镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
Merge branch 'master' into huggingfacelocal
这个提交包含在：
binary-husky
44
.github/workflows/build-with-all-capacity-beta.yml
vendored
普通文件
44
.github/workflows/build-with-all-capacity-beta.yml
vendored
普通文件
@@ -0,0 +1,44 @@
|
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# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
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name: build-with-all-capacity-beta
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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 }}_with_all_capacity_beta
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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+AllCapacityBeta
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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107
README.md
107
README.md
@@ -14,41 +14,69 @@ pinned: false
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>
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> 2023.11.12: 某些依赖包尚不兼容python 3.12，推荐python 3.11。
|
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>
|
||||
> 2023.11.7: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目开源免费，近期发现有人蔑视开源协议并利用本项目违规圈钱，请提高警惕，谨防上当受骗。
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> 2023.12.26: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
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<br>
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<div align=center>
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<h1 aligh="center">
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<img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)
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</h1>
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|
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[![Github][Github-image]][Github-url]
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[![License][License-image]][License-url]
|
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[![Releases][Releases-image]][Releases-url]
|
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[![Installation][Installation-image]][Installation-url]
|
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[![Wiki][Wiki-image]][Wiki-url]
|
||||
[![PR][PRs-image]][PRs-url]
|
||||
|
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[Github-image]: https://img.shields.io/badge/github-12100E.svg?style=flat-square
|
||||
[License-image]: https://img.shields.io/github/license/binary-husky/gpt_academic?label=License&style=flat-square&color=orange
|
||||
[Releases-image]: https://img.shields.io/github/release/binary-husky/gpt_academic?label=Release&style=flat-square&color=blue
|
||||
[Installation-image]: https://img.shields.io/badge/dynamic/json?color=blue&url=https://raw.githubusercontent.com/binary-husky/gpt_academic/master/version&query=$.version&label=Installation&style=flat-square
|
||||
[Wiki-image]: https://img.shields.io/badge/wiki-项目文档-black?style=flat-square
|
||||
[PRs-image]: https://img.shields.io/badge/PRs-welcome-pink?style=flat-square
|
||||
|
||||
[Github-url]: https://github.com/binary-husky/gpt_academic
|
||||
[License-url]: https://github.com/binary-husky/gpt_academic/blob/master/LICENSE
|
||||
[Releases-url]: https://github.com/binary-husky/gpt_academic/releases
|
||||
[Installation-url]: https://github.com/binary-husky/gpt_academic#installation
|
||||
[Wiki-url]: https://github.com/binary-husky/gpt_academic/wiki
|
||||
[PRs-url]: https://github.com/binary-husky/gpt_academic/pulls
|
||||
|
||||
|
||||
|
||||
# <div align=center><img src="docs/logo.png" width="40"> GPT 学术优化 (GPT Academic)</div>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
**如果喜欢这个项目，请给它一个Star；如果您发明了好用的快捷键或插件，欢迎发pull requests！**
|
||||
|
||||
If you like this project, please give it a Star. We also have a README in [English|](docs/README.English.md)[日本語|](docs/README.Japanese.md)[한국어|](docs/README.Korean.md)[Русский|](docs/README.Russian.md)[Français](docs/README.French.md) translated by this project itself.
|
||||
To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
|
||||
If you like this project, please give it a Star.
|
||||
Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanese.md) | [한국어](docs/README.Korean.md) | [Русский](docs/README.Russian.md) | [Français](docs/README.French.md). All translations have been provided by the project itself. To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
|
||||
<br>
|
||||
|
||||
|
||||
> **Note**
|
||||
>
|
||||
> 1.请注意只有 **高亮** 标识的插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR。
|
||||
>
|
||||
> 2.本项目中每个文件的功能都在[自译解报告`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)。[常规安装方法](#installation) | [一键安装脚本](https://github.com/binary-husky/gpt_academic/releases) | [配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
|
||||
> 2.本项目中每个文件的功能都在[自译解报告](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)`self_analysis.md`详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题请查阅wiki。
|
||||
> [](#installation) [](https://github.com/binary-husky/gpt_academic/releases) [](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明) []([https://github.com/binary-husky/gpt_academic/wiki/项目配置说明](https://github.com/binary-husky/gpt_academic/wiki))
|
||||
>
|
||||
> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。
|
||||
> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交即可生效。
|
||||
|
||||
|
||||
|
||||
<br><br>
|
||||
|
||||
<div align="center">
|
||||
|
||||
功能（⭐= 近期新增功能） | 描述
|
||||
--- | ---
|
||||
⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，[智谱API](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
|
||||
⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，[智谱API](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
|
||||
润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
|
||||
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
|
||||
模块化设计 | 支持自定义强大的[插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [插件] 一键可以剖析Python/C/C++/Java/Lua/...项目树 或 [自我剖析](https://www.bilibili.com/video/BV1cj411A7VW)
|
||||
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [插件] 一键剖析Python/C/C++/Java/Lua/...项目树 或 [自我剖析](https://www.bilibili.com/video/BV1cj411A7VW)
|
||||
读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
|
||||
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
|
||||
批量注释生成 | [插件] 一键批量生成函数注释
|
||||
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？
|
||||
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？就是出自他的手笔
|
||||
chat分析报告生成 | [插件] 运行后自动生成总结汇报
|
||||
[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [插件] PDF论文提取题目&摘要+翻译全文（多线程）
|
||||
[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
|
||||
@@ -60,22 +88,22 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
|
||||
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
|
||||
⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
|
||||
启动暗色[主题](https://github.com/binary-husky/gpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)伺候的感觉一定会很不错吧？
|
||||
⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件
|
||||
更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
|
||||
⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
|
||||
⭐虚空终端插件 | [插件] 用自然语言，直接调度本项目其他插件
|
||||
⭐虚空终端插件 | [插件] 能够使用自然语言直接调度本项目其他插件
|
||||
更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
|
||||
</div>
|
||||
|
||||
|
||||
- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/d81137c3-affd-4cd1-bb5e-b15610389762" width="700" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/279702205-d81137c3-affd-4cd1-bb5e-b15610389762.gif" width="700" >
|
||||
</div>
|
||||
|
||||
|
||||
- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放粘贴板
|
||||
- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放剪贴板
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
|
||||
</div>
|
||||
@@ -85,21 +113,23 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
|
||||
<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
|
||||
</div>
|
||||
|
||||
- 如果输出包含公式，会同时以tex形式和渲染形式显示，方便复制和阅读
|
||||
- 如果输出包含公式，会以tex形式和渲染形式同时显示，方便复制和阅读
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
|
||||
</div>
|
||||
|
||||
- 懒得看项目代码？整个工程直接给chatgpt炫嘴里
|
||||
- 懒得看项目代码？直接把整个工程炫ChatGPT嘴里
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
|
||||
</div>
|
||||
|
||||
- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
|
||||
- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + GPT4）
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
|
||||
</div>
|
||||
|
||||
<br><br>
|
||||
|
||||
# Installation
|
||||
### 安装方法I：直接运行 (Windows, Linux or MacOS)
|
||||
|
||||
@@ -110,13 +140,13 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
|
||||
cd gpt_academic
|
||||
```
|
||||
|
||||
2. 配置API_KEY
|
||||
2. 配置API_KEY等变量
|
||||
|
||||
在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
|
||||
在`config.py`中，配置API KEY等变量。[特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1)、[Wiki-项目配置说明](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
|
||||
|
||||
「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解该读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。 」
|
||||
「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解以上读取逻辑，我们强烈建议您在`config.py`同路径下创建一个名为`config_private.py`的新配置文件，并使用`config_private.py`配置项目，以确保更新或其他用户无法轻易查看您的私有配置 」。
|
||||
|
||||
「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。配置读取优先级: `环境变量` > `config_private.py` > `config.py`。 」
|
||||
「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。配置读取优先级: `环境变量` > `config_private.py` > `config.py` 」。
|
||||
|
||||
|
||||
3. 安装依赖
|
||||
@@ -149,6 +179,14 @@ git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss #
|
||||
|
||||
# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
|
||||
pip install bitsandbyte
|
||||
# windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
|
||||
python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
|
||||
pip install -U git+https://github.com/huggingface/transformers.git
|
||||
pip install -U git+https://github.com/huggingface/accelerate.git
|
||||
pip install peft
|
||||
```
|
||||
|
||||
</p>
|
||||
@@ -163,7 +201,7 @@ AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-
|
||||
|
||||
### 安装方法II：使用Docker
|
||||
|
||||
0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小，则不推荐使用这个）
|
||||
0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小，则不推荐该方法部署完整项目）
|
||||
[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml)
|
||||
|
||||
``` sh
|
||||
@@ -192,26 +230,26 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
|
||||
```
|
||||
|
||||
|
||||
### 安装方法III：其他部署姿势
|
||||
### 安装方法III：其他部署方法
|
||||
1. **Windows一键运行脚本**。
|
||||
完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。
|
||||
脚本的贡献来源是[oobabooga](https://github.com/oobabooga/one-click-installers)。
|
||||
完全不熟悉python环境的Windows用户可以下载[Release](https://github.com/binary-husky/gpt_academic/releases)中发布的一键运行脚本安装无本地模型的版本。脚本贡献来源：[oobabooga](https://github.com/oobabooga/one-click-installers)。
|
||||
|
||||
2. 使用第三方API、Azure等、文心一言、星火等，见[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)
|
||||
|
||||
3. 云服务器远程部署避坑指南。
|
||||
请访问[云服务器远程部署wiki](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
|
||||
|
||||
4. 一些新型的部署平台或方法
|
||||
4. 在其他平台部署&二级网址部署
|
||||
- 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
|
||||
- 使用WSL2（Windows Subsystem for Linux 子系统）。请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
|
||||
- 如何在二级网址（如`http://localhost/subpath`）下运行。请访问[FastAPI运行说明](docs/WithFastapi.md)
|
||||
|
||||
<br><br>
|
||||
|
||||
# Advanced Usage
|
||||
### I：自定义新的便捷按钮（学术快捷键）
|
||||
|
||||
任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序。（如按钮已存在，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
|
||||
任意文本编辑器打开`core_functional.py`，添加如下条目，然后重启程序。（如果按钮已存在，那么可以直接修改（前缀、后缀都已支持热修改），无需重启程序即可生效。）
|
||||
例如
|
||||
|
||||
```python
|
||||
@@ -233,6 +271,7 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
|
||||
本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。
|
||||
详情请参考[函数插件指南](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
|
||||
|
||||
<br><br>
|
||||
|
||||
# Updates
|
||||
### I：动态
|
||||
@@ -332,7 +371,7 @@ GPT Academic开发者QQ群：`610599535`
|
||||
|
||||
- 已知问题
|
||||
- 某些浏览器翻译插件干扰此软件前端的运行
|
||||
- 官方Gradio目前有很多兼容性Bug，请务必使用`requirement.txt`安装Gradio
|
||||
- 官方Gradio目前有很多兼容性问题，请**务必使用`requirement.txt`安装Gradio**
|
||||
|
||||
### III：主题
|
||||
可以通过修改`THEME`选项（config.py）变更主题
|
||||
@@ -343,8 +382,8 @@ GPT Academic开发者QQ群：`610599535`
|
||||
|
||||
1. `master` 分支: 主分支，稳定版
|
||||
2. `frontier` 分支: 开发分支，测试版
|
||||
3. 如何接入其他大模型：[接入其他大模型](request_llms/README.md)
|
||||
|
||||
3. 如何[接入其他大模型](request_llms/README.md)
|
||||
4. 访问GPT-Academic的[在线服务并支持我们](https://github.com/binary-husky/gpt_academic/wiki/online)
|
||||
|
||||
### V：参考与学习
|
||||
|
||||
|
||||
125
app.py
125
app.py
@@ -1,6 +1,17 @@
|
||||
import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
|
||||
import pickle
|
||||
import base64
|
||||
|
||||
help_menu_description = \
|
||||
"""Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic),
|
||||
感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
|
||||
</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki),
|
||||
如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
|
||||
</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
|
||||
</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
|
||||
</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
|
||||
</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端
|
||||
</br></br>如何保存对话: 点击保存当前的对话按钮
|
||||
</br></br>如何语音对话: 请阅读Wiki
|
||||
</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
|
||||
|
||||
def main():
|
||||
import subprocess, sys
|
||||
@@ -10,7 +21,7 @@ def main():
|
||||
raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
|
||||
from request_llms.bridge_all import predict
|
||||
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
|
||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
|
||||
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
|
||||
CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
|
||||
ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
|
||||
@@ -20,21 +31,11 @@ def main():
|
||||
# 如果WEB_PORT是-1, 则随机选取WEB端口
|
||||
PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
|
||||
from check_proxy import get_current_version
|
||||
from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme
|
||||
|
||||
from themes.theme import adjust_theme, advanced_css, theme_declaration
|
||||
from themes.theme import js_code_for_css_changing, js_code_for_darkmode_init, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
|
||||
from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
|
||||
title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
|
||||
description = "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), "
|
||||
description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)."
|
||||
description += "</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), "
|
||||
description += "如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues)."
|
||||
description += "</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交"
|
||||
description += "</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮"
|
||||
description += "</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮"
|
||||
description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端"
|
||||
description += "</br></br>如何保存对话: 点击保存当前的对话按钮"
|
||||
description += "</br></br>如何语音对话: 请阅读Wiki"
|
||||
description += "</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"
|
||||
|
||||
|
||||
# 问询记录, python 版本建议3.9+（越新越好）
|
||||
import logging, uuid
|
||||
os.makedirs(PATH_LOGGING, exist_ok=True)
|
||||
@@ -88,7 +89,7 @@ def main():
|
||||
with gr_L2(scale=1, elem_id="gpt-panel"):
|
||||
with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
|
||||
with gr.Row():
|
||||
txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥，输入多个密钥时，用英文逗号间隔。支持OpenAI密钥和API2D密钥共存。").style(container=False)
|
||||
txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥，输入多个密钥时，用英文逗号间隔。支持多个OpenAI密钥共存。").style(container=False)
|
||||
with gr.Row():
|
||||
submitBtn = gr.Button("提交", elem_id="elem_submit", variant="primary")
|
||||
with gr.Row():
|
||||
@@ -149,7 +150,7 @@ def main():
|
||||
with gr.Row():
|
||||
with gr.Tab("上传文件", elem_id="interact-panel"):
|
||||
gr.Markdown("请上传本地文件/压缩包供“函数插件区”功能调用。请注意: 上传文件后会自动把输入区修改为相应路径。")
|
||||
file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple")
|
||||
file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload_float")
|
||||
|
||||
with gr.Tab("更换模型 & Prompt", elem_id="interact-panel"):
|
||||
md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
|
||||
@@ -165,39 +166,24 @@ def main():
|
||||
checkboxes_2 = gr.CheckboxGroup(["自定义菜单"],
|
||||
value=[], label="显示/隐藏自定义菜单", elem_id='cbs').style(container=False)
|
||||
dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")
|
||||
dark_mode_btn.click(None, None, None, _js="""() => {
|
||||
if (document.querySelectorAll('.dark').length) {
|
||||
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
||||
} else {
|
||||
document.querySelector('body').classList.add('dark');
|
||||
}
|
||||
}""",
|
||||
dark_mode_btn.click(None, None, None, _js=js_code_for_toggle_darkmode,
|
||||
)
|
||||
with gr.Tab("帮助", elem_id="interact-panel"):
|
||||
gr.Markdown(description)
|
||||
gr.Markdown(help_menu_description)
|
||||
|
||||
with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_input_secondary:
|
||||
with gr.Accordion("浮动输入区", open=True, elem_id="input-panel2"):
|
||||
with gr.Row() as row:
|
||||
row.style(equal_height=True)
|
||||
with gr.Column(scale=10):
|
||||
txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", lines=8, label="输入区2").style(container=False)
|
||||
txt2 = gr.Textbox(show_label=False, placeholder="Input question here.",
|
||||
elem_id='user_input_float', lines=8, label="输入区2").style(container=False)
|
||||
with gr.Column(scale=1, min_width=40):
|
||||
submitBtn2 = gr.Button("提交", variant="primary"); submitBtn2.style(size="sm")
|
||||
resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm")
|
||||
stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
|
||||
clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
|
||||
|
||||
def to_cookie_str(d):
|
||||
# Pickle the dictionary and encode it as a string
|
||||
pickled_dict = pickle.dumps(d)
|
||||
cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
|
||||
return cookie_value
|
||||
|
||||
def from_cookie_str(c):
|
||||
# Decode the base64-encoded string and unpickle it into a dictionary
|
||||
pickled_dict = base64.b64decode(c.encode('utf-8'))
|
||||
return pickle.loads(pickled_dict)
|
||||
|
||||
with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_customize:
|
||||
with gr.Accordion("自定义菜单", open=True, elem_id="edit-panel"):
|
||||
@@ -229,11 +215,11 @@ def main():
|
||||
else:
|
||||
ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
|
||||
ret.update({cookies: cookies_})
|
||||
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
|
||||
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
|
||||
except: persistent_cookie_ = {}
|
||||
persistent_cookie_["custom_bnt"] = customize_fn_overwrite_ # dict update new value
|
||||
persistent_cookie_ = to_cookie_str(persistent_cookie_) # persistent cookie to dict
|
||||
ret.update({persistent_cookie: persistent_cookie_}) # write persistent cookie
|
||||
persistent_cookie_["custom_bnt"] = customize_fn_overwrite_ # dict update new value
|
||||
persistent_cookie_ = to_cookie_str(persistent_cookie_) # persistent cookie to dict
|
||||
ret.update({persistent_cookie: persistent_cookie_}) # write persistent cookie
|
||||
return ret
|
||||
|
||||
def reflesh_btn(persistent_cookie_, cookies_):
|
||||
@@ -254,10 +240,11 @@ def main():
|
||||
else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
|
||||
return ret
|
||||
|
||||
basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies],[cookies, *customize_btns.values(), *predefined_btns.values()])
|
||||
basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies], [cookies, *customize_btns.values(), *predefined_btns.values()])
|
||||
h = basic_fn_confirm.click(assign_btn, [persistent_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix],
|
||||
[persistent_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
|
||||
h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""") # save persistent cookie
|
||||
# save persistent cookie
|
||||
h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""")
|
||||
|
||||
# 功能区显示开关与功能区的互动
|
||||
def fn_area_visibility(a):
|
||||
@@ -307,8 +294,8 @@ def main():
|
||||
click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
|
||||
cancel_handles.append(click_handle)
|
||||
# 文件上传区，接收文件后与chatbot的互动
|
||||
file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
|
||||
file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
|
||||
file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None, _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
|
||||
file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None, _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
|
||||
# 函数插件-固定按钮区
|
||||
for k in plugins:
|
||||
if not plugins[k].get("AsButton", True): continue
|
||||
@@ -344,18 +331,7 @@ def main():
|
||||
None,
|
||||
[secret_css],
|
||||
None,
|
||||
_js="""(css) => {
|
||||
var existingStyles = document.querySelectorAll("style[data-loaded-css]");
|
||||
for (var i = 0; i < existingStyles.length; i++) {
|
||||
var style = existingStyles[i];
|
||||
style.parentNode.removeChild(style);
|
||||
}
|
||||
var styleElement = document.createElement('style');
|
||||
styleElement.setAttribute('data-loaded-css', css);
|
||||
styleElement.innerHTML = css;
|
||||
document.head.appendChild(styleElement);
|
||||
}
|
||||
"""
|
||||
_js=js_code_for_css_changing
|
||||
)
|
||||
# 随变按钮的回调函数注册
|
||||
def route(request: gr.Request, k, *args, **kwargs):
|
||||
@@ -387,27 +363,10 @@ def main():
|
||||
rad.feed(cookies['uuid'].hex, audio)
|
||||
audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
|
||||
|
||||
def init_cookie(cookies, chatbot):
|
||||
# 为每一位访问的用户赋予一个独一无二的uuid编码
|
||||
cookies.update({'uuid': uuid.uuid4()})
|
||||
return cookies
|
||||
|
||||
demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
|
||||
darkmode_js = """(dark) => {
|
||||
dark = dark == "True";
|
||||
if (document.querySelectorAll('.dark').length) {
|
||||
if (!dark){
|
||||
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
||||
}
|
||||
} else {
|
||||
if (dark){
|
||||
document.querySelector('body').classList.add('dark');
|
||||
}
|
||||
}
|
||||
}"""
|
||||
load_cookie_js = """(persistent_cookie) => {
|
||||
return getCookie("persistent_cookie");
|
||||
}"""
|
||||
demo.load(None, inputs=None, outputs=[persistent_cookie], _js=load_cookie_js)
|
||||
darkmode_js = js_code_for_darkmode_init
|
||||
demo.load(None, inputs=None, outputs=[persistent_cookie], _js=js_code_for_persistent_cookie_init)
|
||||
demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js) # 配置暗色主题或亮色主题
|
||||
demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
|
||||
|
||||
@@ -418,8 +377,18 @@ def main():
|
||||
if DARK_MODE: print(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
|
||||
else: print(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
|
||||
|
||||
def auto_updates(): time.sleep(0); auto_update()
|
||||
def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
|
||||
def warm_up_mods(): time.sleep(6); warm_up_modules()
|
||||
|
||||
threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
|
||||
threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面
|
||||
threading.Thread(target=warm_up_mods, name="warm-up", daemon=True).start() # 预热tiktoken模块
|
||||
|
||||
run_delayed_tasks()
|
||||
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
|
||||
|
||||
|
||||
# 如果需要在二级路径下运行
|
||||
# CUSTOM_PATH = get_conf('CUSTOM_PATH')
|
||||
# if CUSTOM_PATH != "/":
|
||||
|
||||
@@ -159,7 +159,15 @@ def warm_up_modules():
|
||||
enc.encode("模块预热", disallowed_special=())
|
||||
enc = model_info["gpt-4"]['tokenizer']
|
||||
enc.encode("模块预热", disallowed_special=())
|
||||
|
||||
def warm_up_vectordb():
|
||||
print('正在执行一些模块的预热 ...')
|
||||
from toolbox import ProxyNetworkActivate
|
||||
with ProxyNetworkActivate("Warmup_Modules"):
|
||||
import nltk
|
||||
with ProxyNetworkActivate("Warmup_Modules"): nltk.download("punkt")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import os
|
||||
os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
|
||||
|
||||
29
config.py
29
config.py
@@ -19,13 +19,13 @@ API_KEY = "此处填API密钥" # 可同时填写多个API-KEY，用英文逗
|
||||
USE_PROXY = False
|
||||
if USE_PROXY:
|
||||
"""
|
||||
代理网络的地址，打开你的代理软件查看代理协议(socks5h / http)、地址(localhost)和端口(11284)
|
||||
填写格式是 [协议]:// [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
|
||||
<配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
|
||||
[协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
|
||||
[地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
|
||||
[地址] 填localhost或者127.0.0.1（localhost意思是代理软件安装在本机上）
|
||||
[端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
|
||||
"""
|
||||
# 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5h / http)、地址(localhost)和端口(11284)
|
||||
proxies = {
|
||||
# [协议]:// [地址] :[端口]
|
||||
"http": "socks5h://localhost:11284", # 再例如 "http": "http://127.0.0.1:7890",
|
||||
@@ -99,14 +99,25 @@ AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-prev
|
||||
"api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
|
||||
"gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
|
||||
"chatglm3", "moss", "claude-2"]
|
||||
# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random"
|
||||
# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random"
|
||||
# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
|
||||
# “qwen-turbo", "qwen-plus", "qwen-max"]
|
||||
|
||||
|
||||
# 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
|
||||
MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
|
||||
|
||||
|
||||
# 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
|
||||
# 如果你选择Qwen系列的模型，那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型
|
||||
# 也可以是具体的模型路径
|
||||
QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
|
||||
|
||||
|
||||
# 接入通义千问在线大模型 https://dashscope.console.aliyun.com/
|
||||
DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
|
||||
|
||||
|
||||
# 百度千帆（LLM_MODEL="qianfan"）
|
||||
BAIDU_CLOUD_API_KEY = ''
|
||||
BAIDU_CLOUD_SECRET_KEY = ''
|
||||
@@ -121,7 +132,6 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
|
||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
||||
LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
|
||||
|
||||
|
||||
# 设置gradio的并行线程数（不需要修改）
|
||||
CONCURRENT_COUNT = 100
|
||||
|
||||
@@ -239,6 +249,10 @@ WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
|
||||
BLOCK_INVALID_APIKEY = False
|
||||
|
||||
|
||||
# 启用插件热加载
|
||||
PLUGIN_HOT_RELOAD = False
|
||||
|
||||
|
||||
# 自定义按钮的最大数量限制
|
||||
NUM_CUSTOM_BASIC_BTN = 4
|
||||
|
||||
@@ -282,6 +296,9 @@ NUM_CUSTOM_BASIC_BTN = 4
|
||||
│ ├── ZHIPUAI_API_KEY
|
||||
│ └── ZHIPUAI_MODEL
|
||||
│
|
||||
├── "qwen-turbo" 等通义千问大模型
|
||||
│ └── DASHSCOPE_API_KEY
|
||||
│
|
||||
└── "newbing" Newbing接口不再稳定，不推荐使用
|
||||
├── NEWBING_STYLE
|
||||
└── NEWBING_COOKIES
|
||||
@@ -298,7 +315,7 @@ NUM_CUSTOM_BASIC_BTN = 4
|
||||
├── "jittorllms_pangualpha"
|
||||
├── "jittorllms_llama"
|
||||
├── "deepseekcoder"
|
||||
├── "qwen"
|
||||
├── "qwen-local"
|
||||
├── RWKV的支持见Wiki
|
||||
└── "llama2"
|
||||
|
||||
|
||||
@@ -345,7 +345,7 @@ def get_crazy_functions():
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4", # 高级参数输入区的显示提示
|
||||
"ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&gpt-4", # 高级参数输入区的显示提示
|
||||
"Function": HotReload(同时问询_指定模型)
|
||||
},
|
||||
})
|
||||
@@ -354,9 +354,9 @@ def get_crazy_functions():
|
||||
print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3
|
||||
from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
|
||||
function_plugins.update({
|
||||
"图片生成_DALLE2 （先切换模型到openai或api2d）": {
|
||||
"图片生成_DALLE2 （先切换模型到gpt-*）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
@@ -367,16 +367,26 @@ def get_crazy_functions():
|
||||
},
|
||||
})
|
||||
function_plugins.update({
|
||||
"图片生成_DALLE3 （先切换模型到openai或api2d）": {
|
||||
"图片生成_DALLE3 （先切换模型到gpt-*）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
|
||||
"ArgsReminder": "在这里输入分辨率, 如1024x1024（默认），支持 1024x1024, 1792x1024, 1024x1792。如需生成高清图像，请输入 1024x1024-HD, 1792x1024-HD, 1024x1792-HD。", # 高级参数输入区的显示提示
|
||||
"ArgsReminder": "在这里输入自定义参数「分辨率-质量(可选)-风格(可选)」, 参数示例「1024x1024-hd-vivid」 || 分辨率支持 「1024x1024」(默认) /「1792x1024」/「1024x1792」 || 质量支持 「-standard」(默认) /「-hd」 || 风格支持 「-vivid」(默认) /「-natural」", # 高级参数输入区的显示提示
|
||||
"Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
|
||||
"Function": HotReload(图片生成_DALLE3)
|
||||
},
|
||||
})
|
||||
function_plugins.update({
|
||||
"图片修改_DALLE2 （先切换模型到gpt-*）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": False, # 调用时，唤起高级参数输入区（默认False）
|
||||
# "Info": "使用DALLE2修改图片 | 输入参数字符串，提供图像的内容",
|
||||
"Function": HotReload(图片修改_DALLE2)
|
||||
},
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print('Load function plugin failed')
|
||||
@@ -430,7 +440,7 @@ def get_crazy_functions():
|
||||
print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.Langchain知识库 import 知识库问答
|
||||
from crazy_functions.知识库问答 import 知识库文件注入
|
||||
function_plugins.update({
|
||||
"构建知识库（先上传文件素材,再运行此插件）": {
|
||||
"Group": "对话",
|
||||
@@ -438,7 +448,7 @@ def get_crazy_functions():
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
|
||||
"Function": HotReload(知识库问答)
|
||||
"Function": HotReload(知识库文件注入)
|
||||
}
|
||||
})
|
||||
except:
|
||||
@@ -446,9 +456,9 @@ def get_crazy_functions():
|
||||
print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.Langchain知识库 import 读取知识库作答
|
||||
from crazy_functions.知识库问答 import 读取知识库作答
|
||||
function_plugins.update({
|
||||
"知识库问答（构建知识库后,再运行此插件）": {
|
||||
"知识库文件注入（构建知识库后,再运行此插件）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
@@ -489,7 +499,7 @@ def get_crazy_functions():
|
||||
})
|
||||
from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
|
||||
function_plugins.update({
|
||||
"Arixv论文精细翻译（输入arxivID）[需Latex]": {
|
||||
"Arxiv论文精细翻译（输入arxivID）[需Latex]": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
@@ -580,6 +590,20 @@ def get_crazy_functions():
|
||||
print(trimmed_format_exc())
|
||||
print('Load function plugin failed')
|
||||
|
||||
try:
|
||||
from crazy_functions.互动小游戏 import 随机小游戏
|
||||
function_plugins.update({
|
||||
"随机互动小游戏（仅供测试）": {
|
||||
"Group": "智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Function": HotReload(随机小游戏)
|
||||
}
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
print('Load function plugin failed')
|
||||
|
||||
# try:
|
||||
# from crazy_functions.chatglm微调工具 import 微调数据集生成
|
||||
# function_plugins.update({
|
||||
|
||||
@@ -26,8 +26,8 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
|
||||
@@ -26,8 +26,8 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
|
||||
@@ -88,6 +88,9 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
|
||||
target_file = pj(translation_dir, 'translate_zh.pdf')
|
||||
if os.path.exists(target_file):
|
||||
promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
|
||||
target_file_compare = pj(translation_dir, 'comparison.pdf')
|
||||
if os.path.exists(target_file_compare):
|
||||
promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
|
||||
return target_file
|
||||
return False
|
||||
def is_float(s):
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
|
||||
import threading
|
||||
import os
|
||||
import logging
|
||||
@@ -139,6 +139,8 @@ def can_multi_process(llm):
|
||||
if llm.startswith('gpt-'): return True
|
||||
if llm.startswith('api2d-'): return True
|
||||
if llm.startswith('azure-'): return True
|
||||
if llm.startswith('spark'): return True
|
||||
if llm.startswith('zhipuai'): return True
|
||||
return False
|
||||
|
||||
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
@@ -312,95 +314,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
return gpt_response_collection
|
||||
|
||||
|
||||
def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
|
||||
def cut(txt_tocut, must_break_at_empty_line): # 递归
|
||||
if get_token_fn(txt_tocut) <= limit:
|
||||
return [txt_tocut]
|
||||
else:
|
||||
lines = txt_tocut.split('\n')
|
||||
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
|
||||
estimated_line_cut = int(estimated_line_cut)
|
||||
for cnt in reversed(range(estimated_line_cut)):
|
||||
if must_break_at_empty_line:
|
||||
if lines[cnt] != "":
|
||||
continue
|
||||
print(cnt)
|
||||
prev = "\n".join(lines[:cnt])
|
||||
post = "\n".join(lines[cnt:])
|
||||
if get_token_fn(prev) < limit:
|
||||
break
|
||||
if cnt == 0:
|
||||
raise RuntimeError("存在一行极长的文本！")
|
||||
# print(len(post))
|
||||
# 列表递归接龙
|
||||
result = [prev]
|
||||
result.extend(cut(post, must_break_at_empty_line))
|
||||
return result
|
||||
try:
|
||||
return cut(txt, must_break_at_empty_line=True)
|
||||
except RuntimeError:
|
||||
return cut(txt, must_break_at_empty_line=False)
|
||||
|
||||
|
||||
def force_breakdown(txt, limit, get_token_fn):
|
||||
"""
|
||||
当无法用标点、空行分割时，我们用最暴力的方法切割
|
||||
"""
|
||||
for i in reversed(range(len(txt))):
|
||||
if get_token_fn(txt[:i]) < limit:
|
||||
return txt[:i], txt[i:]
|
||||
return "Tiktoken未知错误", "Tiktoken未知错误"
|
||||
|
||||
def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
|
||||
# 递归
|
||||
def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):
|
||||
if get_token_fn(txt_tocut) <= limit:
|
||||
return [txt_tocut]
|
||||
else:
|
||||
lines = txt_tocut.split('\n')
|
||||
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
|
||||
estimated_line_cut = int(estimated_line_cut)
|
||||
cnt = 0
|
||||
for cnt in reversed(range(estimated_line_cut)):
|
||||
if must_break_at_empty_line:
|
||||
if lines[cnt] != "":
|
||||
continue
|
||||
prev = "\n".join(lines[:cnt])
|
||||
post = "\n".join(lines[cnt:])
|
||||
if get_token_fn(prev) < limit:
|
||||
break
|
||||
if cnt == 0:
|
||||
if break_anyway:
|
||||
prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
|
||||
else:
|
||||
raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
|
||||
# print(len(post))
|
||||
# 列表递归接龙
|
||||
result = [prev]
|
||||
result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
|
||||
return result
|
||||
try:
|
||||
# 第1次尝试，将双空行（\n\n）作为切分点
|
||||
return cut(txt, must_break_at_empty_line=True)
|
||||
except RuntimeError:
|
||||
try:
|
||||
# 第2次尝试，将单空行（\n）作为切分点
|
||||
return cut(txt, must_break_at_empty_line=False)
|
||||
except RuntimeError:
|
||||
try:
|
||||
# 第3次尝试，将英文句号（.）作为切分点
|
||||
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
|
||||
return [r.replace('。\n', '.') for r in res]
|
||||
except RuntimeError as e:
|
||||
try:
|
||||
# 第4次尝试，将中文句号（。）作为切分点
|
||||
res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
|
||||
return [r.replace('。。\n', '。') for r in res]
|
||||
except RuntimeError as e:
|
||||
# 第5次尝试，没办法了，随便切一下敷衍吧
|
||||
return cut(txt, must_break_at_empty_line=False, break_anyway=True)
|
||||
|
||||
|
||||
|
||||
def read_and_clean_pdf_text(fp):
|
||||
"""
|
||||
@@ -631,90 +544,6 @@ def get_files_from_everything(txt, type): # type='.md'
|
||||
|
||||
|
||||
|
||||
|
||||
def Singleton(cls):
|
||||
_instance = {}
|
||||
|
||||
def _singleton(*args, **kargs):
|
||||
if cls not in _instance:
|
||||
_instance[cls] = cls(*args, **kargs)
|
||||
return _instance[cls]
|
||||
|
||||
return _singleton
|
||||
|
||||
|
||||
@Singleton
|
||||
class knowledge_archive_interface():
|
||||
def __init__(self) -> None:
|
||||
self.threadLock = threading.Lock()
|
||||
self.current_id = ""
|
||||
self.kai_path = None
|
||||
self.qa_handle = None
|
||||
self.text2vec_large_chinese = None
|
||||
|
||||
def get_chinese_text2vec(self):
|
||||
if self.text2vec_large_chinese is None:
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
from toolbox import ProxyNetworkActivate
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
|
||||
return self.text2vec_large_chinese
|
||||
|
||||
|
||||
def feed_archive(self, file_manifest, id="default"):
|
||||
self.threadLock.acquire()
|
||||
# import uuid
|
||||
self.current_id = id
|
||||
from zh_langchain import construct_vector_store
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
files=file_manifest,
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
|
||||
def get_current_archive_id(self):
|
||||
return self.current_id
|
||||
|
||||
def get_loaded_file(self):
|
||||
return self.qa_handle.get_loaded_file()
|
||||
|
||||
def answer_with_archive_by_id(self, txt, id):
|
||||
self.threadLock.acquire()
|
||||
if not self.current_id == id:
|
||||
self.current_id = id
|
||||
from zh_langchain import construct_vector_store
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
VECTOR_SEARCH_TOP_K = 4
|
||||
CHUNK_SIZE = 512
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
return resp, prompt
|
||||
|
||||
@Singleton
|
||||
class nougat_interface():
|
||||
def __init__(self):
|
||||
|
||||
@@ -0,0 +1,42 @@
|
||||
from toolbox import CatchException, update_ui, update_ui_lastest_msg
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
|
||||
import random
|
||||
|
||||
|
||||
class MiniGame_ASCII_Art(GptAcademicGameBaseState):
|
||||
def step(self, prompt, chatbot, history):
|
||||
if self.step_cnt == 0:
|
||||
chatbot.append(["我画你猜（动物）", "请稍等..."])
|
||||
else:
|
||||
if prompt.strip() == 'exit':
|
||||
self.delete_game = True
|
||||
yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
|
||||
return
|
||||
chatbot.append([prompt, ""])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
if self.step_cnt == 0:
|
||||
self.lock_plugin(chatbot)
|
||||
self.cur_task = 'draw'
|
||||
|
||||
if self.cur_task == 'draw':
|
||||
avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
|
||||
self.obj = random.choice(avail_obj)
|
||||
inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + \
|
||||
f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
|
||||
raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
|
||||
self.cur_task = 'identify user guess'
|
||||
res = get_code_block(raw_res)
|
||||
history += ['', f'the answer is {self.obj}', inputs, res]
|
||||
yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
elif self.cur_task == 'identify user guess':
|
||||
if is_same_thing(self.obj, prompt, self.llm_kwargs):
|
||||
self.delete_game = True
|
||||
yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
|
||||
else:
|
||||
self.cur_task = 'identify user guess'
|
||||
yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
|
||||
@@ -0,0 +1,212 @@
|
||||
prompts_hs = """ 请以“{headstart}”为开头，编写一个小说的第一幕。
|
||||
|
||||
- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
|
||||
- 出现人物时，给出人物的名字。
|
||||
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
|
||||
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
|
||||
- 字数要求：第一幕的字数少于300字，且少于2个段落。
|
||||
"""
|
||||
|
||||
prompts_interact = """ 小说的前文回顾：
|
||||
「
|
||||
{previously_on_story}
|
||||
」
|
||||
|
||||
你是一个作家，根据以上的情节，给出4种不同的后续剧情发展方向，每个发展方向都精明扼要地用一句话说明。稍后，我将在这4个选择中，挑选一种剧情发展。
|
||||
|
||||
输出格式例如：
|
||||
1. 后续剧情发展1
|
||||
2. 后续剧情发展2
|
||||
3. 后续剧情发展3
|
||||
4. 后续剧情发展4
|
||||
"""
|
||||
|
||||
|
||||
prompts_resume = """小说的前文回顾：
|
||||
「
|
||||
{previously_on_story}
|
||||
」
|
||||
|
||||
你是一个作家，我们正在互相讨论，确定后续剧情的发展。
|
||||
在以下的剧情发展中，
|
||||
「
|
||||
{choice}
|
||||
」
|
||||
我认为更合理的是：{user_choice}。
|
||||
请在前文的基础上（不要重复前文），围绕我选定的剧情情节，编写小说的下一幕。
|
||||
|
||||
- 禁止杜撰不符合我选择的剧情。
|
||||
- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
|
||||
- 不要重复前文。
|
||||
- 出现人物时，给出人物的名字。
|
||||
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
|
||||
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
|
||||
- 小说的下一幕字数少于300字，且少于2个段落。
|
||||
"""
|
||||
|
||||
|
||||
prompts_terminate = """小说的前文回顾：
|
||||
「
|
||||
{previously_on_story}
|
||||
」
|
||||
|
||||
你是一个作家，我们正在互相讨论，确定后续剧情的发展。
|
||||
现在，故事该结束了，我认为最合理的故事结局是：{user_choice}。
|
||||
|
||||
请在前文的基础上（不要重复前文），编写小说的最后一幕。
|
||||
|
||||
- 不要重复前文。
|
||||
- 出现人物时，给出人物的名字。
|
||||
- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
|
||||
- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
|
||||
- 字数要求：最后一幕的字数少于1000字。
|
||||
"""
|
||||
|
||||
|
||||
from toolbox import CatchException, update_ui, update_ui_lastest_msg
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
|
||||
import random
|
||||
|
||||
|
||||
class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
story_headstart = [
|
||||
'先行者知道，他现在是全宇宙中唯一的一个人了。',
|
||||
'深夜，一个年轻人穿过天安门广场向纪念堂走去。在二十二世纪编年史中，计算机把他的代号定为M102。',
|
||||
'他知道，这最后一课要提前讲了。又一阵剧痛从肝部袭来，几乎使他晕厥过去。',
|
||||
'在距地球五万光年的远方，在银河系的中心，一场延续了两万年的星际战争已接近尾声。那里的太空中渐渐隐现出一个方形区域，仿佛灿烂的群星的背景被剪出一个方口。',
|
||||
'伊依一行三人乘坐一艘游艇在南太平洋上做吟诗航行，他们的目的地是南极，如果几天后能顺利到达那里，他们将钻出地壳去看诗云。',
|
||||
'很多人生来就会莫名其妙地迷上一样东西，仿佛他的出生就是要和这东西约会似的，正是这样，圆圆迷上了肥皂泡。'
|
||||
]
|
||||
|
||||
|
||||
def begin_game_step_0(self, prompt, chatbot, history):
|
||||
# init game at step 0
|
||||
self.headstart = random.choice(self.story_headstart)
|
||||
self.story = []
|
||||
chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
|
||||
self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
|
||||
|
||||
|
||||
def generate_story_image(self, story_paragraph):
|
||||
try:
|
||||
from crazy_functions.图片生成 import gen_image
|
||||
prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
|
||||
image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
|
||||
return f'<br/><div align="center"><img src="file={image_path}"></div>'
|
||||
except:
|
||||
return ''
|
||||
|
||||
def step(self, prompt, chatbot, history):
|
||||
|
||||
"""
|
||||
首先，处理游戏初始化等特殊情况
|
||||
"""
|
||||
if self.step_cnt == 0:
|
||||
self.begin_game_step_0(prompt, chatbot, history)
|
||||
self.lock_plugin(chatbot)
|
||||
self.cur_task = 'head_start'
|
||||
else:
|
||||
if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
|
||||
# should we terminate game here?
|
||||
self.delete_game = True
|
||||
yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
|
||||
return
|
||||
if '剧情收尾' in prompt:
|
||||
self.cur_task = 'story_terminate'
|
||||
# # well, game resumes
|
||||
# chatbot.append([prompt, ""])
|
||||
# update ui, don't keep the user waiting
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
|
||||
"""
|
||||
处理游戏的主体逻辑
|
||||
"""
|
||||
if self.cur_task == 'head_start':
|
||||
"""
|
||||
这是游戏的第一步
|
||||
"""
|
||||
inputs_ = prompts_hs.format(headstart=self.headstart)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, '故事开头', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
# # 配图
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
# # 构建后续剧情引导
|
||||
previously_on_story = ""
|
||||
for s in self.story:
|
||||
previously_on_story += s + '\n'
|
||||
inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
|
||||
history_ = []
|
||||
self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs,
|
||||
chatbot,
|
||||
history_,
|
||||
self.sys_prompt_
|
||||
)
|
||||
self.cur_task = 'user_choice'
|
||||
|
||||
|
||||
elif self.cur_task == 'user_choice':
|
||||
"""
|
||||
根据用户的提示，确定故事的下一步
|
||||
"""
|
||||
if '请在以下几种故事走向中，选择一种' in chatbot[-1][0]: chatbot.pop(-1)
|
||||
previously_on_story = ""
|
||||
for s in self.story:
|
||||
previously_on_story += s + '\n'
|
||||
inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
# # 配图
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
# # 构建后续剧情引导
|
||||
previously_on_story = ""
|
||||
for s in self.story:
|
||||
previously_on_story += s + '\n'
|
||||
inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
|
||||
history_ = []
|
||||
self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_,
|
||||
'请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs,
|
||||
chatbot,
|
||||
history_,
|
||||
self.sys_prompt_
|
||||
)
|
||||
self.cur_task = 'user_choice'
|
||||
|
||||
|
||||
elif self.cur_task == 'story_terminate':
|
||||
"""
|
||||
根据用户的提示，确定故事的结局
|
||||
"""
|
||||
previously_on_story = ""
|
||||
for s in self.story:
|
||||
previously_on_story += s + '\n'
|
||||
inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
|
||||
history_ = []
|
||||
story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs,
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
# # 配图
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
|
||||
# terminate game
|
||||
self.delete_game = True
|
||||
return
|
||||
@@ -0,0 +1,35 @@
|
||||
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) == 1:
|
||||
return "```" + matches[0] + "```" # code block
|
||||
raise RuntimeError("GPT is not generating proper code.")
|
||||
|
||||
def is_same_thing(a, b, llm_kwargs):
|
||||
from pydantic import BaseModel, Field
|
||||
class IsSameThing(BaseModel):
|
||||
is_same_thing: bool = Field(description="determine whether two objects are same thing.", default=False)
|
||||
|
||||
def run_gpt_fn(inputs, sys_prompt, history=[]):
|
||||
return predict_no_ui_long_connection(
|
||||
inputs=inputs, llm_kwargs=llm_kwargs,
|
||||
history=history, sys_prompt=sys_prompt, observe_window=[]
|
||||
)
|
||||
|
||||
gpt_json_io = GptJsonIO(IsSameThing)
|
||||
inputs_01 = "Identity whether the user input and the target is the same thing: \n target object: {a} \n user input object: {b} \n\n\n".format(a=a, b=b)
|
||||
inputs_01 += "\n\n\n Note that the user may describe the target object with a different language, e.g. cat and 猫 are the same thing."
|
||||
analyze_res_cot_01 = run_gpt_fn(inputs_01, "", [])
|
||||
|
||||
inputs_02 = inputs_01 + gpt_json_io.format_instructions
|
||||
analyze_res = run_gpt_fn(inputs_02, "", [inputs_01, analyze_res_cot_01])
|
||||
|
||||
try:
|
||||
res = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
return res.is_same_thing
|
||||
except JsonStringError as e:
|
||||
return False
|
||||
37
crazy_functions/ipc_fns/mp.py
普通文件
37
crazy_functions/ipc_fns/mp.py
普通文件
@@ -0,0 +1,37 @@
|
||||
import platform
|
||||
import pickle
|
||||
import multiprocessing
|
||||
|
||||
def run_in_subprocess_wrapper_func(v_args):
|
||||
func, args, kwargs, return_dict, exception_dict = pickle.loads(v_args)
|
||||
import sys
|
||||
try:
|
||||
result = func(*args, **kwargs)
|
||||
return_dict['result'] = result
|
||||
except Exception as e:
|
||||
exc_info = sys.exc_info()
|
||||
exception_dict['exception'] = exc_info
|
||||
|
||||
def run_in_subprocess_with_timeout(func, timeout=60):
|
||||
if platform.system() == 'Linux':
|
||||
def wrapper(*args, **kwargs):
|
||||
return_dict = multiprocessing.Manager().dict()
|
||||
exception_dict = multiprocessing.Manager().dict()
|
||||
v_args = pickle.dumps((func, args, kwargs, return_dict, exception_dict))
|
||||
process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, args=(v_args,))
|
||||
process.start()
|
||||
process.join(timeout)
|
||||
if process.is_alive():
|
||||
process.terminate()
|
||||
raise TimeoutError(f'功能单元{str(func)}未能在规定时间内完成任务')
|
||||
process.close()
|
||||
if 'exception' in exception_dict:
|
||||
# ooops, the subprocess ran into an exception
|
||||
exc_info = exception_dict['exception']
|
||||
raise exc_info[1].with_traceback(exc_info[2])
|
||||
if 'result' in return_dict.keys():
|
||||
# If the subprocess ran successfully, return the result
|
||||
return return_dict['result']
|
||||
return wrapper
|
||||
else:
|
||||
return func
|
||||
@@ -175,7 +175,6 @@ class LatexPaperFileGroup():
|
||||
self.sp_file_contents = []
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
@@ -192,13 +191,12 @@ class LatexPaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
|
||||
print('Segmentation: done')
|
||||
|
||||
def merge_result(self):
|
||||
self.file_result = ["" for _ in range(len(self.file_paths))]
|
||||
@@ -404,7 +402,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
result_pdf = pj(work_folder_modified, f'merge_diff.pdf') # get pdf path
|
||||
promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
||||
if modified_pdf_success:
|
||||
yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
|
||||
origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
|
||||
if os.path.exists(pj(work_folder, '..', 'translation')):
|
||||
@@ -416,8 +414,11 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
from .latex_toolbox import merge_pdfs
|
||||
concat_pdf = pj(work_folder_modified, f'comparison.pdf')
|
||||
merge_pdfs(origin_pdf, result_pdf, concat_pdf)
|
||||
if os.path.exists(pj(work_folder, '..', 'translation')):
|
||||
shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
|
||||
promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
return True # 成功啦
|
||||
else:
|
||||
|
||||
@@ -493,11 +493,38 @@ def compile_latex_with_timeout(command, cwd, timeout=60):
|
||||
return False
|
||||
return True
|
||||
|
||||
def run_in_subprocess_wrapper_func(func, args, kwargs, return_dict, exception_dict):
|
||||
import sys
|
||||
try:
|
||||
result = func(*args, **kwargs)
|
||||
return_dict['result'] = result
|
||||
except Exception as e:
|
||||
exc_info = sys.exc_info()
|
||||
exception_dict['exception'] = exc_info
|
||||
|
||||
def run_in_subprocess(func):
|
||||
import multiprocessing
|
||||
def wrapper(*args, **kwargs):
|
||||
return_dict = multiprocessing.Manager().dict()
|
||||
exception_dict = multiprocessing.Manager().dict()
|
||||
process = multiprocessing.Process(target=run_in_subprocess_wrapper_func,
|
||||
args=(func, args, kwargs, return_dict, exception_dict))
|
||||
process.start()
|
||||
process.join()
|
||||
process.close()
|
||||
if 'exception' in exception_dict:
|
||||
# ooops, the subprocess ran into an exception
|
||||
exc_info = exception_dict['exception']
|
||||
raise exc_info[1].with_traceback(exc_info[2])
|
||||
if 'result' in return_dict.keys():
|
||||
# If the subprocess ran successfully, return the result
|
||||
return return_dict['result']
|
||||
return wrapper
|
||||
|
||||
def merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||
import PyPDF2
|
||||
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||
import PyPDF2 # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
|
||||
Percent = 0.95
|
||||
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
|
||||
# Open the first PDF file
|
||||
with open(pdf1_path, 'rb') as pdf1_file:
|
||||
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
|
||||
@@ -531,3 +558,5 @@ def merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||
# Save the merged PDF file
|
||||
with open(output_path, 'wb') as output_file:
|
||||
output_writer.write(output_file)
|
||||
|
||||
merge_pdfs = run_in_subprocess(_merge_pdfs) # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
import time
|
||||
@@ -21,11 +22,7 @@ class GptAcademicState():
|
||||
def reset(self):
|
||||
pass
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['plugin_state'] = pickle.dumps(self)
|
||||
|
||||
def unlock_plugin(self, chatbot):
|
||||
self.reset()
|
||||
def dump_state(self, chatbot):
|
||||
chatbot._cookies['plugin_state'] = pickle.dumps(self)
|
||||
|
||||
def set_state(self, chatbot, key, value):
|
||||
@@ -40,6 +37,57 @@ class GptAcademicState():
|
||||
state.chatbot = chatbot
|
||||
return state
|
||||
|
||||
class GatherMaterials():
|
||||
def __init__(self, materials) -> None:
|
||||
materials = ['image', 'prompt']
|
||||
|
||||
class GptAcademicGameBaseState():
|
||||
"""
|
||||
1. first init: __init__ ->
|
||||
"""
|
||||
def init_game(self, chatbot, lock_plugin):
|
||||
self.plugin_name = None
|
||||
self.callback_fn = None
|
||||
self.delete_game = False
|
||||
self.step_cnt = 0
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
if self.callback_fn is None:
|
||||
raise ValueError("callback_fn is None")
|
||||
chatbot._cookies['lock_plugin'] = self.callback_fn
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def get_plugin_name(self):
|
||||
if self.plugin_name is None:
|
||||
raise ValueError("plugin_name is None")
|
||||
return self.plugin_name
|
||||
|
||||
def dump_state(self, chatbot):
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
|
||||
|
||||
def set_state(self, chatbot, key, value):
|
||||
setattr(self, key, value)
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = pickle.dumps(self)
|
||||
|
||||
@staticmethod
|
||||
def sync_state(chatbot, llm_kwargs, cls, plugin_name, callback_fn, lock_plugin=True):
|
||||
state = chatbot._cookies.get(f'plugin_state/{plugin_name}', None)
|
||||
if state is not None:
|
||||
state = pickle.loads(state)
|
||||
else:
|
||||
state = cls()
|
||||
state.init_game(chatbot, lock_plugin)
|
||||
state.plugin_name = plugin_name
|
||||
state.llm_kwargs = llm_kwargs
|
||||
state.chatbot = chatbot
|
||||
state.callback_fn = callback_fn
|
||||
return state
|
||||
|
||||
def continue_game(self, prompt, chatbot, history):
|
||||
# 游戏主体
|
||||
yield from self.step(prompt, chatbot, history)
|
||||
self.step_cnt += 1
|
||||
# 保存状态，收尾
|
||||
self.dump_state(chatbot)
|
||||
# 如果游戏结束，清理
|
||||
if self.delete_game:
|
||||
chatbot._cookies['lock_plugin'] = None
|
||||
chatbot._cookies[f'plugin_state/{self.get_plugin_name()}'] = None
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
|
||||
|
||||
def force_breakdown(txt, limit, get_token_fn):
|
||||
""" 当无法用标点、空行分割时，我们用最暴力的方法切割
|
||||
"""
|
||||
for i in reversed(range(len(txt))):
|
||||
if get_token_fn(txt[:i]) < limit:
|
||||
return txt[:i], txt[i:]
|
||||
return "Tiktoken未知错误", "Tiktoken未知错误"
|
||||
|
||||
|
||||
def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
|
||||
""" 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
|
||||
当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
|
||||
"""
|
||||
_min = int(5e4)
|
||||
_max = int(1e5)
|
||||
# print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
|
||||
if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
|
||||
remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
|
||||
remain_txt_to_cut_storage = ""
|
||||
if len(remain_txt_to_cut) > _max:
|
||||
remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
|
||||
remain_txt_to_cut = remain_txt_to_cut[:_max]
|
||||
return remain_txt_to_cut, remain_txt_to_cut_storage
|
||||
|
||||
|
||||
def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
|
||||
""" 文本切分
|
||||
"""
|
||||
res = []
|
||||
total_len = len(txt_tocut)
|
||||
fin_len = 0
|
||||
remain_txt_to_cut = txt_tocut
|
||||
remain_txt_to_cut_storage = ""
|
||||
# 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
|
||||
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
|
||||
|
||||
while True:
|
||||
if get_token_fn(remain_txt_to_cut) <= limit:
|
||||
# 如果剩余文本的token数小于限制，那么就不用切了
|
||||
res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
|
||||
break
|
||||
else:
|
||||
# 如果剩余文本的token数大于限制，那么就切
|
||||
lines = remain_txt_to_cut.split('\n')
|
||||
|
||||
# 估计一个切分点
|
||||
estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
|
||||
estimated_line_cut = int(estimated_line_cut)
|
||||
|
||||
# 开始查找合适切分点的偏移（cnt）
|
||||
cnt = 0
|
||||
for cnt in reversed(range(estimated_line_cut)):
|
||||
if must_break_at_empty_line:
|
||||
# 首先尝试用双空行（\n\n）作为切分点
|
||||
if lines[cnt] != "":
|
||||
continue
|
||||
prev = "\n".join(lines[:cnt])
|
||||
post = "\n".join(lines[cnt:])
|
||||
if get_token_fn(prev) < limit:
|
||||
break
|
||||
|
||||
if cnt == 0:
|
||||
# 如果没有找到合适的切分点
|
||||
if break_anyway:
|
||||
# 是否允许暴力切分
|
||||
prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
|
||||
else:
|
||||
# 不允许直接报错
|
||||
raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
|
||||
|
||||
# 追加列表
|
||||
res.append(prev); fin_len+=len(prev)
|
||||
# 准备下一次迭代
|
||||
remain_txt_to_cut = post
|
||||
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
|
||||
process = fin_len/total_len
|
||||
print(f'正在文本切分 {int(process*100)}%')
|
||||
if len(remain_txt_to_cut.strip()) == 0:
|
||||
break
|
||||
return res
|
||||
|
||||
|
||||
def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
|
||||
""" 使用多种方式尝试切分文本，以满足 token 限制
|
||||
"""
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info[llm_model]['tokenizer']
|
||||
def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
try:
|
||||
# 第1次尝试，将双空行（\n\n）作为切分点
|
||||
return cut(limit, get_token_fn, txt, must_break_at_empty_line=True)
|
||||
except RuntimeError:
|
||||
try:
|
||||
# 第2次尝试，将单空行（\n）作为切分点
|
||||
return cut(limit, get_token_fn, txt, must_break_at_empty_line=False)
|
||||
except RuntimeError:
|
||||
try:
|
||||
# 第3次尝试，将英文句号（.）作为切分点
|
||||
res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
|
||||
return [r.replace('。\n', '.') for r in res]
|
||||
except RuntimeError as e:
|
||||
try:
|
||||
# 第4次尝试，将中文句号（。）作为切分点
|
||||
res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
|
||||
return [r.replace('。。\n', '。') for r in res]
|
||||
except RuntimeError as e:
|
||||
# 第5次尝试，没办法了，随便切一下吧
|
||||
return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
|
||||
|
||||
breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
|
||||
|
||||
if __name__ == '__main__':
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
|
||||
|
||||
from request_llms.bridge_all import model_info
|
||||
for i in range(5):
|
||||
file_content += file_content
|
||||
|
||||
print(len(file_content))
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
|
||||
|
||||
@@ -74,7 +74,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
|
||||
|
||||
def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
from crazy_functions.crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
@@ -116,7 +116,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
|
||||
# find a smooth token limit to achieve even seperation
|
||||
count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
|
||||
token_limit_smooth = raw_token_num // count + count
|
||||
return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
|
||||
return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
|
||||
|
||||
for section in article_dict.get('sections'):
|
||||
if len(section['text']) == 0: continue
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
|
||||
from langchain.document_loaders import UnstructuredFileLoader
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
class ChineseTextSplitter(CharacterTextSplitter):
|
||||
def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.pdf = pdf
|
||||
self.sentence_size = sentence_size
|
||||
|
||||
def split_text1(self, text: str) -> List[str]:
|
||||
if self.pdf:
|
||||
text = re.sub(r"\n{3,}", "\n", text)
|
||||
text = re.sub('\s', ' ', text)
|
||||
text = text.replace("\n\n", "")
|
||||
sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del ：；
|
||||
sent_list = []
|
||||
for ele in sent_sep_pattern.split(text):
|
||||
if sent_sep_pattern.match(ele) and sent_list:
|
||||
sent_list[-1] += ele
|
||||
elif ele:
|
||||
sent_list.append(ele)
|
||||
return sent_list
|
||||
|
||||
def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑
|
||||
if self.pdf:
|
||||
text = re.sub(r"\n{3,}", r"\n", text)
|
||||
text = re.sub('\s', " ", text)
|
||||
text = re.sub("\n\n", "", text)
|
||||
|
||||
text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text) # 单字符断句符
|
||||
text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text) # 英文省略号
|
||||
text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text) # 中文省略号
|
||||
text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text)
|
||||
# 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
|
||||
text = text.rstrip() # 段尾如果有多余的\n就去掉它
|
||||
# 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
|
||||
ls = [i for i in text.split("\n") if i]
|
||||
for ele in ls:
|
||||
if len(ele) > self.sentence_size:
|
||||
ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele)
|
||||
ele1_ls = ele1.split("\n")
|
||||
for ele_ele1 in ele1_ls:
|
||||
if len(ele_ele1) > self.sentence_size:
|
||||
ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1)
|
||||
ele2_ls = ele_ele2.split("\n")
|
||||
for ele_ele2 in ele2_ls:
|
||||
if len(ele_ele2) > self.sentence_size:
|
||||
ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2)
|
||||
ele2_id = ele2_ls.index(ele_ele2)
|
||||
ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[
|
||||
ele2_id + 1:]
|
||||
ele_id = ele1_ls.index(ele_ele1)
|
||||
ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]
|
||||
|
||||
id = ls.index(ele)
|
||||
ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]
|
||||
return ls
|
||||
|
||||
def load_file(filepath, sentence_size):
|
||||
loader = UnstructuredFileLoader(filepath, mode="elements")
|
||||
textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
|
||||
docs = loader.load_and_split(text_splitter=textsplitter)
|
||||
# write_check_file(filepath, docs)
|
||||
return docs
|
||||
|
||||
@@ -0,0 +1,338 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
import threading
|
||||
from toolbox import Singleton
|
||||
import os
|
||||
import shutil
|
||||
import os
|
||||
import uuid
|
||||
import tqdm
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain.docstore.document import Document
|
||||
from typing import List, Tuple
|
||||
import numpy as np
|
||||
from crazy_functions.vector_fns.general_file_loader import load_file
|
||||
|
||||
embedding_model_dict = {
|
||||
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
|
||||
"ernie-base": "nghuyong/ernie-3.0-base-zh",
|
||||
"text2vec-base": "shibing624/text2vec-base-chinese",
|
||||
"text2vec": "GanymedeNil/text2vec-large-chinese",
|
||||
}
|
||||
|
||||
# Embedding model name
|
||||
EMBEDDING_MODEL = "text2vec"
|
||||
|
||||
# Embedding running device
|
||||
EMBEDDING_DEVICE = "cpu"
|
||||
|
||||
# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
|
||||
PROMPT_TEMPLATE = """已知信息：
|
||||
{context}
|
||||
|
||||
根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
|
||||
|
||||
# 文本分句长度
|
||||
SENTENCE_SIZE = 100
|
||||
|
||||
# 匹配后单段上下文长度
|
||||
CHUNK_SIZE = 250
|
||||
|
||||
# LLM input history length
|
||||
LLM_HISTORY_LEN = 3
|
||||
|
||||
# return top-k text chunk from vector store
|
||||
VECTOR_SEARCH_TOP_K = 5
|
||||
|
||||
# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
|
||||
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
|
||||
|
||||
FLAG_USER_NAME = uuid.uuid4().hex
|
||||
|
||||
# 是否开启跨域，默认为False，如果需要开启，请设置为True
|
||||
# is open cross domain
|
||||
OPEN_CROSS_DOMAIN = False
|
||||
|
||||
def similarity_search_with_score_by_vector(
|
||||
self, embedding: List[float], k: int = 4
|
||||
) -> List[Tuple[Document, float]]:
|
||||
|
||||
def seperate_list(ls: List[int]) -> List[List[int]]:
|
||||
lists = []
|
||||
ls1 = [ls[0]]
|
||||
for i in range(1, len(ls)):
|
||||
if ls[i - 1] + 1 == ls[i]:
|
||||
ls1.append(ls[i])
|
||||
else:
|
||||
lists.append(ls1)
|
||||
ls1 = [ls[i]]
|
||||
lists.append(ls1)
|
||||
return lists
|
||||
|
||||
scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
|
||||
docs = []
|
||||
id_set = set()
|
||||
store_len = len(self.index_to_docstore_id)
|
||||
for j, i in enumerate(indices[0]):
|
||||
if i == -1 or 0 < self.score_threshold < scores[0][j]:
|
||||
# This happens when not enough docs are returned.
|
||||
continue
|
||||
_id = self.index_to_docstore_id[i]
|
||||
doc = self.docstore.search(_id)
|
||||
if not self.chunk_conent:
|
||||
if not isinstance(doc, Document):
|
||||
raise ValueError(f"Could not find document for id {_id}, got {doc}")
|
||||
doc.metadata["score"] = int(scores[0][j])
|
||||
docs.append(doc)
|
||||
continue
|
||||
id_set.add(i)
|
||||
docs_len = len(doc.page_content)
|
||||
for k in range(1, max(i, store_len - i)):
|
||||
break_flag = False
|
||||
for l in [i + k, i - k]:
|
||||
if 0 <= l < len(self.index_to_docstore_id):
|
||||
_id0 = self.index_to_docstore_id[l]
|
||||
doc0 = self.docstore.search(_id0)
|
||||
if docs_len + len(doc0.page_content) > self.chunk_size:
|
||||
break_flag = True
|
||||
break
|
||||
elif doc0.metadata["source"] == doc.metadata["source"]:
|
||||
docs_len += len(doc0.page_content)
|
||||
id_set.add(l)
|
||||
if break_flag:
|
||||
break
|
||||
if not self.chunk_conent:
|
||||
return docs
|
||||
if len(id_set) == 0 and self.score_threshold > 0:
|
||||
return []
|
||||
id_list = sorted(list(id_set))
|
||||
id_lists = seperate_list(id_list)
|
||||
for id_seq in id_lists:
|
||||
for id in id_seq:
|
||||
if id == id_seq[0]:
|
||||
_id = self.index_to_docstore_id[id]
|
||||
doc = self.docstore.search(_id)
|
||||
else:
|
||||
_id0 = self.index_to_docstore_id[id]
|
||||
doc0 = self.docstore.search(_id0)
|
||||
doc.page_content += " " + doc0.page_content
|
||||
if not isinstance(doc, Document):
|
||||
raise ValueError(f"Could not find document for id {_id}, got {doc}")
|
||||
doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
|
||||
doc.metadata["score"] = int(doc_score)
|
||||
docs.append(doc)
|
||||
return docs
|
||||
|
||||
|
||||
class LocalDocQA:
|
||||
llm: object = None
|
||||
embeddings: object = None
|
||||
top_k: int = VECTOR_SEARCH_TOP_K
|
||||
chunk_size: int = CHUNK_SIZE
|
||||
chunk_conent: bool = True
|
||||
score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
|
||||
|
||||
def init_cfg(self,
|
||||
top_k=VECTOR_SEARCH_TOP_K,
|
||||
):
|
||||
|
||||
self.llm = None
|
||||
self.top_k = top_k
|
||||
|
||||
def init_knowledge_vector_store(self,
|
||||
filepath,
|
||||
vs_path: str or os.PathLike = None,
|
||||
sentence_size=SENTENCE_SIZE,
|
||||
text2vec=None):
|
||||
loaded_files = []
|
||||
failed_files = []
|
||||
if isinstance(filepath, str):
|
||||
if not os.path.exists(filepath):
|
||||
print("路径不存在")
|
||||
return None
|
||||
elif os.path.isfile(filepath):
|
||||
file = os.path.split(filepath)[-1]
|
||||
try:
|
||||
docs = load_file(filepath, SENTENCE_SIZE)
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(filepath)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print(f"{file} 未能成功加载")
|
||||
return None
|
||||
elif os.path.isdir(filepath):
|
||||
docs = []
|
||||
for file in tqdm(os.listdir(filepath), desc="加载文件"):
|
||||
fullfilepath = os.path.join(filepath, file)
|
||||
try:
|
||||
docs += load_file(fullfilepath, SENTENCE_SIZE)
|
||||
loaded_files.append(fullfilepath)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
failed_files.append(file)
|
||||
|
||||
if len(failed_files) > 0:
|
||||
print("以下文件未能成功加载：")
|
||||
for file in failed_files:
|
||||
print(f"{file}\n")
|
||||
|
||||
else:
|
||||
docs = []
|
||||
for file in filepath:
|
||||
docs += load_file(file, SENTENCE_SIZE)
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(file)
|
||||
|
||||
if len(docs) > 0:
|
||||
print("文件加载完毕，正在生成向量库")
|
||||
if vs_path and os.path.isdir(vs_path):
|
||||
try:
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
self.vector_store.add_documents(docs)
|
||||
except:
|
||||
self.vector_store = FAISS.from_documents(docs, text2vec)
|
||||
else:
|
||||
self.vector_store = FAISS.from_documents(docs, text2vec) # docs 为Document列表
|
||||
|
||||
self.vector_store.save_local(vs_path)
|
||||
return vs_path, loaded_files
|
||||
else:
|
||||
raise RuntimeError("文件加载失败，请检查文件格式是否正确")
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
ds = self.vector_store.docstore
|
||||
return set([ds._dict[k].metadata['source'].split(vs_path)[-1] for k in ds._dict])
|
||||
|
||||
|
||||
# query 查询内容
|
||||
# vs_path 知识库路径
|
||||
# chunk_conent 是否启用上下文关联
|
||||
# score_threshold 搜索匹配score阈值
|
||||
# vector_search_top_k 搜索知识库内容条数，默认搜索5条结果
|
||||
# chunk_sizes 匹配单段内容的连接上下文长度
|
||||
def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
|
||||
text2vec=None):
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
self.vector_store.chunk_conent = chunk_conent
|
||||
self.vector_store.score_threshold = score_threshold
|
||||
self.vector_store.chunk_size = chunk_size
|
||||
|
||||
embedding = self.vector_store.embedding_function.embed_query(query)
|
||||
related_docs_with_score = similarity_search_with_score_by_vector(self.vector_store, embedding, k=vector_search_top_k)
|
||||
|
||||
if not related_docs_with_score:
|
||||
response = {"query": query,
|
||||
"source_documents": []}
|
||||
return response, ""
|
||||
# prompt = f"{query}. You should answer this question using information from following documents: \n\n"
|
||||
prompt = f"{query}. 你必须利用以下文档中包含的信息回答这个问题: \n\n---\n\n"
|
||||
prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
|
||||
prompt += "\n\n---\n\n"
|
||||
prompt = prompt.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
# print(prompt)
|
||||
response = {"query": query, "source_documents": related_docs_with_score}
|
||||
return response, prompt
|
||||
|
||||
|
||||
|
||||
|
||||
def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
|
||||
for file in files:
|
||||
assert os.path.exists(file), "输入文件不存在：" + file
|
||||
import nltk
|
||||
if NLTK_DATA_PATH not in nltk.data.path: nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
|
||||
local_doc_qa = LocalDocQA()
|
||||
local_doc_qa.init_cfg()
|
||||
filelist = []
|
||||
if not os.path.exists(os.path.join(vs_path, vs_id)):
|
||||
os.makedirs(os.path.join(vs_path, vs_id))
|
||||
for file in files:
|
||||
file_name = file.name if not isinstance(file, str) else file
|
||||
filename = os.path.split(file_name)[-1]
|
||||
shutil.copyfile(file_name, os.path.join(vs_path, vs_id, filename))
|
||||
filelist.append(os.path.join(vs_path, vs_id, filename))
|
||||
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, os.path.join(vs_path, vs_id), sentence_size, text2vec)
|
||||
|
||||
if len(loaded_files):
|
||||
file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
|
||||
else:
|
||||
pass
|
||||
# file_status = "文件未成功加载，请重新上传文件"
|
||||
# print(file_status)
|
||||
return local_doc_qa, vs_path
|
||||
|
||||
@Singleton
|
||||
class knowledge_archive_interface():
|
||||
def __init__(self) -> None:
|
||||
self.threadLock = threading.Lock()
|
||||
self.current_id = ""
|
||||
self.kai_path = None
|
||||
self.qa_handle = None
|
||||
self.text2vec_large_chinese = None
|
||||
|
||||
def get_chinese_text2vec(self):
|
||||
if self.text2vec_large_chinese is None:
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
from toolbox import ProxyNetworkActivate
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
|
||||
return self.text2vec_large_chinese
|
||||
|
||||
|
||||
def feed_archive(self, file_manifest, vs_path, id="default"):
|
||||
self.threadLock.acquire()
|
||||
# import uuid
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=file_manifest,
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
|
||||
def get_current_archive_id(self):
|
||||
return self.current_id
|
||||
|
||||
def get_loaded_file(self, vs_path):
|
||||
return self.qa_handle.get_loaded_file(vs_path)
|
||||
|
||||
def answer_with_archive_by_id(self, txt, id, vs_path):
|
||||
self.threadLock.acquire()
|
||||
if not self.current_id == id:
|
||||
self.current_id = id
|
||||
self.qa_handle, self.kai_path = construct_vector_store(
|
||||
vs_id=self.current_id,
|
||||
vs_path=vs_path,
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
VECTOR_SEARCH_TOP_K = 4
|
||||
CHUNK_SIZE = 512
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
self.threadLock.release()
|
||||
return resp, prompt
|
||||
40
crazy_functions/互动小游戏.py
普通文件
40
crazy_functions/互动小游戏.py
普通文件
@@ -0,0 +1,40 @@
|
||||
from toolbox import CatchException, update_ui, update_ui_lastest_msg
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
|
||||
|
||||
@CatchException
|
||||
def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
from crazy_functions.game_fns.game_interactive_story import MiniGame_ResumeStory
|
||||
# 清空历史
|
||||
history = []
|
||||
# 选择游戏
|
||||
cls = MiniGame_ResumeStory
|
||||
# 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
|
||||
state = cls.sync_state(chatbot,
|
||||
llm_kwargs,
|
||||
cls,
|
||||
plugin_name='MiniGame_ResumeStory',
|
||||
callback_fn='crazy_functions.互动小游戏->随机小游戏',
|
||||
lock_plugin=True
|
||||
)
|
||||
yield from state.continue_game(prompt, chatbot, history)
|
||||
|
||||
|
||||
@CatchException
|
||||
def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
from crazy_functions.game_fns.game_ascii_art import MiniGame_ASCII_Art
|
||||
# 清空历史
|
||||
history = []
|
||||
# 选择游戏
|
||||
cls = MiniGame_ASCII_Art
|
||||
# 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
|
||||
state = cls.sync_state(chatbot,
|
||||
llm_kwargs,
|
||||
cls,
|
||||
plugin_name='MiniGame_ASCII_Art',
|
||||
callback_fn='crazy_functions.互动小游戏->随机小游戏1',
|
||||
lock_plugin=True
|
||||
)
|
||||
yield from state.continue_game(prompt, chatbot, history)
|
||||
@@ -2,7 +2,7 @@ from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log
|
||||
from crazy_functions.multi_stage.multi_stage_utils import GptAcademicState
|
||||
|
||||
|
||||
def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", quality=None):
|
||||
def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", quality=None, style=None):
|
||||
import requests, json, time, os
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
@@ -25,7 +25,10 @@ def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", qual
|
||||
'model': model,
|
||||
'response_format': 'url'
|
||||
}
|
||||
if quality is not None: data.update({'quality': quality})
|
||||
if quality is not None:
|
||||
data['quality'] = quality
|
||||
if style is not None:
|
||||
data['style'] = style
|
||||
response = requests.post(url, headers=headers, json=data, proxies=proxies)
|
||||
print(response.content)
|
||||
try:
|
||||
@@ -54,19 +57,25 @@ def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="da
|
||||
img_endpoint = chat_endpoint.replace('chat/completions','images/edits')
|
||||
# # Generate the image
|
||||
url = img_endpoint
|
||||
n = 1
|
||||
headers = {
|
||||
'Authorization': f"Bearer {api_key}",
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
data = {
|
||||
'image': open(image_path, 'rb'),
|
||||
'prompt': prompt,
|
||||
'n': 1,
|
||||
'size': resolution,
|
||||
'model': model,
|
||||
'response_format': 'url'
|
||||
}
|
||||
response = requests.post(url, headers=headers, json=data, proxies=proxies)
|
||||
make_transparent(image_path, image_path+'.tsp.png')
|
||||
make_square_image(image_path+'.tsp.png', image_path+'.tspsq.png')
|
||||
resize_image(image_path+'.tspsq.png', image_path+'.ready.png', max_size=1024)
|
||||
image_path = image_path+'.ready.png'
|
||||
with open(image_path, 'rb') as f:
|
||||
file_content = f.read()
|
||||
files = {
|
||||
'image': (os.path.basename(image_path), file_content),
|
||||
# 'mask': ('mask.png', open('mask.png', 'rb'))
|
||||
'prompt': (None, prompt),
|
||||
"n": (None, str(n)),
|
||||
'size': (None, resolution),
|
||||
}
|
||||
|
||||
response = requests.post(url, headers=headers, files=files, proxies=proxies)
|
||||
print(response.content)
|
||||
try:
|
||||
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
|
||||
@@ -95,7 +104,11 @@ def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
if prompt.strip() == "":
|
||||
chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
return
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
resolution = plugin_kwargs.get("advanced_arg", '1024x1024')
|
||||
@@ -112,16 +125,25 @@ def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
@CatchException
|
||||
def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
if prompt.strip() == "":
|
||||
chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
return
|
||||
chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 由于请求gpt需要一段时间,我们先及时地做一次界面更新
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
resolution = plugin_kwargs.get("advanced_arg", '1024x1024').lower()
|
||||
if resolution.endswith('-hd'):
|
||||
resolution = resolution.replace('-hd', '')
|
||||
quality = 'hd'
|
||||
else:
|
||||
quality = 'standard'
|
||||
image_url, image_path = gen_image(llm_kwargs, prompt, resolution, model="dall-e-3", quality=quality)
|
||||
resolution_arg = plugin_kwargs.get("advanced_arg", '1024x1024-standard-vivid').lower()
|
||||
parts = resolution_arg.split('-')
|
||||
resolution = parts[0] # 解析分辨率
|
||||
quality = 'standard' # 质量与风格默认值
|
||||
style = 'vivid'
|
||||
# 遍历检查是否有额外参数
|
||||
for part in parts[1:]:
|
||||
if part in ['hd', 'standard']:
|
||||
quality = part
|
||||
elif part in ['vivid', 'natural']:
|
||||
style = part
|
||||
image_url, image_path = gen_image(llm_kwargs, prompt, resolution, model="dall-e-3", quality=quality, style=style)
|
||||
chatbot.append([prompt,
|
||||
f'图像中转网址: <br/>`{image_url}`<br/>'+
|
||||
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
|
||||
@@ -130,6 +152,7 @@ def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
|
||||
|
||||
class ImageEditState(GptAcademicState):
|
||||
# 尚未完成
|
||||
def get_image_file(self, x):
|
||||
@@ -142,18 +165,27 @@ class ImageEditState(GptAcademicState):
|
||||
file = None if not confirm else file_manifest[0]
|
||||
return confirm, file
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.图片生成->图片修改_DALLE2'
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def unlock_plugin(self, chatbot):
|
||||
self.reset()
|
||||
chatbot._cookies['lock_plugin'] = None
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def get_resolution(self, x):
|
||||
return (x in ['256x256', '512x512', '1024x1024']), x
|
||||
|
||||
|
||||
def get_prompt(self, x):
|
||||
confirm = (len(x)>=5) and (not self.get_resolution(x)[0]) and (not self.get_image_file(x)[0])
|
||||
return confirm, x
|
||||
|
||||
|
||||
def reset(self):
|
||||
self.req = [
|
||||
{'value':None, 'description': '请先上传图像（必须是.png格式）, 然后再次点击本插件', 'verify_fn': self.get_image_file},
|
||||
{'value':None, 'description': '请输入分辨率,可选：256x256, 512x512 或 1024x1024', 'verify_fn': self.get_resolution},
|
||||
{'value':None, 'description': '请输入修改需求,建议您使用英文提示词', 'verify_fn': self.get_prompt},
|
||||
{'value':None, 'description': '请先上传图像（必须是.png格式）, 然后再次点击本插件', 'verify_fn': self.get_image_file},
|
||||
{'value':None, 'description': '请输入分辨率,可选：256x256, 512x512 或 1024x1024, 然后再次点击本插件', 'verify_fn': self.get_resolution},
|
||||
{'value':None, 'description': '请输入修改需求,建议您使用英文提示词, 然后再次点击本插件', 'verify_fn': self.get_prompt},
|
||||
]
|
||||
self.info = ""
|
||||
|
||||
@@ -163,7 +195,7 @@ class ImageEditState(GptAcademicState):
|
||||
confirm, res = r['verify_fn'](prompt)
|
||||
if confirm:
|
||||
r['value'] = res
|
||||
self.set_state(chatbot, 'dummy_key', 'dummy_value')
|
||||
self.dump_state(chatbot)
|
||||
break
|
||||
return self
|
||||
|
||||
@@ -182,23 +214,63 @@ def 图片修改_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
history = [] # 清空历史
|
||||
state = ImageEditState.get_state(chatbot, ImageEditState)
|
||||
state = state.feed(prompt, chatbot)
|
||||
state.lock_plugin(chatbot)
|
||||
if not state.already_obtained_all_materials():
|
||||
chatbot.append(["图片修改（先上传图片,再输入修改需求,最后输入分辨率）", state.next_req()])
|
||||
chatbot.append(["图片修改\n\n1. 上传图片（图片中需要修改的位置用橡皮擦擦除为纯白色，即RGB=255,255,255）\n2. 输入分辨率 \n3. 输入修改需求", state.next_req()])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
image_path = state.req[0]
|
||||
resolution = state.req[1]
|
||||
prompt = state.req[2]
|
||||
image_path = state.req[0]['value']
|
||||
resolution = state.req[1]['value']
|
||||
prompt = state.req[2]['value']
|
||||
chatbot.append(["图片修改, 执行中", f"图片:`{image_path}`<br/>分辨率:`{resolution}`<br/>修改需求:`{prompt}`"])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
image_url, image_path = edit_image(llm_kwargs, prompt, image_path, resolution)
|
||||
chatbot.append([state.prompt,
|
||||
chatbot.append([prompt,
|
||||
f'图像中转网址: <br/>`{image_url}`<br/>'+
|
||||
f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
|
||||
f'本地文件地址: <br/>`{image_path}`<br/>'+
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
|
||||
state.unlock_plugin(chatbot)
|
||||
|
||||
def make_transparent(input_image_path, output_image_path):
|
||||
from PIL import Image
|
||||
image = Image.open(input_image_path)
|
||||
image = image.convert("RGBA")
|
||||
data = image.getdata()
|
||||
new_data = []
|
||||
for item in data:
|
||||
if item[0] == 255 and item[1] == 255 and item[2] == 255:
|
||||
new_data.append((255, 255, 255, 0))
|
||||
else:
|
||||
new_data.append(item)
|
||||
image.putdata(new_data)
|
||||
image.save(output_image_path, "PNG")
|
||||
|
||||
def resize_image(input_path, output_path, max_size=1024):
|
||||
from PIL import Image
|
||||
with Image.open(input_path) as img:
|
||||
width, height = img.size
|
||||
if width > max_size or height > max_size:
|
||||
if width >= height:
|
||||
new_width = max_size
|
||||
new_height = int((max_size / width) * height)
|
||||
else:
|
||||
new_height = max_size
|
||||
new_width = int((max_size / height) * width)
|
||||
|
||||
resized_img = img.resize(size=(new_width, new_height))
|
||||
resized_img.save(output_path)
|
||||
else:
|
||||
img.save(output_path)
|
||||
|
||||
def make_square_image(input_path, output_path):
|
||||
from PIL import Image
|
||||
with Image.open(input_path) as img:
|
||||
width, height = img.size
|
||||
size = max(width, height)
|
||||
new_img = Image.new("RGBA", (size, size), color="black")
|
||||
new_img.paste(img, ((size - width) // 2, (size - height) // 2))
|
||||
new_img.save(output_path)
|
||||
|
||||
@@ -29,17 +29,12 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
|
||||
except:
|
||||
raise RuntimeError('请先将.doc文档转换为.docx文档。')
|
||||
|
||||
print(file_content)
|
||||
# private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
from request_llms.bridge_all import model_info
|
||||
max_token = model_info[llm_kwargs['llm_model']]['max_token']
|
||||
TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content,
|
||||
get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'],
|
||||
limit=TOKEN_LIMIT_PER_FRAGMENT
|
||||
)
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
this_paper_history = []
|
||||
for i, paper_frag in enumerate(paper_fragments):
|
||||
i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
|
||||
|
||||
@@ -28,8 +28,8 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
|
||||
@@ -20,14 +20,9 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
|
||||
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
|
||||
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
|
||||
@@ -91,14 +91,9 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
|
||||
page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
|
||||
# 递归地切割PDF文件
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
|
||||
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
|
||||
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
@@ -18,14 +18,9 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
|
||||
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
@@ -45,7 +40,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]} ...."
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
|
||||
@@ -1,10 +1,19 @@
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
|
||||
|
||||
install_msg ="""
|
||||
|
||||
1. python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
2. python -m pip install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
|
||||
3. python -m pip install unstructured[all-docs] --upgrade
|
||||
|
||||
4. python -c 'import nltk; nltk.download("punkt")'
|
||||
"""
|
||||
|
||||
@CatchException
|
||||
def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
def 知识库文件注入(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
|
||||
@@ -25,15 +34,15 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
|
||||
# resolve deps
|
||||
try:
|
||||
from zh_langchain import construct_vector_store
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from .crazy_utils import knowledge_archive_interface
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import try_install_deps
|
||||
try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < --------------------读取文件--------------- >
|
||||
@@ -42,7 +51,7 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
for sp in spl:
|
||||
_, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')
|
||||
file_manifest += file_manifest_tmp
|
||||
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
chatbot.append(["没有找到任何可读取文件", "当前支持的格式包括: txt, md, docx, pptx, pdf, json等"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
@@ -62,13 +71,14 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
print('Establishing knowledge archive ...')
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
kai = knowledge_archive_interface()
|
||||
kai.feed_archive(file_manifest=file_manifest, id=kai_id)
|
||||
kai_files = kai.get_loaded_file()
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
kai.feed_archive(file_manifest=file_manifest, vs_path=vs_path, id=kai_id)
|
||||
kai_files = kai.get_loaded_file(vs_path=vs_path)
|
||||
kai_files = '<br/>'.join(kai_files)
|
||||
# chatbot.append(['知识库构建成功', "正在将知识库存储至cookie中"])
|
||||
# yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()
|
||||
# chatbot._cookies['lock_plugin'] = 'crazy_functions.Langchain知识库->读取知识库作答'
|
||||
# chatbot._cookies['lock_plugin'] = 'crazy_functions.知识库文件注入->读取知识库作答'
|
||||
# chatbot.append(['完成', "“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。"])
|
||||
chatbot.append(['构建完成', f"当前知识库内的有效文件：\n\n---\n\n{kai_files}\n\n---\n\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
@@ -77,15 +87,15 @@ def 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port=-1):
|
||||
# resolve deps
|
||||
try:
|
||||
from zh_langchain import construct_vector_store
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from .crazy_utils import knowledge_archive_interface
|
||||
# from zh_langchain import construct_vector_store
|
||||
# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from crazy_functions.vector_fns.vector_database import knowledge_archive_interface
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", "导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待..."])
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import try_install_deps
|
||||
try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < ------------------- --------------- >
|
||||
@@ -93,7 +103,8 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
kai_id = plugin_kwargs.get("advanced_arg", 'default')
|
||||
resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
resp, prompt = kai.answer_with_archive_by_id(txt, kai_id, vs_path)
|
||||
|
||||
chatbot.append((txt, f'[知识库 {kai_id}] ' + prompt))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
@@ -12,13 +12,6 @@ class PaperFileGroup():
|
||||
self.sp_file_index = []
|
||||
self.sp_file_tag = []
|
||||
|
||||
# count_token
|
||||
from request_llms.bridge_all import model_info
|
||||
enc = model_info["gpt-3.5-turbo"]['tokenizer']
|
||||
def get_token_num(txt): return len(
|
||||
enc.encode(txt, disallowed_special=()))
|
||||
self.get_token_num = get_token_num
|
||||
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
@@ -29,9 +22,8 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index])
|
||||
else:
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
file_content, self.get_token_num, max_token_limit)
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
|
||||
for j, segment in enumerate(segments):
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacity --network=host --build-arg http_proxy=http://localhost:10881 --build-arg https_proxy=http://localhost:10881 .
|
||||
# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacityBeta --network=host .
|
||||
# docker run -it --net=host gpt-academic-all-capacity bash
|
||||
|
||||
# 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
|
||||
FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
|
||||
|
||||
# use python3 as the system default python
|
||||
WORKDIR /gpt
|
||||
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
|
||||
|
||||
# # 非必要步骤，更换pip源 （以下三行，可以删除）
|
||||
# RUN echo '[global]' > /etc/pip.conf && \
|
||||
# echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
|
||||
# echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
||||
|
||||
# 下载pytorch
|
||||
RUN python3 -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
|
||||
# 准备pip依赖
|
||||
RUN python3 -m pip install openai numpy arxiv rich
|
||||
RUN python3 -m pip install colorama Markdown pygments pymupdf
|
||||
RUN python3 -m pip install python-docx moviepy pdfminer
|
||||
RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
|
||||
RUN python3 -m pip install rarfile py7zr
|
||||
RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
|
||||
# 下载分支
|
||||
WORKDIR /gpt
|
||||
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||
WORKDIR /gpt/gpt_academic
|
||||
RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
|
||||
|
||||
RUN python3 -m pip install -r requirements.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_moss.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_qwen.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||
RUN python3 -m pip install -r request_llms/requirements_newbing.txt
|
||||
RUN python3 -m pip install nougat-ocr
|
||||
|
||||
# 预热Tiktoken模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 安装知识库插件的额外依赖
|
||||
RUN apt-get update && apt-get install libgl1 -y
|
||||
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
RUN pip3 install unstructured[all-docs] --upgrade
|
||||
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||
RUN rm -rf /usr/local/lib/python3.8/dist-packages/tests
|
||||
|
||||
|
||||
# COPY .cache /root/.cache
|
||||
# COPY config_private.py config_private.py
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
@@ -0,0 +1,26 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal-vs -f docs/GithubAction+NoLocal+Vectordb .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal-vs
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 安装知识库插件的额外依赖
|
||||
RUN apt-get update && apt-get install libgl1 -y
|
||||
RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
|
||||
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||
RUN pip3 install unstructured[all-docs] --upgrade
|
||||
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
@@ -923,7 +923,7 @@
|
||||
"的第": "The",
|
||||
"个片段": "fragment",
|
||||
"总结文章": "Summarize the article",
|
||||
"根据以上的对话": "According to the above dialogue",
|
||||
"根据以上的对话": "According to the conversation above",
|
||||
"的主要内容": "The main content of",
|
||||
"所有文件都总结完成了吗": "Are all files summarized?",
|
||||
"如果是.doc文件": "If it is a .doc file",
|
||||
@@ -1501,7 +1501,7 @@
|
||||
"发送请求到OpenAI后": "After sending the request to OpenAI",
|
||||
"上下布局": "Vertical Layout",
|
||||
"左右布局": "Horizontal Layout",
|
||||
"对话窗的高度": "Height of the Dialogue Window",
|
||||
"对话窗的高度": "Height of the Conversation Window",
|
||||
"重试的次数限制": "Retry Limit",
|
||||
"gpt4现在只对申请成功的人开放": "GPT-4 is now only open to those who have successfully applied",
|
||||
"提高限制请查询": "Please check for higher limits",
|
||||
@@ -2183,9 +2183,8 @@
|
||||
"找不到合适插件执行该任务": "Cannot find a suitable plugin to perform this task",
|
||||
"接驳VoidTerminal": "Connect to VoidTerminal",
|
||||
"**很好": "**Very good",
|
||||
"对话|编程": "Conversation|Programming",
|
||||
"对话|编程|学术": "Conversation|Programming|Academic",
|
||||
"4. 建议使用 GPT3.5 或更强的模型": "4. It is recommended to use GPT3.5 or a stronger model",
|
||||
"对话|编程": "Conversation&ImageGenerating|Programming",
|
||||
"对话|编程|学术": "Conversation&ImageGenerating|Programming|Academic", "4. 建议使用 GPT3.5 或更强的模型": "4. It is recommended to use GPT3.5 or a stronger model",
|
||||
"「请调用插件翻译PDF论文": "Please call the plugin to translate the PDF paper",
|
||||
"3. 如果您使用「调用插件xxx」、「修改配置xxx」、「请问」等关键词": "3. If you use keywords such as 'call plugin xxx', 'modify configuration xxx', 'please', etc.",
|
||||
"以下是一篇学术论文的基本信息": "The following is the basic information of an academic paper",
|
||||
@@ -2630,7 +2629,7 @@
|
||||
"已经被记忆": "Already memorized",
|
||||
"默认用英文的": "Default to English",
|
||||
"错误追踪": "Error tracking",
|
||||
"对话|编程|学术|智能体": "Dialogue|Programming|Academic|Intelligent agent",
|
||||
"对话&编程|编程|学术|智能体": "Conversation&ImageGenerating|Programming|Academic|Intelligent agent",
|
||||
"请检查": "Please check",
|
||||
"检测到被滞留的缓存文档": "Detected cached documents being left behind",
|
||||
"还有哪些场合允许使用代理": "What other occasions allow the use of proxies",
|
||||
@@ -2864,7 +2863,7 @@
|
||||
"加载API_KEY": "Loading API_KEY",
|
||||
"协助您编写代码": "Assist you in writing code",
|
||||
"我可以为您提供以下服务": "I can provide you with the following services",
|
||||
"排队中请稍后 ...": "Please wait in line ...",
|
||||
"排队中请稍候 ...": "Please wait in line ...",
|
||||
"建议您使用英文提示词": "It is recommended to use English prompts",
|
||||
"不能支撑AutoGen运行": "Cannot support AutoGen operation",
|
||||
"帮助您解决编程问题": "Help you solve programming problems",
|
||||
@@ -2903,5 +2902,107 @@
|
||||
"高优先级": "High priority",
|
||||
"请配置ZHIPUAI_API_KEY": "Please configure ZHIPUAI_API_KEY",
|
||||
"单个azure模型": "Single Azure model",
|
||||
"预留参数 context 未实现": "Reserved parameter 'context' not implemented"
|
||||
}
|
||||
"预留参数 context 未实现": "Reserved parameter 'context' not implemented",
|
||||
"在输入区输入临时API_KEY后提交": "Submit after entering temporary API_KEY in the input area",
|
||||
"鸟": "Bird",
|
||||
"图片中需要修改的位置用橡皮擦擦除为纯白色": "Erase the areas in the image that need to be modified with an eraser to pure white",
|
||||
"└── PDF文档精准解析": "└── Accurate parsing of PDF documents",
|
||||
"└── ALLOW_RESET_CONFIG 是否允许通过自然语言描述修改本页的配置": "└── ALLOW_RESET_CONFIG Whether to allow modifying the configuration of this page through natural language description",
|
||||
"等待指令": "Waiting for instructions",
|
||||
"不存在": "Does not exist",
|
||||
"选择游戏": "Select game",
|
||||
"本地大模型示意图": "Local large model diagram",
|
||||
"无视此消息即可": "You can ignore this message",
|
||||
"即RGB=255": "That is, RGB=255",
|
||||
"如需追问": "If you have further questions",
|
||||
"也可以是具体的模型路径": "It can also be a specific model path",
|
||||
"才会起作用": "Will take effect",
|
||||
"下载失败": "Download failed",
|
||||
"网页刷新后失效": "Invalid after webpage refresh",
|
||||
"crazy_functions.互动小游戏-": "crazy_functions.Interactive mini game-",
|
||||
"右对齐": "Right alignment",
|
||||
"您可以调用下拉菜单中的“LoadConversationHistoryArchive”还原当下的对话": "You can use the 'LoadConversationHistoryArchive' in the drop-down menu to restore the current conversation",
|
||||
"左对齐": "Left alignment",
|
||||
"使用默认的 FP16": "Use default FP16",
|
||||
"一小时": "One hour",
|
||||
"从而方便内存的释放": "Thus facilitating memory release",
|
||||
"如何临时更换API_KEY": "How to temporarily change API_KEY",
|
||||
"请输入 1024x1024-HD": "Please enter 1024x1024-HD",
|
||||
"使用 INT8 量化": "Use INT8 quantization",
|
||||
"3. 输入修改需求": "3. Enter modification requirements",
|
||||
"刷新界面 由于请求gpt需要一段时间": "Refreshing the interface takes some time due to the request for gpt",
|
||||
"随机小游戏": "Random mini game",
|
||||
"那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型": "So please specify the specific model in QWEN_MODEL_SELECTION below",
|
||||
"表值": "Table value",
|
||||
"我画你猜": "I draw, you guess",
|
||||
"狗": "Dog",
|
||||
"2. 输入分辨率": "2. Enter resolution",
|
||||
"鱼": "Fish",
|
||||
"尚未完成": "Not yet completed",
|
||||
"表头": "Table header",
|
||||
"填localhost或者127.0.0.1": "Fill in localhost or 127.0.0.1",
|
||||
"请上传jpg格式的图片": "Please upload images in jpg format",
|
||||
"API_URL_REDIRECT填写格式是错误的": "The format of API_URL_REDIRECT is incorrect",
|
||||
"├── RWKV的支持见Wiki": "Support for RWKV is available in the Wiki",
|
||||
"如果中文Prompt效果不理想": "If the Chinese prompt is not effective",
|
||||
"/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix": "/SEAFILE_LOCAL/50503047/My Library/Degree/paperlatex/aaai/Fu_8368_with_appendix",
|
||||
"只有当AVAIL_LLM_MODELS包含了对应本地模型时": "Only when AVAIL_LLM_MODELS contains the corresponding local model",
|
||||
"选择本地模型变体": "Choose the local model variant",
|
||||
"如果您确信自己没填错": "If you are sure you haven't made a mistake",
|
||||
"PyPDF2这个库有严重的内存泄露问题": "PyPDF2 library has serious memory leak issues",
|
||||
"整理文件集合 输出消息": "Organize file collection and output message",
|
||||
"没有检测到任何近期上传的图像文件": "No recently uploaded image files detected",
|
||||
"游戏结束": "Game over",
|
||||
"调用结束": "Call ended",
|
||||
"猫": "Cat",
|
||||
"请及时切换模型": "Please switch models in time",
|
||||
"次中": "In the meantime",
|
||||
"如需生成高清图像": "If you need to generate high-definition images",
|
||||
"CPU 模式": "CPU mode",
|
||||
"项目目录": "Project directory",
|
||||
"动物": "Animal",
|
||||
"居中对齐": "Center alignment",
|
||||
"请注意拓展名需要小写": "Please note that the extension name needs to be lowercase",
|
||||
"重试第": "Retry",
|
||||
"实验性功能": "Experimental feature",
|
||||
"猜错了": "Wrong guess",
|
||||
"打开你的代理软件查看代理协议": "Open your proxy software to view the proxy agreement",
|
||||
"您不需要再重复强调该文件的路径了": "You don't need to emphasize the file path again",
|
||||
"请阅读": "Please read",
|
||||
"请直接输入您的问题": "Please enter your question directly",
|
||||
"API_URL_REDIRECT填错了": "API_URL_REDIRECT is filled incorrectly",
|
||||
"谜底是": "The answer is",
|
||||
"第一个模型": "The first model",
|
||||
"你猜对了！": "You guessed it right!",
|
||||
"已经接收到您上传的文件": "The file you uploaded has been received",
|
||||
"您正在调用“图像生成”插件": "You are calling the 'Image Generation' plugin",
|
||||
"刷新界面 界面更新": "Refresh the interface, interface update",
|
||||
"如果之前已经初始化了游戏实例": "If the game instance has been initialized before",
|
||||
"文件": "File",
|
||||
"老鼠": "Mouse",
|
||||
"列2": "Column 2",
|
||||
"等待图片": "Waiting for image",
|
||||
"使用 INT4 量化": "Use INT4 quantization",
|
||||
"from crazy_functions.互动小游戏 import 随机小游戏": "TranslatedText",
|
||||
"游戏主体": "TranslatedText",
|
||||
"该模型不具备上下文对话能力": "TranslatedText",
|
||||
"列3": "TranslatedText",
|
||||
"清理": "TranslatedText",
|
||||
"检查量化配置": "TranslatedText",
|
||||
"如果游戏结束": "TranslatedText",
|
||||
"蛇": "TranslatedText",
|
||||
"则继续该实例；否则重新初始化": "TranslatedText",
|
||||
"e.g. cat and 猫 are the same thing": "TranslatedText",
|
||||
"第三个模型": "TranslatedText",
|
||||
"如果你选择Qwen系列的模型": "TranslatedText",
|
||||
"列4": "TranslatedText",
|
||||
"输入“exit”获取答案": "TranslatedText",
|
||||
"把它放到子进程中运行": "TranslatedText",
|
||||
"列1": "TranslatedText",
|
||||
"使用该模型需要额外依赖": "TranslatedText",
|
||||
"再试试": "TranslatedText",
|
||||
"1. 上传图片": "TranslatedText",
|
||||
"保存状态": "TranslatedText",
|
||||
"GPT-Academic对话存档": "TranslatedText",
|
||||
"Arxiv论文精细翻译": "TranslatedText"
|
||||
}
|
||||
|
||||
@@ -1043,9 +1043,9 @@
|
||||
"jittorllms响应异常": "jittorllms response exception",
|
||||
"在项目根目录运行这两个指令": "Run these two commands in the project root directory",
|
||||
"获取tokenizer": "Get tokenizer",
|
||||
"chatbot 为WebUI中显示的对话列表": "chatbot is the list of dialogues displayed in WebUI",
|
||||
"chatbot 为WebUI中显示的对话列表": "chatbot is the list of conversations displayed in WebUI",
|
||||
"test_解析一个Cpp项目": "test_parse a Cpp project",
|
||||
"将对话记录history以Markdown格式写入文件中": "Write the dialogue record history to a file in Markdown format",
|
||||
"将对话记录history以Markdown格式写入文件中": "Write the conversations record history to a file in Markdown format",
|
||||
"装饰器函数": "Decorator function",
|
||||
"玫瑰色": "Rose color",
|
||||
"将单空行": "刪除單行空白",
|
||||
@@ -2270,4 +2270,4 @@
|
||||
"标注节点的行数范围": "標註節點的行數範圍",
|
||||
"默认 True": "默認 True",
|
||||
"将两个PDF拼接": "將兩個PDF拼接"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -182,12 +182,12 @@ cached_translation = read_map_from_json(language=LANG)
|
||||
def trans(word_to_translate, language, special=False):
|
||||
if len(word_to_translate) == 0: return {}
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from toolbox import get_conf, ChatBotWithCookies
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
|
||||
|
||||
cookies = load_chat_cookies()
|
||||
llm_kwargs = {
|
||||
'api_key': API_KEY,
|
||||
'llm_model': LLM_MODEL,
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'max_length': None,
|
||||
'temperature':0.4,
|
||||
@@ -245,15 +245,15 @@ def trans(word_to_translate, language, special=False):
|
||||
def trans_json(word_to_translate, language, special=False):
|
||||
if len(word_to_translate) == 0: return {}
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from toolbox import get_conf, ChatBotWithCookies
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
|
||||
|
||||
cookies = load_chat_cookies()
|
||||
llm_kwargs = {
|
||||
'api_key': API_KEY,
|
||||
'llm_model': LLM_MODEL,
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': cookies['llm_model'],
|
||||
'top_p':1.0,
|
||||
'max_length': None,
|
||||
'temperature':0.1,
|
||||
'temperature':0.4,
|
||||
}
|
||||
import random
|
||||
N_EACH_REQ = random.randint(16, 32)
|
||||
|
||||
@@ -431,16 +431,48 @@ if "chatglm_onnx" in AVAIL_LLM_MODELS:
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
if "qwen" in AVAIL_LLM_MODELS:
|
||||
if "qwen-local" in AVAIL_LLM_MODELS:
|
||||
try:
|
||||
from .bridge_qwen_local import predict_no_ui_long_connection as qwen_local_noui
|
||||
from .bridge_qwen_local import predict as qwen_local_ui
|
||||
model_info.update({
|
||||
"qwen-local": {
|
||||
"fn_with_ui": qwen_local_ui,
|
||||
"fn_without_ui": qwen_local_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
}
|
||||
})
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS: # zhipuai
|
||||
try:
|
||||
from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
|
||||
from .bridge_qwen import predict as qwen_ui
|
||||
model_info.update({
|
||||
"qwen": {
|
||||
"qwen-turbo": {
|
||||
"fn_with_ui": qwen_ui,
|
||||
"fn_without_ui": qwen_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 4096,
|
||||
"max_token": 6144,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
"qwen-plus": {
|
||||
"fn_with_ui": qwen_ui,
|
||||
"fn_without_ui": qwen_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 30720,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
"qwen-max": {
|
||||
"fn_with_ui": qwen_ui,
|
||||
"fn_without_ui": qwen_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 28672,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
}
|
||||
@@ -552,7 +584,7 @@ if "deepseekcoder" in AVAIL_LLM_MODELS: # deepseekcoder
|
||||
"fn_with_ui": deepseekcoder_ui,
|
||||
"fn_without_ui": deepseekcoder_noui,
|
||||
"endpoint": None,
|
||||
"max_token": 4096,
|
||||
"max_token": 2048,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
}
|
||||
|
||||
@@ -51,7 +51,8 @@ def decode_chunk(chunk):
|
||||
chunkjson = json.loads(chunk_decoded[6:])
|
||||
has_choices = 'choices' in chunkjson
|
||||
if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
|
||||
if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
|
||||
if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
|
||||
if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
|
||||
if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
|
||||
except:
|
||||
pass
|
||||
@@ -101,20 +102,25 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
result = ''
|
||||
json_data = None
|
||||
while True:
|
||||
try: chunk = next(stream_response).decode()
|
||||
try: chunk = next(stream_response)
|
||||
except StopIteration:
|
||||
break
|
||||
except requests.exceptions.ConnectionError:
|
||||
chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
|
||||
if len(chunk)==0: continue
|
||||
if not chunk.startswith('data:'):
|
||||
error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
|
||||
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
|
||||
chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
|
||||
if len(chunk_decoded)==0: continue
|
||||
if not chunk_decoded.startswith('data:'):
|
||||
error_msg = get_full_error(chunk, stream_response).decode()
|
||||
if "reduce the length" in error_msg:
|
||||
raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
|
||||
else:
|
||||
raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
|
||||
if ('data: [DONE]' in chunk): break # api2d 正常完成
|
||||
json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
|
||||
if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
|
||||
# 提前读取一些信息 （用于判断异常）
|
||||
if has_choices and not choice_valid:
|
||||
# 一些垃圾第三方接口的出现这样的错误
|
||||
continue
|
||||
json_data = chunkjson['choices'][0]
|
||||
delta = json_data["delta"]
|
||||
if len(delta) == 0: break
|
||||
if "role" in delta: continue
|
||||
|
||||
@@ -15,29 +15,16 @@ import requests
|
||||
import base64
|
||||
import os
|
||||
import glob
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, \
|
||||
update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files
|
||||
|
||||
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, update_ui_lastest_msg, get_max_token
|
||||
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
|
||||
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
|
||||
|
||||
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
|
||||
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
|
||||
|
||||
def have_any_recent_upload_image_files(chatbot):
|
||||
_5min = 5 * 60
|
||||
if chatbot is None: return False, None # chatbot is None
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
if not most_recent_uploaded: return False, None # most_recent_uploaded is None
|
||||
if time.time() - most_recent_uploaded["time"] < _5min:
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
path = most_recent_uploaded['path']
|
||||
file_manifest = [f for f in glob.glob(f'{path}/**/*.jpg', recursive=True)]
|
||||
file_manifest += [f for f in glob.glob(f'{path}/**/*.jpeg', recursive=True)]
|
||||
file_manifest += [f for f in glob.glob(f'{path}/**/*.png', recursive=True)]
|
||||
if len(file_manifest) == 0: return False, None
|
||||
return True, file_manifest # most_recent_uploaded is new
|
||||
else:
|
||||
return False, None # most_recent_uploaded is too old
|
||||
|
||||
def report_invalid_key(key):
|
||||
if get_conf("BLOCK_INVALID_APIKEY"):
|
||||
@@ -258,10 +245,6 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg,
|
||||
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
|
||||
|
||||
# Function to encode the image
|
||||
def encode_image(image_path):
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode('utf-8')
|
||||
|
||||
def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
|
||||
"""
|
||||
|
||||
@@ -6,6 +6,7 @@ from toolbox import ProxyNetworkActivate
|
||||
from toolbox import get_conf
|
||||
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
|
||||
from threading import Thread
|
||||
import torch
|
||||
|
||||
def download_huggingface_model(model_name, max_retry, local_dir):
|
||||
from huggingface_hub import snapshot_download
|
||||
@@ -36,9 +37,46 @@ class GetCoderLMHandle(LocalLLMHandle):
|
||||
# tokenizer = download_huggingface_model(model_name, max_retry=128, local_dir=local_dir)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
self._streamer = TextIteratorStreamer(tokenizer)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": 0,
|
||||
"lm_head": 0,
|
||||
"transformer.h": 0,
|
||||
"transformer.ln_f": 0,
|
||||
"model.embed_tokens": 0,
|
||||
"model.layers": 0,
|
||||
"model.norm": 0,
|
||||
}
|
||||
|
||||
# 检查量化配置
|
||||
quantization_type = get_conf('LOCAL_MODEL_QUANT')
|
||||
|
||||
if get_conf('LOCAL_MODEL_DEVICE') != 'cpu':
|
||||
model = model.cuda()
|
||||
if quantization_type == "INT8":
|
||||
from transformers import BitsAndBytesConfig
|
||||
# 使用 INT8 量化
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, load_in_8bit=True,
|
||||
device_map=device_map)
|
||||
elif quantization_type == "INT4":
|
||||
from transformers import BitsAndBytesConfig
|
||||
# 使用 INT4 量化
|
||||
bnb_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.bfloat16
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
quantization_config=bnb_config, device_map=device_map)
|
||||
else:
|
||||
# 使用默认的 FP16
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16, device_map=device_map)
|
||||
else:
|
||||
# CPU 模式
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16)
|
||||
|
||||
return model, tokenizer
|
||||
|
||||
def llm_stream_generator(self, **kwargs):
|
||||
@@ -54,7 +92,10 @@ class GetCoderLMHandle(LocalLLMHandle):
|
||||
query, max_length, top_p, temperature, history = adaptor(kwargs)
|
||||
history.append({ 'role': 'user', 'content': query})
|
||||
messages = history
|
||||
inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt").to(self._model.device)
|
||||
inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt")
|
||||
if inputs.shape[1] > max_length:
|
||||
inputs = inputs[:, -max_length:]
|
||||
inputs = inputs.to(self._model.device)
|
||||
generation_kwargs = dict(
|
||||
inputs=inputs,
|
||||
max_new_tokens=max_length,
|
||||
|
||||
@@ -1,67 +1,62 @@
|
||||
model_name = "Qwen"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
|
||||
|
||||
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
import time
|
||||
import threading
|
||||
import importlib
|
||||
from toolbox import update_ui, get_conf, ProxyNetworkActivate
|
||||
from multiprocessing import Process, Pipe
|
||||
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
|
||||
import os
|
||||
from toolbox import update_ui, get_conf, update_ui_lastest_msg
|
||||
from toolbox import check_packages, report_exception
|
||||
|
||||
model_name = 'Qwen'
|
||||
|
||||
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_llms/bridge_all.py
|
||||
"""
|
||||
watch_dog_patience = 5
|
||||
response = ""
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 Local Model
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
class GetQwenLMHandle(LocalLLMHandle):
|
||||
from .com_qwenapi import QwenRequestInstance
|
||||
sri = QwenRequestInstance()
|
||||
for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
|
||||
if len(observe_window) >= 1:
|
||||
observe_window[0] = response
|
||||
if len(observe_window) >= 2:
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
|
||||
return response
|
||||
|
||||
def load_model_info(self):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
self.model_name = model_name
|
||||
self.cmd_to_install = cmd_to_install
|
||||
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_llms/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, ""))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
def load_model_and_tokenizer(self):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
import os, glob
|
||||
import os
|
||||
import platform
|
||||
from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
check_packages(["dashscope"])
|
||||
except:
|
||||
yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade dashscope```。",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
return
|
||||
|
||||
with ProxyNetworkActivate('Download_LLM'):
|
||||
model_id = 'qwen/Qwen-7B-Chat'
|
||||
self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
|
||||
# use fp16
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
|
||||
self._model = model
|
||||
# 检查DASHSCOPE_API_KEY
|
||||
if get_conf("DASHSCOPE_API_KEY") == "":
|
||||
yield from update_ui_lastest_msg(f"请配置 DASHSCOPE_API_KEY。",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
return
|
||||
|
||||
return self._model, self._tokenizer
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
|
||||
def llm_stream_generator(self, **kwargs):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
def adaptor(kwargs):
|
||||
query = kwargs['query']
|
||||
max_length = kwargs['max_length']
|
||||
top_p = kwargs['top_p']
|
||||
temperature = kwargs['temperature']
|
||||
history = kwargs['history']
|
||||
return query, max_length, top_p, temperature, history
|
||||
# 开始接收回复
|
||||
from .com_qwenapi import QwenRequestInstance
|
||||
sri = QwenRequestInstance()
|
||||
for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
|
||||
chatbot[-1] = (inputs, response)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
query, max_length, top_p, temperature, history = adaptor(kwargs)
|
||||
|
||||
for response in self._model.chat(self._tokenizer, query, history=history, stream=True):
|
||||
yield response
|
||||
|
||||
def try_to_import_special_deps(self, **kwargs):
|
||||
# import something that will raise error if the user does not install requirement_*.txt
|
||||
# 🏃♂️🏃♂️🏃♂️ 主进程执行
|
||||
import importlib
|
||||
importlib.import_module('modelscope')
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 GPT-Academic Interface
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetQwenLMHandle, model_name)
|
||||
# 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)
|
||||
@@ -0,0 +1,59 @@
|
||||
model_name = "Qwen_Local"
|
||||
cmd_to_install = "`pip install -r request_llms/requirements_qwen_local.txt`"
|
||||
|
||||
from toolbox import ProxyNetworkActivate, get_conf
|
||||
from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
|
||||
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 Local Model
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
class GetQwenLMHandle(LocalLLMHandle):
|
||||
|
||||
def load_model_info(self):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
self.model_name = model_name
|
||||
self.cmd_to_install = cmd_to_install
|
||||
|
||||
def load_model_and_tokenizer(self):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
# from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from transformers.generation import GenerationConfig
|
||||
with ProxyNetworkActivate('Download_LLM'):
|
||||
model_id = get_conf('QWEN_LOCAL_MODEL_SELECTION')
|
||||
self._tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, resume_download=True)
|
||||
# use fp16
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True).eval()
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
|
||||
self._model = model
|
||||
|
||||
return self._model, self._tokenizer
|
||||
|
||||
def llm_stream_generator(self, **kwargs):
|
||||
# 🏃♂️🏃♂️🏃♂️ 子进程执行
|
||||
def adaptor(kwargs):
|
||||
query = kwargs['query']
|
||||
max_length = kwargs['max_length']
|
||||
top_p = kwargs['top_p']
|
||||
temperature = kwargs['temperature']
|
||||
history = kwargs['history']
|
||||
return query, max_length, top_p, temperature, history
|
||||
|
||||
query, max_length, top_p, temperature, history = adaptor(kwargs)
|
||||
|
||||
for response in self._model.chat_stream(self._tokenizer, query, history=history):
|
||||
yield response
|
||||
|
||||
def try_to_import_special_deps(self, **kwargs):
|
||||
# import something that will raise error if the user does not install requirement_*.txt
|
||||
# 🏃♂️🏃♂️🏃♂️ 主进程执行
|
||||
import importlib
|
||||
importlib.import_module('modelscope')
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
# 🔌💻 GPT-Academic Interface
|
||||
# ------------------------------------------------------------------------------------------------------------------------
|
||||
predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetQwenLMHandle, model_name)
|
||||
@@ -26,7 +26,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
|
||||
from .com_sparkapi import SparkRequestInstance
|
||||
sri = SparkRequestInstance()
|
||||
for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
|
||||
for response in sri.generate(inputs, llm_kwargs, history, sys_prompt, use_image_api=False):
|
||||
if len(observe_window) >= 1:
|
||||
observe_window[0] = response
|
||||
if len(observe_window) >= 2:
|
||||
@@ -52,7 +52,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
# 开始接收回复
|
||||
from .com_sparkapi import SparkRequestInstance
|
||||
sri = SparkRequestInstance()
|
||||
for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
|
||||
for response in sri.generate(inputs, llm_kwargs, history, system_prompt, use_image_api=True):
|
||||
chatbot[-1] = (inputs, response)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
|
||||
94
request_llms/com_qwenapi.py
普通文件
94
request_llms/com_qwenapi.py
普通文件
@@ -0,0 +1,94 @@
|
||||
from http import HTTPStatus
|
||||
from toolbox import get_conf
|
||||
import threading
|
||||
import logging
|
||||
|
||||
timeout_bot_msg = '[Local Message] Request timeout. Network error.'
|
||||
|
||||
class QwenRequestInstance():
|
||||
def __init__(self):
|
||||
import dashscope
|
||||
self.time_to_yield_event = threading.Event()
|
||||
self.time_to_exit_event = threading.Event()
|
||||
self.result_buf = ""
|
||||
|
||||
def validate_key():
|
||||
DASHSCOPE_API_KEY = get_conf("DASHSCOPE_API_KEY")
|
||||
if DASHSCOPE_API_KEY == '': return False
|
||||
return True
|
||||
|
||||
if not validate_key():
|
||||
raise RuntimeError('请配置 DASHSCOPE_API_KEY')
|
||||
dashscope.api_key = get_conf("DASHSCOPE_API_KEY")
|
||||
|
||||
|
||||
def generate(self, inputs, llm_kwargs, history, system_prompt):
|
||||
# import _thread as thread
|
||||
from dashscope import Generation
|
||||
QWEN_MODEL = {
|
||||
'qwen-turbo': Generation.Models.qwen_turbo,
|
||||
'qwen-plus': Generation.Models.qwen_plus,
|
||||
'qwen-max': Generation.Models.qwen_max,
|
||||
}[llm_kwargs['llm_model']]
|
||||
top_p = llm_kwargs.get('top_p', 0.8)
|
||||
if top_p == 0: top_p += 1e-5
|
||||
if top_p == 1: top_p -= 1e-5
|
||||
|
||||
self.result_buf = ""
|
||||
responses = Generation.call(
|
||||
model=QWEN_MODEL,
|
||||
messages=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
|
||||
top_p=top_p,
|
||||
temperature=llm_kwargs.get('temperature', 1.0),
|
||||
result_format='message',
|
||||
stream=True,
|
||||
incremental_output=True
|
||||
)
|
||||
|
||||
for response in responses:
|
||||
if response.status_code == HTTPStatus.OK:
|
||||
if response.output.choices[0].finish_reason == 'stop':
|
||||
yield self.result_buf
|
||||
break
|
||||
elif response.output.choices[0].finish_reason == 'length':
|
||||
self.result_buf += "[Local Message] 生成长度过长，后续输出被截断"
|
||||
yield self.result_buf
|
||||
break
|
||||
else:
|
||||
self.result_buf += response.output.choices[0].message.content
|
||||
yield self.result_buf
|
||||
else:
|
||||
self.result_buf += f"[Local Message] 请求错误：状态码：{response.status_code}，错误码:{response.code}，消息：{response.message}"
|
||||
yield self.result_buf
|
||||
break
|
||||
logging.info(f'[raw_input] {inputs}')
|
||||
logging.info(f'[response] {self.result_buf}')
|
||||
return self.result_buf
|
||||
|
||||
|
||||
def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
|
||||
conversation_cnt = len(history) // 2
|
||||
if system_prompt == '': system_prompt = 'Hello!'
|
||||
messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
|
||||
if conversation_cnt:
|
||||
for index in range(0, 2*conversation_cnt, 2):
|
||||
what_i_have_asked = {}
|
||||
what_i_have_asked["role"] = "user"
|
||||
what_i_have_asked["content"] = history[index]
|
||||
what_gpt_answer = {}
|
||||
what_gpt_answer["role"] = "assistant"
|
||||
what_gpt_answer["content"] = history[index+1]
|
||||
if what_i_have_asked["content"] != "":
|
||||
if what_gpt_answer["content"] == "":
|
||||
continue
|
||||
if what_gpt_answer["content"] == timeout_bot_msg:
|
||||
continue
|
||||
messages.append(what_i_have_asked)
|
||||
messages.append(what_gpt_answer)
|
||||
else:
|
||||
messages[-1]['content'] = what_gpt_answer['content']
|
||||
what_i_ask_now = {}
|
||||
what_i_ask_now["role"] = "user"
|
||||
what_i_ask_now["content"] = inputs
|
||||
messages.append(what_i_ask_now)
|
||||
return messages
|
||||
@@ -1,4 +1,4 @@
|
||||
from toolbox import get_conf
|
||||
from toolbox import get_conf, get_pictures_list, encode_image
|
||||
import base64
|
||||
import datetime
|
||||
import hashlib
|
||||
@@ -65,18 +65,19 @@ class SparkRequestInstance():
|
||||
self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
|
||||
self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
|
||||
self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
|
||||
self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
|
||||
|
||||
self.time_to_yield_event = threading.Event()
|
||||
self.time_to_exit_event = threading.Event()
|
||||
|
||||
self.result_buf = ""
|
||||
|
||||
def generate(self, inputs, llm_kwargs, history, system_prompt):
|
||||
def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False):
|
||||
llm_kwargs = llm_kwargs
|
||||
history = history
|
||||
system_prompt = system_prompt
|
||||
import _thread as thread
|
||||
thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt))
|
||||
thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api))
|
||||
while True:
|
||||
self.time_to_yield_event.wait(timeout=1)
|
||||
if self.time_to_yield_event.is_set():
|
||||
@@ -85,14 +86,20 @@ class SparkRequestInstance():
|
||||
return self.result_buf
|
||||
|
||||
|
||||
def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
|
||||
def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api):
|
||||
if llm_kwargs['llm_model'] == 'sparkv2':
|
||||
gpt_url = self.gpt_url_v2
|
||||
elif llm_kwargs['llm_model'] == 'sparkv3':
|
||||
gpt_url = self.gpt_url_v3
|
||||
else:
|
||||
gpt_url = self.gpt_url
|
||||
|
||||
file_manifest = []
|
||||
if use_image_api and llm_kwargs.get('most_recent_uploaded'):
|
||||
if llm_kwargs['most_recent_uploaded'].get('path'):
|
||||
file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
|
||||
if len(file_manifest) > 0:
|
||||
print('正在使用讯飞图片理解API')
|
||||
gpt_url = self.gpt_url_img
|
||||
wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
|
||||
websocket.enableTrace(False)
|
||||
wsUrl = wsParam.create_url()
|
||||
@@ -101,9 +108,8 @@ class SparkRequestInstance():
|
||||
def on_open(ws):
|
||||
import _thread as thread
|
||||
thread.start_new_thread(run, (ws,))
|
||||
|
||||
def run(ws, *args):
|
||||
data = json.dumps(gen_params(ws.appid, *ws.all_args))
|
||||
data = json.dumps(gen_params(ws.appid, *ws.all_args, file_manifest))
|
||||
ws.send(data)
|
||||
|
||||
# 收到websocket消息的处理
|
||||
@@ -142,9 +148,18 @@ class SparkRequestInstance():
|
||||
ws.all_args = (inputs, llm_kwargs, history, system_prompt)
|
||||
ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
|
||||
|
||||
def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
|
||||
def generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest):
|
||||
conversation_cnt = len(history) // 2
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
messages = []
|
||||
if file_manifest:
|
||||
base64_images = []
|
||||
for image_path in file_manifest:
|
||||
base64_images.append(encode_image(image_path))
|
||||
for img_s in base64_images:
|
||||
if img_s not in str(messages):
|
||||
messages.append({"role": "user", "content": img_s, "content_type": "image"})
|
||||
else:
|
||||
messages = [{"role": "system", "content": system_prompt}]
|
||||
if conversation_cnt:
|
||||
for index in range(0, 2*conversation_cnt, 2):
|
||||
what_i_have_asked = {}
|
||||
@@ -167,7 +182,7 @@ def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
|
||||
return messages
|
||||
|
||||
|
||||
def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
|
||||
def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest):
|
||||
"""
|
||||
通过appid和用户的提问来生成请参数
|
||||
"""
|
||||
@@ -176,6 +191,8 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
|
||||
"sparkv2": "generalv2",
|
||||
"sparkv3": "generalv3",
|
||||
}
|
||||
domains_select = domains[llm_kwargs['llm_model']]
|
||||
if file_manifest: domains_select = 'image'
|
||||
data = {
|
||||
"header": {
|
||||
"app_id": appid,
|
||||
@@ -183,7 +200,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
|
||||
},
|
||||
"parameter": {
|
||||
"chat": {
|
||||
"domain": domains[llm_kwargs['llm_model']],
|
||||
"domain": domains_select,
|
||||
"temperature": llm_kwargs["temperature"],
|
||||
"random_threshold": 0.5,
|
||||
"max_tokens": 4096,
|
||||
@@ -192,7 +209,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
|
||||
},
|
||||
"payload": {
|
||||
"message": {
|
||||
"text": generate_message_payload(inputs, llm_kwargs, history, system_prompt)
|
||||
"text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -183,11 +183,11 @@ class LocalLLMHandle(Process):
|
||||
def stream_chat(self, **kwargs):
|
||||
# ⭐run in main process
|
||||
if self.get_state() == "`准备就绪`":
|
||||
yield "`正在等待线程锁，排队中请稍后 ...`"
|
||||
yield "`正在等待线程锁，排队中请稍候 ...`"
|
||||
|
||||
with self.threadLock:
|
||||
if self.parent.poll():
|
||||
yield "`排队中请稍后 ...`"
|
||||
yield "`排队中请稍候 ...`"
|
||||
self.clear_pending_messages()
|
||||
self.parent.send(kwargs)
|
||||
std_out = ""
|
||||
|
||||
@@ -6,5 +6,3 @@ sentencepiece
|
||||
numpy
|
||||
onnxruntime
|
||||
sentencepiece
|
||||
streamlit
|
||||
streamlit-chat
|
||||
|
||||
@@ -5,5 +5,4 @@ accelerate
|
||||
matplotlib
|
||||
huggingface_hub
|
||||
triton
|
||||
streamlit
|
||||
|
||||
|
||||
@@ -1,2 +1 @@
|
||||
modelscope
|
||||
transformers_stream_generator
|
||||
dashscope
|
||||
@@ -0,0 +1,5 @@
|
||||
modelscope
|
||||
transformers_stream_generator
|
||||
auto-gptq
|
||||
optimum
|
||||
urllib3<2
|
||||
@@ -2,6 +2,7 @@ pydantic==1.10.11
|
||||
pypdf2==2.12.1
|
||||
tiktoken>=0.3.3
|
||||
requests[socks]
|
||||
protobuf==3.18
|
||||
transformers>=4.27.1
|
||||
scipdf_parser>=0.52
|
||||
python-markdown-math
|
||||
|
||||
@@ -16,8 +16,9 @@ if __name__ == "__main__":
|
||||
# from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_claude import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_internlm import predict_no_ui_long_connection
|
||||
from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_qwen import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
|
||||
from request_llms.bridge_qwen_local import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_spark import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_zhipu import predict_no_ui_long_connection
|
||||
# from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
|
||||
|
||||
@@ -48,11 +48,11 @@ if __name__ == "__main__":
|
||||
# for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
|
||||
# plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang})
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->知识库问答', main_input="./")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->知识库文件注入', main_input="./")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->读取知识库作答', main_input="What is the installation method？")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="What is the installation method？")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Langchain知识库->读取知识库作答', main_input="远程云服务器部署？")
|
||||
# plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？")
|
||||
|
||||
# plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2210.03629")
|
||||
|
||||
|
||||
@@ -56,11 +56,11 @@ vt.get_plugin_handle = silence_stdout_fn(get_plugin_handle)
|
||||
vt.get_plugin_default_kwargs = silence_stdout_fn(get_plugin_default_kwargs)
|
||||
vt.get_chat_handle = silence_stdout_fn(get_chat_handle)
|
||||
vt.get_chat_default_kwargs = silence_stdout_fn(get_chat_default_kwargs)
|
||||
vt.chat_to_markdown_str = chat_to_markdown_str
|
||||
vt.chat_to_markdown_str = (chat_to_markdown_str)
|
||||
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
|
||||
vt.get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
|
||||
|
||||
def plugin_test(main_input, plugin, advanced_arg=None):
|
||||
def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
|
||||
from rich.live import Live
|
||||
from rich.markdown import Markdown
|
||||
|
||||
@@ -72,7 +72,10 @@ def plugin_test(main_input, plugin, advanced_arg=None):
|
||||
plugin_kwargs['main_input'] = main_input
|
||||
if advanced_arg is not None:
|
||||
plugin_kwargs['plugin_kwargs'] = advanced_arg
|
||||
my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
|
||||
if debug:
|
||||
my_working_plugin = (plugin)(**plugin_kwargs)
|
||||
else:
|
||||
my_working_plugin = silence_stdout(plugin)(**plugin_kwargs)
|
||||
|
||||
with Live(Markdown(""), auto_refresh=False, vertical_overflow="visible") as live:
|
||||
for cookies, chat, hist, msg in my_working_plugin:
|
||||
|
||||
17
tests/test_vector_plugins.py
普通文件
17
tests/test_vector_plugins.py
普通文件
@@ -0,0 +1,17 @@
|
||||
"""
|
||||
对项目中的各个插件进行测试。运行方法：直接运行 python tests/test_plugins.py
|
||||
"""
|
||||
|
||||
|
||||
import os, sys
|
||||
def validate_path(): dir_name = os.path.dirname(__file__); root_dir_assume = os.path.abspath(dir_name + '/..'); os.chdir(root_dir_assume); sys.path.append(root_dir_assume)
|
||||
validate_path() # 返回项目根路径
|
||||
|
||||
if __name__ == "__main__":
|
||||
from tests.test_utils import plugin_test
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->知识库文件注入', main_input="./README.md")
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="What is the installation method？")
|
||||
|
||||
plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="远程云服务器部署？")
|
||||
436
themes/common.js
436
themes/common.js
@@ -1,9 +1,13 @@
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 1 部分: 工具函数
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function gradioApp() {
|
||||
// https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
|
||||
const elems = document.getElementsByTagName('gradio-app');
|
||||
const elem = elems.length == 0 ? document : elems[0];
|
||||
if (elem !== document) {
|
||||
elem.getElementById = function(id) {
|
||||
elem.getElementById = function (id) {
|
||||
return document.getElementById(id);
|
||||
};
|
||||
}
|
||||
@@ -12,31 +16,76 @@ function gradioApp() {
|
||||
|
||||
function setCookie(name, value, days) {
|
||||
var expires = "";
|
||||
|
||||
|
||||
if (days) {
|
||||
var date = new Date();
|
||||
date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
|
||||
expires = "; expires=" + date.toUTCString();
|
||||
var date = new Date();
|
||||
date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
|
||||
expires = "; expires=" + date.toUTCString();
|
||||
}
|
||||
|
||||
|
||||
document.cookie = name + "=" + value + expires + "; path=/";
|
||||
}
|
||||
|
||||
function getCookie(name) {
|
||||
var decodedCookie = decodeURIComponent(document.cookie);
|
||||
var cookies = decodedCookie.split(';');
|
||||
|
||||
|
||||
for (var i = 0; i < cookies.length; i++) {
|
||||
var cookie = cookies[i].trim();
|
||||
|
||||
if (cookie.indexOf(name + "=") === 0) {
|
||||
return cookie.substring(name.length + 1, cookie.length);
|
||||
}
|
||||
var cookie = cookies[i].trim();
|
||||
|
||||
if (cookie.indexOf(name + "=") === 0) {
|
||||
return cookie.substring(name.length + 1, cookie.length);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
let toastCount = 0;
|
||||
function toast_push(msg, duration) {
|
||||
duration = isNaN(duration) ? 3000 : duration;
|
||||
const existingToasts = document.querySelectorAll('.toast');
|
||||
existingToasts.forEach(toast => {
|
||||
toast.style.top = `${parseInt(toast.style.top, 10) - 70}px`;
|
||||
});
|
||||
const m = document.createElement('div');
|
||||
m.innerHTML = msg;
|
||||
m.classList.add('toast');
|
||||
m.style.cssText = `font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 0, 0.6); padding: 10px 15px; border-radius: 4px; position: fixed; top: ${50 + toastCount * 70}%; left: 50%; transform: translateX(-50%); width: auto; text-align: center; transition: top 0.3s;`;
|
||||
document.body.appendChild(m);
|
||||
setTimeout(function () {
|
||||
m.style.opacity = '0';
|
||||
setTimeout(function () {
|
||||
document.body.removeChild(m);
|
||||
toastCount--;
|
||||
}, 500);
|
||||
}, duration);
|
||||
toastCount++;
|
||||
}
|
||||
|
||||
function toast_up(msg) {
|
||||
var m = document.getElementById('toast_up');
|
||||
if (m) {
|
||||
document.body.removeChild(m); // remove the loader from the body
|
||||
}
|
||||
m = document.createElement('div');
|
||||
m.id = 'toast_up';
|
||||
m.innerHTML = msg;
|
||||
m.style.cssText = "font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 100, 0.6); padding: 10px 15px; margin: 0 0 0 -60px; border-radius: 4px; position: fixed; top: 50%; left: 50%; width: auto; text-align: center;";
|
||||
document.body.appendChild(m);
|
||||
}
|
||||
function toast_down() {
|
||||
var m = document.getElementById('toast_up');
|
||||
if (m) {
|
||||
document.body.removeChild(m); // remove the loader from the body
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 2 部分: 复制按钮
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function addCopyButton(botElement) {
|
||||
// https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
|
||||
// Copy bot button
|
||||
@@ -49,7 +98,7 @@ function addCopyButton(botElement) {
|
||||
// messageBtnColumnElement.remove();
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
var copyButton = document.createElement('button');
|
||||
copyButton.classList.add('copy-bot-btn');
|
||||
copyButton.setAttribute('aria-label', 'Copy');
|
||||
@@ -98,47 +147,61 @@ function chatbotContentChanged(attempt = 1, force = false) {
|
||||
}
|
||||
}
|
||||
|
||||
function chatbotAutoHeight(){
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 3 部分: chatbot动态高度调整
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function chatbotAutoHeight() {
|
||||
// 自动调整高度
|
||||
function update_height(){
|
||||
var { panel_height_target, chatbot_height, chatbot } = get_elements(true);
|
||||
if (panel_height_target!=chatbot_height)
|
||||
{
|
||||
var pixelString = panel_height_target.toString() + 'px';
|
||||
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
|
||||
function update_height() {
|
||||
var { height_target, chatbot_height, chatbot } = get_elements(true);
|
||||
if (height_target != chatbot_height) {
|
||||
var pixelString = height_target.toString() + 'px';
|
||||
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
|
||||
}
|
||||
}
|
||||
|
||||
function update_height_slow(){
|
||||
var { panel_height_target, chatbot_height, chatbot } = get_elements();
|
||||
if (panel_height_target!=chatbot_height)
|
||||
{
|
||||
new_panel_height = (panel_height_target - chatbot_height)*0.5 + chatbot_height;
|
||||
if (Math.abs(new_panel_height - panel_height_target) < 10){
|
||||
new_panel_height = panel_height_target;
|
||||
function update_height_slow() {
|
||||
var { height_target, chatbot_height, chatbot } = get_elements();
|
||||
if (height_target != chatbot_height) {
|
||||
new_panel_height = (height_target - chatbot_height) * 0.5 + chatbot_height;
|
||||
if (Math.abs(new_panel_height - height_target) < 10) {
|
||||
new_panel_height = height_target;
|
||||
}
|
||||
// console.log(chatbot_height, panel_height_target, new_panel_height);
|
||||
// console.log(chatbot_height, height_target, new_panel_height);
|
||||
var pixelString = new_panel_height.toString() + 'px';
|
||||
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
|
||||
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
|
||||
}
|
||||
}
|
||||
|
||||
monitoring_input_box()
|
||||
update_height();
|
||||
setInterval(function() {
|
||||
setInterval(function () {
|
||||
update_height_slow()
|
||||
}, 50); // 每100毫秒执行一次
|
||||
}, 50); // 每50毫秒执行一次
|
||||
}
|
||||
|
||||
function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
|
||||
chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
|
||||
var chatbotObserver = new MutationObserver(() => {
|
||||
chatbotContentChanged(1);
|
||||
});
|
||||
chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
|
||||
if (LAYOUT === "LEFT-RIGHT") {chatbotAutoHeight();}
|
||||
swapped = false;
|
||||
function swap_input_area() {
|
||||
// Get the elements to be swapped
|
||||
var element1 = document.querySelector("#input-panel");
|
||||
var element2 = document.querySelector("#basic-panel");
|
||||
|
||||
// Get the parent of the elements
|
||||
var parent = element1.parentNode;
|
||||
|
||||
// Get the next sibling of element2
|
||||
var nextSibling = element2.nextSibling;
|
||||
|
||||
// Swap the elements
|
||||
parent.insertBefore(element2, element1);
|
||||
parent.insertBefore(element1, nextSibling);
|
||||
if (swapped) {swapped = false;}
|
||||
else {swapped = true;}
|
||||
}
|
||||
|
||||
function get_elements(consider_state_panel=false) {
|
||||
function get_elements(consider_state_panel = false) {
|
||||
var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
|
||||
if (!chatbot) {
|
||||
chatbot = document.querySelector('#gpt-chatbot');
|
||||
@@ -147,17 +210,292 @@ function get_elements(consider_state_panel=false) {
|
||||
const panel2 = document.querySelector('#basic-panel').getBoundingClientRect()
|
||||
const panel3 = document.querySelector('#plugin-panel').getBoundingClientRect();
|
||||
// const panel4 = document.querySelector('#interact-panel').getBoundingClientRect();
|
||||
const panel5 = document.querySelector('#input-panel2').getBoundingClientRect();
|
||||
const panel_active = document.querySelector('#state-panel').getBoundingClientRect();
|
||||
if (consider_state_panel || panel_active.height < 25){
|
||||
if (consider_state_panel || panel_active.height < 25) {
|
||||
document.state_panel_height = panel_active.height;
|
||||
}
|
||||
// 25 是chatbot的label高度, 16 是右侧的gap
|
||||
var panel_height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16*2;
|
||||
var height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16 * 2;
|
||||
// 禁止动态的state-panel高度影响
|
||||
panel_height_target = panel_height_target + (document.state_panel_height-panel_active.height)
|
||||
var panel_height_target = parseInt(panel_height_target);
|
||||
height_target = height_target + (document.state_panel_height - panel_active.height)
|
||||
var height_target = parseInt(height_target);
|
||||
var chatbot_height = chatbot.style.height;
|
||||
// 交换输入区位置，使得输入区始终可用
|
||||
if (!swapped){
|
||||
if (panel1.top!=0 && panel1.top < 0){ swap_input_area(); }
|
||||
}
|
||||
else if (swapped){
|
||||
if (panel2.top!=0 && panel2.top > 0){ swap_input_area(); }
|
||||
}
|
||||
// 调整高度
|
||||
const err_tor = 5;
|
||||
if (Math.abs(panel1.left - chatbot.getBoundingClientRect().left) < err_tor){
|
||||
// 是否处于窄屏模式
|
||||
height_target = window.innerHeight * 0.6;
|
||||
}else{
|
||||
// 调整高度
|
||||
const chatbot_height_exceed = 15;
|
||||
const chatbot_height_exceed_m = 10;
|
||||
b_panel = Math.max(panel1.bottom, panel2.bottom, panel3.bottom)
|
||||
if (b_panel >= window.innerHeight - chatbot_height_exceed) {
|
||||
height_target = window.innerHeight - chatbot.getBoundingClientRect().top - chatbot_height_exceed_m;
|
||||
}
|
||||
else if (b_panel < window.innerHeight * 0.75) {
|
||||
height_target = window.innerHeight * 0.8;
|
||||
}
|
||||
}
|
||||
var chatbot_height = parseInt(chatbot_height);
|
||||
return { panel_height_target, chatbot_height, chatbot };
|
||||
return { height_target, chatbot_height, chatbot };
|
||||
}
|
||||
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 4 部分: 粘贴、拖拽文件上传
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
var elem_upload = null;
|
||||
var elem_upload_float = null;
|
||||
var elem_input_main = null;
|
||||
var elem_input_float = null;
|
||||
var elem_chatbot = null;
|
||||
var exist_file_msg = '⚠️请先删除上传区（左上方）中的历史文件，再尝试上传。'
|
||||
|
||||
function add_func_paste(input) {
|
||||
let paste_files = [];
|
||||
if (input) {
|
||||
input.addEventListener("paste", async function (e) {
|
||||
const clipboardData = e.clipboardData || window.clipboardData;
|
||||
const items = clipboardData.items;
|
||||
if (items) {
|
||||
for (i = 0; i < items.length; i++) {
|
||||
if (items[i].kind === "file") { // 确保是文件类型
|
||||
const file = items[i].getAsFile();
|
||||
// 将每一个粘贴的文件添加到files数组中
|
||||
paste_files.push(file);
|
||||
e.preventDefault(); // 避免粘贴文件名到输入框
|
||||
}
|
||||
}
|
||||
if (paste_files.length > 0) {
|
||||
// 按照文件列表执行批量上传逻辑
|
||||
await upload_files(paste_files);
|
||||
paste_files = []
|
||||
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function add_func_drag(elem) {
|
||||
if (elem) {
|
||||
const dragEvents = ["dragover"];
|
||||
const leaveEvents = ["dragleave", "dragend", "drop"];
|
||||
|
||||
const onDrag = function (e) {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
if (elem_upload_float.querySelector("input[type=file]")) {
|
||||
toast_up('⚠️释放以上传文件')
|
||||
} else {
|
||||
toast_up(exist_file_msg)
|
||||
}
|
||||
};
|
||||
|
||||
const onLeave = function (e) {
|
||||
toast_down();
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
};
|
||||
|
||||
dragEvents.forEach(event => {
|
||||
elem.addEventListener(event, onDrag);
|
||||
});
|
||||
|
||||
leaveEvents.forEach(event => {
|
||||
elem.addEventListener(event, onLeave);
|
||||
});
|
||||
|
||||
elem.addEventListener("drop", async function (e) {
|
||||
const files = e.dataTransfer.files;
|
||||
await upload_files(files);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
async function upload_files(files) {
|
||||
const uploadInputElement = elem_upload_float.querySelector("input[type=file]");
|
||||
let totalSizeMb = 0
|
||||
if (files && files.length > 0) {
|
||||
// 执行具体的上传逻辑
|
||||
if (uploadInputElement) {
|
||||
for (let i = 0; i < files.length; i++) {
|
||||
// 将从文件数组中获取的文件大小(单位为字节)转换为MB，
|
||||
totalSizeMb += files[i].size / 1024 / 1024;
|
||||
}
|
||||
// 检查文件总大小是否超过20MB
|
||||
if (totalSizeMb > 20) {
|
||||
toast_push('⚠️文件夹大于 20MB 🚀上传文件中', 3000)
|
||||
// return; // 如果超过了指定大小, 可以不进行后续上传操作
|
||||
}
|
||||
// 监听change事件， 原生Gradio可以实现
|
||||
// uploadInputElement.addEventListener('change', function(){replace_input_string()});
|
||||
let event = new Event("change");
|
||||
Object.defineProperty(event, "target", { value: uploadInputElement, enumerable: true });
|
||||
Object.defineProperty(event, "currentTarget", { value: uploadInputElement, enumerable: true });
|
||||
Object.defineProperty(uploadInputElement, "files", { value: files, enumerable: true });
|
||||
uploadInputElement.dispatchEvent(event);
|
||||
} else {
|
||||
toast_push(exist_file_msg, 3000)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function begin_loading_status() {
|
||||
// Create the loader div and add styling
|
||||
var loader = document.createElement('div');
|
||||
loader.id = 'Js_File_Loading';
|
||||
loader.style.position = "absolute";
|
||||
loader.style.top = "50%";
|
||||
loader.style.left = "50%";
|
||||
loader.style.width = "60px";
|
||||
loader.style.height = "60px";
|
||||
loader.style.border = "16px solid #f3f3f3";
|
||||
loader.style.borderTop = "16px solid #3498db";
|
||||
loader.style.borderRadius = "50%";
|
||||
loader.style.animation = "spin 2s linear infinite";
|
||||
loader.style.transform = "translate(-50%, -50%)";
|
||||
document.body.appendChild(loader); // Add the loader to the body
|
||||
// Set the CSS animation keyframes
|
||||
var styleSheet = document.createElement('style');
|
||||
// styleSheet.type = 'text/css';
|
||||
styleSheet.id = 'Js_File_Loading_Style'
|
||||
styleSheet.innerText = `
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}`;
|
||||
document.head.appendChild(styleSheet);
|
||||
}
|
||||
|
||||
function cancel_loading_status() {
|
||||
var loadingElement = document.getElementById('Js_File_Loading');
|
||||
if (loadingElement) {
|
||||
document.body.removeChild(loadingElement); // remove the loader from the body
|
||||
}
|
||||
var loadingStyle = document.getElementById('Js_File_Loading_Style');
|
||||
if (loadingStyle) {
|
||||
document.head.removeChild(loadingStyle);
|
||||
}
|
||||
let clearButton = document.querySelectorAll('div[id*="elem_upload"] button[aria-label="Clear"]');
|
||||
for (let button of clearButton) {
|
||||
button.addEventListener('click', function () {
|
||||
setTimeout(function () {
|
||||
register_upload_event();
|
||||
}, 50);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function register_upload_event() {
|
||||
elem_upload_float = document.getElementById('elem_upload_float')
|
||||
const upload_component = elem_upload_float.querySelector("input[type=file]");
|
||||
if (upload_component) {
|
||||
upload_component.addEventListener('change', function (event) {
|
||||
toast_push('正在上传中，请稍等。', 2000);
|
||||
begin_loading_status();
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function monitoring_input_box() {
|
||||
register_upload_event();
|
||||
|
||||
elem_upload = document.getElementById('elem_upload')
|
||||
elem_upload_float = document.getElementById('elem_upload_float')
|
||||
elem_input_main = document.getElementById('user_input_main')
|
||||
elem_input_float = document.getElementById('user_input_float')
|
||||
elem_chatbot = document.getElementById('gpt-chatbot')
|
||||
|
||||
if (elem_input_main) {
|
||||
if (elem_input_main.querySelector("textarea")) {
|
||||
add_func_paste(elem_input_main.querySelector("textarea"))
|
||||
}
|
||||
}
|
||||
if (elem_input_float) {
|
||||
if (elem_input_float.querySelector("textarea")) {
|
||||
add_func_paste(elem_input_float.querySelector("textarea"))
|
||||
}
|
||||
}
|
||||
if (elem_chatbot) {
|
||||
add_func_drag(elem_chatbot)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// 监视页面变化
|
||||
window.addEventListener("DOMContentLoaded", function () {
|
||||
// const ga = document.getElementsByTagName("gradio-app");
|
||||
gradioApp().addEventListener("render", monitoring_input_box);
|
||||
});
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 5 部分: 音频按钮样式变化
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function audio_fn_init() {
|
||||
let audio_component = document.getElementById('elem_audio');
|
||||
if (audio_component) {
|
||||
let buttonElement = audio_component.querySelector('button');
|
||||
let specificElement = audio_component.querySelector('.hide.sr-only');
|
||||
specificElement.remove();
|
||||
|
||||
buttonElement.childNodes[1].nodeValue = '启动麦克风';
|
||||
buttonElement.addEventListener('click', function (event) {
|
||||
event.stopPropagation();
|
||||
toast_push('您启动了麦克风!下一步请点击“实时语音对话”启动语音对话。');
|
||||
});
|
||||
|
||||
// 查找语音插件按钮
|
||||
let buttons = document.querySelectorAll('button');
|
||||
let audio_button = null;
|
||||
for (let button of buttons) {
|
||||
if (button.textContent.includes('语音')) {
|
||||
audio_button = button;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (audio_button) {
|
||||
audio_button.addEventListener('click', function () {
|
||||
toast_push('您点击了“实时语音对话”启动语音对话。');
|
||||
});
|
||||
let parent_element = audio_component.parentElement; // 将buttonElement移动到audio_button的内部
|
||||
audio_button.appendChild(audio_component);
|
||||
buttonElement.style.cssText = 'border-color: #00ffe0;border-width: 2px; height: 25px;'
|
||||
parent_element.remove();
|
||||
audio_component.style.cssText = 'width: 250px;right: 0px;display: inline-flex;flex-flow: row-reverse wrap;place-content: stretch space-between;align-items: center;background-color: #ffffff00;';
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
// 第 6 部分: JS初始化函数
|
||||
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
|
||||
audio_fn_init();
|
||||
chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
|
||||
var chatbotObserver = new MutationObserver(() => {
|
||||
chatbotContentChanged(1);
|
||||
});
|
||||
chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
|
||||
if (LAYOUT === "LEFT-RIGHT") { chatbotAutoHeight(); }
|
||||
}
|
||||
0
themes/cookies.py
普通文件
0
themes/cookies.py
普通文件
@@ -256,13 +256,13 @@ textarea.svelte-1pie7s6 {
|
||||
max-height: 95% !important;
|
||||
overflow-y: auto !important;
|
||||
}*/
|
||||
.app.svelte-1mya07g.svelte-1mya07g {
|
||||
/* .app.svelte-1mya07g.svelte-1mya07g {
|
||||
max-width: 100%;
|
||||
position: relative;
|
||||
padding: var(--size-4);
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
}
|
||||
} */
|
||||
|
||||
.gradio-container-3-32-2 h1 {
|
||||
font-weight: 700 !important;
|
||||
|
||||
101
themes/theme.py
101
themes/theme.py
@@ -1,6 +1,14 @@
|
||||
import gradio as gr
|
||||
import pickle
|
||||
import base64
|
||||
import uuid
|
||||
from toolbox import get_conf
|
||||
THEME = get_conf('THEME')
|
||||
|
||||
"""
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
第 1 部分
|
||||
加载主题相关的工具函数
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
"""
|
||||
|
||||
def load_dynamic_theme(THEME):
|
||||
adjust_dynamic_theme = None
|
||||
@@ -20,4 +28,91 @@ def load_dynamic_theme(THEME):
|
||||
theme_declaration = ""
|
||||
return adjust_theme, advanced_css, theme_declaration, adjust_dynamic_theme
|
||||
|
||||
adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(THEME)
|
||||
adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(get_conf('THEME'))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
"""
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
第 2 部分
|
||||
cookie相关工具函数
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
"""
|
||||
|
||||
def init_cookie(cookies, chatbot):
|
||||
# 为每一位访问的用户赋予一个独一无二的uuid编码
|
||||
cookies.update({'uuid': uuid.uuid4()})
|
||||
return cookies
|
||||
|
||||
def to_cookie_str(d):
|
||||
# Pickle the dictionary and encode it as a string
|
||||
pickled_dict = pickle.dumps(d)
|
||||
cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
|
||||
return cookie_value
|
||||
|
||||
def from_cookie_str(c):
|
||||
# Decode the base64-encoded string and unpickle it into a dictionary
|
||||
pickled_dict = base64.b64decode(c.encode('utf-8'))
|
||||
return pickle.loads(pickled_dict)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
"""
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
第 3 部分
|
||||
内嵌的javascript代码
|
||||
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
|
||||
"""
|
||||
|
||||
js_code_for_css_changing = """(css) => {
|
||||
var existingStyles = document.querySelectorAll("body > gradio-app > div > style")
|
||||
for (var i = 0; i < existingStyles.length; i++) {
|
||||
var style = existingStyles[i];
|
||||
style.parentNode.removeChild(style);
|
||||
}
|
||||
var existingStyles = document.querySelectorAll("style[data-loaded-css]");
|
||||
for (var i = 0; i < existingStyles.length; i++) {
|
||||
var style = existingStyles[i];
|
||||
style.parentNode.removeChild(style);
|
||||
}
|
||||
var styleElement = document.createElement('style');
|
||||
styleElement.setAttribute('data-loaded-css', 'placeholder');
|
||||
styleElement.innerHTML = css;
|
||||
document.body.appendChild(styleElement);
|
||||
}
|
||||
"""
|
||||
|
||||
js_code_for_darkmode_init = """(dark) => {
|
||||
dark = dark == "True";
|
||||
if (document.querySelectorAll('.dark').length) {
|
||||
if (!dark){
|
||||
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
||||
}
|
||||
} else {
|
||||
if (dark){
|
||||
document.querySelector('body').classList.add('dark');
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
js_code_for_toggle_darkmode = """() => {
|
||||
if (document.querySelectorAll('.dark').length) {
|
||||
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
||||
} else {
|
||||
document.querySelector('body').classList.add('dark');
|
||||
}
|
||||
}"""
|
||||
|
||||
|
||||
js_code_for_persistent_cookie_init = """(persistent_cookie) => {
|
||||
return getCookie("persistent_cookie");
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
|
||||
139
toolbox.py
139
toolbox.py
@@ -4,6 +4,7 @@ import time
|
||||
import inspect
|
||||
import re
|
||||
import os
|
||||
import base64
|
||||
import gradio
|
||||
import shutil
|
||||
import glob
|
||||
@@ -79,6 +80,7 @@ def ArgsGeneralWrapper(f):
|
||||
'max_length': max_length,
|
||||
'temperature':temperature,
|
||||
'client_ip': request.client.host,
|
||||
'most_recent_uploaded': cookies.get('most_recent_uploaded')
|
||||
}
|
||||
plugin_kwargs = {
|
||||
"advanced_arg": plugin_advanced_arg,
|
||||
@@ -178,12 +180,15 @@ def HotReload(f):
|
||||
最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
|
||||
最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
|
||||
"""
|
||||
@wraps(f)
|
||||
def decorated(*args, **kwargs):
|
||||
fn_name = f.__name__
|
||||
f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
|
||||
yield from f_hot_reload(*args, **kwargs)
|
||||
return decorated
|
||||
if get_conf('PLUGIN_HOT_RELOAD'):
|
||||
@wraps(f)
|
||||
def decorated(*args, **kwargs):
|
||||
fn_name = f.__name__
|
||||
f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
|
||||
yield from f_hot_reload(*args, **kwargs)
|
||||
return decorated
|
||||
else:
|
||||
return f
|
||||
|
||||
|
||||
"""
|
||||
@@ -561,7 +566,8 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
user_name = get_user(chatbot)
|
||||
else:
|
||||
user_name = default_user_name
|
||||
|
||||
if not os.path.exists(file):
|
||||
raise FileNotFoundError(f'文件{file}不存在')
|
||||
user_path = get_log_folder(user_name, plugin_name=None)
|
||||
if file_already_in_downloadzone(file, user_path):
|
||||
new_path = file
|
||||
@@ -577,7 +583,8 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
if chatbot is not None:
|
||||
if 'files_to_promote' in chatbot._cookies: current = chatbot._cookies['files_to_promote']
|
||||
else: current = []
|
||||
chatbot._cookies.update({'files_to_promote': [new_path] + current})
|
||||
if new_path not in current: # 避免把同一个文件添加多次
|
||||
chatbot._cookies.update({'files_to_promote': [new_path] + current})
|
||||
return new_path
|
||||
|
||||
|
||||
@@ -602,6 +609,64 @@ def del_outdated_uploads(outdate_time_seconds, target_path_base=None):
|
||||
except: pass
|
||||
return
|
||||
|
||||
|
||||
def html_local_file(file):
|
||||
base_path = os.path.dirname(__file__) # 项目目录
|
||||
if os.path.exists(str(file)):
|
||||
file = f'file={file.replace(base_path, ".")}'
|
||||
return file
|
||||
|
||||
|
||||
def html_local_img(__file, layout='left', max_width=None, max_height=None, md=True):
|
||||
style = ''
|
||||
if max_width is not None:
|
||||
style += f"max-width: {max_width};"
|
||||
if max_height is not None:
|
||||
style += f"max-height: {max_height};"
|
||||
__file = html_local_file(__file)
|
||||
a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
|
||||
if md:
|
||||
a = f''
|
||||
return a
|
||||
|
||||
def file_manifest_filter_type(file_list, filter_: list = None):
|
||||
new_list = []
|
||||
if not filter_: filter_ = ['png', 'jpg', 'jpeg']
|
||||
for file in file_list:
|
||||
if str(os.path.basename(file)).split('.')[-1] in filter_:
|
||||
new_list.append(html_local_img(file, md=False))
|
||||
else:
|
||||
new_list.append(file)
|
||||
return new_list
|
||||
|
||||
def to_markdown_tabs(head: list, tabs: list, alignment=':---:', column=False):
|
||||
"""
|
||||
Args:
|
||||
head: 表头：[]
|
||||
tabs: 表值：[[列1], [列2], [列3], [列4]]
|
||||
alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
|
||||
column: True to keep data in columns, False to keep data in rows (default).
|
||||
Returns:
|
||||
A string representation of the markdown table.
|
||||
"""
|
||||
if column:
|
||||
transposed_tabs = list(map(list, zip(*tabs)))
|
||||
else:
|
||||
transposed_tabs = tabs
|
||||
# Find the maximum length among the columns
|
||||
max_len = max(len(column) for column in transposed_tabs)
|
||||
|
||||
tab_format = "| %s "
|
||||
tabs_list = "".join([tab_format % i for i in head]) + '|\n'
|
||||
tabs_list += "".join([tab_format % alignment for i in head]) + '|\n'
|
||||
|
||||
for i in range(max_len):
|
||||
row_data = [tab[i] if i < len(tab) else '' for tab in transposed_tabs]
|
||||
row_data = file_manifest_filter_type(row_data, filter_=None)
|
||||
tabs_list += "".join([tab_format % i for i in row_data]) + '|\n'
|
||||
|
||||
return tabs_list
|
||||
|
||||
def on_file_uploaded(request: gradio.Request, files, chatbot, txt, txt2, checkboxes, cookies):
|
||||
"""
|
||||
当文件被上传时的回调函数
|
||||
@@ -626,16 +691,15 @@ def on_file_uploaded(request: gradio.Request, files, chatbot, txt, txt2, checkbo
|
||||
this_file_path = pj(target_path_base, file_origin_name)
|
||||
shutil.move(file.name, this_file_path)
|
||||
upload_msg += extract_archive(file_path=this_file_path, dest_dir=this_file_path+'.extract')
|
||||
|
||||
# 整理文件集合
|
||||
moved_files = [fp for fp in glob.glob(f'{target_path_base}/**/*', recursive=True)]
|
||||
|
||||
if "浮动输入区" in checkboxes:
|
||||
txt, txt2 = "", target_path_base
|
||||
else:
|
||||
txt, txt2 = target_path_base, ""
|
||||
|
||||
# 输出消息
|
||||
moved_files_str = '\t\n\n'.join(moved_files)
|
||||
# 整理文件集合 输出消息
|
||||
moved_files = [fp for fp in glob.glob(f'{target_path_base}/**/*', recursive=True)]
|
||||
moved_files_str = to_markdown_tabs(head=['文件'], tabs=[moved_files])
|
||||
chatbot.append(['我上传了文件，请查收',
|
||||
f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
|
||||
f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
|
||||
@@ -856,7 +920,14 @@ def read_single_conf_with_lru_cache(arg):
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
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
|
||||
"""
|
||||
本项目的所有配置都集中在config.py中。 修改配置有三种方法，您只需要选择其中一种即可：
|
||||
- 直接修改config.py
|
||||
- 创建并修改config_private.py
|
||||
- 修改环境变量（修改docker-compose.yml等价于修改容器内部的环境变量）
|
||||
|
||||
注意：如果您使用docker-compose部署，请修改docker-compose（等价于修改容器内部的环境变量）
|
||||
"""
|
||||
res = []
|
||||
for arg in args:
|
||||
r = read_single_conf_with_lru_cache(arg)
|
||||
@@ -937,14 +1008,19 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
def get_token_num(txt):
|
||||
return len(tokenizer.encode(txt, disallowed_special=()))
|
||||
input_token_num = get_token_num(inputs)
|
||||
|
||||
if max_token_limit < 5000: output_token_expect = 256 # 4k & 2k models
|
||||
elif max_token_limit < 9000: output_token_expect = 512 # 8k models
|
||||
else: output_token_expect = 1024 # 16k & 32k models
|
||||
|
||||
if input_token_num < max_token_limit * 3 / 4:
|
||||
# 当输入部分的token占比小于限制的3/4时，裁剪时
|
||||
# 1. 把input的余量留出来
|
||||
max_token_limit = max_token_limit - input_token_num
|
||||
# 2. 把输出用的余量留出来
|
||||
max_token_limit = max_token_limit - 128
|
||||
max_token_limit = max_token_limit - output_token_expect
|
||||
# 3. 如果余量太小了，直接清除历史
|
||||
if max_token_limit < 128:
|
||||
if max_token_limit < output_token_expect:
|
||||
history = []
|
||||
return history
|
||||
else:
|
||||
@@ -1053,7 +1129,7 @@ def get_user(chatbotwithcookies):
|
||||
|
||||
class ProxyNetworkActivate():
|
||||
"""
|
||||
这段代码定义了一个名为TempProxy的空上下文管理器, 用于给一小段代码上代理
|
||||
这段代码定义了一个名为ProxyNetworkActivate的空上下文管理器, 用于给一小段代码上代理
|
||||
"""
|
||||
def __init__(self, task=None) -> None:
|
||||
self.task = task
|
||||
@@ -1198,6 +1274,35 @@ def get_chat_default_kwargs():
|
||||
|
||||
return default_chat_kwargs
|
||||
|
||||
|
||||
def get_pictures_list(path):
|
||||
file_manifest = [f for f in glob.glob(f'{path}/**/*.jpg', recursive=True)]
|
||||
file_manifest += [f for f in glob.glob(f'{path}/**/*.jpeg', recursive=True)]
|
||||
file_manifest += [f for f in glob.glob(f'{path}/**/*.png', recursive=True)]
|
||||
return file_manifest
|
||||
|
||||
|
||||
def have_any_recent_upload_image_files(chatbot):
|
||||
_5min = 5 * 60
|
||||
if chatbot is None: return False, None # chatbot is None
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
if not most_recent_uploaded: return False, None # most_recent_uploaded is None
|
||||
if time.time() - most_recent_uploaded["time"] < _5min:
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
path = most_recent_uploaded['path']
|
||||
file_manifest = get_pictures_list(path)
|
||||
if len(file_manifest) == 0: return False, None
|
||||
return True, file_manifest # most_recent_uploaded is new
|
||||
else:
|
||||
return False, None # most_recent_uploaded is too old
|
||||
|
||||
|
||||
# Function to encode the image
|
||||
def encode_image(image_path):
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode('utf-8')
|
||||
|
||||
|
||||
def get_max_token(llm_kwargs):
|
||||
from request_llms.bridge_all import model_info
|
||||
return model_info[llm_kwargs['llm_model']]['max_token']
|
||||
|
||||
4
version
4
version
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.61,
|
||||
"version": 3.64,
|
||||
"show_feature": true,
|
||||
"new_feature": "修复潜在的多用户冲突问题 <-> 接入Deepseek Coder <-> AutoGen多智能体插件测试版 <-> 修复本地模型在Windows下的加载BUG <-> 支持文心一言v4和星火v3 <-> 支持GLM3和智谱的API <-> 解决本地模型并发BUG <-> 支持动态追加基础功能按钮"
|
||||
"new_feature": "支持直接拖拽文件到上传区 <-> 支持将图片粘贴到输入区 <-> 修复若干隐蔽的内存BUG <-> 修复多用户冲突问题 <-> 接入Deepseek Coder <-> AutoGen多智能体插件测试版"
|
||||
}
|
||||
|
||||
在新工单中引用
屏蔽一个用户