]
|
||||
B --> C["生成历史事件查询提示词"]
|
||||
C --> D["调用大模型"]
|
||||
D --> E["更新界面"]
|
||||
E --> F["记录历史"]
|
||||
F --> |"下一天"| B
|
||||
end
|
||||
```
|
||||
"""
|
||||
|
||||
@CatchException
|
||||
def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
# 高阶功能模板函数示意图：https://mermaid.live/edit#pako:eNptk1tvEkEYhv8KmattQpvlvOyFCcdeeaVXuoYssBwie8gyhCIlqVoLhrbbtAWNUpEGUkyMEDW2Fmn_DDOL_8LZHdOwxrnamX3f7_3mmZk6yKhZCfAgV1KrmYKoQ9fDuKC4yChX0nld1Aou1JzjznQ5fWmejh8LYHW6vG2a47YAnlCLNSIRolnenKBXI_zRIBrcuqRT890u7jZx7zMDt-AaMbnW1--5olGiz2sQjwfoQxsZL0hxplSSU0-rop4vrzmKR6O2JxYjHmwcL2Y_HDatVMkXlf86YzHbGY9bO5j8XE7O8Nsbc3iNB3ukL2SMcH-XIQBgWoVOZzxuOxOJOyc63EPGV6ZQLENVrznViYStTiaJ2vw2M2d9bByRnOXkgCnXylCSU5quyto_IcmkbdvctELmJ-j1ASW3uB3g5xOmKqVTmqr_Na3AtuS_dtBFm8H90XJyHkDDT7S9xXWb4HGmRChx64AOL5HRpUm411rM5uh4H78Z4V7fCZzytjZz2seto9XaNPFue07clLaVZF8UNLygJ-VES8lah_n-O-5Ozc7-77NzJ0-K0yr0ZYrmHdqAk50t2RbA4qq9uNohBASw7YpSgaRkLWCCAtxAlnRZLGbJba9bPwUAC5IsCYAnn1kpJ1ZKUACC0iBSsQLVBzUlA3ioVyQ3qGhZEUrxokiehAz4nFgqk1VNVABfB1uAD_g2_AGPl-W8nMcbCvsDblADfNCz4feyobDPy3rYEMtxwYYbPFNVUoHdCPmDHBv2cP4AMfrCbiBli-Q-3afv0X6WdsIjW2-10fgDy1SAig
|
||||
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((
|
||||
"您正在调用插件：历史上的今天",
|
||||
"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！" + 高阶功能模板函数示意图))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
for i in range(5):
|
||||
currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
|
||||
currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
|
||||
i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
|
||||
|
||||
PROMPT = """
|
||||
请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
graph TD
|
||||
P(编程) --> L1(Python)
|
||||
P(编程) --> L2(C)
|
||||
P(编程) --> L3(C++)
|
||||
P(编程) --> L4(Javascipt)
|
||||
P(编程) --> L5(PHP)
|
||||
```
|
||||
"""
|
||||
@CatchException
|
||||
def 测试图表渲染(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "一个测试mermaid绘制图表的功能，您可以在输入框中输入一些关键词，然后使用mermaid+llm绘制图表。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
if txt == "": txt = "空白的输入栏" # 调皮一下
|
||||
|
||||
i_say_show_user = f'请绘制有关“{txt}”的逻辑关系图。'
|
||||
i_say = PROMPT.format(subject=txt)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say,
|
||||
inputs_show_user=i_say_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=""
|
||||
)
|
||||
history.append(i_say); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
231
docker-compose.yml
Normal file
231
docker-compose.yml
Normal file
@@ -0,0 +1,231 @@
|
||||
## ===================================================
|
||||
# docker-compose.yml
|
||||
## ===================================================
|
||||
# 1. 请在以下方案中选择任意一种，然后删除其他的方案
|
||||
# 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py
|
||||
# 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改：
|
||||
# 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置
|
||||
# network_mode: "host"
|
||||
# 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
|
||||
# ports:
|
||||
# - "12345:12345" # 注意！12345必须与WEB_PORT环境变量相互对应
|
||||
# 4. 最后`docker-compose up`运行
|
||||
# 5. 如果希望使用显卡，请关注 LOCAL_MODEL_DEVICE 和 英伟达显卡运行时 选项
|
||||
## ===================================================
|
||||
# 1. Please choose one of the following options and delete the others.
|
||||
# 2. Modify the environment variables in the selected option, see GitHub wiki or config.py for more details.
|
||||
# 3. Choose a method to expose the server port and make the corresponding configuration changes:
|
||||
# [Method 1: Suitable for Linux, convenient, but not supported for Windows] Fusion with the host network, this is the default configuration
|
||||
# network_mode: "host"
|
||||
# [Method 2: Suitable for all systems including Windows and MacOS] Port mapping, mapping the container port to the host port (note that you need to delete network_mode: "host" first, and then add the following content)
|
||||
# ports:
|
||||
# - "12345: 12345" # Note! 12345 must correspond to the WEB_PORT environment variable.
|
||||
# 4. Finally, run `docker-compose up`.
|
||||
# 5. If you want to use a graphics card, pay attention to the LOCAL_MODEL_DEVICE and Nvidia GPU runtime options.
|
||||
## ===================================================
|
||||
|
||||
## ===================================================
|
||||
## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_full_capability:
|
||||
image: ghcr.io/binary-husky/gpt_academic_with_all_capacity:master
|
||||
environment:
|
||||
# 请查阅 `config.py`或者 github wiki 以查看所有的配置信息
|
||||
API_KEY: ' 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 '
|
||||
# USE_PROXY: ' True '
|
||||
# proxies: ' { "http": "http://localhost:10881", "https": "http://localhost:10881", } '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4", "qianfan", "sparkv2", "spark", "chatglm"] '
|
||||
BAIDU_CLOUD_API_KEY : ' bTUtwEAveBrQipEowUvDwYWq '
|
||||
BAIDU_CLOUD_SECRET_KEY : ' jqXtLvXiVw6UNdjliATTS61rllG8Iuni '
|
||||
XFYUN_APPID: ' 53a8d816 '
|
||||
XFYUN_API_SECRET: ' MjMxNDQ4NDE4MzM0OSNlNjQ2NTlhMTkx '
|
||||
XFYUN_API_KEY: ' 95ccdec285364869d17b33e75ee96447 '
|
||||
ENABLE_AUDIO: ' False '
|
||||
DEFAULT_WORKER_NUM: ' 20 '
|
||||
WEB_PORT: ' 12345 '
|
||||
ADD_WAIFU: ' False '
|
||||
ALIYUN_APPKEY: ' RxPlZrM88DnAFkZK '
|
||||
THEME: ' Chuanhu-Small-and-Beautiful '
|
||||
ALIYUN_ACCESSKEY: ' LTAI5t6BrFUzxRXVGUWnekh1 '
|
||||
ALIYUN_SECRET: ' eHmI20SVWIwQZxCiTD2bGQVspP9i68 '
|
||||
# LOCAL_MODEL_DEVICE: ' cuda '
|
||||
|
||||
# 加载英伟达显卡运行时
|
||||
# runtime: nvidia
|
||||
# deploy:
|
||||
# resources:
|
||||
# reservations:
|
||||
# devices:
|
||||
# - driver: nvidia
|
||||
# count: 1
|
||||
# capabilities: [gpu]
|
||||
|
||||
# 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
|
||||
# ports:
|
||||
# - "12345:12345" # 12345必须与WEB_PORT相互对应
|
||||
|
||||
# 启动容器后，运行main.py主程序
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_nolocalllms:
|
||||
image: ghcr.io/binary-husky/gpt_academic_nolocal:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
USE_PROXY: ' True '
|
||||
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "sparkv2", "qianfan"] '
|
||||
WEB_PORT: ' 22303 '
|
||||
ADD_WAIFU: ' True '
|
||||
# THEME: ' Chuanhu-Small-and-Beautiful '
|
||||
# DEFAULT_WORKER_NUM: ' 10 '
|
||||
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
### ===================================================
|
||||
### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
|
||||
### ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_chatglm:
|
||||
image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
USE_PROXY: ' True '
|
||||
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"] '
|
||||
LOCAL_MODEL_DEVICE: ' cuda '
|
||||
DEFAULT_WORKER_NUM: ' 10 '
|
||||
WEB_PORT: ' 12303 '
|
||||
ADD_WAIFU: ' True '
|
||||
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
|
||||
|
||||
# 显卡的使用，nvidia0指第0个GPU
|
||||
runtime: nvidia
|
||||
devices:
|
||||
- /dev/nvidia0:/dev/nvidia0
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
# P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
|
||||
# command: >
|
||||
# bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
|
||||
|
||||
### ===================================================
|
||||
### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
|
||||
### ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_rwkv:
|
||||
image: ghcr.io/binary-husky/gpt_academic_jittorllms:master
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
USE_PROXY: ' True '
|
||||
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "newbing", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] '
|
||||
LOCAL_MODEL_DEVICE: ' cuda '
|
||||
DEFAULT_WORKER_NUM: ' 10 '
|
||||
WEB_PORT: ' 12305 '
|
||||
ADD_WAIFU: ' True '
|
||||
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
|
||||
|
||||
# 显卡的使用，nvidia0指第0个GPU
|
||||
runtime: nvidia
|
||||
devices:
|
||||
- /dev/nvidia0:/dev/nvidia0
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
python3 -u main.py
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 【方案四】 ChatGPT + Latex
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_latex:
|
||||
image: ghcr.io/binary-husky/gpt_academic_with_latex:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex)
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
USE_PROXY: ' True '
|
||||
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] '
|
||||
LOCAL_MODEL_DEVICE: ' cuda '
|
||||
DEFAULT_WORKER_NUM: ' 10 '
|
||||
WEB_PORT: ' 12303 '
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_audio:
|
||||
image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS '
|
||||
USE_PROXY: ' False '
|
||||
proxies: ' None '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] '
|
||||
ENABLE_AUDIO: ' True '
|
||||
LOCAL_MODEL_DEVICE: ' cuda '
|
||||
DEFAULT_WORKER_NUM: ' 20 '
|
||||
WEB_PORT: ' 12343 '
|
||||
ADD_WAIFU: ' True '
|
||||
THEME: ' Chuanhu-Small-and-Beautiful '
|
||||
ALIYUN_APPKEY: ' RoP1ZrM84DnAFkZK '
|
||||
ALIYUN_TOKEN: ' f37f30e0f9934c34a992f6f64f7eba4f '
|
||||
# (无需填写) ALIYUN_ACCESSKEY: ' LTAI5q6BrFUzoRXVGUWnekh1 '
|
||||
# (无需填写) ALIYUN_SECRET: ' eHmI20AVWIaQZ0CiTD2bGQVsaP9i68 '
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
1
docs/Dockerfile+ChatGLM
Normal file
1
docs/Dockerfile+ChatGLM
Normal file
@@ -0,0 +1 @@
|
||||
# 此Dockerfile不再维护，请前往docs/GithubAction+ChatGLM+Moss
|
||||
1
docs/Dockerfile+JittorLLM
Normal file
1
docs/Dockerfile+JittorLLM
Normal file
@@ -0,0 +1 @@
|
||||
# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs
|
||||
1
docs/Dockerfile+NoLocal+Latex
Normal file
1
docs/Dockerfile+NoLocal+Latex
Normal file
@@ -0,0 +1 @@
|
||||
# 此Dockerfile不再维护，请前往docs/GithubAction+NoLocal+Latex
|
||||
36
docs/GithubAction+AllCapacity
Normal file
36
docs/GithubAction+AllCapacity
Normal file
@@ -0,0 +1,36 @@
|
||||
# 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 rarfile py7zr
|
||||
RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad 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 --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
|
||||
|
||||
RUN python3 -m pip install -r requirements.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_moss.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_qwen.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_newbing.txt
|
||||
RUN python3 -m pip install nougat-ocr
|
||||
|
||||
|
||||
# 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"]
|
||||
53
docs/GithubAction+AllCapacityBeta
Normal file
53
docs/GithubAction+AllCapacityBeta
Normal file
@@ -0,0 +1,53 @@
|
||||
# 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 .
|
||||
# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacityBeta --network=host .
|
||||
# docker run -it --net=host gpt-academic-all-capacity bash
|
||||
|
||||
# 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
|
||||
|
||||
# # 非必要步骤，更换pip源 （以下三行，可以删除）
|
||||
# RUN echo '[global]' > /etc/pip.conf && \
|
||||
# echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
|
||||
# echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
||||
|
||||
# 下载pytorch
|
||||
RUN python3 -m pip install torch torchvision --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 rarfile py7zr
|
||||
RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad 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 --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
|
||||
|
||||
RUN python3 -m pip install -r requirements.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_moss.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_qwen.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_newbing.txt
|
||||
RUN python3 -m pip install nougat-ocr
|
||||
|
||||
# 预热Tiktoken模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 安装知识库插件的额外依赖
|
||||
RUN apt-get update && apt-get install libgl1 -y
|
||||
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
RUN pip3 install unstructured[all-docs] --upgrade
|
||||
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||
RUN rm -rf /usr/local/lib/python3.8/dist-packages/tests
|
||||
|
||||
|
||||
# COPY .cache /root/.cache
|
||||
# COPY config_private.py config_private.py
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
30
docs/GithubAction+ChatGLM+Moss
Normal file
30
docs/GithubAction+ChatGLM+Moss
Normal file
@@ -0,0 +1,30 @@
|
||||
|
||||
# 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
|
||||
RUN apt-get update
|
||||
RUN apt-get install -y curl proxychains curl gcc
|
||||
RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
|
||||
|
||||
|
||||
# use python3 as the system default python
|
||||
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
|
||||
# 下载pytorch
|
||||
RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
|
||||
# 下载分支
|
||||
WORKDIR /gpt
|
||||
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||
WORKDIR /gpt/gpt_academic
|
||||
RUN git clone https://github.com/OpenLMLab/MOSS.git request_llms/moss
|
||||
RUN python3 -m pip install -r requirements.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_moss.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_qwen.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||
RUN python3 -m pip install -r request_llms/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"]
|
||||
34
docs/GithubAction+JittorLLMs
Normal file
34
docs/GithubAction+JittorLLMs
Normal file
@@ -0,0 +1,34 @@
|
||||
# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
|
||||
FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
|
||||
ARG useProxyNetwork=''
|
||||
RUN apt-get update
|
||||
RUN apt-get install -y curl proxychains curl g++
|
||||
RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
|
||||
|
||||
# use python3 as the system default python
|
||||
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
|
||||
|
||||
# 下载pytorch
|
||||
RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
|
||||
|
||||
# 下载分支
|
||||
WORKDIR /gpt
|
||||
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||
WORKDIR /gpt/gpt_academic
|
||||
RUN python3 -m pip install -r requirements.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_newbing.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
|
||||
|
||||
# 下载JittorLLMs
|
||||
RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llms/jittorllms
|
||||
|
||||
# 禁用缓存，确保更新代码
|
||||
ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
|
||||
RUN git pull
|
||||
|
||||
# 预热Tiktoken模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
20
docs/GithubAction+NoLocal
Normal file
20
docs/GithubAction+NoLocal
Normal file
@@ -0,0 +1,20 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
22
docs/GithubAction+NoLocal+AudioAssistant
Normal file
22
docs/GithubAction+NoLocal+AudioAssistant
Normal file
@@ -0,0 +1,22 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 安装语音插件的额外依赖
|
||||
RUN pip3 install pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
32
docs/GithubAction+NoLocal+Latex
Normal file
32
docs/GithubAction+NoLocal+Latex
Normal file
@@ -0,0 +1,32 @@
|
||||
# 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/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
|
||||
ENV PATH "$PATH:/usr/local/texlive/2022/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2023/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2024/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2025/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2026/bin/x86_64-linux"
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
RUN pip3 install openai numpy arxiv rich
|
||||
RUN pip3 install colorama Markdown pygments pymupdf
|
||||
RUN pip3 install python-docx pdfminer
|
||||
RUN pip3 install nougat-ocr
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
26
docs/GithubAction+NoLocal+Vectordb
Normal file
26
docs/GithubAction+NoLocal+Vectordb
Normal file
@@ -0,0 +1,26 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal-vs -f docs/GithubAction+NoLocal+Vectordb .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal-vs
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 安装知识库插件的额外依赖
|
||||
RUN apt-get update && apt-get install libgl1 -y
|
||||
RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
|
||||
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
RUN pip3 install unstructured[all-docs] --upgrade
|
||||
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||
|
||||
# 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"]
|
||||
343
docs/README.Arabic.md
Normal file
343
docs/README.Arabic.md
Normal file
@@ -0,0 +1,343 @@
|
||||
|
||||
|
||||
|
||||
> **ملحوظة**
|
||||
>
|
||||
> تمت ترجمة هذا الملف README باستخدام GPT (بواسطة المكون الإضافي لهذا المشروع) وقد لا تكون الترجمة 100٪ موثوقة، يُرجى التمييز بعناية بنتائج الترجمة.
|
||||
>
|
||||
> 2023.11.7: عند تثبيت التبعيات، يُرجى اختيار الإصدار المُحدد في `requirements.txt`. الأمر للتثبيت: `pip install -r requirements.txt`.
|
||||
|
||||
# <div align=center><img src="logo.png" width="40"> GPT الأكاديمي</div>
|
||||
|
||||
**إذا كنت تحب هذا المشروع، فيُرجى إعطاؤه Star. لترجمة هذا المشروع إلى لغة عشوائية باستخدام GPT، قم بقراءة وتشغيل [`multi_language.py`](multi_language.py) (تجريبي).
|
||||
|
||||
> **ملحوظة**
|
||||
>
|
||||
> 1. يُرجى ملاحظة أنها الإضافات (الأزرار) المميزة فقط التي تدعم قراءة الملفات، وبعض الإضافات توجد في قائمة منسدلة في منطقة الإضافات. بالإضافة إلى ذلك، نرحب بأي Pull Request جديد بأعلى أولوية لأي إضافة جديدة.
|
||||
>
|
||||
> 2. تُوضّح كل من الملفات في هذا المشروع وظيفتها بالتفصيل في [تقرير الفهم الذاتي `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). يمكنك في أي وقت أن تنقر على إضافة وظيفة ذات صلة لاستدعاء GPT وإعادة إنشاء تقرير الفهم الذاتي للمشروع. للأسئلة الشائعة [`الويكي`](https://github.com/binary-husky/gpt_academic/wiki). [طرق التثبيت العادية](#installation) | [نصب بنقرة واحدة](https://github.com/binary-husky/gpt_academic/releases) | [تعليمات التكوين](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
|
||||
>
|
||||
> 3. يتم توافق هذا المشروع مع ودعم توصيات اللغة البيجائية الأكبر شمولًا وشجاعة لمثل ChatGLM. يمكنك توفير العديد من مفاتيح Api المشتركة في تكوين الملف، مثل `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. عند تبديل مؤقت لـ `API_KEY`، قم بإدخال `API_KEY` المؤقت في منطقة الإدخال ثم اضغط على زر "إدخال" لجعله ساري المفعول.
|
||||
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
الوظائف (⭐= وظائف مُضافة حديثًا) | الوصف
|
||||
--- | ---
|
||||
⭐[التوصل لنموذج جديد](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)! | بحث بيدو[تشيان فان](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu) ووينسين[جينرال](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)، مختبرات شنغهاي للذكاء الصناعي[شو شينغ](https://github.com/InternLM/InternLM)، إكسنفلام[زينغهو]https://xinghuo.xfyun.cn/)، [LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)، واجهة بيانية ذكية و3 خدمات إضافية [DALLE3]
|
||||
الجودة الفائقة، الترجمة، شرح الكود | الإصلاح الفوري للاخطاء النحوية في الأبحاث وترجمة وتحسين التصريف اللغوي للأكواد
|
||||
[اختصارات مخصصة](https://www.bilibili.com/video/BV14s4y1E7jN) | دعم الاختصارات المخصصة
|
||||
تصميم قابل للتوسيع | دعم الإضافات القوية المخصصة (الوظائف)، الإضافات قابلة للتحديث بشكل فوري
|
||||
[تحليل البرنامج](https://www.bilibili.com/video/BV1cj411A7VW) | [وظائف] التحليل الشجري بناءً على البرنامج من Python/C/C++/Java/Lua/..., أو [التحليل الذاتي](https://www.bilibili.com/video/BV1cj411A7VW)
|
||||
قراءة وترجمة الأبحاث | [وظائف] فك تشفير كامل لأوراق البحث بتنسيق LaTeX/PDF وإنشاء مستخلص
|
||||
ترجمة وتحسين أوراق اللاتكس | [وظائف] ترجمة أو تحسين الأوراق المكتوبة بلاتكس
|
||||
إنشاء تعليقات الدوال دفعة واحدة | [وظائف] إنشاء تعليقات الدوال بدفعة واحدة
|
||||
ترجمة Markdown بين اللغتين العربية والإنجليزية | [وظائف] هل رأيت الـ 5 لغات المستخدمة في منشور [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) ؟
|
||||
إنشاء تقرير تحليل الدردشة | [وظائف] إنشاء تقرير ملخص بعد تشغيله
|
||||
ترجمة كاملة لأوراق PDF | [وظائف] تحليل الأوراق بتنسيق PDF لتحديد العنوان وملخصها وترجمتها (متعدد الخيوط)
|
||||
مساعدة Arxiv | [وظائف] قم بإدخال رابط مقال Arxiv لترجمة الملخص وتحميل ملف PDF
|
||||
تصحيح لاتكس بضغطة زر واحدة | [وظائف] إكمال تصحيح لاتكس بناءً على التركيبة النحوية، إخراج همز المقابل للمقارنة PDF
|
||||
مساعد بحث Google بنسخة محلية | [وظائف] قم بتقديم رابط لصفحة بحث Google Scholar العشوائي حتى يساعدك GPT في كتابة [الأبحاث المتعلقة](https://www.bilibili.com/video/BV1GP411U7Az/)
|
||||
تجميع معلومات الويب + GPT | [وظائف] جمع المعلومات من الويب بشكل سهل للرد على الأسئلة لجعل المعلومات محدثة باستمرار
|
||||
⭐ترجمة دقيقة لأوراق Arxiv ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [وظائف] ترجمة مقالات Arxiv عالية الجودة بنقرة واحدة، أفضل أداة حاليا للترجمة
|
||||
⭐[إدخال الصوت الفوري](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [وظائف] (غير متزامن) استماع الصوت وقطعه تلقائيًا وتحديد وقت الإجابة تلقائيًا
|
||||
عرض الصيغ/الصور/الجداول | يمكن عرض الصيغ بشكل [TEX](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) وأيضًا بتنسيق رسومي، يدعم عرض الصيغ وإبراز الكود
|
||||
⭐إضغط على وكيل "شارلوت الذكي" | [وظائف] استكمال الذكاء للكأس الأول للذكاء المكتسب من مايكروسوفت، اكتشاف وتطوير عالمي العميل
|
||||
تبديل الواجهة المُظلمة | يمكنك التبديل إلى الواجهة المظلمة بإضافة ```/?__theme=dark``` إلى نهاية عنوان URL في المتصفح
|
||||
دعم المزيد من نماذج LLM | دعم لجميع GPT3.5 وGPT4 و[ChatGLM2 في جامعة ثوه في لين](https://github.com/THUDM/ChatGLM2-6B) و[MOSS في جامعة فودان](https://github.com/OpenLMLab/MOSS)
|
||||
⭐تحوي انطباعة "ChatGLM2" | يدعم استيراد "ChatGLM2" ويوفر إضافة المساعدة في تعديله
|
||||
دعم المزيد من نماذج "LLM"، دعم [نشر الحديس](https://huggingface.co/spaces/qingxu98/gpt-academic) | انضم إلى واجهة "Newbing" (Bing الجديدة)،نقدم نماذج Jittorllms الجديدة تؤيدهم [LLaMA](https://github.com/facebookresearch/llama) و [盘古α](https://openi.org.cn/pangu/)
|
||||
⭐حزمة "void-terminal" للشبكة (pip) | قم بطلب كافة وظائف إضافة هذا المشروع في python بدون واجهة رسومية (قيد التطوير)
|
||||
⭐PCI-Express لإعلام (PCI) | [وظائف] باللغة الطبيعية، قم بتنفيذ المِهام الأخرى في المشروع
|
||||
المزيد من العروض (إنشاء الصور وغيرها)……| شاهد أكثر في نهاية هذا المستند ...
|
||||
</div>
|
||||
|
||||
|
||||
- شكل جديد (عن طريق تعديل الخيار LAYOUT في `config.py` لقانون التوزيع "اليمين أو اليسار" أو "الأعلى أو الأسفل")
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
|
||||
</div>
|
||||
|
||||
|
||||
- جميع الأزرار يتم إنشاؤها ديناميكيًا من خلال قراءة functional.py ويمكن إضافة وظائف مخصصة بحرية وتحرير الحافظة
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
|
||||
</div>
|
||||
|
||||
- التجميل / التحوير
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
- إذا تضمّن الإخراج معادلات، فسيتم عرضها بشكلٍ يمكّن من النسخ والقراءة على النحوين: TEX ورسومية.
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
|
||||
</div>
|
||||
|
||||
- هل تشعر بالكسل من قراءة كود المشروع؟ قم بمدها مباشرةً إلى ChatGPT
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
|
||||
</div>
|
||||
|
||||
- دمج نماذج اللغات الكبيرة المختلفة (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
|
||||
</div>
|
||||
|
||||
# Installation
|
||||
### طريقة التثبيت الأولى: التشغيل المباشر (Windows، Linux أو MacOS)
|
||||
|
||||
1. قم بتنزيل المشروع
|
||||
```sh
|
||||
git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||
cd gpt_academic
|
||||
```
|
||||
|
||||
2. قم بتكوين لغة البرمجة Python
|
||||
|
||||
في ملف `config.py`، قم بتكوين مفتاح الواجهة API والإعدادات الأخرى، [انقر هنا للاطلاع على طريقة تكوين الإعدادات في بيئة شبكة خاصة](https://github.com/binary-husky/gpt_academic/issues/1). [انقر هنا لزيارة صفحة الويكي](https://github.com/binary-husky/gpt_academic/wiki/توضيحات-تكوين-المشروع).
|
||||
|
||||
" ستقوم البرنامج بفحص وجود ملف تكوين خاص يسمى `config_private.py` بأولوية، وسيستخدم التكوينات الموجودة فيه لتجاوز التكوينات ذات الأسماء المطابقة في `config.py`. إذا كنت تفهم هذه الطريقة ونظام القراءة، فإننا نوصي بشدة بإنشاء ملف تكوين جديد يسمى `config_private.py` بجوار `config.py` ونقل (نسخ) التكوينات الموجودة في `config.py` إلى `config_private.py` (يجب نسخ العناصر التي قمت بتعديلها فقط). "
|
||||
|
||||
" يدعم المشروع التكوين من خلال `المتغيرات المحيطية`، ويمكن تحديد تنسيق كتابة المتغيرات المحيطية من خلال ملف `docker-compose.yml` أو صفحة الويكي الخاصة بنا. تعتمد أولوية القراءة على التكوينات على التالي: `المتغيرات المحيطية` > `config_private.py` > `config.py`. "
|
||||
|
||||
3. قم بتثبيت التبعيات
|
||||
```sh
|
||||
# (الخيار الأول: إذا كنت تعرف Python، python>=3.9) الملحوظة: استخدم مستودع pip الرسمي أو مستودع pip آلي بباي، يمكن تغيير المستودع المؤقت بواسطة الأمر: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
|
||||
python -m pip install -r requirements.txt
|
||||
|
||||
# (الخيار الثاني: باستخدام Anaconda) الخطوات مشابهة (https://www.bilibili.com/video/BV1rc411W7Dr):
|
||||
conda create -n gptac_venv python=3.11 # إنشاء بيئة Anaconda
|
||||
conda activate gptac_venv # تنشيط بيئة Anaconda
|
||||
python -m pip install -r requirements.txt # هذه الخطوة مطابقة لخطوة تثبيت pip
|
||||
```
|
||||
|
||||
|
||||
<details><summary>إذا كنت بحاجة إلى دعم ChatGLM2 من الجامعة الصينية للاقتصاد وإدارة الأعمال وموس من جامعة فودان كخادم وجودة عالية لطرح الأسئلة، انقر هنا للعرض</summary>
|
||||
<p>
|
||||
|
||||
【خطوات اختيارية】إذا كنت بحاجة إلى دعم جودة عالية لتشات جامعة تسينهوا (ChatGLM2) الصينية وجامعة فودان (MOSS)، يتعين عليك تثبيت تبعيات إضافية (شرط مسبق: التعامل مع Python واستخدام Pytorch وتوفر الحاسوب الشخصي بمواصفات قوية):
|
||||
```sh
|
||||
# 【خطوات اختيارية 1】دعم جودة عالية لتشات جامعة تسينهوا (ChatGLM2)
|
||||
python -m pip install -r request_llms/requirements_chatglm.txt
|
||||
|
||||
# 【خطوات اختيارية 2】دعم جودة عالية لتشات جامعة فودان (MOSS)
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # عند تنفيذ هذا الأمر، يجب أن تكون في مسار المشروع الرئيسي
|
||||
|
||||
# 【خطوات اختيارية 3】دعم RWKV Runner
|
||||
راجع الويكي: https://github.com/binary-husky/gpt_academic/wiki/دليل-تكوين-RWKV
|
||||
|
||||
# 【خطوات اختيارية 4】تأكد من أن ملف التكوين config.py يحتوي على النماذج المرجوة، وهناك النماذج المدعومة حاليًا التالية (توجد خطط لتشغيل "jittorllms" في docker فقط):
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
```
|
||||
|
||||
</p>
|
||||
</details>
|
||||
|
||||
|
||||
4. تشغيل البرنامج
|
||||
```sh
|
||||
python main.py
|
||||
```
|
||||
|
||||
### طريقة التثبيت الثانية: استخدام Docker
|
||||
|
||||
0. نصب القدرات الكاملة للمشروع (هذا هو الصورة الكبيرة التي تحتوي على CUDA و LaTeX. ولكن إذا كانت سرعة الإنترنت بطيئة أو القرص الصلب صغير، فإننا لا نوصي باستخدام هذا الخيار)
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
|
||||
|
||||
``` sh
|
||||
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 0 وحذف الخطط الأخرى. ثم أشغل:
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
1. تشغيل نموذج ChatGPT فقط + 文心一言 (Wenxin YIYan) + Spark عبر الإنترنت (يُوصى بهذا الخيار للمعظم)
|
||||
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
|
||||
|
||||
``` sh
|
||||
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 1 وحذف الخطط الأخرى. ثم أشغل:
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
P.S. للاستفادة من إمكانية اللافتكس الإضافية، يرجى الرجوع إلى الويكي. بالإضافة إلى ذلك، يمكنك استخدام الخطة 4 أو الخطة 0 مباشرة للحصول على إمكانية اللافتكس.
|
||||
|
||||
2. تشغيل نموذج ChatGPT + نموذج ChatGLM2 + نموذج MOSS + نموذج LLAMA2 + تون يي تشين ون (QiChaYiWen) (يتطلب معرفة بتشغيل نيفيديا دوكر (Nvidia Docker))
|
||||
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
|
||||
|
||||
``` sh
|
||||
# قم بتعديل ملف docker-compose.yml للحفاظ على الخطة رقم 2 وحذف الخطط الأخرى. ثم أشغل:
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
### طريقة التثبيت الثالثة: طرائق نشر أخرى
|
||||
1. **نصوص بنقرة واحدة لأنظمة Windows**.
|
||||
يمكن لمستخدمي Windows الذين لا يعرفون بيئة Python تنزيل سكربت التشغيل بنقرة واحدة من [الإصدارات](https://github.com/binary-husky/gpt_academic/releases) المنشورة لتثبيت الإصدار الذي لا يحتوي على نماذج محلية.
|
||||
المساهمة في السكربت تعود لـ[oobabooga](https://github.com/oobabooga/one-click-installers).
|
||||
|
||||
2. استخدام واجهة برمجة تطبيقات (API) مطراف ثالثة، Microsoft Azure، ونشوة النص، وغيرها، يرجى الرجوع إلى [صفحة الويكي](https://github.com/binary-husky/gpt_academic/wiki/إعدادات-التكوين-للمشروع) الخاصة بنا
|
||||
|
||||
3. دليل تجنب المشاكل عند نشر المشروع في خوادم السحابة.
|
||||
يرجى زيارة صفحة [دليل نشر خوادم السحابة في المحيط](https://github.com/binary-husky/gpt_academic/wiki/دليل-نشر-خوادم-السحابة)
|
||||
|
||||
4. طرائق نشر المشروع بأحدث الأساليب
|
||||
- استخدام Sealos للنشر السريع [بنقرة واحدة](https://github.com/binary-husky/gpt_academic/issues/993).
|
||||
- استخدم WSL2 (Windows Subsystem for Linux). يُرجى زيارة صفحة الويكي [لدليل التثبيت-2](https://github.com/binary-husky/gpt_academic/wiki/دليل-تشغيل-WSL2-(Windows-Subsystem-for-Linux)
|
||||
- كيفية تشغيل البرنامج تحت عنوان فرعي (على سبيل المثال: `http://localhost/subpath`). يُرجى زيارة [إرشادات FastAPI](docs/WithFastapi.md)
|
||||
|
||||
|
||||
|
||||
# الاستخدام المتقدم
|
||||
### I: إنشاء أزرار مخصصة (اختصارات أكاديمية)
|
||||
افتح أي محرر نصوص وافتح `core_functional.py` وأضف الإدخالات التالية ثم أعد تشغيل البرنامج. (إذا كانت الأزرار موجودة بالفعل، بإمكانك تعديل البادئة واللاحقة حراريًا دون الحاجة لإعادة تشغيل البرنامج)
|
||||
على سبيل المثال:
|
||||
```
|
||||
"ترجمة سوبر الإنجليزية إلى العربية": {
|
||||
# البادئة، ستتم إضافتها قبل إدخالاتك. مثلاً، لوصف ما تريده مثل ترجمة أو شرح كود أو تلوين وهلم جرا
|
||||
"بادئة": "يرجى ترجمة النص التالي إلى العربية ثم استخدم جدول Markdown لشرح المصطلحات المختصة المذكورة في النص:\n\n",
|
||||
|
||||
# اللاحقة، سيتم إضافتها بعد إدخالاتك. يمكن استخدامها لوضع علامات اقتباس حول إدخالك.
|
||||
"لاحقة": "",
|
||||
},
|
||||
```
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
|
||||
</div>
|
||||
|
||||
### II: إنشاء مكونات وظيفية مخصصة
|
||||
قم بكتابة مكونات وظيفية قوية لتنفيذ أي مهمة ترغب في الحصول عليها وحتى تلك التي لم تخطر لك على بال.
|
||||
إن إنشاء وتصحيح المكونات في هذا المشروع سهل للغاية، فما عليك سوى أن تمتلك بعض المعرفة الأساسية في لغة البرمجة بايثون وتستند على القالب الذي نقدمه.
|
||||
للمزيد من التفاصيل، يُرجى الاطلاع على [دليل المكونات الوظيفية](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).
|
||||
|
||||
|
||||
# التحديثات
|
||||
### I: تحديثات
|
||||
|
||||
1. ميزة حفظ الدردشة: يمكن حفظ الدردشة الحالية كملف HTML قابل للقراءة والاسترداد ببساطة عند استدعاء الوظيفة في منطقة المكونات `حفظ الدردشة الحالية` ، ويمكن استرجاع المحادثة السابقة ببساطة عند استدعاء الوظيفة في منطقة المكونات (القائمة المنسدلة) `تحميل سجل الدردشة` .
|
||||
نصيحة: يمكنك النقر المباشر على `تحميل سجل الدردشة` بدون تحديد ملف لعرض ذاكرة التخزين المؤقت لسجلات HTML.
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
|
||||
</div>
|
||||
|
||||
2. ميزة ترجمة المقالات العلمية بواسطة Latex/Arxiv
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/002a1a75-ace0-4e6a-94e2-ec1406a746f1" height="250" > ===>
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/9fdcc391-f823-464f-9322-f8719677043b" height="250" >
|
||||
</div>
|
||||
|
||||
3. محطة فراغ (فهم نغمة المستخدم من داخل اللغة الطبيعية واستدعاء وظائف أخرى تلقائيًا)
|
||||
|
||||
- الخطوة 1: اكتب "بالرجاء استدعاء وظيفة ترجمة المقالة الأكاديمية من PDF وعنوان المقال هو https://openreview.net/pdf?id=rJl0r3R9KX".
|
||||
- الخطوة 2: انقر فوق "محطة الفراغ".
|
||||
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/66f1b044-e9ff-4eed-9126-5d4f3668f1ed" width="500" >
|
||||
</div>
|
||||
|
||||
4. تصميم الوظائف المتعددة القادرة على توفير وظائف قوية بواجهات بسيطة
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
|
||||
</div>
|
||||
|
||||
5. ترجمة وإلغاء ترجمة المشاريع الأخرى مفتوحة المصدر
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" height="250" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" height="250" >
|
||||
</div>
|
||||
|
||||
6. ميزة تزيين [live2d](https://github.com/fghrsh/live2d_demo) (مغلقة بشكل افتراضي، يتطلب تعديل `config.py`)
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
|
||||
</div>
|
||||
|
||||
7. إنتاج الصور من OpenAI
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
|
||||
</div>
|
||||
|
||||
8. تحليل وإجماع الصوت من OpenAI
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
|
||||
</div>
|
||||
|
||||
9. إصلاح أخطاء اللغة الطبيعة في Latex
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" height="200" > ===>
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
|
||||
</div>
|
||||
|
||||
10. تغيير اللغة والموضوع
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
### II: الإصدارات:
|
||||
- الإصدار 3.70 (قريبًا): تحسينات لوظائف AutoGen وتصميم سلسلة من المكونات المشتقة
|
||||
- الإصدار 3.60: إدخال AutoGen كأساس لوظائف الجيل الجديد
|
||||
- الإصدار 3.57: دعم GLM3، نار النجوم v3، وشجرة الكلمات v4، وإصلاح خطأ الازدحام في النماذج المحلية
|
||||
- الإصدار 3.56: الدعم لإضافة مزامنة الأزرار الأساسية حسب الطلب، وصفحة تجميع تقارير البيانات في ملف PDF
|
||||
- الإصدار 3.55: إعادة هيكلة واجهة المستخدم الأمامية، وإضافة نافذة عائمة وشريط قائمة
|
||||
- الإصدار 3.54: إضافة مترجم الكود المباشر (Code Interpreter) (قيد الانجاز)
|
||||
- الإصدار 3.53: دعم اختيار موضوعات واجهة مختلفة، وزيادة الاستقرار وحل مشاكل التعارض بين المستخدمين المتعدد
|
||||
- الإصدار 3.50: استخدام اللغة الطبيعية لاستدعاء جميع وظائف المشروع هذا (محطة فراغ)، ودعم تصنيف الوظائف وتحسين واجهة المستخدم وتصميم مواضيع جديدة
|
||||
- الإصدار 3.49: دعم المنصات البحثية في بيدو كونفان وشجرة الكلمات
|
||||
- الإصدار 3.48: دعم علي بابا, بوكما رش حتكيا, إكسونامبلومانت النار
|
||||
- الإصدار 3.46: دعم محادثة نصية في الوقت الحقيقي غير مراقبة
|
||||
- الإصدار 3.45: دعم تخصيص LatexChatglm النموذج التعديل
|
||||
- الإصدار 3.44: دعم Azure رسميًا، وتحسين سهولة الاستخدام للواجهات الأمامية
|
||||
- الإصدار 3.4: +ترجمة النصوص الكاملة للمقالات من خلال ملف PDF، +اختيار موضع المنطقة النصية، +خيار التخطيط الرأسي، +تحسينات في وظائف التداخل العديدة
|
||||
- الإصدار 3.3: +وظائف متكاملة للمعلومات عبر الإنترنت
|
||||
- الإصدار 3.2: دعم وظائف المكونات التي تحتوي معلمات أكثر (حفظ النص، فهم أي لغة برمجة، طلب أي تركيبة LLM في وقت واحد)
|
||||
- الإصدار 3.1: دعم السؤال نحو نماذج GPT المتعددة! دعم واجهة api2d، دعم توازن الأحمال بين المفاتيح الخاصة المتعددة
|
||||
- الإصدار 3.0: دعم لنماذج جات، واحدة منها لشتلس الصغيرة
|
||||
- الإصدار 2.6: إعادة تصميم بنية الوظائف، وتحسين التفاعل وإضافة مزيد من الوظائف
|
||||
- الإصدار 2.5: التحديث التلقائي، وحل مشكلة النص الطويل عند ملخص المشاريع الضخمة وتجاوز النصوص.
|
||||
- الإصدار 2.4: (١) إضافة ميزة ترجمة المقالات الدورية. (٢) إضافة ميزة لتحويل مكان منطقة الإدخال. (٣) إضافة خيار التخطيط العمودي (vertical layout). (٤) تحسين وظائف المكونات متعددة الخيوط.
|
||||
- الإصدار 2.3: تحسين التفاعل مع مواضيع متعددة
|
||||
- الإصدار 2.2: دعم إعادة تحميل الوظائف المكونة حراريًا
|
||||
- الإصدار 2.1: تصميم قابل للطي
|
||||
- الإصدار 2.0: إدخال وحدات الوظائف المكونة
|
||||
- الإصدار 1.0: الوظائف الأساسية
|
||||
|
||||
مجموعة المطورين GPT Academic QQ: `610599535`
|
||||
|
||||
- مشكلات معروفة
|
||||
- بعض ملحقات متصفح الترجمة تتداخل مع تشغيل الواجهة الأمامية لهذا البرنامج
|
||||
- يحتوي Gradio الرسمي حاليًا على عدد كبير من مشاكل التوافق. يُرجى استخدام `requirement.txt` لتثبيت Gradio.
|
||||
|
||||
### III: الأنساق
|
||||
يمكن تغيير الأنساق بتعديل خيار `THEME` (config.py)
|
||||
1. `Chuanhu-Small-and-Beautiful` [الرابط](https://github.com/GaiZhenbiao/ChuanhuChatGPT/)
|
||||
|
||||
|
||||
### IV: فروع تطوير هذا المشروع
|
||||
|
||||
1. الفرع `master`: الفرع الرئيسي، إصدار مستقر
|
||||
2. الفرع `frontier`: الفرع التطويري، إصدار تجريبي
|
||||
|
||||
|
||||
### V: المراجع والفروض التعليمية
|
||||
|
||||
```
|
||||
استخدمت العديد من التصاميم الموجودة في مشاريع ممتازة أخرى في الأكواد التالية، للمراجع عشوائية:
|
||||
|
||||
# ViewGradio:
|
||||
https://github.com/THUD
|
||||
|
||||
|
||||
|
||||
# مُثبّت بضغطة واحدة Oobabooga:
|
||||
https://github.com/oobabooga/one-click-installers
|
||||
|
||||
# المزيد:
|
||||
https://github.com/gradio-app/gradio
|
||||
https://github.com/fghrsh/live2d_demo
|
||||
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