Compare commits
3 Commits
enable_cle
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threejs-ap
| Author | SHA1 | Date | |
|---|---|---|---|
| 3071057e6d | |||
| 271379cdee | |||
| 0d655f2d18 |
44
.github/workflows/build-with-audio-assistant.yml
vendored
44
.github/workflows/build-with-audio-assistant.yml
vendored
@ -1,44 +0,0 @@
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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-audio-assistant
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on:
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push:
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branches:
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- 'master'
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env:
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REGISTRY: ghcr.io
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IMAGE_NAME: ${{ github.repository }}_audio_assistant
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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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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
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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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password: ${{ secrets.GITHUB_TOKEN }}
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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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- 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+NoLocal+AudioAssistant
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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15
README.md
15
README.md
@ -44,7 +44,7 @@ chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
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Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
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Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
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[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
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[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
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互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
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互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
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⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
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⭐Arxiv论文精细翻译 | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
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⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
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⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
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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
|
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
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多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
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多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
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@ -93,7 +93,7 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
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1. 下载项目
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1. 下载项目
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```sh
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```sh
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git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
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git clone https://github.com/binary-husky/gpt_academic.git
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cd gpt_academic
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cd gpt_academic
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```
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```
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@ -126,7 +126,7 @@ python -m pip install -r request_llm/requirements_chatglm.txt
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# 【可选步骤II】支持复旦MOSS
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# 【可选步骤II】支持复旦MOSS
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python -m pip install -r request_llm/requirements_moss.txt
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python -m pip install -r request_llm/requirements_moss.txt
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git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径
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git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径
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# 【可选步骤III】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)：
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# 【可选步骤III】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)：
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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@ -147,10 +147,9 @@ python main.py
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1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
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1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
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||||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
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||||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
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``` sh
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``` sh
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git clone --depth=1 https://github.com/binary-husky/gpt_academic.git # 下载项目
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git clone https://github.com/binary-husky/gpt_academic.git # 下载项目
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cd gpt_academic # 进入路径
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cd gpt_academic # 进入路径
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nano config.py # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
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nano config.py # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
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docker build -t gpt-academic . # 安装
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docker build -t gpt-academic . # 安装
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@ -196,12 +195,10 @@ docker-compose up
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5. 远程云服务器部署（需要云服务器知识与经验）。
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5. 远程云服务器部署（需要云服务器知识与经验）。
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||||||
请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
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请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
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6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
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6. 使用WSL2（Windows Subsystem for Linux 子系统）。
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7. 使用WSL2（Windows Subsystem for Linux 子系统）。
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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)
|
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)
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8. 如何在二级网址（如`http://localhost/subpath`）下运行。
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7. 如何在二级网址（如`http://localhost/subpath`）下运行。
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请访问[FastAPI运行说明](docs/WithFastapi.md)
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请访问[FastAPI运行说明](docs/WithFastapi.md)
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39
cc.json
Normal file
39
cc.json
Normal file
@ -0,0 +1,39 @@
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[
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{
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"name": "Box-1",
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"width": 1,
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"height": 1,
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"depth": 1,
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"location_x": 1,
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"location_y": 0,
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"location_z": 0
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},
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{
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"name": "Box-2",
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"width": 1,
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"height": 1,
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"depth": 1,
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"location_x": -1,
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"location_y": 0,
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"location_z": 0
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},
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{
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"name": "Box-3",
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"width": 1,
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"height": 1,
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"depth": 1,
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"location_x": 0,
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"location_y": 1,
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"location_z": 0
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},
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{
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"name": "Box-4",
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"width": 1,
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"height": 1,
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"depth": 1,
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"location_x": 0,
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"location_y": -1,
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"location_z": 0
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}
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]
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22
config.py
22
config.py
@ -32,9 +32,9 @@ else:
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# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
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# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
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# 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！）
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# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!! 高危设置！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！）
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# 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"}
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# 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"}
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# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
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# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions":"https://reverse-proxy-url/v1/chat/completions"}
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API_URL_REDIRECT = {}
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API_URL_REDIRECT = {}
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@ -71,7 +71,7 @@ MAX_RETRY = 2
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# 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 )
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# 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 )
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LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
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LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
|
AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
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# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
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||||||
|
|
||||||
# ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
|
# ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
|
||||||
@ -80,7 +80,6 @@ ChatGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
|
|||||||
|
|
||||||
# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
|
# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
|
||||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
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）
|
# 设置gradio的并行线程数（不需要修改）
|
||||||
@ -132,14 +131,9 @@ 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
|
# 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
|
ENABLE_AUDIO = False
|
||||||
ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
||||||
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
||||||
ALIYUN_ACCESSKEY="" # （无需填写）
|
|
||||||
ALIYUN_SECRET="" # （无需填写）
|
|
||||||
|
|
||||||
# Claude API KEY
|
# Claude API KEY
|
||||||
ANTHROPIC_API_KEY = ""
|
ANTHROPIC_API_KEY = ""
|
||||||
|
|
||||||
|
|
||||||
# 自定义API KEY格式
|
|
||||||
CUSTOM_API_KEY_PATTERN = ""
|
|
||||||
@ -1,7 +1,7 @@
|
|||||||
# 'primary' 颜色对应 theme.py 中的 primary_hue
|
# 'primary' 颜色对应 theme.py 中的 primary_hue
|
||||||
# 'secondary' 颜色对应 theme.py 中的 neutral_hue
|
# 'secondary' 颜色对应 theme.py 中的 neutral_hue
|
||||||
# 'stop' 颜色对应 theme.py 中的 color_er
|
# 'stop' 颜色对应 theme.py 中的 color_er
|
||||||
import importlib
|
# 默认按钮颜色是 secondary
|
||||||
from toolbox import clear_line_break
|
from toolbox import clear_line_break
|
||||||
|
|
||||||
|
|
||||||
@ -14,12 +14,7 @@ def get_core_functions():
|
|||||||
r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
|
r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
|
||||||
# 后语
|
# 后语
|
||||||
"Suffix": r"",
|
"Suffix": r"",
|
||||||
# 按钮颜色 (默认 secondary)
|
"Color": r"secondary", # 按钮颜色
|
||||||
"Color": r"secondary",
|
|
||||||
# 按钮是否可见 (默认 True，即可见)
|
|
||||||
"Visible": True,
|
|
||||||
# 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
|
|
||||||
"AutoClearHistory": False
|
|
||||||
},
|
},
|
||||||
"中文学术润色": {
|
"中文学术润色": {
|
||||||
"Prefix": r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
|
"Prefix": r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
|
||||||
@ -81,14 +76,3 @@ def get_core_functions():
|
|||||||
"Suffix": r"",
|
"Suffix": r"",
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
def handle_core_functionality(additional_fn, inputs, history, chatbot):
|
|
||||||
import core_functional
|
|
||||||
importlib.reload(core_functional) # 热更新prompt
|
|
||||||
core_functional = core_functional.get_core_functions()
|
|
||||||
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
|
||||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
|
||||||
if core_functional[additional_fn].get("AutoClearHistory", False):
|
|
||||||
history = []
|
|
||||||
return inputs, history
|
|
||||||
|
|||||||
@ -416,6 +416,17 @@ def get_crazy_functions():
|
|||||||
except:
|
except:
|
||||||
print('Load function plugin failed')
|
print('Load function plugin failed')
|
||||||
|
|
||||||
|
try:
|
||||||
|
from crazy_functions.Three场景交互3D import 三维生成
|
||||||
|
function_plugins.update({
|
||||||
|
"ThreeJS 三维交互": {
|
||||||
|
"Color": "stop",
|
||||||
|
"AsButton": False,
|
||||||
|
"Function": HotReload(三维生成)
|
||||||
|
}
|
||||||
|
})
|
||||||
|
except:
|
||||||
|
print('Load function plugin failed')
|
||||||
|
|
||||||
try:
|
try:
|
||||||
from toolbox import get_conf
|
from toolbox import get_conf
|
||||||
|
|||||||
@ -157,7 +157,7 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
|
|||||||
try:
|
try:
|
||||||
import glob, os, time, subprocess
|
import glob, os, time, subprocess
|
||||||
subprocess.Popen(['pdflatex', '-version'])
|
subprocess.Popen(['pdflatex', '-version'])
|
||||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
from .latex_utils import Latex精细分解与转化, 编译Latex
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
chatbot.append([ f"解析项目: {txt}",
|
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"])
|
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"])
|
||||||
@ -234,7 +234,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
|||||||
try:
|
try:
|
||||||
import glob, os, time, subprocess
|
import glob, os, time, subprocess
|
||||||
subprocess.Popen(['pdflatex', '-version'])
|
subprocess.Popen(['pdflatex', '-version'])
|
||||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
from .latex_utils import Latex精细分解与转化, 编译Latex
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
chatbot.append([ f"解析项目: {txt}",
|
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"])
|
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"])
|
||||||
|
|||||||
249
crazy_functions/Three场景交互3D.py
Normal file
249
crazy_functions/Three场景交互3D.py
Normal file
@ -0,0 +1,249 @@
|
|||||||
|
from toolbox import CatchException, update_ui, gen_time_str
|
||||||
|
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||||
|
from .crazy_utils import input_clipping
|
||||||
|
|
||||||
|
def inspect_dependency(chatbot, history):
|
||||||
|
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||||
|
try:
|
||||||
|
from VISUALIZE.mcom import mcom
|
||||||
|
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 vhmap```"])
|
||||||
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
|
return False
|
||||||
|
|
||||||
|
def get_code_block(reply):
|
||||||
|
try:
|
||||||
|
import json
|
||||||
|
json.loads(reply)
|
||||||
|
return reply
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
|
||||||
|
import re
|
||||||
|
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||||
|
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||||
|
res = ""
|
||||||
|
for match in matches:
|
||||||
|
if 'import ' not in match:
|
||||||
|
res = match.strip('python').strip('json')
|
||||||
|
break
|
||||||
|
if len(res) == 0:
|
||||||
|
print(reply)
|
||||||
|
raise RuntimeError("GPT is not generating proper Json.")
|
||||||
|
return res # code block
|
||||||
|
|
||||||
|
def get_json_blocks(reply):
|
||||||
|
import re, json
|
||||||
|
pattern = r"{([\s\S]*?)}" # regex pattern to match code blocks
|
||||||
|
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||||
|
res = []
|
||||||
|
for match in matches:
|
||||||
|
if '"name"' in match:
|
||||||
|
try:
|
||||||
|
res.append(json.loads("{" + f'{match}' + "}"))
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
return res # code block
|
||||||
|
|
||||||
|
def read_json(code):
|
||||||
|
import json
|
||||||
|
return json.loads(code)
|
||||||
|
|
||||||
|
def parse_partial(vi, gpt_say):
|
||||||
|
# 解析Json
|
||||||
|
js = get_json_blocks(gpt_say)
|
||||||
|
vi.update(js)
|
||||||
|
|
||||||
|
|
||||||
|
@CatchException
|
||||||
|
def 三维生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||||
|
"""
|
||||||
|
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||||
|
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||||
|
plugin_kwargs 插件模型的参数，暂时没有用武之地
|
||||||
|
chatbot 聊天显示框的句柄，用于显示给用户
|
||||||
|
history 聊天历史，前情提要
|
||||||
|
system_prompt 给gpt的静默提醒
|
||||||
|
web_port 当前软件运行的端口号
|
||||||
|
"""
|
||||||
|
from .vhmap_interact.vhmap import vhmp_interface
|
||||||
|
vi = vhmp_interface()
|
||||||
|
# 基本信息：功能、贡献者
|
||||||
|
chatbot.append([
|
||||||
|
"函数插件功能？",
|
||||||
|
"生成3D, 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 = prompt(txt)
|
||||||
|
# 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=[],
|
||||||
|
sys_prompt=r"You are a Json generator",
|
||||||
|
on_reply_update=lambda t:parse_partial(vi, t)
|
||||||
|
)
|
||||||
|
chatbot.append(["开始生成执行", "..."])
|
||||||
|
history.extend([i_say, gpt_say])
|
||||||
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||||
|
|
||||||
|
# 解析Json
|
||||||
|
code = get_code_block(gpt_say)
|
||||||
|
js = read_json(code)
|
||||||
|
vi.update(js)
|
||||||
|
return
|
||||||
|
|
||||||
|
|
||||||
|
def prompt(text):
|
||||||
|
return r"""
|
||||||
|
> Requirements:
|
||||||
|
1. You can only use square Boxes to build cubes and walls.
|
||||||
|
2. The space you can work in is a sphere with origin (0,0,0) and radius 100.
|
||||||
|
3. The ground is z=0.
|
||||||
|
4. You can only use 100 boxes.
|
||||||
|
5. Format of each box is json, e.g.
|
||||||
|
{
|
||||||
|
"name": "Box-1",
|
||||||
|
"geometry": "box", // choose from "box", "octahedron", "sphere", "cylinder"
|
||||||
|
"size": 1.0,
|
||||||
|
"color": "rgb(255,165,0)",
|
||||||
|
"location_x": 1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
6. Only produce json as output. Use markdown code block to wrap the json output.
|
||||||
|
|
||||||
|
> Example:
|
||||||
|
User: Generate 4 different objects around the origin.
|
||||||
|
You:
|
||||||
|
```
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"name": "Box-1",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "box",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-2",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "octahedron",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": -1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-3",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "sphere",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 0.0,
|
||||||
|
"location_y": 1.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-4",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "cylinder",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 0.0,
|
||||||
|
"location_y": -1.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
}
|
||||||
|
]
|
||||||
|
```
|
||||||
|
|
||||||
|
> User: """ + text
|
||||||
|
|
||||||
|
"""
|
||||||
|
Please construct a 3D environment where a girl is sitting under a tree in a garden.
|
||||||
|
|
||||||
|
Requirements:
|
||||||
|
1. List objects in this scene and make a markdown list.
|
||||||
|
2. The list must contain creative details, give at least 20 objects
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
Convert the result to json,
|
||||||
|
Requirements:
|
||||||
|
1. Format: [
|
||||||
|
{
|
||||||
|
"name": "object-1",
|
||||||
|
"location": [position_x, position_y, position_z]
|
||||||
|
}
|
||||||
|
]
|
||||||
|
2. Generate relative position of objects
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
> Requirements:
|
||||||
|
1. You can use box, octahedron, sphere, cylinder to build objects.
|
||||||
|
2. The ground is z=0.
|
||||||
|
3. You can only use 100 boxes.
|
||||||
|
4. Format of each box is json, e.g.
|
||||||
|
{
|
||||||
|
"name": "Box-1",
|
||||||
|
"geometry": "box", // choose from "box", "octahedron", "sphere", "cylinder"
|
||||||
|
"size": 1.0,
|
||||||
|
"color": "rgb(255,165,0)",
|
||||||
|
"location_x": 1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
5. Only produce json as output. Use markdown code block to wrap the json output.
|
||||||
|
|
||||||
|
> Example:
|
||||||
|
```
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"name": "Box-1",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "box",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-2",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "octahedron",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": -1.0,
|
||||||
|
"location_y": 0.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-3",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "sphere",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 0.0,
|
||||||
|
"location_y": 1.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "Box-4",
|
||||||
|
"size": 1.0,
|
||||||
|
"geometry": "cylinder",
|
||||||
|
"color": "rgb(255,11,10)",
|
||||||
|
"location_x": 0.0,
|
||||||
|
"location_y": -1.0,
|
||||||
|
"location_z": 0.0
|
||||||
|
}
|
||||||
|
]
|
||||||
|
```
|
||||||
|
"""
|
||||||
@ -17,7 +17,7 @@ validate_path() # validate path so you can run from base directory
|
|||||||
# ==============================================================================================================================
|
# ==============================================================================================================================
|
||||||
|
|
||||||
from colorful import *
|
from colorful import *
|
||||||
from toolbox import get_conf, ChatBotWithCookies
|
from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
|
||||||
import contextlib
|
import contextlib
|
||||||
import os
|
import os
|
||||||
import sys
|
import sys
|
||||||
@ -32,6 +32,7 @@ llm_kwargs = {
|
|||||||
'max_length': None,
|
'max_length': None,
|
||||||
'temperature':1.0,
|
'temperature':1.0,
|
||||||
}
|
}
|
||||||
|
llm_kwargs.update(load_chat_cookies())
|
||||||
plugin_kwargs = { }
|
plugin_kwargs = { }
|
||||||
chatbot = ChatBotWithCookies(llm_kwargs)
|
chatbot = ChatBotWithCookies(llm_kwargs)
|
||||||
history = []
|
history = []
|
||||||
@ -195,12 +196,9 @@ def test_Latex():
|
|||||||
# txt = r"https://arxiv.org/abs/2303.08774"
|
# txt = r"https://arxiv.org/abs/2303.08774"
|
||||||
# txt = r"https://arxiv.org/abs/2303.12712"
|
# txt = r"https://arxiv.org/abs/2303.12712"
|
||||||
# txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
|
# txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
|
||||||
# txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误！
|
txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误！
|
||||||
# txt = "https://arxiv.org/abs/2205.14135"
|
|
||||||
# txt = r"C:\Users\fuqingxu\arxiv_cache\2205.14135\workfolder"
|
|
||||||
# txt = r"C:\Users\fuqingxu\arxiv_cache\2205.14135\workfolder"
|
|
||||||
txt = r"2210.03629"
|
|
||||||
txt = r"2307.04964"
|
|
||||||
for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||||
cli_printer.print(cb) # print(cb)
|
cli_printer.print(cb) # print(cb)
|
||||||
|
|
||||||
@ -229,6 +227,15 @@ def test_chatglm_finetune():
|
|||||||
cli_printer.print(cb)
|
cli_printer.print(cb)
|
||||||
|
|
||||||
|
|
||||||
|
def 三维生成():
|
||||||
|
from crazy_functions.Three场景交互3D import 三维生成
|
||||||
|
txt = "Generate 10 boxes to form a triangle formation with random color."
|
||||||
|
plugin_kwargs = {"advanced_arg":""}
|
||||||
|
|
||||||
|
for cookies, cb, hist, msg in (三维生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||||
|
cli_printer.print(cb)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# test_解析一个Python项目()
|
# test_解析一个Python项目()
|
||||||
# test_Latex英文润色()
|
# test_Latex英文润色()
|
||||||
@ -243,7 +250,7 @@ if __name__ == "__main__":
|
|||||||
# test_数学动画生成manim()
|
# test_数学动画生成manim()
|
||||||
# test_Langchain知识库()
|
# test_Langchain知识库()
|
||||||
# test_Langchain知识库读取()
|
# test_Langchain知识库读取()
|
||||||
test_Latex()
|
# test_Latex()
|
||||||
# test_chatglm_finetune()
|
三维生成()
|
||||||
input("程序完成，回车退出。")
|
input("程序完成，回车退出。")
|
||||||
print("退出。")
|
print("退出。")
|
||||||
@ -40,6 +40,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
|||||||
chatbot, history, sys_prompt, refresh_interval=0.2,
|
chatbot, history, sys_prompt, refresh_interval=0.2,
|
||||||
handle_token_exceed=True,
|
handle_token_exceed=True,
|
||||||
retry_times_at_unknown_error=2,
|
retry_times_at_unknown_error=2,
|
||||||
|
on_reply_update=None
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
Request GPT model，请求GPT模型同时维持用户界面活跃。
|
Request GPT model，请求GPT模型同时维持用户界面活跃。
|
||||||
@ -123,6 +124,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
|||||||
if future.done():
|
if future.done():
|
||||||
break
|
break
|
||||||
chatbot[-1] = [chatbot[-1][0], mutable[0]]
|
chatbot[-1] = [chatbot[-1][0], mutable[0]]
|
||||||
|
if on_reply_update: on_reply_update(mutable[0])
|
||||||
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
|
||||||
|
|
||||||
final_result = future.result()
|
final_result = future.result()
|
||||||
|
|||||||
@ -1,456 +0,0 @@
|
|||||||
import os, shutil
|
|
||||||
import re
|
|
||||||
import numpy as np
|
|
||||||
PRESERVE = 0
|
|
||||||
TRANSFORM = 1
|
|
||||||
|
|
||||||
pj = os.path.join
|
|
||||||
|
|
||||||
class LinkedListNode():
|
|
||||||
"""
|
|
||||||
Linked List Node
|
|
||||||
"""
|
|
||||||
def __init__(self, string, preserve=True) -> None:
|
|
||||||
self.string = string
|
|
||||||
self.preserve = preserve
|
|
||||||
self.next = None
|
|
||||||
self.range = None
|
|
||||||
# self.begin_line = 0
|
|
||||||
# self.begin_char = 0
|
|
||||||
|
|
||||||
def convert_to_linklist(text, mask):
|
|
||||||
root = LinkedListNode("", preserve=True)
|
|
||||||
current_node = root
|
|
||||||
for c, m, i in zip(text, mask, range(len(text))):
|
|
||||||
if (m==PRESERVE and current_node.preserve) \
|
|
||||||
or (m==TRANSFORM and not current_node.preserve):
|
|
||||||
# add
|
|
||||||
current_node.string += c
|
|
||||||
else:
|
|
||||||
current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
|
|
||||||
current_node = current_node.next
|
|
||||||
return root
|
|
||||||
|
|
||||||
def post_process(root):
|
|
||||||
# 修复括号
|
|
||||||
node = root
|
|
||||||
while True:
|
|
||||||
string = node.string
|
|
||||||
if node.preserve:
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
continue
|
|
||||||
def break_check(string):
|
|
||||||
str_stack = [""] # (lv, index)
|
|
||||||
for i, c in enumerate(string):
|
|
||||||
if c == '{':
|
|
||||||
str_stack.append('{')
|
|
||||||
elif c == '}':
|
|
||||||
if len(str_stack) == 1:
|
|
||||||
print('stack fix')
|
|
||||||
return i
|
|
||||||
str_stack.pop(-1)
|
|
||||||
else:
|
|
||||||
str_stack[-1] += c
|
|
||||||
return -1
|
|
||||||
bp = break_check(string)
|
|
||||||
|
|
||||||
if bp == -1:
|
|
||||||
pass
|
|
||||||
elif bp == 0:
|
|
||||||
node.string = string[:1]
|
|
||||||
q = LinkedListNode(string[1:], False)
|
|
||||||
q.next = node.next
|
|
||||||
node.next = q
|
|
||||||
else:
|
|
||||||
node.string = string[:bp]
|
|
||||||
q = LinkedListNode(string[bp:], False)
|
|
||||||
q.next = node.next
|
|
||||||
node.next = q
|
|
||||||
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
|
|
||||||
# 屏蔽空行和太短的句子
|
|
||||||
node = root
|
|
||||||
while True:
|
|
||||||
if len(node.string.strip('\n').strip(''))==0: node.preserve = True
|
|
||||||
if len(node.string.strip('\n').strip(''))<42: node.preserve = True
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
node = root
|
|
||||||
while True:
|
|
||||||
if node.next and node.preserve and node.next.preserve:
|
|
||||||
node.string += node.next.string
|
|
||||||
node.next = node.next.next
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
|
|
||||||
# 将前后断行符脱离
|
|
||||||
node = root
|
|
||||||
prev_node = None
|
|
||||||
while True:
|
|
||||||
if not node.preserve:
|
|
||||||
lstriped_ = node.string.lstrip().lstrip('\n')
|
|
||||||
if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
|
|
||||||
prev_node.string += node.string[:-len(lstriped_)]
|
|
||||||
node.string = lstriped_
|
|
||||||
rstriped_ = node.string.rstrip().rstrip('\n')
|
|
||||||
if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
|
|
||||||
node.next.string = node.string[len(rstriped_):] + node.next.string
|
|
||||||
node.string = rstriped_
|
|
||||||
# =====
|
|
||||||
prev_node = node
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
|
|
||||||
# 标注节点的行数范围
|
|
||||||
node = root
|
|
||||||
n_line = 0
|
|
||||||
expansion = 2
|
|
||||||
while True:
|
|
||||||
n_l = node.string.count('\n')
|
|
||||||
node.range = [n_line-expansion, n_line+n_l+expansion] # 失败时，扭转的范围
|
|
||||||
n_line = n_line+n_l
|
|
||||||
node = node.next
|
|
||||||
if node is None: break
|
|
||||||
return root
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
def set_forbidden_text(text, mask, pattern, flags=0):
|
|
||||||
"""
|
|
||||||
Add a preserve text area in this paper
|
|
||||||
e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
|
|
||||||
you can mask out (mask = PRESERVE so that text become untouchable for GPT)
|
|
||||||
everything between "\begin{equation}" and "\end{equation}"
|
|
||||||
"""
|
|
||||||
if isinstance(pattern, list): pattern = '|'.join(pattern)
|
|
||||||
pattern_compile = re.compile(pattern, flags)
|
|
||||||
for res in pattern_compile.finditer(text):
|
|
||||||
mask[res.span()[0]:res.span()[1]] = PRESERVE
|
|
||||||
return text, mask
|
|
||||||
|
|
||||||
def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
|
|
||||||
"""
|
|
||||||
Move area out of preserve area (make text editable for GPT)
|
|
||||||
count the number of the braces so as to catch compelete text area.
|
|
||||||
e.g.
|
|
||||||
\begin{abstract} blablablablablabla. \end{abstract}
|
|
||||||
"""
|
|
||||||
if isinstance(pattern, list): pattern = '|'.join(pattern)
|
|
||||||
pattern_compile = re.compile(pattern, flags)
|
|
||||||
for res in pattern_compile.finditer(text):
|
|
||||||
if not forbid_wrapper:
|
|
||||||
mask[res.span()[0]:res.span()[1]] = TRANSFORM
|
|
||||||
else:
|
|
||||||
mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE # '\\begin{abstract}'
|
|
||||||
mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM # abstract
|
|
||||||
mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE # abstract
|
|
||||||
return text, mask
|
|
||||||
|
|
||||||
def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
|
|
||||||
"""
|
|
||||||
Add a preserve text area in this paper (text become untouchable for GPT).
|
|
||||||
count the number of the braces so as to catch compelete text area.
|
|
||||||
e.g.
|
|
||||||
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
|
||||||
"""
|
|
||||||
pattern_compile = re.compile(pattern, flags)
|
|
||||||
for res in pattern_compile.finditer(text):
|
|
||||||
brace_level = -1
|
|
||||||
p = begin = end = res.regs[0][0]
|
|
||||||
for _ in range(1024*16):
|
|
||||||
if text[p] == '}' and brace_level == 0: break
|
|
||||||
elif text[p] == '}': brace_level -= 1
|
|
||||||
elif text[p] == '{': brace_level += 1
|
|
||||||
p += 1
|
|
||||||
end = p+1
|
|
||||||
mask[begin:end] = PRESERVE
|
|
||||||
return text, mask
|
|
||||||
|
|
||||||
def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
|
|
||||||
"""
|
|
||||||
Move area out of preserve area (make text editable for GPT)
|
|
||||||
count the number of the braces so as to catch compelete text area.
|
|
||||||
e.g.
|
|
||||||
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
|
||||||
"""
|
|
||||||
pattern_compile = re.compile(pattern, flags)
|
|
||||||
for res in pattern_compile.finditer(text):
|
|
||||||
brace_level = 0
|
|
||||||
p = begin = end = res.regs[1][0]
|
|
||||||
for _ in range(1024*16):
|
|
||||||
if text[p] == '}' and brace_level == 0: break
|
|
||||||
elif text[p] == '}': brace_level -= 1
|
|
||||||
elif text[p] == '{': brace_level += 1
|
|
||||||
p += 1
|
|
||||||
end = p
|
|
||||||
mask[begin:end] = TRANSFORM
|
|
||||||
if forbid_wrapper:
|
|
||||||
mask[res.regs[0][0]:begin] = PRESERVE
|
|
||||||
mask[end:res.regs[0][1]] = PRESERVE
|
|
||||||
return text, mask
|
|
||||||
|
|
||||||
def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
|
|
||||||
"""
|
|
||||||
Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
|
|
||||||
Add it to preserve area
|
|
||||||
"""
|
|
||||||
pattern_compile = re.compile(pattern, flags)
|
|
||||||
def search_with_line_limit(text, mask):
|
|
||||||
for res in pattern_compile.finditer(text):
|
|
||||||
cmd = res.group(1) # begin{what}
|
|
||||||
this = res.group(2) # content between begin and end
|
|
||||||
this_mask = mask[res.regs[2][0]:res.regs[2][1]]
|
|
||||||
white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof',
|
|
||||||
'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
|
|
||||||
if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
|
|
||||||
this, this_mask = search_with_line_limit(this, this_mask)
|
|
||||||
mask[res.regs[2][0]:res.regs[2][1]] = this_mask
|
|
||||||
else:
|
|
||||||
mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
|
|
||||||
return text, mask
|
|
||||||
return search_with_line_limit(text, mask)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
Latex Merge File
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
"""
|
|
||||||
|
|
||||||
def find_main_tex_file(file_manifest, mode):
|
|
||||||
"""
|
|
||||||
在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
|
|
||||||
P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
|
|
||||||
"""
|
|
||||||
canidates = []
|
|
||||||
for texf in file_manifest:
|
|
||||||
if os.path.basename(texf).startswith('merge'):
|
|
||||||
continue
|
|
||||||
with open(texf, 'r', encoding='utf8', errors='ignore') as f:
|
|
||||||
file_content = f.read()
|
|
||||||
if r'\documentclass' in file_content:
|
|
||||||
canidates.append(texf)
|
|
||||||
else:
|
|
||||||
continue
|
|
||||||
|
|
||||||
if len(canidates) == 0:
|
|
||||||
raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
|
|
||||||
elif len(canidates) == 1:
|
|
||||||
return canidates[0]
|
|
||||||
else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
|
|
||||||
canidates_score = []
|
|
||||||
# 给出一些判定模板文档的词作为扣分项
|
|
||||||
unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
|
|
||||||
expected_words = ['\input', '\ref', '\cite']
|
|
||||||
for texf in canidates:
|
|
||||||
canidates_score.append(0)
|
|
||||||
with open(texf, 'r', encoding='utf8', errors='ignore') as f:
|
|
||||||
file_content = f.read()
|
|
||||||
for uw in unexpected_words:
|
|
||||||
if uw in file_content:
|
|
||||||
canidates_score[-1] -= 1
|
|
||||||
for uw in expected_words:
|
|
||||||
if uw in file_content:
|
|
||||||
canidates_score[-1] += 1
|
|
||||||
select = np.argmax(canidates_score) # 取评分最高者返回
|
|
||||||
return canidates[select]
|
|
||||||
|
|
||||||
def rm_comments(main_file):
|
|
||||||
new_file_remove_comment_lines = []
|
|
||||||
for l in main_file.splitlines():
|
|
||||||
# 删除整行的空注释
|
|
||||||
if l.lstrip().startswith("%"):
|
|
||||||
pass
|
|
||||||
else:
|
|
||||||
new_file_remove_comment_lines.append(l)
|
|
||||||
main_file = '\n'.join(new_file_remove_comment_lines)
|
|
||||||
# main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file) # 将 \include 命令转换为 \input 命令
|
|
||||||
main_file = re.sub(r'(?<!\\)%.*', '', main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
|
|
||||||
return main_file
|
|
||||||
|
|
||||||
def find_tex_file_ignore_case(fp):
|
|
||||||
dir_name = os.path.dirname(fp)
|
|
||||||
base_name = os.path.basename(fp)
|
|
||||||
if not base_name.endswith('.tex'): base_name+='.tex'
|
|
||||||
if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
|
|
||||||
# go case in-sensitive
|
|
||||||
import glob
|
|
||||||
for f in glob.glob(dir_name+'/*.tex'):
|
|
||||||
base_name_s = os.path.basename(fp)
|
|
||||||
if base_name_s.lower() == base_name.lower(): return f
|
|
||||||
return None
|
|
||||||
|
|
||||||
def merge_tex_files_(project_foler, main_file, mode):
|
|
||||||
"""
|
|
||||||
Merge Tex project recrusively
|
|
||||||
"""
|
|
||||||
main_file = rm_comments(main_file)
|
|
||||||
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
|
|
||||||
f = s.group(1)
|
|
||||||
fp = os.path.join(project_foler, f)
|
|
||||||
fp = find_tex_file_ignore_case(fp)
|
|
||||||
if fp:
|
|
||||||
with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
|
|
||||||
else:
|
|
||||||
raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
|
|
||||||
c = merge_tex_files_(project_foler, c, mode)
|
|
||||||
main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
|
|
||||||
return main_file
|
|
||||||
|
|
||||||
def merge_tex_files(project_foler, main_file, mode):
|
|
||||||
"""
|
|
||||||
Merge Tex project recrusively
|
|
||||||
P.S. 顺便把CTEX塞进去以支持中文
|
|
||||||
P.S. 顺便把Latex的注释去除
|
|
||||||
"""
|
|
||||||
main_file = merge_tex_files_(project_foler, main_file, mode)
|
|
||||||
main_file = rm_comments(main_file)
|
|
||||||
|
|
||||||
if mode == 'translate_zh':
|
|
||||||
# find paper documentclass
|
|
||||||
pattern = re.compile(r'\\documentclass.*\n')
|
|
||||||
match = pattern.search(main_file)
|
|
||||||
assert match is not None, "Cannot find documentclass statement!"
|
|
||||||
position = match.end()
|
|
||||||
add_ctex = '\\usepackage{ctex}\n'
|
|
||||||
add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
|
|
||||||
main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
|
|
||||||
# fontset=windows
|
|
||||||
import platform
|
|
||||||
main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
|
|
||||||
main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
|
|
||||||
# find paper abstract
|
|
||||||
pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
|
|
||||||
pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
|
|
||||||
match_opt1 = pattern_opt1.search(main_file)
|
|
||||||
match_opt2 = pattern_opt2.search(main_file)
|
|
||||||
assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
|
|
||||||
return main_file
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
Post process
|
|
||||||
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
||||||
"""
|
|
||||||
def mod_inbraket(match):
|
|
||||||
"""
|
|
||||||
为啥chatgpt会把cite里面的逗号换成中文逗号呀
|
|
||||||
"""
|
|
||||||
# get the matched string
|
|
||||||
cmd = match.group(1)
|
|
||||||
str_to_modify = match.group(2)
|
|
||||||
# modify the matched string
|
|
||||||
str_to_modify = str_to_modify.replace('：', ':') # 前面是中文冒号，后面是英文冒号
|
|
||||||
str_to_modify = str_to_modify.replace('，', ',') # 前面是中文逗号，后面是英文逗号
|
|
||||||
# str_to_modify = 'BOOM'
|
|
||||||
return "\\" + cmd + "{" + str_to_modify + "}"
|
|
||||||
|
|
||||||
def fix_content(final_tex, node_string):
|
|
||||||
"""
|
|
||||||
Fix common GPT errors to increase success rate
|
|
||||||
"""
|
|
||||||
final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
|
|
||||||
final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
|
|
||||||
final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
|
|
||||||
final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
|
|
||||||
|
|
||||||
if "Traceback" in final_tex and "[Local Message]" in final_tex:
|
|
||||||
final_tex = node_string # 出问题了，还原原文
|
|
||||||
if node_string.count('\\begin') != final_tex.count('\\begin'):
|
|
||||||
final_tex = node_string # 出问题了，还原原文
|
|
||||||
if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
|
|
||||||
# walk and replace any _ without \
|
|
||||||
final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
|
|
||||||
|
|
||||||
def compute_brace_level(string):
|
|
||||||
# this function count the number of { and }
|
|
||||||
brace_level = 0
|
|
||||||
for c in string:
|
|
||||||
if c == "{": brace_level += 1
|
|
||||||
elif c == "}": brace_level -= 1
|
|
||||||
return brace_level
|
|
||||||
def join_most(tex_t, tex_o):
|
|
||||||
# this function join translated string and original string when something goes wrong
|
|
||||||
p_t = 0
|
|
||||||
p_o = 0
|
|
||||||
def find_next(string, chars, begin):
|
|
||||||
p = begin
|
|
||||||
while p < len(string):
|
|
||||||
if string[p] in chars: return p, string[p]
|
|
||||||
p += 1
|
|
||||||
return None, None
|
|
||||||
while True:
|
|
||||||
res1, char = find_next(tex_o, ['{','}'], p_o)
|
|
||||||
if res1 is None: break
|
|
||||||
res2, char = find_next(tex_t, [char], p_t)
|
|
||||||
if res2 is None: break
|
|
||||||
p_o = res1 + 1
|
|
||||||
p_t = res2 + 1
|
|
||||||
return tex_t[:p_t] + tex_o[p_o:]
|
|
||||||
|
|
||||||
if compute_brace_level(final_tex) != compute_brace_level(node_string):
|
|
||||||
# 出问题了，还原部分原文，保证括号正确
|
|
||||||
final_tex = join_most(final_tex, node_string)
|
|
||||||
return final_tex
|
|
||||||
|
|
||||||
def compile_latex_with_timeout(command, cwd, timeout=60):
|
|
||||||
import subprocess
|
|
||||||
process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
|
|
||||||
try:
|
|
||||||
stdout, stderr = process.communicate(timeout=timeout)
|
|
||||||
except subprocess.TimeoutExpired:
|
|
||||||
process.kill()
|
|
||||||
stdout, stderr = process.communicate()
|
|
||||||
print("Process timed out!")
|
|
||||||
return False
|
|
||||||
return True
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def merge_pdfs(pdf1_path, pdf2_path, output_path):
|
|
||||||
import PyPDF2
|
|
||||||
Percent = 0.8
|
|
||||||
# Open the first PDF file
|
|
||||||
with open(pdf1_path, 'rb') as pdf1_file:
|
|
||||||
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
|
|
||||||
# Open the second PDF file
|
|
||||||
with open(pdf2_path, 'rb') as pdf2_file:
|
|
||||||
pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
|
|
||||||
# Create a new PDF file to store the merged pages
|
|
||||||
output_writer = PyPDF2.PdfFileWriter()
|
|
||||||
# Determine the number of pages in each PDF file
|
|
||||||
num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
|
|
||||||
# Merge the pages from the two PDF files
|
|
||||||
for page_num in range(num_pages):
|
|
||||||
# Add the page from the first PDF file
|
|
||||||
if page_num < pdf1_reader.numPages:
|
|
||||||
page1 = pdf1_reader.getPage(page_num)
|
|
||||||
else:
|
|
||||||
page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
|
||||||
# Add the page from the second PDF file
|
|
||||||
if page_num < pdf2_reader.numPages:
|
|
||||||
page2 = pdf2_reader.getPage(page_num)
|
|
||||||
else:
|
|
||||||
page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
|
||||||
# Create a new empty page with double width
|
|
||||||
new_page = PyPDF2.PageObject.createBlankPage(
|
|
||||||
width = int(int(page1.mediaBox.getWidth()) + int(page2.mediaBox.getWidth()) * Percent),
|
|
||||||
height = max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight())
|
|
||||||
)
|
|
||||||
new_page.mergeTranslatedPage(page1, 0, 0)
|
|
||||||
new_page.mergeTranslatedPage(page2, int(int(page1.mediaBox.getWidth())-int(page2.mediaBox.getWidth())* (1-Percent)), 0)
|
|
||||||
output_writer.addPage(new_page)
|
|
||||||
# Save the merged PDF file
|
|
||||||
with open(output_path, 'wb') as output_file:
|
|
||||||
output_writer.write(output_file)
|
|
||||||
@ -1,16 +1,320 @@
|
|||||||
from toolbox import update_ui, update_ui_lastest_msg # 刷新Gradio前端界面
|
from toolbox import update_ui, update_ui_lastest_msg # 刷新Gradio前端界面
|
||||||
from toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone
|
from toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone
|
||||||
from .latex_toolbox import PRESERVE, TRANSFORM
|
|
||||||
from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
|
||||||
from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
|
|
||||||
from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
|
|
||||||
|
|
||||||
import os, shutil
|
import os, shutil
|
||||||
import re
|
import re
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
pj = os.path.join
|
pj = os.path.join
|
||||||
|
|
||||||
|
"""
|
||||||
|
========================================================================
|
||||||
|
Part One
|
||||||
|
Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
|
||||||
|
========================================================================
|
||||||
|
"""
|
||||||
|
PRESERVE = 0
|
||||||
|
TRANSFORM = 1
|
||||||
|
|
||||||
|
def set_forbidden_text(text, mask, pattern, flags=0):
|
||||||
|
"""
|
||||||
|
Add a preserve text area in this paper
|
||||||
|
e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
|
||||||
|
you can mask out (mask = PRESERVE so that text become untouchable for GPT)
|
||||||
|
everything between "\begin{equation}" and "\end{equation}"
|
||||||
|
"""
|
||||||
|
if isinstance(pattern, list): pattern = '|'.join(pattern)
|
||||||
|
pattern_compile = re.compile(pattern, flags)
|
||||||
|
for res in pattern_compile.finditer(text):
|
||||||
|
mask[res.span()[0]:res.span()[1]] = PRESERVE
|
||||||
|
return text, mask
|
||||||
|
|
||||||
|
def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
|
||||||
|
"""
|
||||||
|
Move area out of preserve area (make text editable for GPT)
|
||||||
|
count the number of the braces so as to catch compelete text area.
|
||||||
|
e.g.
|
||||||
|
\begin{abstract} blablablablablabla. \end{abstract}
|
||||||
|
"""
|
||||||
|
if isinstance(pattern, list): pattern = '|'.join(pattern)
|
||||||
|
pattern_compile = re.compile(pattern, flags)
|
||||||
|
for res in pattern_compile.finditer(text):
|
||||||
|
if not forbid_wrapper:
|
||||||
|
mask[res.span()[0]:res.span()[1]] = TRANSFORM
|
||||||
|
else:
|
||||||
|
mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE # '\\begin{abstract}'
|
||||||
|
mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM # abstract
|
||||||
|
mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE # abstract
|
||||||
|
return text, mask
|
||||||
|
|
||||||
|
def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
|
||||||
|
"""
|
||||||
|
Add a preserve text area in this paper (text become untouchable for GPT).
|
||||||
|
count the number of the braces so as to catch compelete text area.
|
||||||
|
e.g.
|
||||||
|
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
||||||
|
"""
|
||||||
|
pattern_compile = re.compile(pattern, flags)
|
||||||
|
for res in pattern_compile.finditer(text):
|
||||||
|
brace_level = -1
|
||||||
|
p = begin = end = res.regs[0][0]
|
||||||
|
for _ in range(1024*16):
|
||||||
|
if text[p] == '}' and brace_level == 0: break
|
||||||
|
elif text[p] == '}': brace_level -= 1
|
||||||
|
elif text[p] == '{': brace_level += 1
|
||||||
|
p += 1
|
||||||
|
end = p+1
|
||||||
|
mask[begin:end] = PRESERVE
|
||||||
|
return text, mask
|
||||||
|
|
||||||
|
def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
|
||||||
|
"""
|
||||||
|
Move area out of preserve area (make text editable for GPT)
|
||||||
|
count the number of the braces so as to catch compelete text area.
|
||||||
|
e.g.
|
||||||
|
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
||||||
|
"""
|
||||||
|
pattern_compile = re.compile(pattern, flags)
|
||||||
|
for res in pattern_compile.finditer(text):
|
||||||
|
brace_level = 0
|
||||||
|
p = begin = end = res.regs[1][0]
|
||||||
|
for _ in range(1024*16):
|
||||||
|
if text[p] == '}' and brace_level == 0: break
|
||||||
|
elif text[p] == '}': brace_level -= 1
|
||||||
|
elif text[p] == '{': brace_level += 1
|
||||||
|
p += 1
|
||||||
|
end = p
|
||||||
|
mask[begin:end] = TRANSFORM
|
||||||
|
if forbid_wrapper:
|
||||||
|
mask[res.regs[0][0]:begin] = PRESERVE
|
||||||
|
mask[end:res.regs[0][1]] = PRESERVE
|
||||||
|
return text, mask
|
||||||
|
|
||||||
|
def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
|
||||||
|
"""
|
||||||
|
Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
|
||||||
|
Add it to preserve area
|
||||||
|
"""
|
||||||
|
pattern_compile = re.compile(pattern, flags)
|
||||||
|
def search_with_line_limit(text, mask):
|
||||||
|
for res in pattern_compile.finditer(text):
|
||||||
|
cmd = res.group(1) # begin{what}
|
||||||
|
this = res.group(2) # content between begin and end
|
||||||
|
this_mask = mask[res.regs[2][0]:res.regs[2][1]]
|
||||||
|
white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof',
|
||||||
|
'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
|
||||||
|
if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
|
||||||
|
this, this_mask = search_with_line_limit(this, this_mask)
|
||||||
|
mask[res.regs[2][0]:res.regs[2][1]] = this_mask
|
||||||
|
else:
|
||||||
|
mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
|
||||||
|
return text, mask
|
||||||
|
return search_with_line_limit(text, mask)
|
||||||
|
|
||||||
|
class LinkedListNode():
|
||||||
|
"""
|
||||||
|
Linked List Node
|
||||||
|
"""
|
||||||
|
def __init__(self, string, preserve=True) -> None:
|
||||||
|
self.string = string
|
||||||
|
self.preserve = preserve
|
||||||
|
self.next = None
|
||||||
|
# self.begin_line = 0
|
||||||
|
# self.begin_char = 0
|
||||||
|
|
||||||
|
def convert_to_linklist(text, mask):
|
||||||
|
root = LinkedListNode("", preserve=True)
|
||||||
|
current_node = root
|
||||||
|
for c, m, i in zip(text, mask, range(len(text))):
|
||||||
|
if (m==PRESERVE and current_node.preserve) \
|
||||||
|
or (m==TRANSFORM and not current_node.preserve):
|
||||||
|
# add
|
||||||
|
current_node.string += c
|
||||||
|
else:
|
||||||
|
current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
|
||||||
|
current_node = current_node.next
|
||||||
|
return root
|
||||||
|
"""
|
||||||
|
========================================================================
|
||||||
|
Latex Merge File
|
||||||
|
========================================================================
|
||||||
|
"""
|
||||||
|
|
||||||
|
def 寻找Latex主文件(file_manifest, mode):
|
||||||
|
"""
|
||||||
|
在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
|
||||||
|
P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
|
||||||
|
"""
|
||||||
|
canidates = []
|
||||||
|
for texf in file_manifest:
|
||||||
|
if os.path.basename(texf).startswith('merge'):
|
||||||
|
continue
|
||||||
|
with open(texf, 'r', encoding='utf8', errors='ignore') as f:
|
||||||
|
file_content = f.read()
|
||||||
|
if r'\documentclass' in file_content:
|
||||||
|
canidates.append(texf)
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
|
||||||
|
if len(canidates) == 0:
|
||||||
|
raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
|
||||||
|
elif len(canidates) == 1:
|
||||||
|
return canidates[0]
|
||||||
|
else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
|
||||||
|
canidates_score = []
|
||||||
|
# 给出一些判定模板文档的词作为扣分项
|
||||||
|
unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
|
||||||
|
expected_words = ['\input', '\ref', '\cite']
|
||||||
|
for texf in canidates:
|
||||||
|
canidates_score.append(0)
|
||||||
|
with open(texf, 'r', encoding='utf8', errors='ignore') as f:
|
||||||
|
file_content = f.read()
|
||||||
|
for uw in unexpected_words:
|
||||||
|
if uw in file_content:
|
||||||
|
canidates_score[-1] -= 1
|
||||||
|
for uw in expected_words:
|
||||||
|
if uw in file_content:
|
||||||
|
canidates_score[-1] += 1
|
||||||
|
select = np.argmax(canidates_score) # 取评分最高者返回
|
||||||
|
return canidates[select]
|
||||||
|
|
||||||
|
def rm_comments(main_file):
|
||||||
|
new_file_remove_comment_lines = []
|
||||||
|
for l in main_file.splitlines():
|
||||||
|
# 删除整行的空注释
|
||||||
|
if l.lstrip().startswith("%"):
|
||||||
|
pass
|
||||||
|
else:
|
||||||
|
new_file_remove_comment_lines.append(l)
|
||||||
|
main_file = '\n'.join(new_file_remove_comment_lines)
|
||||||
|
# main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file) # 将 \include 命令转换为 \input 命令
|
||||||
|
main_file = re.sub(r'(?<!\\)%.*', '', main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
|
||||||
|
return main_file
|
||||||
|
|
||||||
|
def find_tex_file_ignore_case(fp):
|
||||||
|
dir_name = os.path.dirname(fp)
|
||||||
|
base_name = os.path.basename(fp)
|
||||||
|
if not base_name.endswith('.tex'): base_name+='.tex'
|
||||||
|
if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
|
||||||
|
# go case in-sensitive
|
||||||
|
import glob
|
||||||
|
for f in glob.glob(dir_name+'/*.tex'):
|
||||||
|
base_name_s = os.path.basename(fp)
|
||||||
|
if base_name_s.lower() == base_name.lower(): return f
|
||||||
|
return None
|
||||||
|
|
||||||
|
def merge_tex_files_(project_foler, main_file, mode):
|
||||||
|
"""
|
||||||
|
Merge Tex project recrusively
|
||||||
|
"""
|
||||||
|
main_file = rm_comments(main_file)
|
||||||
|
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
|
||||||
|
f = s.group(1)
|
||||||
|
fp = os.path.join(project_foler, f)
|
||||||
|
fp = find_tex_file_ignore_case(fp)
|
||||||
|
if fp:
|
||||||
|
with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
|
||||||
|
else:
|
||||||
|
raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
|
||||||
|
c = merge_tex_files_(project_foler, c, mode)
|
||||||
|
main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
|
||||||
|
return main_file
|
||||||
|
|
||||||
|
def merge_tex_files(project_foler, main_file, mode):
|
||||||
|
"""
|
||||||
|
Merge Tex project recrusively
|
||||||
|
P.S. 顺便把CTEX塞进去以支持中文
|
||||||
|
P.S. 顺便把Latex的注释去除
|
||||||
|
"""
|
||||||
|
main_file = merge_tex_files_(project_foler, main_file, mode)
|
||||||
|
main_file = rm_comments(main_file)
|
||||||
|
|
||||||
|
if mode == 'translate_zh':
|
||||||
|
# find paper documentclass
|
||||||
|
pattern = re.compile(r'\\documentclass.*\n')
|
||||||
|
match = pattern.search(main_file)
|
||||||
|
assert match is not None, "Cannot find documentclass statement!"
|
||||||
|
position = match.end()
|
||||||
|
add_ctex = '\\usepackage{ctex}\n'
|
||||||
|
add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
|
||||||
|
main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
|
||||||
|
# fontset=windows
|
||||||
|
import platform
|
||||||
|
main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
|
||||||
|
main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
|
||||||
|
# find paper abstract
|
||||||
|
pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
|
||||||
|
pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
|
||||||
|
match_opt1 = pattern_opt1.search(main_file)
|
||||||
|
match_opt2 = pattern_opt2.search(main_file)
|
||||||
|
assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
|
||||||
|
return main_file
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
========================================================================
|
||||||
|
Post process
|
||||||
|
========================================================================
|
||||||
|
"""
|
||||||
|
def mod_inbraket(match):
|
||||||
|
"""
|
||||||
|
为啥chatgpt会把cite里面的逗号换成中文逗号呀
|
||||||
|
"""
|
||||||
|
# get the matched string
|
||||||
|
cmd = match.group(1)
|
||||||
|
str_to_modify = match.group(2)
|
||||||
|
# modify the matched string
|
||||||
|
str_to_modify = str_to_modify.replace('：', ':') # 前面是中文冒号，后面是英文冒号
|
||||||
|
str_to_modify = str_to_modify.replace('，', ',') # 前面是中文逗号，后面是英文逗号
|
||||||
|
# str_to_modify = 'BOOM'
|
||||||
|
return "\\" + cmd + "{" + str_to_modify + "}"
|
||||||
|
|
||||||
|
def fix_content(final_tex, node_string):
|
||||||
|
"""
|
||||||
|
Fix common GPT errors to increase success rate
|
||||||
|
"""
|
||||||
|
final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
|
||||||
|
final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
|
||||||
|
final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
|
||||||
|
final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
|
||||||
|
|
||||||
|
if "Traceback" in final_tex and "[Local Message]" in final_tex:
|
||||||
|
final_tex = node_string # 出问题了，还原原文
|
||||||
|
if node_string.count('\\begin') != final_tex.count('\\begin'):
|
||||||
|
final_tex = node_string # 出问题了，还原原文
|
||||||
|
if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
|
||||||
|
# walk and replace any _ without \
|
||||||
|
final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
|
||||||
|
|
||||||
|
def compute_brace_level(string):
|
||||||
|
# this function count the number of { and }
|
||||||
|
brace_level = 0
|
||||||
|
for c in string:
|
||||||
|
if c == "{": brace_level += 1
|
||||||
|
elif c == "}": brace_level -= 1
|
||||||
|
return brace_level
|
||||||
|
def join_most(tex_t, tex_o):
|
||||||
|
# this function join translated string and original string when something goes wrong
|
||||||
|
p_t = 0
|
||||||
|
p_o = 0
|
||||||
|
def find_next(string, chars, begin):
|
||||||
|
p = begin
|
||||||
|
while p < len(string):
|
||||||
|
if string[p] in chars: return p, string[p]
|
||||||
|
p += 1
|
||||||
|
return None, None
|
||||||
|
while True:
|
||||||
|
res1, char = find_next(tex_o, ['{','}'], p_o)
|
||||||
|
if res1 is None: break
|
||||||
|
res2, char = find_next(tex_t, [char], p_t)
|
||||||
|
if res2 is None: break
|
||||||
|
p_o = res1 + 1
|
||||||
|
p_t = res2 + 1
|
||||||
|
return tex_t[:p_t] + tex_o[p_o:]
|
||||||
|
|
||||||
|
if compute_brace_level(final_tex) != compute_brace_level(node_string):
|
||||||
|
# 出问题了，还原部分原文，保证括号正确
|
||||||
|
final_tex = join_most(final_tex, node_string)
|
||||||
|
return final_tex
|
||||||
|
|
||||||
def split_subprocess(txt, project_folder, return_dict, opts):
|
def split_subprocess(txt, project_folder, return_dict, opts):
|
||||||
"""
|
"""
|
||||||
@ -22,8 +326,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
|||||||
mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
|
mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
|
||||||
|
|
||||||
# 吸收title与作者以上的部分
|
# 吸收title与作者以上的部分
|
||||||
text, mask = set_forbidden_text(text, mask, r"^(.*?)\\maketitle", re.DOTALL)
|
text, mask = set_forbidden_text(text, mask, r"(.*?)\\maketitle", re.DOTALL)
|
||||||
text, mask = set_forbidden_text(text, mask, r"^(.*?)\\begin{document}", re.DOTALL)
|
|
||||||
# 吸收iffalse注释
|
# 吸收iffalse注释
|
||||||
text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
|
text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
|
||||||
# 吸收在42行以内的begin-end组合
|
# 吸收在42行以内的begin-end组合
|
||||||
@ -53,9 +356,77 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
|||||||
text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
|
text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
|
||||||
root = convert_to_linklist(text, mask)
|
root = convert_to_linklist(text, mask)
|
||||||
|
|
||||||
# 最后一步处理，增强稳健性
|
# 修复括号
|
||||||
root = post_process(root)
|
node = root
|
||||||
|
while True:
|
||||||
|
string = node.string
|
||||||
|
if node.preserve:
|
||||||
|
node = node.next
|
||||||
|
if node is None: break
|
||||||
|
continue
|
||||||
|
def break_check(string):
|
||||||
|
str_stack = [""] # (lv, index)
|
||||||
|
for i, c in enumerate(string):
|
||||||
|
if c == '{':
|
||||||
|
str_stack.append('{')
|
||||||
|
elif c == '}':
|
||||||
|
if len(str_stack) == 1:
|
||||||
|
print('stack fix')
|
||||||
|
return i
|
||||||
|
str_stack.pop(-1)
|
||||||
|
else:
|
||||||
|
str_stack[-1] += c
|
||||||
|
return -1
|
||||||
|
bp = break_check(string)
|
||||||
|
|
||||||
|
if bp == -1:
|
||||||
|
pass
|
||||||
|
elif bp == 0:
|
||||||
|
node.string = string[:1]
|
||||||
|
q = LinkedListNode(string[1:], False)
|
||||||
|
q.next = node.next
|
||||||
|
node.next = q
|
||||||
|
else:
|
||||||
|
node.string = string[:bp]
|
||||||
|
q = LinkedListNode(string[bp:], False)
|
||||||
|
q.next = node.next
|
||||||
|
node.next = q
|
||||||
|
|
||||||
|
node = node.next
|
||||||
|
if node is None: break
|
||||||
|
|
||||||
|
# 屏蔽空行和太短的句子
|
||||||
|
node = root
|
||||||
|
while True:
|
||||||
|
if len(node.string.strip('\n').strip(''))==0: node.preserve = True
|
||||||
|
if len(node.string.strip('\n').strip(''))<42: node.preserve = True
|
||||||
|
node = node.next
|
||||||
|
if node is None: break
|
||||||
|
node = root
|
||||||
|
while True:
|
||||||
|
if node.next and node.preserve and node.next.preserve:
|
||||||
|
node.string += node.next.string
|
||||||
|
node.next = node.next.next
|
||||||
|
node = node.next
|
||||||
|
if node is None: break
|
||||||
|
|
||||||
|
# 将前后断行符脱离
|
||||||
|
node = root
|
||||||
|
prev_node = None
|
||||||
|
while True:
|
||||||
|
if not node.preserve:
|
||||||
|
lstriped_ = node.string.lstrip().lstrip('\n')
|
||||||
|
if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
|
||||||
|
prev_node.string += node.string[:-len(lstriped_)]
|
||||||
|
node.string = lstriped_
|
||||||
|
rstriped_ = node.string.rstrip().rstrip('\n')
|
||||||
|
if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
|
||||||
|
node.next.string = node.string[len(rstriped_):] + node.next.string
|
||||||
|
node.string = rstriped_
|
||||||
|
# =====
|
||||||
|
prev_node = node
|
||||||
|
node = node.next
|
||||||
|
if node is None: break
|
||||||
# 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）
|
# 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）
|
||||||
with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:
|
with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:
|
||||||
segment_parts_for_gpt = []
|
segment_parts_for_gpt = []
|
||||||
@ -66,7 +437,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
|||||||
show_html = node.string.replace('\n','<br/>')
|
show_html = node.string.replace('\n','<br/>')
|
||||||
if not node.preserve:
|
if not node.preserve:
|
||||||
segment_parts_for_gpt.append(node.string)
|
segment_parts_for_gpt.append(node.string)
|
||||||
f.write(f'<p style="color:black;">#{node.range}{show_html}#</p>')
|
f.write(f'<p style="color:black;">#{show_html}#</p>')
|
||||||
else:
|
else:
|
||||||
f.write(f'<p style="color:red;">{show_html}</p>')
|
f.write(f'<p style="color:red;">{show_html}</p>')
|
||||||
node = node.next
|
node = node.next
|
||||||
@ -77,6 +448,8 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
|||||||
return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt
|
return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt
|
||||||
return return_dict
|
return return_dict
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class LatexPaperSplit():
|
class LatexPaperSplit():
|
||||||
"""
|
"""
|
||||||
break down latex file to a linked list,
|
break down latex file to a linked list,
|
||||||
@ -91,32 +464,18 @@ class LatexPaperSplit():
|
|||||||
# 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）
|
# 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。}}\\\\"
|
||||||
|
|
||||||
|
def merge_result(self, arr, mode, msg):
|
||||||
def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):
|
|
||||||
"""
|
"""
|
||||||
Merge the result after the GPT process completed
|
Merge the result after the GPT process completed
|
||||||
"""
|
"""
|
||||||
result_string = ""
|
result_string = ""
|
||||||
node_cnt = 0
|
p = 0
|
||||||
line_cnt = 0
|
|
||||||
|
|
||||||
for node in self.nodes:
|
for node in self.nodes:
|
||||||
if node.preserve:
|
if node.preserve:
|
||||||
line_cnt += node.string.count('\n')
|
|
||||||
result_string += node.string
|
result_string += node.string
|
||||||
else:
|
else:
|
||||||
translated_txt = fix_content(arr[node_cnt], node.string)
|
result_string += fix_content(arr[p], node.string)
|
||||||
begin_line = line_cnt
|
p += 1
|
||||||
end_line = line_cnt + translated_txt.count('\n')
|
|
||||||
|
|
||||||
# reverse translation if any error
|
|
||||||
if any([begin_line-buggy_line_surgery_n_lines <= b_line <= end_line+buggy_line_surgery_n_lines for b_line in buggy_lines]):
|
|
||||||
translated_txt = node.string
|
|
||||||
|
|
||||||
result_string += translated_txt
|
|
||||||
node_cnt += 1
|
|
||||||
line_cnt += translated_txt.count('\n')
|
|
||||||
|
|
||||||
if mode == 'translate_zh':
|
if mode == 'translate_zh':
|
||||||
pattern = re.compile(r'\\begin\{abstract\}.*\n')
|
pattern = re.compile(r'\\begin\{abstract\}.*\n')
|
||||||
match = pattern.search(result_string)
|
match = pattern.search(result_string)
|
||||||
@ -131,7 +490,6 @@ class LatexPaperSplit():
|
|||||||
result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]
|
result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]
|
||||||
return result_string
|
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,
|
break down latex file to a linked list,
|
||||||
@ -153,6 +511,7 @@ class LatexPaperSplit():
|
|||||||
return self.sp
|
return self.sp
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class LatexPaperFileGroup():
|
class LatexPaperFileGroup():
|
||||||
"""
|
"""
|
||||||
use tokenizer to break down text according to max_token_limit
|
use tokenizer to break down text according to max_token_limit
|
||||||
@ -180,7 +539,7 @@ class LatexPaperFileGroup():
|
|||||||
self.sp_file_index.append(index)
|
self.sp_file_index.append(index)
|
||||||
self.sp_file_tag.append(self.file_paths[index])
|
self.sp_file_tag.append(self.file_paths[index])
|
||||||
else:
|
else:
|
||||||
from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
||||||
for j, segment in enumerate(segments):
|
for j, segment in enumerate(segments):
|
||||||
self.sp_file_contents.append(segment)
|
self.sp_file_contents.append(segment)
|
||||||
@ -201,14 +560,41 @@ class LatexPaperFileGroup():
|
|||||||
f.write(res)
|
f.write(res)
|
||||||
return manifest
|
return manifest
|
||||||
|
|
||||||
|
def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
||||||
|
|
||||||
|
# write html
|
||||||
|
try:
|
||||||
|
import shutil
|
||||||
|
from .crazy_utils import construct_html
|
||||||
|
from toolbox import gen_time_str
|
||||||
|
ch = construct_html()
|
||||||
|
orig = ""
|
||||||
|
trans = ""
|
||||||
|
final = []
|
||||||
|
for c,r in zip(sp_file_contents, sp_file_result):
|
||||||
|
final.append(c)
|
||||||
|
final.append(r)
|
||||||
|
for i, k in enumerate(final):
|
||||||
|
if i%2==0:
|
||||||
|
orig = k
|
||||||
|
if i%2==1:
|
||||||
|
trans = k
|
||||||
|
ch.add_row(a=orig, b=trans)
|
||||||
|
create_report_file_name = f"{gen_time_str()}.trans.html"
|
||||||
|
ch.save_file(create_report_file_name)
|
||||||
|
shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))
|
||||||
|
promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)
|
||||||
|
except:
|
||||||
|
from toolbox import trimmed_format_exc
|
||||||
|
print('writing html result failed:', trimmed_format_exc())
|
||||||
|
|
||||||
def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):
|
def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):
|
||||||
import time, os, re
|
import time, os, re
|
||||||
from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||||
from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
|
from .latex_utils import LatexPaperFileGroup, merge_tex_files, LatexPaperSplit, 寻找Latex主文件
|
||||||
|
|
||||||
# <-------- 寻找主tex文件 ---------->
|
# <-------- 寻找主tex文件 ---------->
|
||||||
maintex = find_main_tex_file(file_manifest, mode)
|
maintex = 寻找Latex主文件(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。'))
|
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
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
time.sleep(3)
|
time.sleep(3)
|
||||||
@ -282,51 +668,54 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
|||||||
# <-------- 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']}。"
|
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)
|
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:
|
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)
|
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'))
|
chatbot.append((f"完成了吗？", 'GPT结果已输出, 正在编译PDF'))
|
||||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
|
|
||||||
# <-------- 返回 ---------->
|
# <-------- 返回 ---------->
|
||||||
return project_folder + f'/merge_{mode}.tex'
|
return project_folder + f'/merge_{mode}.tex'
|
||||||
|
|
||||||
|
|
||||||
def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified, fixed_line=[]):
|
|
||||||
|
def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified):
|
||||||
try:
|
try:
|
||||||
with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
|
with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
|
||||||
log = f.read()
|
log = f.read()
|
||||||
|
with open(file_path, 'r', encoding='utf-8', errors='replace') as f:
|
||||||
|
file_lines = f.readlines()
|
||||||
import re
|
import re
|
||||||
buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)
|
buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)
|
||||||
buggy_lines = [int(l) for l in buggy_lines]
|
buggy_lines = [int(l) for l in buggy_lines]
|
||||||
buggy_lines = sorted(buggy_lines)
|
buggy_lines = sorted(buggy_lines)
|
||||||
buggy_line = buggy_lines[0]-1
|
print("removing lines that has errors", buggy_lines)
|
||||||
print("reversing tex line that has errors", buggy_line)
|
file_lines.pop(buggy_lines[0]-1)
|
||||||
|
|
||||||
# 重组，逆转出错的段落
|
|
||||||
if buggy_line not in fixed_line:
|
|
||||||
fixed_line.append(buggy_line)
|
|
||||||
|
|
||||||
lps, file_result, mode, msg = objload(file=pj(work_folder_modified,'merge_result.pkl'))
|
|
||||||
final_tex = lps.merge_result(file_result, mode, msg, buggy_lines=fixed_line, buggy_line_surgery_n_lines=5*n_fix)
|
|
||||||
|
|
||||||
with open(pj(work_folder_modified, f"{tex_name_pure}_fix_{n_fix}.tex"), 'w', encoding='utf-8', errors='replace') as f:
|
with open(pj(work_folder_modified, f"{tex_name_pure}_fix_{n_fix}.tex"), 'w', encoding='utf-8', errors='replace') as f:
|
||||||
f.write(final_tex)
|
f.writelines(file_lines)
|
||||||
|
|
||||||
return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
|
return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
|
||||||
except:
|
except:
|
||||||
print("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]
|
return False, -1, [-1]
|
||||||
|
|
||||||
|
def compile_latex_with_timeout(command, cwd, timeout=60):
|
||||||
|
import subprocess
|
||||||
|
process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
|
||||||
|
try:
|
||||||
|
stdout, stderr = process.communicate(timeout=timeout)
|
||||||
|
except subprocess.TimeoutExpired:
|
||||||
|
process.kill()
|
||||||
|
stdout, stderr = process.communicate()
|
||||||
|
print("Process timed out!")
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
||||||
def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):
|
def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):
|
||||||
import os, time
|
import os, time
|
||||||
|
current_dir = os.getcwd()
|
||||||
n_fix = 1
|
n_fix = 1
|
||||||
fixed_line = []
|
|
||||||
max_try = 32
|
max_try = 32
|
||||||
chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
|
chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
|
||||||
chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
|
chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
|
||||||
@ -334,10 +723,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
|
|
||||||
while True:
|
while True:
|
||||||
import os
|
import os
|
||||||
may_exist_bbl = pj(work_folder_modified, f'merge.bbl')
|
|
||||||
target_bbl = pj(work_folder_modified, f'{main_file_modified}.bbl')
|
|
||||||
if os.path.exists(may_exist_bbl) and not os.path.exists(target_bbl):
|
|
||||||
shutil.copyfile(may_exist_bbl, target_bbl)
|
|
||||||
|
|
||||||
# https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
|
# https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
|
||||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history) # 刷新Gradio前端界面
|
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||||
@ -371,6 +756,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
||||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
||||||
|
|
||||||
|
|
||||||
# <---------- 检查结果 ----------->
|
# <---------- 检查结果 ----------->
|
||||||
results_ = ""
|
results_ = ""
|
||||||
original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
|
original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
|
||||||
@ -387,19 +773,9 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
if modified_pdf_success:
|
if modified_pdf_success:
|
||||||
yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history) # 刷新Gradio前端界面
|
yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history) # 刷新Gradio前端界面
|
||||||
result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
|
result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
|
||||||
origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
|
|
||||||
if os.path.exists(pj(work_folder, '..', 'translation')):
|
if os.path.exists(pj(work_folder, '..', 'translation')):
|
||||||
shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))
|
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
|
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:
|
|
||||||
try:
|
|
||||||
from .latex_toolbox import merge_pdfs
|
|
||||||
concat_pdf = pj(work_folder_modified, f'comparison.pdf')
|
|
||||||
merge_pdfs(origin_pdf, result_pdf, concat_pdf)
|
|
||||||
promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
|
||||||
except Exception as e:
|
|
||||||
pass
|
|
||||||
return True # 成功啦
|
return True # 成功啦
|
||||||
else:
|
else:
|
||||||
if n_fix>=max_try: break
|
if n_fix>=max_try: break
|
||||||
@ -411,7 +787,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
tex_name_pure=f'{main_file_modified}',
|
tex_name_pure=f'{main_file_modified}',
|
||||||
n_fix=n_fix,
|
n_fix=n_fix,
|
||||||
work_folder_modified=work_folder_modified,
|
work_folder_modified=work_folder_modified,
|
||||||
fixed_line=fixed_line
|
|
||||||
)
|
)
|
||||||
yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history) # 刷新Gradio前端界面
|
yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history) # 刷新Gradio前端界面
|
||||||
if not can_retry: break
|
if not can_retry: break
|
||||||
@ -419,29 +794,4 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
return False # 失败啦
|
return False # 失败啦
|
||||||
|
|
||||||
|
|
||||||
def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
|
||||||
# write html
|
|
||||||
try:
|
|
||||||
import shutil
|
|
||||||
from ..crazy_utils import construct_html
|
|
||||||
from toolbox import gen_time_str
|
|
||||||
ch = construct_html()
|
|
||||||
orig = ""
|
|
||||||
trans = ""
|
|
||||||
final = []
|
|
||||||
for c,r in zip(sp_file_contents, sp_file_result):
|
|
||||||
final.append(c)
|
|
||||||
final.append(r)
|
|
||||||
for i, k in enumerate(final):
|
|
||||||
if i%2==0:
|
|
||||||
orig = k
|
|
||||||
if i%2==1:
|
|
||||||
trans = k
|
|
||||||
ch.add_row(a=orig, b=trans)
|
|
||||||
create_report_file_name = f"{gen_time_str()}.trans.html"
|
|
||||||
ch.save_file(create_report_file_name)
|
|
||||||
shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))
|
|
||||||
promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)
|
|
||||||
except:
|
|
||||||
from toolbox import trimmed_format_exc
|
|
||||||
print('writing html result failed:', trimmed_format_exc())
|
|
||||||
@ -19,7 +19,7 @@ class AliyunASR():
|
|||||||
pass
|
pass
|
||||||
|
|
||||||
def test_on_error(self, message, *args):
|
def test_on_error(self, message, *args):
|
||||||
print("on_error args=>{}".format(args))
|
# print("on_error args=>{}".format(args))
|
||||||
pass
|
pass
|
||||||
|
|
||||||
def test_on_close(self, *args):
|
def test_on_close(self, *args):
|
||||||
@ -50,8 +50,6 @@ class AliyunASR():
|
|||||||
rad.clean_up()
|
rad.clean_up()
|
||||||
temp_folder = tempfile.gettempdir()
|
temp_folder = tempfile.gettempdir()
|
||||||
TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
|
TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
|
||||||
if len(TOKEN) == 0:
|
|
||||||
TOKEN = self.get_token()
|
|
||||||
self.aliyun_service_ok = True
|
self.aliyun_service_ok = True
|
||||||
URL="wss://nls-gateway.aliyuncs.com/ws/v1"
|
URL="wss://nls-gateway.aliyuncs.com/ws/v1"
|
||||||
sr = nls.NlsSpeechTranscriber(
|
sr = nls.NlsSpeechTranscriber(
|
||||||
@ -93,38 +91,3 @@ class AliyunASR():
|
|||||||
self.stop = True
|
self.stop = True
|
||||||
self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'
|
self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'
|
||||||
r = sr.stop()
|
r = sr.stop()
|
||||||
|
|
||||||
def get_token(self):
|
|
||||||
from toolbox import get_conf
|
|
||||||
import json
|
|
||||||
from aliyunsdkcore.request import CommonRequest
|
|
||||||
from aliyunsdkcore.client import AcsClient
|
|
||||||
AccessKey_ID, AccessKey_secret = get_conf('ALIYUN_ACCESSKEY', 'ALIYUN_SECRET')
|
|
||||||
|
|
||||||
# 创建AcsClient实例
|
|
||||||
client = AcsClient(
|
|
||||||
AccessKey_ID,
|
|
||||||
AccessKey_secret,
|
|
||||||
"cn-shanghai"
|
|
||||||
)
|
|
||||||
|
|
||||||
# 创建request，并设置参数。
|
|
||||||
request = CommonRequest()
|
|
||||||
request.set_method('POST')
|
|
||||||
request.set_domain('nls-meta.cn-shanghai.aliyuncs.com')
|
|
||||||
request.set_version('2019-02-28')
|
|
||||||
request.set_action_name('CreateToken')
|
|
||||||
|
|
||||||
try:
|
|
||||||
response = client.do_action_with_exception(request)
|
|
||||||
print(response)
|
|
||||||
jss = json.loads(response)
|
|
||||||
if 'Token' in jss and 'Id' in jss['Token']:
|
|
||||||
token = jss['Token']['Id']
|
|
||||||
expireTime = jss['Token']['ExpireTime']
|
|
||||||
print("token = " + token)
|
|
||||||
print("expireTime = " + str(expireTime))
|
|
||||||
except Exception as e:
|
|
||||||
print(e)
|
|
||||||
|
|
||||||
return token
|
|
||||||
|
|||||||
38
crazy_functions/vhmap_interact/vhmap.py
Normal file
38
crazy_functions/vhmap_interact/vhmap.py
Normal file
@ -0,0 +1,38 @@
|
|||||||
|
from toolbox import update_ui, get_conf, trimmed_format_exc
|
||||||
|
import threading
|
||||||
|
|
||||||
|
def Singleton(cls):
|
||||||
|
_instance = {}
|
||||||
|
|
||||||
|
def _singleton(*args, **kargs):
|
||||||
|
if cls not in _instance:
|
||||||
|
_instance[cls] = cls(*args, **kargs)
|
||||||
|
return _instance[cls]
|
||||||
|
|
||||||
|
return _singleton
|
||||||
|
|
||||||
|
@Singleton
|
||||||
|
class vhmp_interface():
|
||||||
|
def __init__(self) -> None:
|
||||||
|
from VISUALIZE.mcom_rt import mcom
|
||||||
|
self.vis3d = mcom(path='TEMP/v2d_logger/', draw_mode='Threejs')
|
||||||
|
self.vis3d.v2d_init()
|
||||||
|
self.vis3d.设置样式('star')
|
||||||
|
# vis3d.设置样式('star') # 布置星空
|
||||||
|
self.vis3d.其他几何体之旋转缩放和平移('box', 'BoxGeometry(1,1,1)', 0,0,0, 1,1,1, 0,0,0)
|
||||||
|
# declare geo 'oct1', init with OctahedronGeometry, then (1)rotate & (2)scale & (3)translate
|
||||||
|
self.vis3d.其他几何体之旋转缩放和平移('octahedron', 'OctahedronGeometry(1,0)', 0,0,0, 1,1,1, 0,0,0) # 八面体
|
||||||
|
# 需要换成其他几何体，请把'OctahedronGeometry(1,0)'替换，参考网址 https://threejs.org/docs/index.html?q=Geometry
|
||||||
|
self.vis3d.其他几何体之旋转缩放和平移('sphere', 'SphereGeometry(1)', 0,0,0, 1,1,1, 0,0,0) # 球体
|
||||||
|
self.vis3d.其他几何体之旋转缩放和平移('cylinder', 'CylinderGeometry(1,1,5,32)', 0,0,0, 1,1,1, 0,0,0) # 球体
|
||||||
|
|
||||||
|
def update(self, json):
|
||||||
|
for obj in json:
|
||||||
|
self.vis3d.发送几何体(
|
||||||
|
f'{obj["geometry"]}|{obj["name"]}|{obj["color"]}|{obj["size"]}', # 填入 ‘形状|几何体之ID标识|颜色|大小’即可
|
||||||
|
obj["location_x"],
|
||||||
|
obj["location_y"],
|
||||||
|
obj["location_z"],
|
||||||
|
ro_x=0, ro_y=0, ro_z=0, # 三维位置+欧拉旋转变换，六自由度
|
||||||
|
track_n_frame=0) # 显示历史20帧留下的轨迹
|
||||||
|
self.vis3d.结束关键帧()
|
||||||
@ -179,12 +179,12 @@ def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
|||||||
import nls
|
import nls
|
||||||
from scipy import io
|
from scipy import io
|
||||||
except:
|
except:
|
||||||
chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
|
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 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
|
||||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
return
|
return
|
||||||
|
|
||||||
APPKEY = get_conf('ALIYUN_APPKEY')
|
TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
|
||||||
if APPKEY == "":
|
if TOKEN == "" or 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"])
|
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
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
return
|
return
|
||||||
|
|||||||
@ -115,36 +115,3 @@ services:
|
|||||||
command: >
|
command: >
|
||||||
bash -c "python3 -u main.py"
|
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"
|
|
||||||
|
|
||||||
|
|||||||
@ -1,22 +0,0 @@
|
|||||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
|
||||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
|
|
||||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
|
|
||||||
FROM python:3.11
|
|
||||||
|
|
||||||
# 指定路径
|
|
||||||
WORKDIR /gpt
|
|
||||||
|
|
||||||
# 装载项目文件
|
|
||||||
COPY . .
|
|
||||||
|
|
||||||
# 安装依赖
|
|
||||||
RUN pip3 install -r requirements.txt
|
|
||||||
|
|
||||||
# 安装语音插件的额外依赖
|
|
||||||
RUN pip3 install pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
|
|
||||||
|
|
||||||
# 可选步骤，用于预热模块
|
|
||||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
|
||||||
|
|
||||||
# 启动
|
|
||||||
CMD ["python3", "-u", "main.py"]
|
|
||||||
@ -28,16 +28,6 @@ ALIYUN_APPKEY = "RoPlZrM88DnAFkZK" # 此appkey已经失效
|
|||||||
参考 https://help.aliyun.com/document_detail/450255.html
|
参考 https://help.aliyun.com/document_detail/450255.html
|
||||||
先有阿里云开发者账号，登录之后，需要开通 智能语音交互 的功能，可以免费获得一个token，然后在 全部项目 中，创建一个项目，可以获得一个appkey.
|
先有阿里云开发者账号，登录之后，需要开通 智能语音交互 的功能，可以免费获得一个token，然后在 全部项目 中，创建一个项目，可以获得一个appkey.
|
||||||
|
|
||||||
- 进阶功能
|
|
||||||
进一步填写ALIYUN_ACCESSKEY和ALIYUN_SECRET实现自动获取ALIYUN_TOKEN
|
|
||||||
```
|
|
||||||
ALIYUN_APPKEY = "RoP1ZrM84DnAFkZK"
|
|
||||||
ALIYUN_TOKEN = ""
|
|
||||||
ALIYUN_ACCESSKEY = "LTAI5q6BrFUzoRXVGUWnekh1"
|
|
||||||
ALIYUN_SECRET = "eHmI20AVWIaQZ0CiTD2bGQVsaP9i68"
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
## 3.启动
|
## 3.启动
|
||||||
|
|
||||||
启动gpt-academic `python main.py`
|
启动gpt-academic `python main.py`
|
||||||
@ -58,7 +48,7 @@ III `[把特殊软件（如腾讯会议）的外放声音用VoiceMeeter截留]`
|
|||||||
|
|
||||||
VI 两种音频监听模式切换时，需要刷新页面才有效。
|
VI 两种音频监听模式切换时，需要刷新页面才有效。
|
||||||
|
|
||||||
VII 非localhost运行+非https情况下无法打开录音功能的坑：https://blog.csdn.net/weixin_39461487/article/details/109594434
|
|
||||||
|
|
||||||
## 5.点击函数插件区“实时音频采集” 或者其他音频交互功能
|
## 5.点击函数插件区“实时音频采集” 或者其他音频交互功能
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
6
main.py
6
main.py
@ -22,10 +22,8 @@ def main():
|
|||||||
# 问询记录, python 版本建议3.9+（越新越好）
|
# 问询记录, python 版本建议3.9+（越新越好）
|
||||||
import logging, uuid
|
import logging, uuid
|
||||||
os.makedirs("gpt_log", exist_ok=True)
|
os.makedirs("gpt_log", exist_ok=True)
|
||||||
try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
|
try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
|
||||||
except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
|
except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
|
||||||
# Disable logging output from the 'httpx' logger
|
|
||||||
logging.getLogger("httpx").setLevel(logging.WARNING)
|
|
||||||
print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
|
print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
|
||||||
|
|
||||||
# 一些普通功能模块
|
# 一些普通功能模块
|
||||||
|
|||||||
@ -248,6 +248,7 @@ if "moss" in AVAIL_LLM_MODELS:
|
|||||||
if "stack-claude" in AVAIL_LLM_MODELS:
|
if "stack-claude" in AVAIL_LLM_MODELS:
|
||||||
from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui
|
from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui
|
||||||
from .bridge_stackclaude import predict as claude_ui
|
from .bridge_stackclaude import predict as claude_ui
|
||||||
|
# claude
|
||||||
model_info.update({
|
model_info.update({
|
||||||
"stack-claude": {
|
"stack-claude": {
|
||||||
"fn_with_ui": claude_ui,
|
"fn_with_ui": claude_ui,
|
||||||
@ -262,6 +263,7 @@ if "newbing-free" in AVAIL_LLM_MODELS:
|
|||||||
try:
|
try:
|
||||||
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
|
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
|
||||||
from .bridge_newbingfree import predict as newbingfree_ui
|
from .bridge_newbingfree import predict as newbingfree_ui
|
||||||
|
# claude
|
||||||
model_info.update({
|
model_info.update({
|
||||||
"newbing-free": {
|
"newbing-free": {
|
||||||
"fn_with_ui": newbingfree_ui,
|
"fn_with_ui": newbingfree_ui,
|
||||||
@ -278,6 +280,7 @@ if "newbing" in AVAIL_LLM_MODELS: # same with newbing-free
|
|||||||
try:
|
try:
|
||||||
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
|
from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
|
||||||
from .bridge_newbingfree import predict as newbingfree_ui
|
from .bridge_newbingfree import predict as newbingfree_ui
|
||||||
|
# claude
|
||||||
model_info.update({
|
model_info.update({
|
||||||
"newbing": {
|
"newbing": {
|
||||||
"fn_with_ui": newbingfree_ui,
|
"fn_with_ui": newbingfree_ui,
|
||||||
@ -294,6 +297,7 @@ if "chatglmft" in AVAIL_LLM_MODELS: # same with newbing-free
|
|||||||
try:
|
try:
|
||||||
from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
|
from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
|
||||||
from .bridge_chatglmft import predict as chatglmft_ui
|
from .bridge_chatglmft import predict as chatglmft_ui
|
||||||
|
# claude
|
||||||
model_info.update({
|
model_info.update({
|
||||||
"chatglmft": {
|
"chatglmft": {
|
||||||
"fn_with_ui": chatglmft_ui,
|
"fn_with_ui": chatglmft_ui,
|
||||||
@ -306,22 +310,7 @@ if "chatglmft" in AVAIL_LLM_MODELS: # same with newbing-free
|
|||||||
})
|
})
|
||||||
except:
|
except:
|
||||||
print(trimmed_format_exc())
|
print(trimmed_format_exc())
|
||||||
if "internlm" in AVAIL_LLM_MODELS:
|
|
||||||
try:
|
|
||||||
from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
|
|
||||||
from .bridge_internlm import predict as internlm_ui
|
|
||||||
model_info.update({
|
|
||||||
"internlm": {
|
|
||||||
"fn_with_ui": internlm_ui,
|
|
||||||
"fn_without_ui": internlm_noui,
|
|
||||||
"endpoint": None,
|
|
||||||
"max_token": 4096,
|
|
||||||
"tokenizer": tokenizer_gpt35,
|
|
||||||
"token_cnt": get_token_num_gpt35,
|
|
||||||
}
|
|
||||||
})
|
|
||||||
except:
|
|
||||||
print(trimmed_format_exc())
|
|
||||||
|
|
||||||
def LLM_CATCH_EXCEPTION(f):
|
def LLM_CATCH_EXCEPTION(f):
|
||||||
"""
|
"""
|
||||||
|
|||||||
@ -37,23 +37,15 @@ class GetGLMHandle(Process):
|
|||||||
# 子进程执行
|
# 子进程执行
|
||||||
# 第一次运行，加载参数
|
# 第一次运行，加载参数
|
||||||
retry = 0
|
retry = 0
|
||||||
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
|
|
||||||
|
|
||||||
if LOCAL_MODEL_QUANT == "INT4": # INT4
|
|
||||||
_model_name_ = "THUDM/chatglm2-6b-int4"
|
|
||||||
elif LOCAL_MODEL_QUANT == "INT8": # INT8
|
|
||||||
_model_name_ = "THUDM/chatglm2-6b-int8"
|
|
||||||
else:
|
|
||||||
_model_name_ = "THUDM/chatglm2-6b" # FP16
|
|
||||||
|
|
||||||
while True:
|
while True:
|
||||||
try:
|
try:
|
||||||
if self.chatglm_model is None:
|
if self.chatglm_model is None:
|
||||||
self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
|
self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
|
||||||
|
device, = get_conf('LOCAL_MODEL_DEVICE')
|
||||||
if device=='cpu':
|
if device=='cpu':
|
||||||
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
|
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).float()
|
||||||
else:
|
else:
|
||||||
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
|
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
|
||||||
self.chatglm_model = self.chatglm_model.eval()
|
self.chatglm_model = self.chatglm_model.eval()
|
||||||
break
|
break
|
||||||
else:
|
else:
|
||||||
@ -144,8 +136,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -185,8 +185,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -129,8 +129,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
raw_input = inputs
|
raw_input = inputs
|
||||||
logging.info(f'[raw_input] {raw_input}')
|
logging.info(f'[raw_input] {raw_input}')
|
||||||
@ -171,10 +174,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
chunk = next(stream_response)
|
chunk = next(stream_response)
|
||||||
except StopIteration:
|
except StopIteration:
|
||||||
# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
|
# 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
|
||||||
chunk_decoded = chunk.decode()
|
from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||||
error_msg = chunk_decoded
|
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode())}")
|
||||||
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
|
yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk.decode()) # 刷新界面
|
||||||
yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
|
|
||||||
return
|
return
|
||||||
|
|
||||||
# print(chunk.decode()[6:])
|
# print(chunk.decode()[6:])
|
||||||
@ -185,7 +187,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
if chunk:
|
if chunk:
|
||||||
try:
|
try:
|
||||||
chunk_decoded = chunk.decode()
|
chunk_decoded = chunk.decode()
|
||||||
# 前者是API2D的结束条件，后者是OPENAI的结束条件
|
# 前者API2D的
|
||||||
if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
|
if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
|
||||||
# 判定为数据流的结束，gpt_replying_buffer也写完了
|
# 判定为数据流的结束，gpt_replying_buffer也写完了
|
||||||
logging.info(f'[response] {gpt_replying_buffer}')
|
logging.info(f'[response] {gpt_replying_buffer}')
|
||||||
@ -198,44 +200,40 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
history[-1] = gpt_replying_buffer
|
history[-1] = gpt_replying_buffer
|
||||||
chatbot[-1] = (history[-2], history[-1])
|
chatbot[-1] = (history[-2], history[-1])
|
||||||
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
|
traceback.print_exc()
|
||||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
|
||||||
chunk = get_full_error(chunk, stream_response)
|
chunk = get_full_error(chunk, stream_response)
|
||||||
chunk_decoded = chunk.decode()
|
chunk_decoded = chunk.decode()
|
||||||
error_msg = chunk_decoded
|
error_msg = chunk_decoded
|
||||||
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
|
openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
|
||||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
if "reduce the length" in error_msg:
|
||||||
print(error_msg)
|
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
|
||||||
return
|
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
|
||||||
|
|
||||||
def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
|
|
||||||
from .bridge_all import model_info
|
|
||||||
openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
|
|
||||||
if "reduce the length" in error_msg:
|
|
||||||
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
|
|
||||||
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
|
|
||||||
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
|
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
|
||||||
# history = [] # 清除历史
|
# history = [] # 清除历史
|
||||||
elif "does not exist" in error_msg:
|
elif "does not exist" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
|
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
|
||||||
elif "Incorrect API key" in error_msg:
|
elif "Incorrect API key" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
|
||||||
elif "exceeded your current quota" in error_msg:
|
elif "exceeded your current quota" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
|
||||||
elif "account is not active" in error_msg:
|
elif "account is not active" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
||||||
elif "associated with a deactivated account" in error_msg:
|
elif "associated with a deactivated account" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
|
||||||
elif "bad forward key" in error_msg:
|
elif "bad forward key" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
|
||||||
elif "Not enough point" in error_msg:
|
elif "Not enough point" in error_msg:
|
||||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
|
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
|
||||||
else:
|
else:
|
||||||
from toolbox import regular_txt_to_markdown
|
from toolbox import regular_txt_to_markdown
|
||||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
|
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
|
||||||
return chatbot, history
|
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
||||||
|
return
|
||||||
|
|
||||||
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
|
||||||
"""
|
"""
|
||||||
|
|||||||
@ -116,8 +116,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
raw_input = inputs
|
raw_input = inputs
|
||||||
logging.info(f'[raw_input] {raw_input}')
|
logging.info(f'[raw_input] {raw_input}')
|
||||||
|
|||||||
@ -1,312 +0,0 @@
|
|||||||
|
|
||||||
from transformers import AutoModel, AutoTokenizer
|
|
||||||
import time
|
|
||||||
import threading
|
|
||||||
import importlib
|
|
||||||
from toolbox import update_ui, get_conf, Singleton
|
|
||||||
from multiprocessing import Process, Pipe
|
|
||||||
|
|
||||||
model_name = "InternLM"
|
|
||||||
cmd_to_install = "`pip install ???`"
|
|
||||||
load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
|
|
||||||
def try_to_import_special_deps():
|
|
||||||
import sentencepiece
|
|
||||||
|
|
||||||
user_prompt = "<|User|>:{user}<eoh>\n"
|
|
||||||
robot_prompt = "<|Bot|>:{robot}<eoa>\n"
|
|
||||||
cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"
|
|
||||||
|
|
||||||
|
|
||||||
def combine_history(prompt, hist):
|
|
||||||
messages = hist
|
|
||||||
total_prompt = ""
|
|
||||||
for message in messages:
|
|
||||||
cur_content = message
|
|
||||||
cur_prompt = user_prompt.replace("{user}", cur_content[0])
|
|
||||||
total_prompt += cur_prompt
|
|
||||||
cur_prompt = robot_prompt.replace("{robot}", cur_content[1])
|
|
||||||
total_prompt += cur_prompt
|
|
||||||
total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
|
|
||||||
return total_prompt
|
|
||||||
|
|
||||||
|
|
||||||
@Singleton
|
|
||||||
class GetInternlmHandle(Process):
|
|
||||||
def __init__(self):
|
|
||||||
# ⭐主进程执行
|
|
||||||
super().__init__(daemon=True)
|
|
||||||
self.parent, self.child = Pipe()
|
|
||||||
self._model = None
|
|
||||||
self._tokenizer = None
|
|
||||||
self.info = ""
|
|
||||||
self.success = True
|
|
||||||
self.check_dependency()
|
|
||||||
self.start()
|
|
||||||
self.threadLock = threading.Lock()
|
|
||||||
|
|
||||||
def ready(self):
|
|
||||||
# ⭐主进程执行
|
|
||||||
return self._model is not None
|
|
||||||
|
|
||||||
def load_model_and_tokenizer(self):
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
|
||||||
import torch
|
|
||||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
||||||
device, = get_conf('LOCAL_MODEL_DEVICE')
|
|
||||||
if self._model is None:
|
|
||||||
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
|
|
||||||
if device=='cpu':
|
|
||||||
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
|
|
||||||
else:
|
|
||||||
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
|
|
||||||
|
|
||||||
model = model.eval()
|
|
||||||
return model, tokenizer
|
|
||||||
|
|
||||||
def llm_stream_generator(self, **kwargs):
|
|
||||||
import torch
|
|
||||||
import logging
|
|
||||||
import copy
|
|
||||||
import warnings
|
|
||||||
import torch.nn as nn
|
|
||||||
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
|
|
||||||
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
|
||||||
def adaptor():
|
|
||||||
model = self._model
|
|
||||||
tokenizer = self._tokenizer
|
|
||||||
prompt = kwargs['query']
|
|
||||||
max_length = kwargs['max_length']
|
|
||||||
top_p = kwargs['top_p']
|
|
||||||
temperature = kwargs['temperature']
|
|
||||||
history = kwargs['history']
|
|
||||||
real_prompt = combine_history(prompt, history)
|
|
||||||
return model, tokenizer, real_prompt, max_length, top_p, temperature
|
|
||||||
|
|
||||||
model, tokenizer, prompt, max_length, top_p, temperature = adaptor()
|
|
||||||
prefix_allowed_tokens_fn = None
|
|
||||||
logits_processor = None
|
|
||||||
stopping_criteria = None
|
|
||||||
additional_eos_token_id = 103028
|
|
||||||
generation_config = None
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25
|
|
||||||
|
|
||||||
inputs = tokenizer([prompt], padding=True, return_tensors="pt")
|
|
||||||
input_length = len(inputs["input_ids"][0])
|
|
||||||
for k, v in inputs.items():
|
|
||||||
inputs[k] = v.cuda()
|
|
||||||
input_ids = inputs["input_ids"]
|
|
||||||
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
|
|
||||||
if generation_config is None:
|
|
||||||
generation_config = model.generation_config
|
|
||||||
generation_config = copy.deepcopy(generation_config)
|
|
||||||
model_kwargs = generation_config.update(**kwargs)
|
|
||||||
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
|
|
||||||
if isinstance(eos_token_id, int):
|
|
||||||
eos_token_id = [eos_token_id]
|
|
||||||
if additional_eos_token_id is not None:
|
|
||||||
eos_token_id.append(additional_eos_token_id)
|
|
||||||
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
|
|
||||||
if has_default_max_length and generation_config.max_new_tokens is None:
|
|
||||||
warnings.warn(
|
|
||||||
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
|
|
||||||
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
|
|
||||||
" recommend using `max_new_tokens` to control the maximum length of the generation.",
|
|
||||||
UserWarning,
|
|
||||||
)
|
|
||||||
elif generation_config.max_new_tokens is not None:
|
|
||||||
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
|
|
||||||
if not has_default_max_length:
|
|
||||||
logging.warn(
|
|
||||||
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
|
|
||||||
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
|
|
||||||
"Please refer to the documentation for more information. "
|
|
||||||
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
|
|
||||||
UserWarning,
|
|
||||||
)
|
|
||||||
|
|
||||||
if input_ids_seq_length >= generation_config.max_length:
|
|
||||||
input_ids_string = "input_ids"
|
|
||||||
logging.warning(
|
|
||||||
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
|
|
||||||
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
|
|
||||||
" increasing `max_new_tokens`."
|
|
||||||
)
|
|
||||||
|
|
||||||
# 2. Set generation parameters if not already defined
|
|
||||||
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
|
|
||||||
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
|
|
||||||
|
|
||||||
logits_processor = model._get_logits_processor(
|
|
||||||
generation_config=generation_config,
|
|
||||||
input_ids_seq_length=input_ids_seq_length,
|
|
||||||
encoder_input_ids=input_ids,
|
|
||||||
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
|
||||||
logits_processor=logits_processor,
|
|
||||||
)
|
|
||||||
|
|
||||||
stopping_criteria = model._get_stopping_criteria(
|
|
||||||
generation_config=generation_config, stopping_criteria=stopping_criteria
|
|
||||||
)
|
|
||||||
logits_warper = model._get_logits_warper(generation_config)
|
|
||||||
|
|
||||||
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
|
|
||||||
scores = None
|
|
||||||
while True:
|
|
||||||
model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
|
|
||||||
# forward pass to get next token
|
|
||||||
outputs = model(
|
|
||||||
**model_inputs,
|
|
||||||
return_dict=True,
|
|
||||||
output_attentions=False,
|
|
||||||
output_hidden_states=False,
|
|
||||||
)
|
|
||||||
|
|
||||||
next_token_logits = outputs.logits[:, -1, :]
|
|
||||||
|
|
||||||
# pre-process distribution
|
|
||||||
next_token_scores = logits_processor(input_ids, next_token_logits)
|
|
||||||
next_token_scores = logits_warper(input_ids, next_token_scores)
|
|
||||||
|
|
||||||
# sample
|
|
||||||
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
|
||||||
if generation_config.do_sample:
|
|
||||||
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
|
||||||
else:
|
|
||||||
next_tokens = torch.argmax(probs, dim=-1)
|
|
||||||
|
|
||||||
# update generated ids, model inputs, and length for next step
|
|
||||||
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
|
||||||
model_kwargs = model._update_model_kwargs_for_generation(
|
|
||||||
outputs, model_kwargs, is_encoder_decoder=False
|
|
||||||
)
|
|
||||||
unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
|
|
||||||
|
|
||||||
output_token_ids = input_ids[0].cpu().tolist()
|
|
||||||
output_token_ids = output_token_ids[input_length:]
|
|
||||||
for each_eos_token_id in eos_token_id:
|
|
||||||
if output_token_ids[-1] == each_eos_token_id:
|
|
||||||
output_token_ids = output_token_ids[:-1]
|
|
||||||
response = tokenizer.decode(output_token_ids)
|
|
||||||
|
|
||||||
yield response
|
|
||||||
# stop when each sentence is finished, or if we exceed the maximum length
|
|
||||||
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def check_dependency(self):
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
|
||||||
try:
|
|
||||||
try_to_import_special_deps()
|
|
||||||
self.info = "依赖检测通过"
|
|
||||||
self.success = True
|
|
||||||
except:
|
|
||||||
self.info = f"缺少{model_name}的依赖，如果要使用{model_name}，除了基础的pip依赖以外，您还需要运行{cmd_to_install}安装{model_name}的依赖。"
|
|
||||||
self.success = False
|
|
||||||
|
|
||||||
def run(self):
|
|
||||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
|
||||||
# 第一次运行，加载参数
|
|
||||||
try:
|
|
||||||
self._model, self._tokenizer = self.load_model_and_tokenizer()
|
|
||||||
except:
|
|
||||||
from toolbox import trimmed_format_exc
|
|
||||||
self.child.send(f'[Local Message] 不能正常加载{model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
|
|
||||||
raise RuntimeError(f"不能正常加载{model_name}的参数！")
|
|
||||||
|
|
||||||
while True:
|
|
||||||
# 进入任务等待状态
|
|
||||||
kwargs = self.child.recv()
|
|
||||||
# 收到消息，开始请求
|
|
||||||
try:
|
|
||||||
for response_full in self.llm_stream_generator(**kwargs):
|
|
||||||
self.child.send(response_full)
|
|
||||||
except:
|
|
||||||
from toolbox import trimmed_format_exc
|
|
||||||
self.child.send(f'[Local Message] 调用{model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
|
|
||||||
# 请求处理结束，开始下一个循环
|
|
||||||
self.child.send('[Finish]')
|
|
||||||
|
|
||||||
def stream_chat(self, **kwargs):
|
|
||||||
# ⭐主进程执行
|
|
||||||
self.threadLock.acquire()
|
|
||||||
self.parent.send(kwargs)
|
|
||||||
while True:
|
|
||||||
res = self.parent.recv()
|
|
||||||
if res != '[Finish]':
|
|
||||||
yield res
|
|
||||||
else:
|
|
||||||
break
|
|
||||||
self.threadLock.release()
|
|
||||||
|
|
||||||
|
|
||||||
# ------------------------------------------------------------------------------------------------------------------------
|
|
||||||
# 🔌💻 GPT-Academic
|
|
||||||
# ------------------------------------------------------------------------------------------------------------------------
|
|
||||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
|
||||||
"""
|
|
||||||
⭐多线程方法
|
|
||||||
函数的说明请见 request_llm/bridge_all.py
|
|
||||||
"""
|
|
||||||
_llm_handle = GetInternlmHandle()
|
|
||||||
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
|
|
||||||
if not _llm_handle.success:
|
|
||||||
error = _llm_handle.info
|
|
||||||
_llm_handle = None
|
|
||||||
raise RuntimeError(error)
|
|
||||||
|
|
||||||
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
|
|
||||||
history_feedin = []
|
|
||||||
history_feedin.append(["What can I do?", sys_prompt])
|
|
||||||
for i in range(len(history)//2):
|
|
||||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
|
||||||
|
|
||||||
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
|
|
||||||
response = ""
|
|
||||||
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
|
||||||
if len(observe_window) >= 1: observe_window[0] = response
|
|
||||||
if len(observe_window) >= 2:
|
|
||||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
|
||||||
raise RuntimeError("程序终止。")
|
|
||||||
return response
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
|
||||||
"""
|
|
||||||
⭐单线程方法
|
|
||||||
函数的说明请见 request_llm/bridge_all.py
|
|
||||||
"""
|
|
||||||
chatbot.append((inputs, ""))
|
|
||||||
|
|
||||||
_llm_handle = GetInternlmHandle()
|
|
||||||
chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
|
|
||||||
yield from update_ui(chatbot=chatbot, history=[])
|
|
||||||
if not _llm_handle.success:
|
|
||||||
_llm_handle = None
|
|
||||||
return
|
|
||||||
|
|
||||||
if additional_fn is not None:
|
|
||||||
from core_functional import handle_core_functionality
|
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
|
||||||
|
|
||||||
# 处理历史信息
|
|
||||||
history_feedin = []
|
|
||||||
history_feedin.append(["What can I do?", system_prompt] )
|
|
||||||
for i in range(len(history)//2):
|
|
||||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
|
||||||
|
|
||||||
# 开始接收chatglm的回复
|
|
||||||
response = f"[Local Message]: 等待{model_name}响应中 ..."
|
|
||||||
for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
|
||||||
chatbot[-1] = (inputs, response)
|
|
||||||
yield from update_ui(chatbot=chatbot, history=history)
|
|
||||||
|
|
||||||
# 总结输出
|
|
||||||
if response == f"[Local Message]: 等待{model_name}响应中 ...":
|
|
||||||
response = f"[Local Message]: {model_name}响应异常 ..."
|
|
||||||
history.extend([inputs, response])
|
|
||||||
yield from update_ui(chatbot=chatbot, history=history)
|
|
||||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
yield from update_ui(chatbot=chatbot, history=history)
|
yield from update_ui(chatbot=chatbot, history=history)
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
# 处理历史信息
|
# 处理历史信息
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
|
|||||||
@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
for i in range(len(history)//2):
|
for i in range(len(history)//2):
|
||||||
|
|||||||
@ -248,8 +248,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
return
|
return
|
||||||
|
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]:
|
||||||
|
inputs = core_functional[additional_fn]["PreProcess"](
|
||||||
|
inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + \
|
||||||
|
inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
history_feedin = []
|
history_feedin = []
|
||||||
for i in range(len(history)//2):
|
for i in range(len(history)//2):
|
||||||
|
|||||||
@ -96,8 +96,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
|||||||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||||||
"""
|
"""
|
||||||
if additional_fn is not None:
|
if additional_fn is not None:
|
||||||
from core_functional import handle_core_functionality
|
import core_functional
|
||||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
importlib.reload(core_functional) # 热更新prompt
|
||||||
|
core_functional = core_functional.get_core_functions()
|
||||||
|
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||||
|
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||||
|
|
||||||
raw_input = "What I would like to say is the following: " + inputs
|
raw_input = "What I would like to say is the following: " + inputs
|
||||||
history.extend([inputs, ""])
|
history.extend([inputs, ""])
|
||||||
|
|||||||
@ -447,15 +447,6 @@ class _ChatHub:
|
|||||||
"""
|
"""
|
||||||
Ask a question to the bot
|
Ask a question to the bot
|
||||||
"""
|
"""
|
||||||
req_header = HEADERS
|
|
||||||
if self.cookies is not None:
|
|
||||||
ws_cookies = []
|
|
||||||
for cookie in self.cookies:
|
|
||||||
ws_cookies.append(f"{cookie['name']}={cookie['value']}")
|
|
||||||
req_header.update({
|
|
||||||
'Cookie': ';'.join(ws_cookies),
|
|
||||||
})
|
|
||||||
|
|
||||||
timeout = aiohttp.ClientTimeout(total=30)
|
timeout = aiohttp.ClientTimeout(total=30)
|
||||||
self.session = aiohttp.ClientSession(timeout=timeout)
|
self.session = aiohttp.ClientSession(timeout=timeout)
|
||||||
|
|
||||||
@ -464,7 +455,7 @@ class _ChatHub:
|
|||||||
# Check if websocket is closed
|
# Check if websocket is closed
|
||||||
self.wss = await self.session.ws_connect(
|
self.wss = await self.session.ws_connect(
|
||||||
wss_link,
|
wss_link,
|
||||||
headers=req_header,
|
headers=HEADERS,
|
||||||
ssl=ssl_context,
|
ssl=ssl_context,
|
||||||
proxy=self.proxy,
|
proxy=self.proxy,
|
||||||
autoping=False,
|
autoping=False,
|
||||||
@ -519,11 +510,7 @@ class _ChatHub:
|
|||||||
resp_txt_no_link = ""
|
resp_txt_no_link = ""
|
||||||
while not final:
|
while not final:
|
||||||
msg = await self.wss.receive()
|
msg = await self.wss.receive()
|
||||||
try:
|
objects = msg.data.split(DELIMITER)
|
||||||
objects = msg.data.split(DELIMITER)
|
|
||||||
except :
|
|
||||||
continue
|
|
||||||
|
|
||||||
for obj in objects:
|
for obj in objects:
|
||||||
if obj is None or not obj:
|
if obj is None or not obj:
|
||||||
continue
|
continue
|
||||||
@ -1122,4 +1109,4 @@ class ImageQuery(Query):
|
|||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
main()
|
main()
|
||||||
@ -14,8 +14,7 @@ if __name__ == "__main__":
|
|||||||
# from request_llm.bridge_moss import predict_no_ui_long_connection
|
# from request_llm.bridge_moss import predict_no_ui_long_connection
|
||||||
# from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
|
# from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
|
||||||
# from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
|
# from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
|
||||||
# from request_llm.bridge_claude import predict_no_ui_long_connection
|
from request_llm.bridge_claude import predict_no_ui_long_connection
|
||||||
from request_llm.bridge_internlm import predict_no_ui_long_connection
|
|
||||||
|
|
||||||
llm_kwargs = {
|
llm_kwargs = {
|
||||||
'max_length': 512,
|
'max_length': 512,
|
||||||
@ -23,8 +22,45 @@ if __name__ == "__main__":
|
|||||||
'temperature': 1,
|
'temperature': 1,
|
||||||
}
|
}
|
||||||
|
|
||||||
result = predict_no_ui_long_connection( inputs="请问什么是质子？",
|
result = predict_no_ui_long_connection(inputs="你好",
|
||||||
llm_kwargs=llm_kwargs,
|
llm_kwargs=llm_kwargs,
|
||||||
history=["你好", "我好！"],
|
history=[],
|
||||||
sys_prompt="")
|
sys_prompt="")
|
||||||
print('final result:', result)
|
print('final result:', result)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# # print(result)
|
||||||
|
# from multiprocessing import Process, Pipe
|
||||||
|
# class GetGLMHandle(Process):
|
||||||
|
# def __init__(self):
|
||||||
|
# super().__init__(daemon=True)
|
||||||
|
# pass
|
||||||
|
# def run(self):
|
||||||
|
# # 子进程执行
|
||||||
|
# # 第一次运行，加载参数
|
||||||
|
# def validate_path():
|
||||||
|
# import os, sys
|
||||||
|
# dir_name = os.path.dirname(__file__)
|
||||||
|
# root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
|
||||||
|
# os.chdir(root_dir_assume + '/request_llm/jittorllms')
|
||||||
|
# sys.path.append(root_dir_assume + '/request_llm/jittorllms')
|
||||||
|
# validate_path() # validate path so you can run from base directory
|
||||||
|
# jittorllms_model = None
|
||||||
|
# import types
|
||||||
|
# try:
|
||||||
|
# if jittorllms_model is None:
|
||||||
|
# from models import get_model
|
||||||
|
# # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
|
||||||
|
# args_dict = {'model': 'chatrwkv'}
|
||||||
|
# print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
|
||||||
|
# jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
|
||||||
|
# print('done get model')
|
||||||
|
# except:
|
||||||
|
# # self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
|
||||||
|
# raise RuntimeError("不能正常加载jittorllms的参数！")
|
||||||
|
# x = GetGLMHandle()
|
||||||
|
# x.start()
|
||||||
|
|
||||||
|
|
||||||
|
# input()
|
||||||
@ -18,4 +18,3 @@ openai
|
|||||||
numpy
|
numpy
|
||||||
arxiv
|
arxiv
|
||||||
rich
|
rich
|
||||||
pypdf2==2.12.1
|
|
||||||
|
|||||||
28
toolbox.py
28
toolbox.py
@ -538,11 +538,7 @@ def load_chat_cookies():
|
|||||||
return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
|
return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
|
||||||
|
|
||||||
def is_openai_api_key(key):
|
def is_openai_api_key(key):
|
||||||
CUSTOM_API_KEY_PATTERN, = get_conf('CUSTOM_API_KEY_PATTERN')
|
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
||||||
if len(CUSTOM_API_KEY_PATTERN) != 0:
|
|
||||||
API_MATCH_ORIGINAL = re.match(CUSTOM_API_KEY_PATTERN, key)
|
|
||||||
else:
|
|
||||||
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
|
|
||||||
return bool(API_MATCH_ORIGINAL)
|
return bool(API_MATCH_ORIGINAL)
|
||||||
|
|
||||||
def is_azure_api_key(key):
|
def is_azure_api_key(key):
|
||||||
@ -598,7 +594,7 @@ def select_api_key(keys, llm_model):
|
|||||||
if is_azure_api_key(k): avail_key_list.append(k)
|
if is_azure_api_key(k): avail_key_list.append(k)
|
||||||
|
|
||||||
if len(avail_key_list) == 0:
|
if len(avail_key_list) == 0:
|
||||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
|
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure和api2d请求源）")
|
||||||
|
|
||||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||||
return api_key
|
return api_key
|
||||||
@ -674,14 +670,13 @@ def read_single_conf_with_lru_cache(arg):
|
|||||||
|
|
||||||
# 在读取API_KEY时，检查一下是不是忘了改config
|
# 在读取API_KEY时，检查一下是不是忘了改config
|
||||||
if arg == 'API_KEY':
|
if arg == 'API_KEY':
|
||||||
print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
|
print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,api2d-key3\"")
|
||||||
print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
|
print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
|
||||||
if is_any_api_key(r):
|
if is_any_api_key(r):
|
||||||
print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
|
print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
|
||||||
else:
|
else:
|
||||||
print亮红( "[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
|
print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。")
|
||||||
if arg == 'proxies':
|
if arg == 'proxies':
|
||||||
if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
|
|
||||||
if r is None:
|
if r is None:
|
||||||
print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
|
print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
|
||||||
else:
|
else:
|
||||||
@ -690,7 +685,6 @@ def read_single_conf_with_lru_cache(arg):
|
|||||||
return r
|
return r
|
||||||
|
|
||||||
|
|
||||||
@lru_cache(maxsize=128)
|
|
||||||
def get_conf(*args):
|
def get_conf(*args):
|
||||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
||||||
res = []
|
res = []
|
||||||
@ -889,16 +883,4 @@ def objload(file='objdump.tmp'):
|
|||||||
return
|
return
|
||||||
with open(file, 'rb') as f:
|
with open(file, 'rb') as f:
|
||||||
return pickle.load(f)
|
return pickle.load(f)
|
||||||
|
|
||||||
def Singleton(cls):
|
|
||||||
"""
|
|
||||||
一个单实例装饰器
|
|
||||||
"""
|
|
||||||
_instance = {}
|
|
||||||
|
|
||||||
def _singleton(*args, **kargs):
|
|
||||||
if cls not in _instance:
|
|
||||||
_instance[cls] = cls(*args, **kargs)
|
|
||||||
return _instance[cls]
|
|
||||||
|
|
||||||
return _singleton
|
|
||||||
6
version
6
version
@ -1,5 +1,5 @@
|
|||||||
{
|
{
|
||||||
"version": 3.47,
|
"version": 3.46,
|
||||||
"show_feature": true,
|
"show_feature": true,
|
||||||
"new_feature": "优化一键升级 <-> 提高arxiv翻译速度和成功率 <-> 支持自定义APIKEY格式 <-> 临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持"
|
"new_feature": "临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
|
||||||
}
|
}
|
||||||
Reference in New Issue
Block a user