h

.github/workflows/build-with-audio-assistant.yml

@@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-audio-assistant
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_audio_assistant
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+NoLocal+AudioAssistant
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}

README.md

@@ -44,7 +44,7 @@ chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
 Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
 [谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
 互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
-⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
+⭐Arxiv论文精细翻译 | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
 ⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
 公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
 多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
@@ -52,8 +52,8 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
 [多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
 ⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件
 更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)和[盘古α](https://openi.org.cn/pangu/)
-⭐[虚空终端](https://github.com/binary-husky/void-terminal)pip包 | 脱离GUI，在Python中直接调用本项目的函数插件（开发中）
 更多新功能展示 (图像生成等) …… | 见本文档结尾处 ……
+
 </div>
 
 
@@ -93,7 +93,7 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
 
 1. 下载项目
 ```sh
-git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
+git clone https://github.com/binary-husky/gpt_academic.git
 cd gpt_academic
 ```
 
@@ -116,7 +116,7 @@ python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步
 ```
 
 
-<details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
+<details><summary>如果需要支持清华ChatGLM2/复旦MOSS作为后端，请点击展开此处</summary>
 <p>
 
 【可选步骤】如果需要支持清华ChatGLM2/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
@@ -126,12 +126,9 @@ python -m pip install -r request_llm/requirements_chatglm.txt
 
 # 【可选步骤II】支持复旦MOSS
 python -m pip install -r request_llm/requirements_moss.txt
-git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 注意执行此行代码时，必须处于项目根路径
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 注意执行此行代码时，必须处于项目根路径
 
-# 【可选步骤III】支持RWKV Runner
+# 【可选步骤III】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
-参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
-
-# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
 ```
 
@@ -150,10 +147,9 @@ python main.py
 1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
 [![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
 [![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
-[![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
 
 ``` sh
-git clone --depth=1 https://github.com/binary-husky/gpt_academic.git  # 下载项目
+git clone https://github.com/binary-husky/gpt_academic.git  # 下载项目
 cd gpt_academic                                 # 进入路径
 nano config.py                                      # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
 docker build -t gpt-academic .                      # 安装
@@ -199,12 +195,10 @@ docker-compose up
 5. 远程云服务器部署（需要云服务器知识与经验）。
 请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
 
-6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
+6. 使用WSL2（Windows Subsystem for Linux 子系统）。
-
-7. 使用WSL2（Windows Subsystem for Linux 子系统）。
 请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
 
-8. 如何在二级网址（如`http://localhost/subpath`）下运行。
+7. 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)
 
 
@@ -292,10 +286,6 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/476f66d9-7716-4537-b5c1-735372c25adb" height="200">
 </div>
 
-11. 语言、主题切换
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/b6799499-b6fb-4f0c-9c8e-1b441872f4e8" width="500" >
-</div>
 
 
 ### II：版本:

cc.json

@@ -0,0 +1,39 @@
+[
+    {
+        "name": "Box-1",
+        "width": 1,
+        "height": 1,
+        "depth": 1,
+        "location_x": 1,
+        "location_y": 0,
+        "location_z": 0
+    },
+    {
+        "name": "Box-2",
+        "width": 1,
+        "height": 1,
+        "depth": 1,
+        "location_x": -1,
+        "location_y": 0,
+        "location_z": 0
+    },
+    {
+        "name": "Box-3",
+        "width": 1,
+        "height": 1,
+        "depth": 1,
+        "location_x": 0,
+        "location_y": 1,
+        "location_z": 0
+    },
+    {
+        "name": "Box-4",
+        "width": 1,
+        "height": 1,
+        "depth": 1,
+        "location_x": 0,
+        "location_y": -1,
+        "location_z": 0
+    }
+
+]

check_proxy.py

@@ -3,18 +3,15 @@ def check_proxy(proxies):
     import requests
     proxies_https = proxies['https'] if proxies is not None else '无'
     try:
-        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
+        response = requests.get("https://ipapi.co/json/",
+                                proxies=proxies, timeout=4)
         data = response.json()
         print(f'查询代理的地理位置，返回的结果是{data}')
         if 'country_name' in data:
             country = data['country_name']
             result = f"代理配置 {proxies_https}, 代理所在地：{country}"
         elif 'error' in data:
-            alternative = _check_with_backup_source(proxies)
+            result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
-            if alternative is None:
-                result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
-            else:
-                result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
         else:
             result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
         print(result)
@@ -24,11 +21,6 @@ def check_proxy(proxies):
         print(result)
         return result
 
-def _check_with_backup_source(proxies):
-    import random, string, requests
-    random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
-    try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
-    except: return None
 
 def backup_and_download(current_version, remote_version):
     """

config.py

@@ -32,9 +32,9 @@ else:
 
 # ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
 
-# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
+# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!! 高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
+# 格式 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
+# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions":"https://reverse-proxy-url/v1/chat/completions"}
 API_URL_REDIRECT = {}
 
 
@@ -71,7 +71,7 @@ MAX_RETRY = 2
 # 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
-# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
 
 
 # ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
@@ -80,7 +80,6 @@ ChatGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
 
 # 本地LLM模型如ChatGLM的执行方式 CPU/GPU
 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）
@@ -132,14 +131,9 @@ put your new bing cookies here
 
 # 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
 ENABLE_AUDIO = False
-ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
+ALIYUN_TOKEN=""    # 例如 f37f30e0f9934c34a992f6f64f7eba4f
-ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
+ALIYUN_APPKEY=""   # 例如 RoPlZrM88DnAFkZK
-ALIYUN_ACCESSKEY="" # （无需填写）
+
-ALIYUN_SECRET=""    # （无需填写）
 
 # Claude API KEY
-ANTHROPIC_API_KEY = ""
+ANTHROPIC_API_KEY = ""
-
-
-# 自定义API KEY格式
-CUSTOM_API_KEY_PATTERN = ""

core_functional.py

@@ -1,25 +1,20 @@
 # 'primary' 颜色对应 theme.py 中的 primary_hue
 # 'secondary' 颜色对应 theme.py 中的 neutral_hue
 # 'stop' 颜色对应 theme.py 中的 color_er
-import importlib
+# 默认按钮颜色是 secondary
 from toolbox import clear_line_break
 
 
 def get_core_functions():
     return {
         "英语学术润色": {
-            # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
+            # 前言
             "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
                         r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
                         r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
+            # 后语
             "Suffix":   r"",
-            # 按钮颜色 (默认 secondary)
+            "Color":    r"secondary",    # 按钮颜色
-            "Color":    r"secondary",
-            # 按钮是否可见 (默认 True，即可见)
-            "Visible": True,
-            # 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
-            "AutoClearHistory": False
         },
         "中文学术润色": {
             "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
@@ -81,14 +76,3 @@ def get_core_functions():
             "Suffix":   r"",
         }
     }
-
-
-def handle_core_functionality(additional_fn, inputs, history, chatbot):
-    import core_functional
-    importlib.reload(core_functional)    # 热更新prompt
-    core_functional = core_functional.get_core_functions()
-    if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-    inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-    if core_functional[additional_fn].get("AutoClearHistory", False):
-        history = []
-    return inputs, history

crazy_functional.py

@@ -416,6 +416,17 @@ def get_crazy_functions():
     except:
         print('Load function plugin failed')
 
+    try:
+        from crazy_functions.Three场景交互3D import 三维生成
+        function_plugins.update({
+            "ThreeJS 三维交互": {
+                "Color": "stop",
+                "AsButton": False,
+                "Function": HotReload(三维生成)
+            }
+        })
+    except:
+        print('Load function plugin failed')
 
     try:
         from toolbox import get_conf

crazy_functions/Latex输出PDF结果.py

@@ -157,7 +157,7 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
     try:
         import glob, os, time, subprocess
         subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+        from .latex_utils import Latex精细分解与转化, 编译Latex
     except Exception as e:
         chatbot.append([ f"解析项目: {txt}",
             f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
@@ -234,7 +234,7 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
     try:
         import glob, os, time, subprocess
         subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+        from .latex_utils import Latex精细分解与转化, 编译Latex
     except Exception as e:
         chatbot.append([ f"解析项目: {txt}",
             f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])

crazy_functions/Three场景交互3D.py

@@ -0,0 +1,249 @@
+from toolbox import CatchException, update_ui, gen_time_str
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from .crazy_utils import input_clipping
+
+def inspect_dependency(chatbot, history):
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        from VISUALIZE.mcom import mcom
+        return True
+    except:
+        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install vhmap```"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return False
+
+def get_code_block(reply):
+    try:
+        import json
+        json.loads(reply)
+        return reply
+    except:
+        pass
+
+    import re
+    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
+    matches = re.findall(pattern, reply) # find all code blocks in text
+    res = ""
+    for match in matches:
+        if 'import ' not in match:
+            res = match.strip('python').strip('json')
+            break
+    if len(res) == 0:
+        print(reply)
+        raise RuntimeError("GPT is not generating proper Json.")
+    return res  #  code block
+
+def get_json_blocks(reply):
+    import re, json
+    pattern = r"{([\s\S]*?)}" # regex pattern to match code blocks
+    matches = re.findall(pattern, reply) # find all code blocks in text
+    res = []
+    for match in matches:
+        if '"name"' in match:
+            try:
+                res.append(json.loads("{" + f'{match}' + "}"))
+            except:
+                pass
+    return res  #  code block
+
+def read_json(code):
+    import json
+    return json.loads(code)
+     
+def parse_partial(vi, gpt_say):
+    # 解析Json
+    js = get_json_blocks(gpt_say)
+    vi.update(js)
+
+
+@CatchException
+def 三维生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    from .vhmap_interact.vhmap import vhmp_interface
+    vi = vhmp_interface()
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "生成3D, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..."
+    ])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖, 如果缺少依赖, 则给出安装建议
+    dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
+    if not dep_ok: return
+    
+    # 输入
+    i_say = prompt(txt)
+    # 开始
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=i_say, inputs_show_user=i_say, 
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+        sys_prompt=r"You are a Json generator",
+        on_reply_update=lambda t:parse_partial(vi, t)
+    )
+    chatbot.append(["开始生成执行", "..."])
+    history.extend([i_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+    
+    # 解析Json
+    code = get_code_block(gpt_say)
+    js = read_json(code)
+    vi.update(js)
+    return
+
+
+def prompt(text):
+    return r"""
+> Requirements:
+    1. You can only use square Boxes to build cubes and walls.
+    2. The space you can work in is a sphere with origin (0,0,0) and radius 100.
+    3. The ground is z=0.
+    4. You can only use 100 boxes.
+    5. Format of each box is json, e.g.
+    {
+        "name": "Box-1",
+        "geometry": "box", // choose from "box", "octahedron", "sphere", "cylinder"
+        "size": 1.0,
+        "color": "rgb(255,165,0)",
+        "location_x": 1.0,
+        "location_y": 0.0,
+        "location_z": 0.0
+    },
+    6. Only produce json as output. Use markdown code block to wrap the json output.
+
+> Example:
+    User: Generate 4 different objects around the origin.
+    You:
+    ```
+    [
+        {
+            "name": "Box-1",
+            "size": 1.0,
+            "geometry": "box",
+            "color": "rgb(255,11,10)",
+            "location_x": 1.0,
+            "location_y": 0.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-2",
+            "size": 1.0,
+            "geometry": "octahedron",
+            "color": "rgb(255,11,10)",
+            "location_x": -1.0,
+            "location_y": 0.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-3",
+            "size": 1.0,
+            "geometry": "sphere",
+            "color": "rgb(255,11,10)",
+            "location_x": 0.0,
+            "location_y": 1.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-4",
+            "size": 1.0,
+            "geometry": "cylinder",
+            "color": "rgb(255,11,10)",
+            "location_x": 0.0,
+            "location_y": -1.0,
+            "location_z": 0.0
+        }
+    ]
+    ```
+
+> User: """ + text
+
+"""
+Please construct a 3D environment where a girl is sitting under a tree in a garden.
+
+Requirements:
+1. List objects in this scene and make a markdown list.
+2. The list must contain creative details, give at least 20 objects
+"""
+
+
+"""
+Convert the result to json, 
+Requirements:
+1. Format: [
+    {
+        "name": "object-1",
+        "location": [position_x, position_y, position_z]
+    }
+]
+2. Generate relative position of objects
+"""
+
+
+
+"""
+> Requirements:
+    1. You can use box, octahedron, sphere, cylinder to build objects.
+    2. The ground is z=0.
+    3. You can only use 100 boxes.
+    4. Format of each box is json, e.g.
+    {
+        "name": "Box-1",
+        "geometry": "box", // choose from "box", "octahedron", "sphere", "cylinder"
+        "size": 1.0,
+        "color": "rgb(255,165,0)",
+        "location_x": 1.0,
+        "location_y": 0.0,
+        "location_z": 0.0
+    },
+    5. Only produce json as output. Use markdown code block to wrap the json output.
+
+> Example:
+    ```
+    [
+        {
+            "name": "Box-1",
+            "size": 1.0,
+            "geometry": "box",
+            "color": "rgb(255,11,10)",
+            "location_x": 1.0,
+            "location_y": 0.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-2",
+            "size": 1.0,
+            "geometry": "octahedron",
+            "color": "rgb(255,11,10)",
+            "location_x": -1.0,
+            "location_y": 0.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-3",
+            "size": 1.0,
+            "geometry": "sphere",
+            "color": "rgb(255,11,10)",
+            "location_x": 0.0,
+            "location_y": 1.0,
+            "location_z": 0.0
+        },
+        {
+            "name": "Box-4",
+            "size": 1.0,
+            "geometry": "cylinder",
+            "color": "rgb(255,11,10)",
+            "location_x": 0.0,
+            "location_y": -1.0,
+            "location_z": 0.0
+        }
+    ]
+    ```
+"""

crazy_functions/crazy_functions_test.py

@@ -17,7 +17,7 @@ validate_path() # validate path so you can run from base directory
 # ==============================================================================================================================
 
 from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
+from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
 import contextlib
 import os
 import sys
@@ -32,6 +32,7 @@ llm_kwargs = {
     'max_length': None,
     'temperature':1.0,
 }
+llm_kwargs.update(load_chat_cookies())
 plugin_kwargs = { }
 chatbot = ChatBotWithCookies(llm_kwargs)
 history = []
@@ -195,12 +196,9 @@ def test_Latex():
     # txt = r"https://arxiv.org/abs/2303.08774"
     # txt = r"https://arxiv.org/abs/2303.12712"
     # txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
-    # txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误！
+    txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误！
-    # txt = "https://arxiv.org/abs/2205.14135"
+    
-    # txt = r"C:\Users\fuqingxu\arxiv_cache\2205.14135\workfolder"
+
-    # txt = r"C:\Users\fuqingxu\arxiv_cache\2205.14135\workfolder"
-    txt = r"2210.03629"
-    txt = r"2307.04964"
     for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
         cli_printer.print(cb)   #  print(cb)
 
@@ -229,6 +227,15 @@ def test_chatglm_finetune():
         cli_printer.print(cb)
 
 
+def 三维生成():
+    from crazy_functions.Three场景交互3D import 三维生成
+    txt = "Generate 10 boxes to form a triangle formation with random color."
+    plugin_kwargs = {"advanced_arg":""}
+
+    for cookies, cb, hist, msg in (三维生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        cli_printer.print(cb)
+
+
 if __name__ == "__main__":
     # test_解析一个Python项目()
     # test_Latex英文润色()
@@ -243,7 +250,7 @@ if __name__ == "__main__":
     # test_数学动画生成manim()
     # test_Langchain知识库()
     # test_Langchain知识库读取()
-    test_Latex()
+    # test_Latex()
-    # test_chatglm_finetune()
+    三维生成()
     input("程序完成，回车退出。")
     print("退出。")

crazy_functions/crazy_utils.py

@@ -40,6 +40,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
         chatbot, history, sys_prompt, refresh_interval=0.2,
         handle_token_exceed=True, 
         retry_times_at_unknown_error=2,
+        on_reply_update=None
         ):
     """
     Request GPT model，请求GPT模型同时维持用户界面活跃。
@@ -123,6 +124,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
         if future.done():
             break
         chatbot[-1] = [chatbot[-1][0], mutable[0]]
+        if on_reply_update: on_reply_update(mutable[0])
         yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
 
     final_result = future.result()

crazy_functions/latex_fns/latex_toolbox.py

@@ -1,456 +0,0 @@
-import os, shutil
-import re
-import numpy as np
-PRESERVE = 0
-TRANSFORM = 1
-
-pj = os.path.join
-
-class LinkedListNode():
-    """
-    Linked List Node
-    """
-    def __init__(self, string, preserve=True) -> None:
-        self.string = string
-        self.preserve = preserve
-        self.next = None
-        self.range = None
-        # self.begin_line = 0
-        # self.begin_char = 0
-
-def convert_to_linklist(text, mask):
-    root = LinkedListNode("", preserve=True)
-    current_node = root
-    for c, m, i in zip(text, mask, range(len(text))):
-        if (m==PRESERVE and current_node.preserve) \
-            or (m==TRANSFORM and not current_node.preserve):
-            # add
-            current_node.string += c
-        else:
-            current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
-            current_node = current_node.next
-    return root
-
-def post_process(root):
-    # 修复括号
-    node = root
-    while True:
-        string = node.string
-        if node.preserve: 
-            node = node.next
-            if node is None: break
-            continue
-        def break_check(string):
-            str_stack = [""] # (lv, index)
-            for i, c in enumerate(string):
-                if c == '{':
-                    str_stack.append('{')
-                elif c == '}':
-                    if len(str_stack) == 1:
-                        print('stack fix')
-                        return i
-                    str_stack.pop(-1)
-                else:
-                    str_stack[-1] += c
-            return -1
-        bp = break_check(string)
-
-        if bp == -1:
-            pass
-        elif bp == 0:
-            node.string = string[:1]
-            q = LinkedListNode(string[1:], False)
-            q.next = node.next
-            node.next = q
-        else:
-            node.string = string[:bp]
-            q = LinkedListNode(string[bp:], False)
-            q.next = node.next
-            node.next = q
-
-        node = node.next
-        if node is None: break
-
-    # 屏蔽空行和太短的句子
-    node = root
-    while True:
-        if len(node.string.strip('\n').strip(''))==0: node.preserve = True
-        if len(node.string.strip('\n').strip(''))<42: node.preserve = True
-        node = node.next
-        if node is None: break
-    node = root
-    while True:
-        if node.next and node.preserve and node.next.preserve:
-            node.string += node.next.string
-            node.next = node.next.next
-        node = node.next
-        if node is None: break
-
-    # 将前后断行符脱离
-    node = root
-    prev_node = None
-    while True:
-        if not node.preserve:
-            lstriped_ = node.string.lstrip().lstrip('\n')
-            if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
-                prev_node.string += node.string[:-len(lstriped_)]
-                node.string = lstriped_
-            rstriped_ = node.string.rstrip().rstrip('\n')
-            if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
-                node.next.string = node.string[len(rstriped_):] + node.next.string
-                node.string = rstriped_
-        # =====
-        prev_node = node
-        node = node.next
-        if node is None: break
-
-    # 标注节点的行数范围
-    node = root
-    n_line = 0
-    expansion = 2
-    while True:
-        n_l = node.string.count('\n')
-        node.range = [n_line-expansion, n_line+n_l+expansion]   # 失败时，扭转的范围
-        n_line = n_line+n_l
-        node = node.next
-        if node is None: break
-    return root
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-
-
-def set_forbidden_text(text, mask, pattern, flags=0):
-    """
-    Add a preserve text area in this paper
-    e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
-    you can mask out (mask = PRESERVE so that text become untouchable for GPT) 
-    everything between "\begin{equation}" and "\end{equation}"
-    """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        mask[res.span()[0]:res.span()[1]] = PRESERVE
-    return text, mask
-
-def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
-    """
-    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area. 
-    e.g.
-    \begin{abstract} blablablablablabla. \end{abstract} 
-    """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        if not forbid_wrapper:
-            mask[res.span()[0]:res.span()[1]] = TRANSFORM
-        else:
-            mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE   # '\\begin{abstract}'
-            mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM   # abstract
-            mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE   # abstract
-    return text, mask
-
-def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
-    """
-    Add a preserve text area in this paper (text become untouchable for GPT).
-    count the number of the braces so as to catch compelete text area. 
-    e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.} 
-    """
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        brace_level = -1
-        p = begin = end = res.regs[0][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
-            p += 1
-        end = p+1
-        mask[begin:end] = PRESERVE
-    return text, mask
-
-def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
-    """
-    Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area. 
-    e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.} 
-    """
-    pattern_compile = re.compile(pattern, flags)
-    for res in pattern_compile.finditer(text):
-        brace_level = 0
-        p = begin = end = res.regs[1][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
-            p += 1
-        end = p
-        mask[begin:end] = TRANSFORM
-        if forbid_wrapper:
-            mask[res.regs[0][0]:begin] = PRESERVE
-            mask[end:res.regs[0][1]] = PRESERVE
-    return text, mask
-
-def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
-    """
-    Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
-    Add it to preserve area
-    """
-    pattern_compile = re.compile(pattern, flags)
-    def search_with_line_limit(text, mask):
-        for res in pattern_compile.finditer(text):
-            cmd = res.group(1)  # begin{what}
-            this = res.group(2) # content between begin and end
-            this_mask = mask[res.regs[2][0]:res.regs[2][1]]
-            white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof', 
-                          'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
-            if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
-                this, this_mask = search_with_line_limit(this, this_mask)
-                mask[res.regs[2][0]:res.regs[2][1]] = this_mask
-            else:
-                mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
-        return text, mask
-    return search_with_line_limit(text, mask) 
-
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Latex Merge File
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-
-def find_main_tex_file(file_manifest, mode):
-    """
-    在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
-    P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
-    """
-    canidates = []
-    for texf in file_manifest:
-        if os.path.basename(texf).startswith('merge'):
-            continue
-        with open(texf, 'r', encoding='utf8', errors='ignore') as f:
-            file_content = f.read()
-        if r'\documentclass' in file_content:
-            canidates.append(texf)
-        else:
-            continue
-
-    if len(canidates) == 0:
-        raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
-    elif len(canidates) == 1:
-        return canidates[0]
-    else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
-        canidates_score = []
-        # 给出一些判定模板文档的词作为扣分项
-        unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
-        expected_words = ['\input', '\ref', '\cite']
-        for texf in canidates:
-            canidates_score.append(0)
-            with open(texf, 'r', encoding='utf8', errors='ignore') as f:
-                file_content = f.read()
-            for uw in unexpected_words:
-                if uw in file_content:
-                    canidates_score[-1] -= 1
-            for uw in expected_words:
-                if uw in file_content:
-                    canidates_score[-1] += 1
-        select = np.argmax(canidates_score) # 取评分最高者返回
-        return canidates[select]
-    
-def rm_comments(main_file):
-    new_file_remove_comment_lines = []
-    for l in main_file.splitlines():
-        # 删除整行的空注释
-        if l.lstrip().startswith("%"):
-            pass
-        else:
-            new_file_remove_comment_lines.append(l)
-    main_file = '\n'.join(new_file_remove_comment_lines)
-    # main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file)  # 将 \include 命令转换为 \input 命令
-    main_file = re.sub(r'(?<!\\)%.*', '', main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
-    return main_file
-
-def find_tex_file_ignore_case(fp):
-    dir_name = os.path.dirname(fp)
-    base_name = os.path.basename(fp)
-    if not base_name.endswith('.tex'): base_name+='.tex'
-    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
-    # go case in-sensitive
-    import glob
-    for f in glob.glob(dir_name+'/*.tex'):
-        base_name_s = os.path.basename(fp)
-        if base_name_s.lower() == base_name.lower(): return f
-    return None
-
-def merge_tex_files_(project_foler, main_file, mode):
-    """
-    Merge Tex project recrusively
-    """
-    main_file = rm_comments(main_file)
-    for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
-        f = s.group(1)
-        fp = os.path.join(project_foler, f)
-        fp = find_tex_file_ignore_case(fp)
-        if fp:
-            with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
-        else:
-            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
-        c = merge_tex_files_(project_foler, c, mode)
-        main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
-    return main_file
-
-def merge_tex_files(project_foler, main_file, mode):
-    """
-    Merge Tex project recrusively
-    P.S. 顺便把CTEX塞进去以支持中文
-    P.S. 顺便把Latex的注释去除
-    """
-    main_file = merge_tex_files_(project_foler, main_file, mode)
-    main_file = rm_comments(main_file)
-
-    if mode == 'translate_zh':
-        # find paper documentclass
-        pattern = re.compile(r'\\documentclass.*\n')
-        match = pattern.search(main_file)
-        assert match is not None, "Cannot find documentclass statement!"
-        position = match.end()
-        add_ctex = '\\usepackage{ctex}\n'
-        add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
-        main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
-        # fontset=windows
-        import platform
-        main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
-        main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
-        # find paper abstract
-        pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
-        pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
-        match_opt1 = pattern_opt1.search(main_file)
-        match_opt2 = pattern_opt2.search(main_file)
-        assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
-    return main_file
-
-
-"""
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-Post process
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-"""
-def mod_inbraket(match):
-    """
-    为啥chatgpt会把cite里面的逗号换成中文逗号呀 
-    """
-    # get the matched string
-    cmd = match.group(1)
-    str_to_modify = match.group(2)
-    # modify the matched string
-    str_to_modify = str_to_modify.replace('：', ':')    # 前面是中文冒号，后面是英文冒号
-    str_to_modify = str_to_modify.replace('，', ',')    # 前面是中文逗号，后面是英文逗号
-    # str_to_modify = 'BOOM'
-    return "\\" + cmd + "{" + str_to_modify + "}"
-
-def fix_content(final_tex, node_string):
-    """
-    Fix common GPT errors to increase success rate
-    """
-    final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
-    final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
-    final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
-    final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
-
-    if "Traceback" in final_tex and "[Local Message]" in final_tex:
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\\begin') != final_tex.count('\\begin'):
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
-        # walk and replace any _ without \
-        final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
-
-    def compute_brace_level(string):
-        # this function count the number of { and }
-        brace_level = 0
-        for c in string:
-            if c == "{": brace_level += 1
-            elif c == "}": brace_level -= 1
-        return brace_level
-    def join_most(tex_t, tex_o):
-        # this function join translated string and original string when something goes wrong
-        p_t = 0
-        p_o = 0
-        def find_next(string, chars, begin):
-            p = begin
-            while p < len(string):
-                if string[p] in chars: return p, string[p]
-                p += 1
-            return None, None
-        while True:
-            res1, char = find_next(tex_o, ['{','}'], p_o)
-            if res1 is None: break
-            res2, char = find_next(tex_t, [char], p_t)
-            if res2 is None: break
-            p_o = res1 + 1
-            p_t = res2 + 1
-        return tex_t[:p_t] + tex_o[p_o:]
-
-    if compute_brace_level(final_tex) != compute_brace_level(node_string):
-        # 出问题了，还原部分原文，保证括号正确
-        final_tex = join_most(final_tex, node_string)
-    return final_tex
-    
-def compile_latex_with_timeout(command, cwd, timeout=60):
-    import subprocess
-    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
-    try:
-        stdout, stderr = process.communicate(timeout=timeout)
-    except subprocess.TimeoutExpired:
-        process.kill()
-        stdout, stderr = process.communicate()
-        print("Process timed out!")
-        return False
-    return True
-
-
-
-def merge_pdfs(pdf1_path, pdf2_path, output_path):
-    import PyPDF2
-    Percent = 0.8
-    # Open the first PDF file
-    with open(pdf1_path, 'rb') as pdf1_file:
-        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
-        # Open the second PDF file
-        with open(pdf2_path, 'rb') as pdf2_file:
-            pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
-            # Create a new PDF file to store the merged pages
-            output_writer = PyPDF2.PdfFileWriter()
-            # Determine the number of pages in each PDF file
-            num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
-            # Merge the pages from the two PDF files
-            for page_num in range(num_pages):
-                # Add the page from the first PDF file
-                if page_num < pdf1_reader.numPages:
-                    page1 = pdf1_reader.getPage(page_num)
-                else:
-                    page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Add the page from the second PDF file
-                if page_num < pdf2_reader.numPages:
-                    page2 = pdf2_reader.getPage(page_num)
-                else:
-                    page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
-                # Create a new empty page with double width
-                new_page = PyPDF2.PageObject.createBlankPage(
-                    width = int(int(page1.mediaBox.getWidth()) + int(page2.mediaBox.getWidth()) * Percent),
-                    height = max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight())
-                )
-                new_page.mergeTranslatedPage(page1, 0, 0)
-                new_page.mergeTranslatedPage(page2, int(int(page1.mediaBox.getWidth())-int(page2.mediaBox.getWidth())* (1-Percent)), 0)
-                output_writer.addPage(new_page)
-            # Save the merged PDF file
-            with open(output_path, 'wb') as output_file:
-                output_writer.write(output_file)

crazy_functions/latex_utils.py

@@ -1,16 +1,320 @@
 from toolbox import update_ui, update_ui_lastest_msg    # 刷新Gradio前端界面
 from toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone
-from .latex_toolbox import PRESERVE, TRANSFORM
-from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
-from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
-from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
-
 import os, shutil
 import re
 import numpy as np
-
 pj = os.path.join
 
+"""
+========================================================================
+Part One
+Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
+========================================================================
+"""
+PRESERVE = 0
+TRANSFORM = 1
+
+def set_forbidden_text(text, mask, pattern, flags=0):
+    """
+    Add a preserve text area in this paper
+    e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
+    you can mask out (mask = PRESERVE so that text become untouchable for GPT) 
+    everything between "\begin{equation}" and "\end{equation}"
+    """
+    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    pattern_compile = re.compile(pattern, flags)
+    for res in pattern_compile.finditer(text):
+        mask[res.span()[0]:res.span()[1]] = PRESERVE
+    return text, mask
+
+def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
+    """
+    Move area out of preserve area (make text editable for GPT)
+    count the number of the braces so as to catch compelete text area. 
+    e.g.
+    \begin{abstract} blablablablablabla. \end{abstract} 
+    """
+    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    pattern_compile = re.compile(pattern, flags)
+    for res in pattern_compile.finditer(text):
+        if not forbid_wrapper:
+            mask[res.span()[0]:res.span()[1]] = TRANSFORM
+        else:
+            mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE   # '\\begin{abstract}'
+            mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM   # abstract
+            mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE   # abstract
+    return text, mask
+
+def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
+    """
+    Add a preserve text area in this paper (text become untouchable for GPT).
+    count the number of the braces so as to catch compelete text area. 
+    e.g.
+    \caption{blablablablabla\texbf{blablabla}blablabla.} 
+    """
+    pattern_compile = re.compile(pattern, flags)
+    for res in pattern_compile.finditer(text):
+        brace_level = -1
+        p = begin = end = res.regs[0][0]
+        for _ in range(1024*16):
+            if text[p] == '}' and brace_level == 0: break
+            elif text[p] == '}':  brace_level -= 1
+            elif text[p] == '{':  brace_level += 1
+            p += 1
+        end = p+1
+        mask[begin:end] = PRESERVE
+    return text, mask
+
+def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
+    """
+    Move area out of preserve area (make text editable for GPT)
+    count the number of the braces so as to catch compelete text area. 
+    e.g.
+    \caption{blablablablabla\texbf{blablabla}blablabla.} 
+    """
+    pattern_compile = re.compile(pattern, flags)
+    for res in pattern_compile.finditer(text):
+        brace_level = 0
+        p = begin = end = res.regs[1][0]
+        for _ in range(1024*16):
+            if text[p] == '}' and brace_level == 0: break
+            elif text[p] == '}':  brace_level -= 1
+            elif text[p] == '{':  brace_level += 1
+            p += 1
+        end = p
+        mask[begin:end] = TRANSFORM
+        if forbid_wrapper:
+            mask[res.regs[0][0]:begin] = PRESERVE
+            mask[end:res.regs[0][1]] = PRESERVE
+    return text, mask
+
+def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
+    """
+    Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
+    Add it to preserve area
+    """
+    pattern_compile = re.compile(pattern, flags)
+    def search_with_line_limit(text, mask):
+        for res in pattern_compile.finditer(text):
+            cmd = res.group(1)  # begin{what}
+            this = res.group(2) # content between begin and end
+            this_mask = mask[res.regs[2][0]:res.regs[2][1]]
+            white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof', 
+                          'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
+            if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
+                this, this_mask = search_with_line_limit(this, this_mask)
+                mask[res.regs[2][0]:res.regs[2][1]] = this_mask
+            else:
+                mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
+        return text, mask
+    return search_with_line_limit(text, mask) 
+
+class LinkedListNode():
+    """
+    Linked List Node
+    """
+    def __init__(self, string, preserve=True) -> None:
+        self.string = string
+        self.preserve = preserve
+        self.next = None
+        # self.begin_line = 0
+        # self.begin_char = 0
+
+def convert_to_linklist(text, mask):
+    root = LinkedListNode("", preserve=True)
+    current_node = root
+    for c, m, i in zip(text, mask, range(len(text))):
+        if (m==PRESERVE and current_node.preserve) \
+            or (m==TRANSFORM and not current_node.preserve):
+            # add
+            current_node.string += c
+        else:
+            current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
+            current_node = current_node.next
+    return root
+"""
+========================================================================
+Latex Merge File
+========================================================================
+"""
+
+def 寻找Latex主文件(file_manifest, mode):
+    """
+    在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
+    P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
+    """
+    canidates = []
+    for texf in file_manifest:
+        if os.path.basename(texf).startswith('merge'):
+            continue
+        with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+            file_content = f.read()
+        if r'\documentclass' in file_content:
+            canidates.append(texf)
+        else:
+            continue
+
+    if len(canidates) == 0:
+        raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
+    elif len(canidates) == 1:
+        return canidates[0]
+    else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
+        canidates_score = []
+        # 给出一些判定模板文档的词作为扣分项
+        unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
+        expected_words = ['\input', '\ref', '\cite']
+        for texf in canidates:
+            canidates_score.append(0)
+            with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+                file_content = f.read()
+            for uw in unexpected_words:
+                if uw in file_content:
+                    canidates_score[-1] -= 1
+            for uw in expected_words:
+                if uw in file_content:
+                    canidates_score[-1] += 1
+        select = np.argmax(canidates_score) # 取评分最高者返回
+        return canidates[select]
+    
+def rm_comments(main_file):
+    new_file_remove_comment_lines = []
+    for l in main_file.splitlines():
+        # 删除整行的空注释
+        if l.lstrip().startswith("%"):
+            pass
+        else:
+            new_file_remove_comment_lines.append(l)
+    main_file = '\n'.join(new_file_remove_comment_lines)
+    # main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file)  # 将 \include 命令转换为 \input 命令
+    main_file = re.sub(r'(?<!\\)%.*', '', main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
+    return main_file
+
+def find_tex_file_ignore_case(fp):
+    dir_name = os.path.dirname(fp)
+    base_name = os.path.basename(fp)
+    if not base_name.endswith('.tex'): base_name+='.tex'
+    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    # go case in-sensitive
+    import glob
+    for f in glob.glob(dir_name+'/*.tex'):
+        base_name_s = os.path.basename(fp)
+        if base_name_s.lower() == base_name.lower(): return f
+    return None
+
+def merge_tex_files_(project_foler, main_file, mode):
+    """
+    Merge Tex project recrusively
+    """
+    main_file = rm_comments(main_file)
+    for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
+        f = s.group(1)
+        fp = os.path.join(project_foler, f)
+        fp = find_tex_file_ignore_case(fp)
+        if fp:
+            with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
+        else:
+            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
+        c = merge_tex_files_(project_foler, c, mode)
+        main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
+    return main_file
+
+def merge_tex_files(project_foler, main_file, mode):
+    """
+    Merge Tex project recrusively
+    P.S. 顺便把CTEX塞进去以支持中文
+    P.S. 顺便把Latex的注释去除
+    """
+    main_file = merge_tex_files_(project_foler, main_file, mode)
+    main_file = rm_comments(main_file)
+
+    if mode == 'translate_zh':
+        # find paper documentclass
+        pattern = re.compile(r'\\documentclass.*\n')
+        match = pattern.search(main_file)
+        assert match is not None, "Cannot find documentclass statement!"
+        position = match.end()
+        add_ctex = '\\usepackage{ctex}\n'
+        add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
+        main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
+        # fontset=windows
+        import platform
+        main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
+        main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
+        # find paper abstract
+        pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
+        pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
+        match_opt1 = pattern_opt1.search(main_file)
+        match_opt2 = pattern_opt2.search(main_file)
+        assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
+    return main_file
+
+
+
+"""
+========================================================================
+Post process
+========================================================================
+"""
+def mod_inbraket(match):
+    """
+    为啥chatgpt会把cite里面的逗号换成中文逗号呀 
+    """
+    # get the matched string
+    cmd = match.group(1)
+    str_to_modify = match.group(2)
+    # modify the matched string
+    str_to_modify = str_to_modify.replace('：', ':')    # 前面是中文冒号，后面是英文冒号
+    str_to_modify = str_to_modify.replace('，', ',')    # 前面是中文逗号，后面是英文逗号
+    # str_to_modify = 'BOOM'
+    return "\\" + cmd + "{" + str_to_modify + "}"
+
+def fix_content(final_tex, node_string):
+    """
+    Fix common GPT errors to increase success rate
+    """
+    final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
+    final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
+    final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
+    final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
+
+    if "Traceback" in final_tex and "[Local Message]" in final_tex:
+        final_tex = node_string # 出问题了，还原原文
+    if node_string.count('\\begin') != final_tex.count('\\begin'):
+        final_tex = node_string # 出问题了，还原原文
+    if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
+        # walk and replace any _ without \
+        final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
+
+    def compute_brace_level(string):
+        # this function count the number of { and }
+        brace_level = 0
+        for c in string:
+            if c == "{": brace_level += 1
+            elif c == "}": brace_level -= 1
+        return brace_level
+    def join_most(tex_t, tex_o):
+        # this function join translated string and original string when something goes wrong
+        p_t = 0
+        p_o = 0
+        def find_next(string, chars, begin):
+            p = begin
+            while p < len(string):
+                if string[p] in chars: return p, string[p]
+                p += 1
+            return None, None
+        while True:
+            res1, char = find_next(tex_o, ['{','}'], p_o)
+            if res1 is None: break
+            res2, char = find_next(tex_t, [char], p_t)
+            if res2 is None: break
+            p_o = res1 + 1
+            p_t = res2 + 1
+        return tex_t[:p_t] + tex_o[p_o:]
+
+    if compute_brace_level(final_tex) != compute_brace_level(node_string):
+        # 出问题了，还原部分原文，保证括号正确
+        final_tex = join_most(final_tex, node_string)
+    return final_tex
 
 def split_subprocess(txt, project_folder, return_dict, opts):
     """
@@ -22,8 +326,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
     mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
 
     # 吸收title与作者以上的部分
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\maketitle", re.DOTALL)
+    text, mask = set_forbidden_text(text, mask, r"(.*?)\\maketitle", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\begin{document}", re.DOTALL)
     # 吸收iffalse注释
     text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
     # 吸收在42行以内的begin-end组合
@@ -53,9 +356,77 @@ def split_subprocess(txt, project_folder, return_dict, opts):
     text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
     root = convert_to_linklist(text, mask)
 
-    # 最后一步处理，增强稳健性
+    # 修复括号
-    root = post_process(root)
+    node = root
+    while True:
+        string = node.string
+        if node.preserve: 
+            node = node.next
+            if node is None: break
+            continue
+        def break_check(string):
+            str_stack = [""] # (lv, index)
+            for i, c in enumerate(string):
+                if c == '{':
+                    str_stack.append('{')
+                elif c == '}':
+                    if len(str_stack) == 1:
+                        print('stack fix')
+                        return i
+                    str_stack.pop(-1)
+                else:
+                    str_stack[-1] += c
+            return -1
+        bp = break_check(string)
 
+        if bp == -1:
+            pass
+        elif bp == 0:
+            node.string = string[:1]
+            q = LinkedListNode(string[1:], False)
+            q.next = node.next
+            node.next = q
+        else:
+            node.string = string[:bp]
+            q = LinkedListNode(string[bp:], False)
+            q.next = node.next
+            node.next = q
+
+        node = node.next
+        if node is None: break
+
+    # 屏蔽空行和太短的句子
+    node = root
+    while True:
+        if len(node.string.strip('\n').strip(''))==0: node.preserve = True
+        if len(node.string.strip('\n').strip(''))<42: node.preserve = True
+        node = node.next
+        if node is None: break
+    node = root
+    while True:
+        if node.next and node.preserve and node.next.preserve:
+            node.string += node.next.string
+            node.next = node.next.next
+        node = node.next
+        if node is None: break
+
+    # 将前后断行符脱离
+    node = root
+    prev_node = None
+    while True:
+        if not node.preserve:
+            lstriped_ = node.string.lstrip().lstrip('\n')
+            if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
+                prev_node.string += node.string[:-len(lstriped_)]
+                node.string = lstriped_
+            rstriped_ = node.string.rstrip().rstrip('\n')
+            if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
+                node.next.string = node.string[len(rstriped_):] + node.next.string
+                node.string = rstriped_
+        # =====
+        prev_node = node
+        node = node.next
+        if node is None: break
     # 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）
     with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:
         segment_parts_for_gpt = []
@@ -66,7 +437,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
             show_html = node.string.replace('\n','<br/>')
             if not node.preserve:
                 segment_parts_for_gpt.append(node.string)
-                f.write(f'<p style="color:black;">#{node.range}{show_html}#</p>')
+                f.write(f'<p style="color:black;">#{show_html}#</p>')
             else:
                 f.write(f'<p style="color:red;">{show_html}</p>')
             node = node.next
@@ -77,6 +448,8 @@ def split_subprocess(txt, project_folder, return_dict, opts):
     return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt
     return return_dict
 
+
+
 class LatexPaperSplit():
     """
     break down latex file to a linked list,
@@ -91,32 +464,18 @@ class LatexPaperSplit():
         # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
         self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\" 
 
-
+    def merge_result(self, arr, mode, msg):
-    def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):
         """
         Merge the result after the GPT process completed
         """
         result_string = ""
-        node_cnt = 0
+        p = 0
-        line_cnt = 0
-        
         for node in self.nodes:
             if node.preserve:
-                line_cnt += node.string.count('\n')
                 result_string += node.string
             else:
-                translated_txt = fix_content(arr[node_cnt], node.string)
+                result_string += fix_content(arr[p], node.string)
-                begin_line = line_cnt
+                p += 1
-                end_line = line_cnt + translated_txt.count('\n')
-
-                # reverse translation if any error
-                if any([begin_line-buggy_line_surgery_n_lines <= b_line <= end_line+buggy_line_surgery_n_lines for b_line in buggy_lines]):
-                    translated_txt = node.string
-
-                result_string += translated_txt
-                node_cnt += 1
-                line_cnt += translated_txt.count('\n')
-
         if mode == 'translate_zh':
             pattern = re.compile(r'\\begin\{abstract\}.*\n')
             match = pattern.search(result_string)
@@ -131,7 +490,6 @@ class LatexPaperSplit():
             result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]
         return result_string
 
-
     def split(self, txt, project_folder, opts): 
         """
         break down latex file to a linked list,
@@ -153,6 +511,7 @@ class LatexPaperSplit():
         return self.sp
 
 
+
 class LatexPaperFileGroup():
     """
     use tokenizer to break down text according to max_token_limit
@@ -180,7 +539,7 @@ class LatexPaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
                 segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
@@ -201,14 +560,41 @@ class LatexPaperFileGroup():
                 f.write(res)
         return manifest
 
+def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
+
+    # write html
+    try:
+        import shutil
+        from .crazy_utils import construct_html
+        from toolbox import gen_time_str
+        ch = construct_html() 
+        orig = ""
+        trans = ""
+        final = []
+        for c,r in zip(sp_file_contents, sp_file_result): 
+            final.append(c)
+            final.append(r)
+        for i, k in enumerate(final): 
+            if i%2==0:
+                orig = k
+            if i%2==1:
+                trans = k
+                ch.add_row(a=orig, b=trans)
+        create_report_file_name = f"{gen_time_str()}.trans.html"
+        ch.save_file(create_report_file_name)
+        shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))
+        promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)
+    except:
+        from toolbox import trimmed_format_exc
+        print('writing html result failed:', trimmed_format_exc())
 
 def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):
     import time, os, re
-    from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
+    from .latex_utils import LatexPaperFileGroup, merge_tex_files, LatexPaperSplit, 寻找Latex主文件
 
     #  <-------- 寻找主tex文件 ----------> 
-    maintex = find_main_tex_file(file_manifest, mode)
+    maintex = 寻找Latex主文件(file_manifest, mode)
     chatbot.append((f"定位主Latex文件", f'[Local Message] 分析结果：该项目的Latex主文件是{maintex}, 如果分析错误, 请立即终止程序, 删除或修改歧义文件, 然后重试。主程序即将开始, 请稍候。'))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
     time.sleep(3)
@@ -282,51 +668,54 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
     #  <-------- 写出文件 ----------> 
     msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
     final_tex = lps.merge_result(pfg.file_result, mode, msg)
-    objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
-
     with open(project_folder + f'/merge_{mode}.tex', 'w', encoding='utf-8', errors='replace') as f:
         if mode != 'translate_zh' or "binary" in final_tex: f.write(final_tex)
         
 
     #  <-------- 整理结果, 退出 ----------> 
-    chatbot.append((f"完成了吗？", 'GPT结果已输出, 即将编译PDF'))
+    chatbot.append((f"完成了吗？", 'GPT结果已输出, 正在编译PDF'))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
     #  <-------- 返回 ----------> 
     return project_folder + f'/merge_{mode}.tex'
 
 
-def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified, fixed_line=[]):
+
+def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified):
     try:
         with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
             log = f.read()
+        with open(file_path, 'r', encoding='utf-8', errors='replace') as f:
+            file_lines = f.readlines()
         import re
         buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)
         buggy_lines = [int(l) for l in buggy_lines]
         buggy_lines = sorted(buggy_lines)
-        buggy_line = buggy_lines[0]-1
+        print("removing lines that has errors", buggy_lines)
-        print("reversing tex line that has errors", buggy_line)
+        file_lines.pop(buggy_lines[0]-1)
-
-        # 重组，逆转出错的段落
-        if buggy_line not in fixed_line:
-            fixed_line.append(buggy_line)
-
-        lps, file_result, mode, msg = objload(file=pj(work_folder_modified,'merge_result.pkl'))
-        final_tex = lps.merge_result(file_result, mode, msg, buggy_lines=fixed_line, buggy_line_surgery_n_lines=5*n_fix)
-
         with open(pj(work_folder_modified, f"{tex_name_pure}_fix_{n_fix}.tex"), 'w', encoding='utf-8', errors='replace') as f:
-            f.write(final_tex)
+            f.writelines(file_lines)
-
         return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
     except:
         print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
         return False, -1, [-1]
-
+    
+def compile_latex_with_timeout(command, cwd, timeout=60):
+    import subprocess
+    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
+    try:
+        stdout, stderr = process.communicate(timeout=timeout)
+    except subprocess.TimeoutExpired:
+        process.kill()
+        stdout, stderr = process.communicate()
+        print("Process timed out!")
+        return False
+    return True
 
 def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):
     import os, time
+    current_dir = os.getcwd()
     n_fix = 1
-    fixed_line = []
     max_try = 32
     chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
     chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
@@ -334,10 +723,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
 
     while True:
         import os
-        may_exist_bbl = pj(work_folder_modified, f'merge.bbl')
-        target_bbl = pj(work_folder_modified, f'{main_file_modified}.bbl')
-        if os.path.exists(may_exist_bbl) and not os.path.exists(target_bbl):
-            shutil.copyfile(may_exist_bbl, target_bbl)
 
         # https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
         yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面
@@ -371,6 +756,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
                 ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
                 ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
 
+
         # <---------- 检查结果 ----------->
         results_ = ""
         original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
@@ -387,19 +773,9 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
         if modified_pdf_success:
             yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面
             result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
-            origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
             if os.path.exists(pj(work_folder, '..', 'translation')):
                 shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))
             promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-            # 将两个PDF拼接
-            if original_pdf_success: 
-                try:
-                    from .latex_toolbox import merge_pdfs
-                    concat_pdf = pj(work_folder_modified, f'comparison.pdf')
-                    merge_pdfs(origin_pdf, result_pdf, concat_pdf)
-                    promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-                except Exception as e:
-                    pass
             return True # 成功啦
         else:
             if n_fix>=max_try: break
@@ -411,7 +787,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
                 tex_name_pure=f'{main_file_modified}',
                 n_fix=n_fix,
                 work_folder_modified=work_folder_modified,
-                fixed_line=fixed_line
             )
             yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面
             if not can_retry: break
@@ -419,29 +794,4 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
     return False # 失败啦
 
 
-def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
+
-    # write html
-    try:
-        import shutil
-        from ..crazy_utils import construct_html
-        from toolbox import gen_time_str
-        ch = construct_html() 
-        orig = ""
-        trans = ""
-        final = []
-        for c,r in zip(sp_file_contents, sp_file_result): 
-            final.append(c)
-            final.append(r)
-        for i, k in enumerate(final): 
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{gen_time_str()}.trans.html"
-        ch.save_file(create_report_file_name)
-        shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))
-        promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)
-    except:
-        from toolbox import trimmed_format_exc
-        print('writing html result failed:', trimmed_format_exc())

crazy_functions/live_audio/aliyunASR.py

@@ -19,7 +19,7 @@ class AliyunASR():
         pass
 
     def test_on_error(self, message, *args):
-        print("on_error args=>{}".format(args))
+        # print("on_error args=>{}".format(args))
         pass
 
     def test_on_close(self, *args):
@@ -50,8 +50,6 @@ class AliyunASR():
         rad.clean_up()
         temp_folder = tempfile.gettempdir()
         TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
-        if len(TOKEN) == 0:
-            TOKEN = self.get_token()
         self.aliyun_service_ok = True
         URL="wss://nls-gateway.aliyuncs.com/ws/v1"
         sr = nls.NlsSpeechTranscriber(
@@ -93,38 +91,3 @@ class AliyunASR():
                 self.stop = True
                 self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'
         r = sr.stop()
-
-    def get_token(self):
-        from toolbox import get_conf
-        import json
-        from aliyunsdkcore.request import CommonRequest
-        from aliyunsdkcore.client import AcsClient
-        AccessKey_ID, AccessKey_secret = get_conf('ALIYUN_ACCESSKEY', 'ALIYUN_SECRET')
-
-        # 创建AcsClient实例
-        client = AcsClient(
-            AccessKey_ID,
-            AccessKey_secret,
-            "cn-shanghai"
-        )
-
-        # 创建request，并设置参数。
-        request = CommonRequest()
-        request.set_method('POST')
-        request.set_domain('nls-meta.cn-shanghai.aliyuncs.com')
-        request.set_version('2019-02-28')
-        request.set_action_name('CreateToken')
-
-        try:
-            response = client.do_action_with_exception(request)
-            print(response)
-            jss = json.loads(response)
-            if 'Token' in jss and 'Id' in jss['Token']:
-                token = jss['Token']['Id']
-                expireTime = jss['Token']['ExpireTime']
-                print("token = " + token)
-                print("expireTime = " + str(expireTime))
-        except Exception as e:
-            print(e)
-
-        return token

crazy_functions/vhmap_interact/vhmap.py

@@ -0,0 +1,38 @@
+from toolbox import update_ui, get_conf, trimmed_format_exc
+import threading
+
+def Singleton(cls):
+    _instance = {}
+ 
+    def _singleton(*args, **kargs):
+        if cls not in _instance:
+            _instance[cls] = cls(*args, **kargs)
+        return _instance[cls]
+ 
+    return _singleton
+
+@Singleton
+class vhmp_interface():
+    def __init__(self) -> None:
+        from VISUALIZE.mcom_rt import mcom
+        self.vis3d = mcom(path='TEMP/v2d_logger/', draw_mode='Threejs')
+        self.vis3d.v2d_init()
+        self.vis3d.设置样式('star')
+        # vis3d.设置样式('star')       # 布置星空
+        self.vis3d.其他几何体之旋转缩放和平移('box', 'BoxGeometry(1,1,1)',   0,0,0,  1,1,1, 0,0,0) 
+        # declare geo 'oct1', init with OctahedronGeometry, then (1)rotate & (2)scale & (3)translate
+        self.vis3d.其他几何体之旋转缩放和平移('octahedron', 'OctahedronGeometry(1,0)', 0,0,0,  1,1,1, 0,0,0)   # 八面体
+        # 需要换成其他几何体，请把'OctahedronGeometry(1,0)'替换，参考网址 https://threejs.org/docs/index.html?q=Geometry
+        self.vis3d.其他几何体之旋转缩放和平移('sphere', 'SphereGeometry(1)',   0,0,0,  1,1,1, 0,0,0) # 球体
+        self.vis3d.其他几何体之旋转缩放和平移('cylinder', 'CylinderGeometry(1,1,5,32)',   0,0,0,  1,1,1, 0,0,0) # 球体
+
+    def update(self, json):
+        for obj in json:
+            self.vis3d.发送几何体(
+                f'{obj["geometry"]}|{obj["name"]}|{obj["color"]}|{obj["size"]}',   # 填入 ‘形状|几何体之ID标识|颜色|大小’即可
+                obj["location_x"], 
+                obj["location_y"], 
+                obj["location_z"],
+                ro_x=0, ro_y=0, ro_z=0,                         # 三维位置+欧拉旋转变换，六自由度
+                track_n_frame=0)                                # 显示历史20帧留下的轨迹
+        self.vis3d.结束关键帧()

crazy_functions/图片生成.py

@@ -55,7 +55,7 @@ def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     web_port        当前软件运行的端口号
     """
     history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 请尝试英文Prompt。正在处理中 ....."))
+    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-xxxx或者api2d-xxxx。如果中文效果不理想, 尝试Prompt。正在处理中 ....."))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
     if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
     resolution = plugin_kwargs.get("advanced_arg", '256x256')

crazy_functions/批量Markdown翻译.py

@@ -1,7 +1,5 @@
-import glob, time, os, re
+from toolbox import update_ui, trimmed_format_exc, gen_time_str
-from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
+from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import CatchException, report_execption, write_history_to_file
-from toolbox import promote_file_to_downloadzone, get_log_folder
 fast_debug = False
 
 class PaperFileGroup():
@@ -44,13 +42,13 @@ class PaperFileGroup():
     def write_result(self, language):
         manifest = []
         for path, res in zip(self.file_paths, self.file_result):
-            dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')
+            with open(path + f'.{gen_time_str()}.{language}.md', 'w', encoding='utf8') as f:
-            with open(dst_file, 'w', encoding='utf8') as f:
+                manifest.append(path + f'.{gen_time_str()}.{language}.md')
-                manifest.append(dst_file)
                 f.write(res)
         return manifest
 
 def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
+    import time, os, re
     from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
     #  <-------- 读取Markdown文件，删除其中的所有注释 ----------> 
@@ -104,38 +102,28 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         print(trimmed_format_exc())
 
     #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = gen_time_str() + f"-chatgpt.md"
+    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
+    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
     history = gpt_response_collection
     chatbot.append((f"{fp}完成了吗？", res))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
 
-def get_files_from_everything(txt, preference=''):
+def get_files_from_everything(txt):
-    if txt == "": return False, None, None
+    import glob, os
+
     success = True
     if txt.startswith('http'):
+        # 网络的远程文件
+        txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
+        txt = txt.replace("/blob/", "/")
         import requests
         from toolbox import get_conf
         proxies, = get_conf('proxies')
-        # 网络的远程文件
-        if preference == 'Github':
-            print('正在从github下载资源 ...')
-            if not txt.endswith('.md'):
-                # Make a request to the GitHub API to retrieve the repository information
-                url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'
-                response = requests.get(url, proxies=proxies)
-                txt = response.json()['download_url']
-            else:
-                txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
-                txt = txt.replace("/blob/", "/")
-
         r = requests.get(txt, proxies=proxies)
-        download_local = f'{get_log_folder(plugin_name="批量Markdown翻译")}/raw-readme-{gen_time_str()}.md'
+        with open('./gpt_log/temp.md', 'wb+') as f: f.write(r.content)
-        project_folder = f'{get_log_folder(plugin_name="批量Markdown翻译")}'
+        project_folder = './gpt_log/'
-        with open(download_local, 'wb+') as f: f.write(r.content)
+        file_manifest = ['./gpt_log/temp.md']
-        file_manifest = [download_local]
     elif txt.endswith('.md'):
         # 直接给定文件
         file_manifest = [txt]
@@ -157,11 +145,11 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
         import tiktoken
+        import glob, os
     except:
         report_execption(chatbot, history,
                          a=f"解析项目: {txt}",
@@ -170,7 +158,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         return
     history = []    # 清空历史，以免输入溢出
 
-    success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
+    success, file_manifest, project_folder = get_files_from_everything(txt)
 
     if not success:
         # 什么都没有
@@ -197,11 +185,11 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
         import tiktoken
+        import glob, os
     except:
         report_execption(chatbot, history,
                          a=f"解析项目: {txt}",
@@ -230,11 +218,11 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
         "函数插件功能？",
         "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
         import tiktoken
+        import glob, os
     except:
         report_execption(chatbot, history,
                          a=f"解析项目: {txt}",

crazy_functions/语音助手.py

@@ -97,7 +97,7 @@ class InterviewAssistant(AliyunASR):
         # 初始化音频采集线程
         self.captured_audio = np.array([])
         self.keep_latest_n_second = 10
-        self.commit_after_pause_n_second = 2.0
+        self.commit_after_pause_n_second = 1.5
         self.ready_audio_flagment = None
         self.stop = False
         self.plugin_wd = WatchDog(timeout=5, bark_fn=self.__del__, msg="程序终止")
@@ -179,12 +179,12 @@ def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
         import nls
         from scipy import io
     except:
-        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
+        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return
 
-    APPKEY = get_conf('ALIYUN_APPKEY')
+    TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
-    if APPKEY == "":
+    if TOKEN == "" or APPKEY == "":
         chatbot.append(["导入依赖失败", "没有阿里云语音识别APPKEY和TOKEN, 详情见https://help.aliyun.com/document_detail/450255.html"])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         return

docker-compose.yml

@@ -115,36 +115,3 @@ services:
     command: >
       bash -c "python3 -u main.py"
 
-
-## ===================================================
-## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
-## ===================================================
-version: '3'
-services:
-  gpt_academic_with_audio:
-    image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master
-    environment:
-      # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS                        '
-      USE_PROXY:                '    False                                                            '
-      proxies:                  '    None                                                             '
-      LLM_MODEL:                '    gpt-3.5-turbo                                                    '
-      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "gpt-4"]                                       '
-      ENABLE_AUDIO:             '    True                                                             '
-      LOCAL_MODEL_DEVICE:       '    cuda                                                             '
-      DEFAULT_WORKER_NUM:       '    20                                                               '
-      WEB_PORT:                 '    12343                                                            '
-      ADD_WAIFU:                '    True                                                             '
-      THEME:                    '    Chuanhu-Small-and-Beautiful                                      '
-      ALIYUN_APPKEY:            '    RoP1ZrM84DnAFkZK                                                 '
-      ALIYUN_TOKEN:             '    f37f30e0f9934c34a992f6f64f7eba4f                                 '
-      # (无需填写) ALIYUN_ACCESSKEY:         '    LTAI5q6BrFUzoRXVGUWnekh1                                         '
-      # (无需填写) ALIYUN_SECRET:            '    eHmI20AVWIaQZ0CiTD2bGQVsaP9i68                                   '
-
-    # 与宿主的网络融合
-    network_mode: "host"
-
-    # 不使用代理网络拉取最新代码
-    command: >
-      bash -c "python3 -u main.py"
-

docs/GithubAction+NoLocal+AudioAssistant

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

docs/translate_english.json

@@ -2085,66 +2085,5 @@
     "欢迎使用 MOSS 人工智能助手！输入内容即可进行对话": "Welcome to use MOSS AI assistant! Enter the content to start the conversation.",
     "记住当前的label": "Remember the current label.",
     "不能正常加载ChatGLMFT的参数！": "Cannot load ChatGLMFT parameters normally!",
-    "建议直接在API_KEY处填写": "It is recommended to fill in directly at API_KEY.",
+    "建议直接在API_KEY处填写": "It is recommended to fill in directly at API_KEY."
-    "创建request": "Create request",
-    "默认 secondary": "Default secondary",
-    "会被加在你的输入之前": "Will be added before your input",
-    "缺少": "Missing",
-    "前者是API2D的结束条件": "The former is the termination condition of API2D",
-    "无需填写": "No need to fill in",
-    "后缀": "Suffix",
-    "扭转的范围": "Range of twisting",
-    "是否在触发时清除历史": "Whether to clear history when triggered",
-    "⭐多线程方法": "⭐Multi-threaded method",
-    "消耗大量的内存": "Consumes a large amount of memory",
-    "重组": "Reorganize",
-    "高危设置! 常规情况下不要修改! 通过修改此设置": "High-risk setting! Do not modify under normal circumstances! Modify this setting",
-    "检查USE_PROXY": "Check USE_PROXY",
-    "标注节点的行数范围": "Range of line numbers for annotated nodes",
-    "即不处理之前的对话历史": "That is, do not process previous conversation history",
-    "即将编译PDF": "Compiling PDF",
-    "没有设置ANTHROPIC_API_KEY选项": "ANTHROPIC_API_KEY option is not set",
-    "非Openai官方接口返回了错误": "Non-Openai official interface returned an error",
-    "您的 API_KEY 不满足任何一种已知的密钥格式": "Your API_KEY does not meet any known key format",
-    "格式": "Format",
-    "不能正常加载": "Cannot load properly",
-    "🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行": "🏃‍♂️🏃‍♂️🏃‍♂️ Subprocess execution",
-    "前缀": "Prefix",
-    "创建AcsClient实例": "Create AcsClient instance",
-    "⭐主进程执行": "⭐Main process execution",
-    "增强稳健性": "Enhance robustness",
-    "用来描述你的要求": "Used to describe your requirements",
-    "举例": "For example",
-    "⭐单线程方法": "⭐Single-threaded method",
-    "后者是OPENAI的结束条件": "The latter is the termination condition of OPENAI",
-    "防止proxies单独起作用": "Prevent proxies from working alone",
-    "将两个PDF拼接": "Concatenate two PDFs",
-    "最后一步处理": "The last step processing",
-    "正在从github下载资源": "Downloading resources from github",
-    "失败时": "When failed",
-    "尚未加载": "Not loaded yet",
-    "配合前缀可以把你的输入内容用引号圈起来": "With the prefix, you can enclose your input content in quotation marks",
-    "我好！": "I'm good!",
-    "默认 False": "Default False",
-    "的依赖": "Dependencies of",
-    "并设置参数": "and set parameters",
-    "会被加在你的输入之后": "Will be added after your input",
-    "安装": "Installation",
-    "一个单实例装饰器": "Single instance decorator",
-    "自定义API KEY格式": "Customize API KEY format",
-    "的参数": "Parameters of",
-    "api2d等请求源": "api2d and other request sources",
-    "逆转出错的段落": "Reverse the wrong paragraph",
-    "没有设置ANTHROPIC_API_KEY": "ANTHROPIC_API_KEY is not set",
-    "默认 True": "Default True",
-    "本项目现已支持OpenAI和Azure的api-key": "This project now supports OpenAI and Azure's api-key",
-    "即可见": "Visible immediately",
-    "请问什么是质子": "What is a proton?",
-    "按钮是否可见": "Is the button visible?",
-    "调用": "Call",
-    "如果要使用": "If you want to use",
-    "的参数！": "parameters!",
-    "例如翻译、解释代码、润色等等": "such as translation, code interpretation, polishing, etc.",
-    "响应异常": "Response exception",
-    "响应中": "Responding"
 }

docs/translate_japanese.json

@@ -939,6 +939,7 @@
     "以下は学術論文の基本情報です": "以下は学術論文の基本情報です",
     "出力が不完全になる原因となる": "出力が不完全になる原因となる",
     "ハイフンを使って": "ハイフンを使って",
+    "シングルスレッド": "シングルスレッド",
     "请先把模型切换至gpt-xxxx或者api2d-xxxx": "Please switch the model to gpt-xxxx or api2d-xxxx first.",
     "路径或网址": "Path or URL",
     "*代表通配符": "* represents a wildcard",
@@ -1483,632 +1484,5 @@
     "请提交新问题": "新しい問題を提出してください",
     "您正在调用一个": "あなたは呼び出しています",
     "请编辑以下文本": "以下のテキストを編集してください",
-    "常见协议无非socks5h/http": "一般的なプロトコルはsocks5h/http以外ありません",
+    "常见协议无非socks5h/http": "一般的なプロトコルはsocks5h/http以外ありません"
-    "Latex英文纠错": "LatexEnglishErrorCorrection",
-    "连接bing搜索回答问题": "ConnectBingSearchAnswerQuestion",
-    "联网的ChatGPT_bing版": "OnlineChatGPT_BingVersion",
-    "总结音视频": "SummarizeAudioVideo",
-    "动画生成": "GenerateAnimation",
-    "数学动画生成manim": "GenerateMathematicalAnimationManim",
-    "Markdown翻译指定语言": "TranslateMarkdownSpecifiedLanguage",
-    "知识库问答": "KnowledgeBaseQuestionAnswer",
-    "Langchain知识库": "LangchainKnowledgeBase",
-    "读取知识库作答": "ReadKnowledgeBaseAnswer",
-    "交互功能模板函数": "InteractiveFunctionTemplateFunction",
-    "交互功能函数模板": "InteractiveFunctionFunctionTemplate",
-    "Latex英文纠错加PDF对比": "LatexEnglishErrorCorrectionWithPDFComparison",
-    "Latex输出PDF结果": "LatexOutputPDFResult",
-    "Latex翻译中文并重新编译PDF": "TranslateChineseAndRecompilePDF",
-    "语音助手": "VoiceAssistant",
-    "微调数据集生成": "FineTuneDatasetGeneration",
-    "chatglm微调工具": "ChatGLMFineTuningTool",
-    "启动微调": "StartFineTuning",
-    "sprint亮靛": "SprintAzureIndigo",
-    "专业词汇声明": "ProfessionalVocabularyDeclaration",
-    "Latex精细分解与转化": "LatexDetailedDecompositionAndConversion",
-    "编译Latex": "CompileLatex",
-    "将代码转为动画": "コードをアニメーションに変換する",
-    "解析arxiv网址失败": "arxivのURLの解析に失敗しました",
-    "其他模型转化效果未知": "他のモデルの変換効果は不明です",
-    "把文件复制过去": "ファイルをコピーする",
-    "！！！如果需要运行量化版本": "！！！量子化バージョンを実行する必要がある場合",
-    "报错信息如下. 如果是与网络相关的问题": "エラーメッセージは次のとおりです。ネットワークに関連する問題の場合",
-    "请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期": "ALIYUN_TOKENとALIYUN_APPKEYの有効期限を確認してください",
-    "编译结束": "コンパイル終了",
-    "只读": "読み取り専用",
-    "模型选择是": "モデルの選択は",
-    "正在从github下载资源": "GitHubからリソースをダウンロードしています",
-    "同时分解长句": "同時に長い文を分解する",
-    "寻找主tex文件": "メインのtexファイルを検索する",
-    "例如您可以将以下命令复制到下方": "たとえば、以下のコマンドを下にコピーできます",
-    "使用中文总结音频“": "中国語で音声を要約する",
-    "此处填API密钥": "ここにAPIキーを入力してください",
-    "裁剪输入": "入力をトリミングする",
-    "当前语言模型温度设定": "現在の言語モデルの温度設定",
-    "history 是之前的对话列表": "historyは以前の対話リストです",
-    "对输入的word文档进行摘要生成": "入力されたWord文書の要約を生成する",
-    "输入问题后点击该插件": "質問を入力した後、このプラグインをクリックします",
-    "仅在Windows系统进行了测试": "Windowsシステムでのみテストされています",
-    "reverse 操作必须放在最后": "reverse操作は最後に配置する必要があります",
-    "即将编译PDF": "PDFをコンパイルする予定です",
-    "执行错误": "エラーが発生しました",
-    "段音频完成了吗": "セグメントのオーディオは完了しましたか",
-    "然后重启程序": "それからプログラムを再起動してください",
-    "是所有LLM的通用接口": "これはすべてのLLMの共通インターフェースです",
-    "当前报错的latex代码处于第": "現在のエラーのあるLaTeXコードは第",
-    "🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行": "🏃‍♂️🏃‍♂️🏃‍♂️ サブプロセスの実行",
-    "用来描述你的要求": "要求を説明するために使用されます",
-    "原始PDF编译是否成功": "元のPDFのコンパイルは成功しましたか",
-    "本地Latex论文精细翻译": "ローカルのLaTeX論文の詳細な翻訳",
-    "设置OpenAI密钥和模型": "OpenAIキーとモデルの設定",
-    "如果使用ChatGLM2微调模型": "ChatGLM2ファインチューニングモデルを使用する場合",
-    "项目Github地址 \\url{https": "プロジェクトのGithubアドレス \\url{https",
-    "将前后断行符脱离": "前後の改行文字を削除します",
-    "该项目的Latex主文件是": "このプロジェクトのLaTeXメインファイルは",
-    "编译已经开始": "コンパイルが開始されました",
-    "*{\\scriptsize\\textbf{警告": "*{\\scriptsize\\textbf{警告",
-    "从一批文件": "一連のファイルから",
-    "等待用户的再次调用": "ユーザーの再呼び出しを待っています",
-    "目前仅支持GPT3.5/GPT4": "現在、GPT3.5/GPT4のみをサポートしています",
-    "如果一句话小于7个字": "1つの文が7文字未満の場合",
-    "目前对机器学习类文献转化效果最好": "現在、機械学習の文献変換効果が最も良いです",
-    "寻找主文件": "メインファイルを検索中",
-    "解除插件状态": "プラグインの状態を解除します",
-    "默认为Chinese": "デフォルトはChineseです",
-    "依赖不足": "不足の依存関係",
-    "编译文献交叉引用": "文献の相互参照をコンパイルする",
-    "对不同latex源文件扣分": "異なるLaTeXソースファイルに罰則を課す",
-    "再列出用户可能提出的三个问题": "ユーザーが提出する可能性のある3つの問題を再リスト化する",
-    "建议排查": "トラブルシューティングの提案",
-    "生成时间戳": "タイムスタンプの生成",
-    "检查config中的AVAIL_LLM_MODELS选项": "configのAVAIL_LLM_MODELSオプションを確認する",
-    "chatglmft 没有 sys_prompt 接口": "chatglmftにはsys_promptインターフェースがありません",
-    "在一个异步线程中采集音频": "非同期スレッドでオーディオを収集する",
-    "初始化插件状态": "プラグインの状態を初期化する",
-    "内含已经翻译的Tex文档": "翻訳済みのTexドキュメントが含まれています",
-    "请注意自我隐私保护哦！": "プライバシー保護に注意してください！",
-    "使用正则表达式查找半行注释": "正規表現を使用して半行コメントを検索する",
-    "不能正常加载ChatGLMFT的参数！": "ChatGLMFTのパラメータを正常にロードできません！",
-    "首先你在中文语境下通读整篇论文": "まず、中国語の文脈で論文全体を読んでください",
-    "如 绿帽子*深蓝色衬衫*黑色运动裤": "例えば、緑の帽子*濃い青のシャツ*黒のスポーツパンツ",
-    "默认为default": "デフォルトはdefaultです",
-    "将": "置き換える",
-    "使用 Unsplash API": "Unsplash APIを使用する",
-    "会被加在你的输入之前": "あなたの入力の前に追加されます",
-    "还需要填写组织": "組織を入力する必要があります",
-    "test_LangchainKnowledgeBase读取": "test_LangchainKnowledgeBaseの読み込み",
-    "目前不支持历史消息查询": "現在、過去のメッセージのクエリはサポートされていません",
-    "临时存储用于调试": "デバッグ用の一時的なストレージ",
-    "提取总结": "テキストの翻訳",
-    "每秒采样数量": "テキストの翻訳",
-    "但通常不会出现在正文": "テキストの翻訳",
-    "通过调用conversations_open方法打开一个频道": "テキストの翻訳",
-    "导致输出不完整": "テキストの翻訳",
-    "获取已打开频道的最新消息并返回消息列表": "テキストの翻訳",
-    "Tex源文件缺失！": "テキストの翻訳",
-    "如果需要使用Slack Claude": "テキストの翻訳",
-    "扭转的范围": "テキストの翻訳",
-    "使用latexdiff生成论文转化前后对比": "テキストの翻訳",
-    "--读取文件": "テキストの翻訳",
-    "调用openai api 使用whisper-1模型": "テキストの翻訳",
-    "避免遗忘导致死锁": "テキストの翻訳",
-    "在多Tex文档中": "テキストの翻訳",
-    "失败时": "テキストの翻訳",
-    "然后转移到指定的另一个路径中": "テキストの翻訳",
-    "使用Newbing": "テキストの翻訳",
-    "的参数": "テキストの翻訳",
-    "后者是OPENAI的结束条件": "テキストの翻訳",
-    "构建知识库": "テキストの翻訳",
-    "吸收匿名公式": "テキストの翻訳",
-    "前缀": "テキストの翻訳",
-    "会直接转到该函数": "テキストの翻訳",
-    "Claude失败": "テキストの翻訳",
-    "P.S. 但愿没人把latex模板放在里面传进来": "P.S. 但愿没人把latex模板放在里面传进来",
-    "临时地启动代理网络": "临时地启动代理网络",
-    "读取文件内容到内存": "読み込んだファイルの内容をメモリに保存する",
-    "总结音频": "音声をまとめる",
-    "没有找到任何可读取文件": "読み込み可能なファイルが見つかりません",
-    "获取Slack消息失败": "Slackメッセージの取得に失敗しました",
-    "用黑色标注转换区": "黒い注釈で変換エリアをマークする",
-    "此插件处于开发阶段": "このプラグインは開発中です",
-    "其他操作系统表现未知": "他のオペレーティングシステムの動作は不明です",
-    "返回找到的第一个": "最初に見つかったものを返す",
-    "发现已经存在翻译好的PDF文档": "翻訳済みのPDFドキュメントが既に存在することがわかりました",
-    "不包含任何可用于": "使用できるものは含まれていません",
-    "发送到openai音频解析终端": "openai音声解析端に送信する",
-    "========================================= 插件主程序2 =====================================================": "========================================= プラグインメインプログラム2 =====================================================",
-    "正在重试": "再試行中",
-    "从而更全面地理解项目的整体功能": "プロジェクトの全体的な機能をより理解するために",
-    "正在等您说完问题": "質問が完了するのをお待ちしています",
-    "使用教程详情见 request_llm/README.md": "使用方法の詳細については、request_llm/README.mdを参照してください",
-    "6.25 加入判定latex模板的代码": "6.25 テンプレートの判定コードを追加",
-    "找不到任何音频或视频文件": "音声またはビデオファイルが見つかりません",
-    "请求GPT模型的": "GPTモデルのリクエスト",
-    "行": "行",
-    "分析上述回答": "上記の回答を分析する",
-    "如果要使用ChatGLMFT": "ChatGLMFTを使用する場合",
-    "上传Latex项目": "Latexプロジェクトをアップロードする",
-    "如参考文献、脚注、图注等": "参考文献、脚注、図のキャプションなど",
-    "未配置": "設定されていません",
-    "请在此处给出自定义翻译命令": "カスタム翻訳コマンドをここに入力してください",
-    "第二部分": "第2部分",
-    "解压失败! 需要安装pip install py7zr来解压7z文件": "解凍に失敗しました！7zファイルを解凍するにはpip install py7zrをインストールする必要があります",
-    "吸收在42行以内的begin-end组合": "42行以内のbegin-endの組み合わせを取り込む",
-    "Latex文件融合完成": "Latexファイルの統合が完了しました",
-    "输出html调试文件": "HTMLデバッグファイルの出力",
-    "论文概况": "論文の概要",
-    "修复括号": "括弧の修復",
-    "赋予插件状态": "プラグインの状態を付与する",
-    "标注节点的行数范围": "ノードの行数範囲を注釈する",
-    "MOSS can understand and communicate fluently in the language chosen by the user such as English and 中文. MOSS can perform any language-based tasks.": "MOSSは、ユーザーが選択した言語（英語や中文など）でスムーズに理解し、コミュニケーションすることができます。MOSSは、言語に基づくさまざまなタスクを実行できます。",
-    "LLM_MODEL是默认选中的模型": "LLM_MODELはデフォルトで選択されたモデルです",
-    "配合前缀可以把你的输入内容用引号圈起来": "接頭辞と組み合わせて、入力内容を引用符で囲むことができます",
-    "获取关键词": "キーワードの取得",
-    "本项目现已支持OpenAI和Azure的api-key": "このプロジェクトは、OpenAIおよびAzureのAPIキーをサポートしています",
-    "欢迎使用 MOSS 人工智能助手！": "MOSS AIアシスタントをご利用いただきありがとうございます！",
-    "在执行完成之后": "実行が完了した後",
-    "正在听您讲话": "お話をお聞きしています",
-    "Claude回复的片段": "Claudeの返信の一部",
-    "返回": "戻る",
-    "期望格式例如": "期待される形式の例",
-    "gpt 多线程请求": "GPTマルチスレッドリクエスト",
-    "当前工作路径为": "現在の作業パスは",
-    "该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成": "このPDFはGPT-Academicオープンソースプロジェクトによって大規模言語モデル+Latex翻訳プラグインを使用して一括生成されました",
-    "解决插件锁定时的界面显示问题": "プラグインのロック時のインターフェース表示の問題を解決する",
-    "默认 secondary": "デフォルトのセカンダリ",
-    "会把列表拆解": "リストを分解します",
-    "暂时不支持历史消息": "一時的に歴史メッセージはサポートされていません",
-    "或者重启之后再度尝试": "または再起動後に再試行してください",
-    "吸收其他杂项": "他の雑項を吸収する",
-    "双手离开鼠标键盘吧": "両手をマウスとキーボードから離してください",
-    "建议更换代理协议": "プロキシプロトコルの変更をお勧めします",
-    "音频助手": "オーディオアシスタント",
-    "请耐心等待": "お待ちください",
-    "翻译结果": "翻訳結果",
-    "请在此处追加更细致的矫错指令": "ここにより詳細なエラー修正命令を追加してください",
-    "编译原始PDF": "元のPDFをコンパイルする",
-    "-构建知识库": "-ナレッジベースの構築",
-    "删除中间文件夹": "中間フォルダを削除する",
-    "这段代码定义了一个名为TempProxy的空上下文管理器": "このコードはTempProxyという名前の空のコンテキストマネージャを定義しています",
-    "参数说明": "パラメータの説明",
-    "正在预热文本向量化模组": "テキストベクトル化モジュールのプリヒート中",
-    "函数插件": "関数プラグイン",
-    "右下角更换模型菜单中可切换openai": "右下のモデルメニューでopenaiを切り替えることができます",
-    "先上传数据集": "まずデータセットをアップロードしてください",
-    "LatexEnglishErrorCorrection+高亮修正位置": "テキストの翻訳",
-    "正在构建知识库": "テキストの翻訳",
-    "用红色标注处保留区": "テキストの翻訳",
-    "安装Claude的依赖": "テキストの翻訳",
-    "已禁用": "テキストの翻訳",
-    "是否在提交时自动清空输入框": "テキストの翻訳",
-    "GPT 学术优化": "テキストの翻訳",
-    "需要特殊依赖": "テキストの翻訳",
-    "test_联网回答问题": "テキストの翻訳",
-    "除非您是论文的原作者": "テキストの翻訳",
-    "即可见": "テキストの翻訳",
-    "解析为简体中文": "テキストの翻訳",
-    "解析整个Python项目": "テキストの翻訳",
-    "========================================= 插件主程序1 =====================================================": "テキストの翻訳",
-    "当前参数": "テキストの翻訳",
-    "处理个别特殊插件的锁定状态": "テキストの翻訳",
-    "已知某些代码的局部作用是": "テキストの翻訳",
-    "请务必用 pip install -r requirements.txt 指令安装依赖": "テキストの翻訳",
-    "安装": "テキストの翻訳",
-    "请登录OpenAI查看详情 https": "テキストの翻訳",
-    "必须包含documentclass": "テキストの翻訳",
-    "极少数情况下": "テキストの翻訳",
-    "并将返回的频道ID保存在属性CHANNEL_ID中": "テキストの翻訳",
-    "您的 API_KEY 不满足任何一种已知的密钥格式": "テキストの翻訳",
-    "-预热文本向量化模组": "テキストの翻訳",
-    "什么都没有": "テキストの翻訳",
-    "等待GPT响应": "テキストの翻訳",
-    "请尝试把以下指令复制到高级参数区": "テキストの翻訳",
-    "模型参数": "テキストの翻訳",
-    "先删除": "テキストの翻訳",
-    "响应中": "テキストの翻訳",
-    "开始接收chatglmft的回复": "テキストの翻訳",
-    "手动指定语言": "テキストの翻訳",
-    "获取线程锁": "テキストの翻訳",
-    "当前大语言模型": "テキストの翻訳",
-    "段音频的第": "テキストの翻訳",
-    "正在编译对比PDF": "テキストの翻訳",
-    "根据需要切换prompt": "テキストの翻訳",
-    "取评分最高者返回": "テキストの翻訳",
-    "如果您是论文原作者": "テキストの翻訳",
-    "段音频的主要内容": "テキストの翻訳",
-    "为啥chatgpt会把cite里面的逗号换成中文逗号呀": "テキストの翻訳",
-    "为每一位访问的用户赋予一个独一无二的uuid编码": "テキストの翻訳",
-    "将每次对话记录写入Markdown格式的文件中": "テキストの翻訳",
-    "ChatGLMFT尚未加载": "テキストの翻訳",
-    "切割音频文件": "テキストの翻訳",
-    "例如 f37f30e0f9934c34a992f6f64f7eba4f": "テキストの翻訳",
-    "work_folder = Latex预处理": "テキストの翻訳",
-    "出问题了": "問題が発生しました",
-    "等待Claude响应中": "Claudeの応答を待っています",
-    "增强稳健性": "信頼性を向上させる",
-    "赋予插件锁定 锁定插件回调路径": "プラグインにコールバックパスをロックする",
-    "将多文件tex工程融合为一个巨型tex": "複数のファイルのtexプロジェクトを1つの巨大なtexに統合する",
-    "参考文献转Bib": "参考文献をBibに変換する",
-    "由于提问含不合规内容被Azure过滤": "質問が規則に違反しているため、Azureによってフィルタリングされました",
-    "读取优先级": "優先度を読み取る",
-    "格式如org-xxxxxxxxxxxxxxxxxxxxxxxx": "形式はorg-xxxxxxxxxxxxxxxxxxxxxxxxのようです",
-    "辅助gpt生成代码": "GPTのコード生成を補助する",
-    "读取音频文件": "音声ファイルを読み取る",
-    "输入arxivID": "arxivIDを入力する",
-    "转化PDF编译是否成功": "PDFのコンパイルが成功したかどうかを変換する",
-    "Call ChatGLMFT fail 不能正常加载ChatGLMFT的参数": "ChatGLMFTのパラメータを正常にロードできませんでした",
-    "创建AcsClient实例": "AcsClientのインスタンスを作成する",
-    "将 chatglm 直接对齐到 chatglm2": "chatglmをchatglm2に直接整列させる",
-    "要求": "要求",
-    "子任务失败时的重试次数": "サブタスクが失敗した場合のリトライ回数",
-    "请求子进程": "サブプロセスを要求する",
-    "按钮是否可见": "ボタンが表示可能かどうか",
-    "将 \\include 命令转换为 \\input 命令": "\\includeコマンドを\\inputコマンドに変換する",
-    "用户填3": "ユーザーが3を入力する",
-    "后面是英文逗号": "後ろに英語のカンマがあります",
-    "吸收iffalse注释": "iffalseコメントを吸収する",
-    "请稍候": "お待ちください",
-    "摘要生成后的文档路径": "要約生成後のドキュメントのパス",
-    "主程序即将开始": "メインプログラムがすぐに開始されます",
-    "处理历史信息": "履歴情報の処理",
-    "根据给定的切割时长将音频文件切割成多个片段": "指定された分割時間に基づいてオーディオファイルを複数のセグメントに分割する",
-    "解决部分词汇翻译不准确的问题": "一部の用語の翻訳の不正確さを解決する",
-    "即将退出": "すぐに終了します",
-    "用于给一小段代码上代理": "一部のコードにプロキシを適用するために使用されます",
-    "提取文件扩展名": "ファイルの拡張子を抽出する",
-    "目前支持的格式": "現在サポートされている形式",
-    "第一次调用": "最初の呼び出し",
-    "异步方法": "非同期メソッド",
-    "P.S. 顺便把Latex的注释去除": "P.S. LaTeXのコメントを削除する",
-    "构建完成": "ビルドが完了しました",
-    "缺少": "不足しています",
-    "建议暂时不要使用": "一時的に使用しないことをお勧めします",
-    "对比PDF编译是否成功": "PDFのコンパイルが成功したかどうかを比較する",
-    "填入azure openai api的密钥": "Azure OpenAI APIのキーを入力してください",
-    "功能尚不稳定": "機能はまだ安定していません",
-    "则跳过GPT请求环节": "GPTリクエストのスキップ",
-    "即不处理之前的对话历史": "以前の対話履歴を処理しない",
-    "非Openai官方接口返回了错误": "非公式のOpenAI APIがエラーを返しました",
-    "其他类型文献转化效果未知": "他のタイプの文献の変換効果は不明です",
-    "给出一些判定模板文档的词作为扣分项": "テンプレートドキュメントの単語を減点項目として提供する",
-    "找 API_ORG 设置项": "API_ORGの設定項目を検索します",
-    "调用函数": "関数を呼び出します",
-    "需要手动安装新增的依赖库": "新しい依存ライブラリを手動でインストールする必要があります",
-    "或者使用此插件继续上传更多文件": "または、このプラグインを使用してさらにファイルをアップロードします",
-    "640个字节为一组": "640バイトごとにグループ化します",
-    "逆转出错的段落": "エラーのあるパラグラフを逆転させます",
-    "对话助手函数插件": "対話アシスタント関数プラグイン",
-    "前者是API2D的结束条件": "前者はAPI2Dの終了条件です",
-    "终端": "ターミナル",
-    "仅调试": "デバッグのみ",
-    "论文": "論文",
-    "想象一个穿着者": "着用者を想像してください",
-    "音频内容是": "音声の内容は",
-    "如果需要使用AZURE 详情请见额外文档 docs\\use_azure.md": "AZUREを使用する必要がある場合は、詳細については別のドキュメント docs\\use_azure.md を参照してください",
-    "请先将.doc文档转换为.docx文档": ".docドキュメントを.docxドキュメントに変換してください",
-    "请查看终端的输出或耐心等待": "ターミナルの出力を確認するか、お待ちください",
-    "初始化音频采集线程": "オーディオキャプチャスレッドを初期化します",
-    "用该压缩包+ConversationHistoryArchive进行反馈": "この圧縮ファイル+ConversationHistoryArchiveを使用してフィードバックします",
-    "阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https": "阿里云リアルタイム音声認識の設定は難しいため、上級ユーザーのみに推奨されます 参考 https",
-    "多线程翻译开始": "マルチスレッド翻訳が開始されました",
-    "只有GenerateImage和生成图像相关": "GenerateImageと関連する画像の生成のみ",
-    "代理数据解析失败": "プロキシデータの解析に失敗しました",
-    "建议使用英文单词": "英単語の使用をお勧めします",
-    "功能描述": "機能の説明",
-    "读 docs\\use_azure.md": "ドキュメントを読む",
-    "将消耗较长时间下载中文向量化模型": "中国語のベクトル化モデルをダウンロードするのに時間がかかります",
-    "表示频道ID": "チャネルIDを表示する",
-    "未知指令": "不明なコマンド",
-    "包含documentclass关键字": "documentclassキーワードを含む",
-    "中读取数据构建知识库": "データを読み取って知識ベースを構築する",
-    "远程云服务器部署": "リモートクラウドサーバーにデプロイする",
-    "输入部分太自由": "入力が自由すぎる",
-    "读取pdf文件": "PDFファイルを読み込む",
-    "将两个PDF拼接": "2つのPDFを結合する",
-    "默认值为1000": "デフォルト値は1000です",
-    "写出文件": "ファイルに書き出す",
-    "生成的视频文件路径": "生成されたビデオファイルのパス",
-    "Arixv论文精细翻译": "Arixv論文の詳細な翻訳",
-    "用latex编译为PDF对修正处做高亮": "LaTeXでコンパイルしてPDFに修正をハイライトする",
-    "点击“停止”键可终止程序": "「停止」ボタンをクリックしてプログラムを終了できます",
-    "否则将导致每个人的Claude问询历史互相渗透": "さもないと、各人のClaudeの問い合わせ履歴が相互に侵入します",
-    "音频文件名": "オーディオファイル名",
-    "的参数！": "のパラメータ！",
-    "对话历史": "対話履歴",
-    "当下一次用户提交时": "次のユーザーの提出時に",
-    "数学GenerateAnimation": "数学GenerateAnimation",
-    "如果要使用Claude": "Claudeを使用する場合は",
-    "请向下翻": "下にスクロールしてください",
-    "报告已经添加到右侧“文件上传区”": "報告は右側の「ファイルアップロードエリア」に追加されました",
-    "删除整行的空注释": "空のコメントを含む行を削除する",
-    "建议直接在API_KEY处填写": "API_KEYの場所に直接入力することをお勧めします",
-    "暗色模式 / 亮色模式": "ダークモード/ライトモード",
-    "做一些外观色彩上的调整": "外観の色調整を行う",
-    "请切换至“KnowledgeBaseQuestionAnswer”插件进行知识库访问": "ナレッジベースのアクセスには「KnowledgeBaseQuestionAnswer」プラグインに切り替えてください",
-    "它*必须*被包含在AVAIL_LLM_MODELS列表中": "それはAVAIL_LLM_MODELSリストに含まれている必要があります",
-    "并设置参数": "パラメータを設定する",
-    "待处理的word文档路径": "処理待ちのWord文書のパス",
-    "调用缓存": "キャッシュを呼び出す",
-    "片段": "フラグメント",
-    "否则结束循环": "それ以外の場合はループを終了する",
-    "请对下面的音频片段做概述": "以下のオーディオフラグメントについて概要を作成してください",
-    "高危设置! 常规情况下不要修改! 通过修改此设置": "高リスクの設定！通常は変更しないでください！この設定を変更することで",
-    "插件锁定中": "プラグインがロックされています",
-    "开始": "開始",
-    "但请查收结果": "結果を確認してください",
-    "刷新Gradio前端界面": "Gradioフロントエンドインターフェースをリフレッシュする",
-    "批量SummarizeAudioVideo": "オーディオビデオを一括要約する",
-    "一个单实例装饰器": "単一のインスタンスデコレータ",
-    "Claude响应异常": "Claudeの応答が異常です",
-    "但内部用stream的方法避免中途网线被掐": "ただし、途中でネットワーク接続が切断されることを避けるために、内部ではストリームを使用しています",
-    "检查USE_PROXY": "USE_PROXYを確認する",
-    "永远给定None": "常にNoneを指定する",
-    "报告如何远程获取": "報告のリモート取得方法",
-    "您可以到Github Issue区": "GithubのIssueエリアにアクセスできます",
-    "如果只询问1个大语言模型": "1つの大規模言語モデルにのみ質問する場合",
-    "为了防止大语言模型的意外谬误产生扩散影响": "大規模言語モデルの誤った結果が広がるのを防ぐために",
-    "编译BibTex": "BibTexのコンパイル",
-    "⭐多线程方法": "マルチスレッドの方法",
-    "推荐http": "httpをおすすめします",
-    "如果要使用": "使用する場合",
-    "的单词": "の単語",
-    "如果本地使用不建议加这个": "ローカルで使用する場合はお勧めしません",
-    "避免线程阻塞": "スレッドのブロックを回避する",
-    "吸收title与作者以上的部分": "タイトルと著者以上の部分を吸収する",
-    "作者": "著者",
-    "5刀": "5ドル",
-    "ChatGLMFT响应异常": "ChatGLMFTの応答異常",
-    "才能继续下面的步骤": "次の手順に進むために",
-    "对这个人外貌、身处的环境、内心世界、过去经历进行描写": "この人の外見、環境、内面世界、過去の経験について描写する",
-    "找不到微调模型检查点": "ファインチューニングモデルのチェックポイントが見つかりません",
-    "请仔细鉴别并以原文为准": "注意深く確認し、元のテキストを参照してください",
-    "计算文件总时长和切割点": "ファイルの総時間とカットポイントを計算する",
-    "我将为您查找相关壁纸": "関連する壁紙を検索します",
-    "此插件Windows支持最佳": "このプラグインはWindowsに最適です",
-    "请输入关键词": "キーワードを入力してください",
-    "以下所有配置也都支持利用环境变量覆写": "以下のすべての設定は環境変数を使用して上書きすることもサポートしています",
-    "尝试第": "第#",
-    "开始生成动画": "アニメーションの生成を開始します",
-    "免费": "無料",
-    "我好！": "私は元気です！",
-    "str类型": "strタイプ",
-    "生成数学动画": "数学アニメーションの生成",
-    "GPT结果已输出": "GPTの結果が出力されました",
-    "PDF文件所在的路径": "PDFファイルのパス",
-    "源码自译解": "ソースコードの自動翻訳解析",
-    "格式如org-123456789abcdefghijklmno的": "org-123456789abcdefghijklmnoの形式",
-    "请对这部分内容进行语法矫正": "この部分の内容に文法修正を行ってください",
-    "调用whisper模型音频转文字": "whisperモデルを使用して音声をテキストに変換する",
-    "编译转化后的PDF": "変換されたPDFをコンパイルする",
-    "将音频解析为简体中文": "音声を簡体字中国語に解析する",
-    "删除或修改歧义文件": "曖昧なファイルを削除または修正する",
-    "ChatGLMFT消耗大量的内存": "ChatGLMFTは大量のメモリを消費します",
-    "图像生成所用到的提示文本": "画像生成に使用されるヒントテキスト",
-    "如果已经存在": "既に存在する場合",
-    "以下是一篇学术论文的基础信息": "以下は学術論文の基本情報です",
-    "解压失败! 需要安装pip install rarfile来解压rar文件": "解凍に失敗しました！rarファイルを解凍するにはpip install rarfileをインストールする必要があります",
-    "一般是文本过长": "通常、テキストが長すぎます",
-    "单线程": "シングルスレッド",
-    "Linux下必须使用Docker安装": "LinuxではDockerを使用してインストールする必要があります",
-    "请先上传文件素材": "まずファイル素材をアップロードしてください",
-    "如果分析错误": "もし解析エラーがある場合",
-    "快捷的调试函数": "便利なデバッグ関数",
-    "欢迎使用 MOSS 人工智能助手！输入内容即可进行对话": "MOSS AIアシスタントをご利用いただきありがとうございます！入力内容を入力すると、対話ができます",
-    "json等": "jsonなど",
-    "--读取参数": "--パラメータの読み込み",
-    "⭐单线程方法": "⭐シングルスレッドメソッド",
-    "请用一句话概括这些文件的整体功能": "これらのファイルの全体的な機能を一文で要約してください",
-    "用于灵活调整复杂功能的各种参数": "複雑な機能を柔軟に調整するためのさまざまなパラメータ",
-    "默认 False": "デフォルトはFalseです",
-    "生成中文PDF": "中国語のPDFを生成する",
-    "正在处理": "処理中",
-    "需要被切割的音频文件名": "分割する必要のある音声ファイル名",
-    "根据文本使用GPT模型生成相应的图像": "テキストに基づいてGPTモデルを使用して対応する画像を生成する",
-    "可选": "オプション",
-    "Aliyun音频服务异常": "Aliyunオーディオサービスの異常",
-    "尝试下载": "ダウンロードを試みる",
-    "需Latex": "LaTeXが必要です",
-    "拆分过长的Markdown文件": "長すぎるMarkdownファイルを分割する",
-    "当前支持的格式包括": "現在サポートされている形式には",
-    "=================================== 工具函数 ===============================================": "=================================== ユーティリティ関数 ===============================================",
-    "所有音频都总结完成了吗": "すべてのオーディオが要約されましたか",
-    "没有设置ANTHROPIC_API_KEY": "ANTHROPIC_API_KEYが設定されていません",
-    "详见项目主README.md": "詳細はプロジェクトのメインREADME.mdを参照してください",
-    "使用": "使用する",
-    "P.S. 其他可用的模型还包括": "P.S. 其他可用的模型还包括",
-    "保证括号正确": "保证括号正确",
-    "或代理节点": "或代理节点",
-    "整理结果为压缩包": "整理结果为压缩包",
-    "实时音频采集": "实时音频采集",
-    "获取回复": "获取回复",
-    "插件可读取“输入区”文本/路径作为参数": "插件可读取“输入区”文本/路径作为参数",
-    "请讲话": "请讲话",
-    "将文件复制一份到下载区": "将文件复制一份到下载区",
-    "from crazy_functions.虚空终端 import 终端": "from crazy_functions.虚空终端 import 终端",
-    "这个paper有个input命令文件名大小写错误！": "这个paper有个input命令文件名大小写错误！",
-    "解除插件锁定": "解除插件锁定",
-    "不能加载Claude组件": "不能加载Claude组件",
-    "如果有必要": "如果有必要",
-    "禁止移除或修改此警告": "禁止移除或修改此警告",
-    "然后进行问答": "然后进行问答",
-    "响应异常": "响应异常",
-    "使用英文": "使用英文",
-    "add gpt task 创建子线程请求gpt": "add gpt task 创建子线程请求gpt",
-    "实际得到格式": "实际得到格式",
-    "请继续分析其他源代码": "请继续分析其他源代码",
-    "”的主要内容": "”的主要内容",
-    "防止proxies单独起作用": "防止proxies单独起作用",
-    "临时地激活代理网络": "临时地激活代理网络",
-    "屏蔽空行和太短的句子": "屏蔽空行和太短的句子",
-    "把某个路径下所有文件压缩": "把某个路径下所有文件压缩",
-    "您需要首先调用构建知识库": "您需要首先调用构建知识库",
-    "翻译-": "翻译-",
-    "Newbing 请求失败": "Newbing 请求失败",
-    "次编译": "次编译",
-    "后缀": "后缀",
-    "文本碎片重组为完整的tex片段": "文本碎片重组为完整的tex片段",
-    "待注入的知识库名称id": "待注入的知识库名称id",
-    "消耗时间的函数": "消耗时间的函数",
-    "You are associated with a deactivated account. OpenAI以账户失效为由": "You are associated with a deactivated account. OpenAI以账户失效为由",
-    "成功啦": "成功啦",
-    "音频文件的路径": "音频文件的路径",
-    "英文Latex项目全文纠错": "英文Latex项目全文纠错",
-    "将子线程的gpt结果写入chatbot": "将子线程的gpt结果写入chatbot",
-    "开始最终总结": "开始最终总结",
-    "调用": "调用",
-    "正在锁定插件": "正在锁定插件",
-    "记住当前的label": "记住当前的label",
-    "根据自然语言执行插件命令": "根据自然语言执行插件命令",
-    "response中会携带traceback报错信息": "response中会携带traceback报错信息",
-    "避免多用户干扰": "避免多用户干扰",
-    "顺利完成": "顺利完成",
-    "详情见https": "详情见https",
-    "清空label": "ラベルをクリアする",
-    "这需要一段时间计算": "これには時間がかかります",
-    "找不到": "見つかりません",
-    "消耗大量的内存": "大量のメモリを消費する",
-    "安装方法https": "インストール方法https",
-    "为发送请求做准备": "リクエストの準備をする",
-    "第1次尝试": "1回目の試み",
-    "检查结果": "結果をチェックする",
-    "精细切分latex文件": "LaTeXファイルを細かく分割する",
-    "api2d等请求源": "api2dなどのリクエストソース",
-    "填入你亲手写的部署名": "あなたが手書きしたデプロイ名を入力してください",
-    "给出指令": "指示を与える",
-    "请问什么是质子": "プロトンとは何ですか",
-    "请直接去该路径下取回翻译结果": "直接そのパスに移動して翻訳結果を取得してください",
-    "等待Claude回复的片段": "Claudeの返信を待っているフラグメント",
-    "Latex没有安装": "LaTeXがインストールされていません",
-    "文档越长耗时越长": "ドキュメントが長いほど時間がかかります",
-    "没有阿里云语音识别APPKEY和TOKEN": "阿里雲の音声認識のAPPKEYとTOKENがありません",
-    "分析结果": "結果を分析する",
-    "请立即终止程序": "プログラムを即座に終了してください",
-    "正在尝试自动安装": "自動インストールを試みています",
-    "请直接提交即可": "直接提出してください",
-    "将指定目录下的PDF文件从英文翻译成中文": "指定されたディレクトリ内のPDFファイルを英語から中国語に翻訳する",
-    "请查收结果": "結果を確認してください",
-    "上下布局": "上下布局",
-    "此处可以输入解析提示": "此处可以输入解析提示",
-    "前面是中文逗号": "前面是中文逗号",
-    "的依赖": "的依赖",
-    "材料如下": "材料如下",
-    "欢迎加REAME中的QQ联系开发者": "欢迎加REAME中的QQ联系开发者",
-    "开始下载": "開始ダウンロード",
-    "100字以内": "100文字以内",
-    "创建request": "リクエストの作成",
-    "创建存储切割音频的文件夹": "切り取られた音声を保存するフォルダの作成",
-    "⭐主进程执行": "⭐メインプロセスの実行",
-    "音频解析结果": "音声解析結果",
-    "Your account is not active. OpenAI以账户失效为由": "アカウントがアクティブではありません。OpenAIはアカウントの無効化を理由にしています",
-    "虽然PDF生成失败了": "PDFの生成に失敗しました",
-    "如果这里报错": "ここでエラーが発生した場合",
-    "前面是中文冒号": "前面は中国語のコロンです",
-    "SummarizeAudioVideo内容": "SummarizeAudioVideoの内容",
-    "openai的官方KEY需要伴随组织编码": "openaiの公式KEYは組織コードと一緒に必要です",
-    "是本次输入": "これは今回の入力です",
-    "色彩主体": "色彩の主体",
-    "Markdown翻译": "Markdownの翻訳",
-    "会被加在你的输入之后": "あなたの入力の後に追加されます",
-    "失败啦": "失敗しました",
-    "每个切割音频片段的时长": "各切り取り音声の長さ",
-    "拆分过长的latex片段": "原始文本",
-    "待提取的知识库名称id": "原始文本",
-    "在这里放一些网上搜集的demo": "原始文本",
-    "环境变量配置格式见docker-compose.yml": "原始文本",
-    "Claude组件初始化成功": "原始文本",
-    "尚未加载": "原始文本",
-    "等待Claude响应": "原始文本",
-    "重组": "原始文本",
-    "将文件添加到chatbot cookie中": "原始文本",
-    "回答完问题后": "原始文本",
-    "将根据报错信息修正tex源文件并重试": "原始文本",
-    "是否在触发时清除历史": "原始文本",
-    "尝试执行Latex指令失败": "原始文本",
-    "默认 True": "原始文本",
-    "文本碎片重组为完整的tex文件": "原始文本",
-    "注意事项": "原始文本",
-    "您接下来不能再使用其他插件了": "原始文本",
-    "属性": "原始文本",
-    "正在编译PDF文档": "原始文本",
-    "提取视频中的音频": "原始文本",
-    "正在同时咨询ChatGPT和ChatGLM……": "原始文本",
-    "Chuanhu-Small-and-Beautiful主题": "原始文本",
-    "版权归原文作者所有": "原始文本",
-    "如果程序停顿5分钟以上": "原始文本",
-    "请输入要翻译成哪种语言": "日本語",
-    "以秒为单位": "秒単位で",
-    "请以以下方式load模型！！！": "以下の方法でモデルをロードしてください！！！",
-    "使用时": "使用時",
-    "对这个人外貌、身处的环境、内心世界、人设进行描写": "この人の外見、環境、内面世界、キャラクターを描写する",
-    "例如翻译、解释代码、润色等等": "例えば翻訳、コードの説明、修正など",
-    "多线程Demo": "マルチスレッドデモ",
-    "不能正常加载": "正常にロードできません",
-    "还原部分原文": "一部の元のテキストを復元する",
-    "可以将自身的状态存储到cookie中": "自身の状態をcookieに保存することができます",
-    "释放线程锁": "スレッドロックを解放する",
-    "当前知识库内的有效文件": "現在のナレッジベース内の有効なファイル",
-    "也是可读的": "読み取り可能です",
-    "等待ChatGLMFT响应中": "ChatGLMFTの応答を待っています",
-    "输入 stop 以终止对话": "stopを入力して対話を終了します",
-    "对整个Latex项目进行纠错": "全体のLatexプロジェクトを修正する",
-    "报错信息": "エラーメッセージ",
-    "下载pdf文件未成功": "PDFファイルのダウンロードに失敗しました",
-    "正在加载Claude组件": "Claudeコンポーネントを読み込んでいます",
-    "格式": "フォーマット",
-    "Claude响应缓慢": "Claudeの応答が遅い",
-    "该选项即将被弃用": "このオプションはまもなく廃止されます",
-    "正常状态": "正常な状態",
-    "中文Bing版": "中国語Bing版",
-    "代理网络配置": "プロキシネットワークの設定",
-    "Openai 限制免费用户每分钟20次请求": "Openaiは無料ユーザーに対して1分間に20回のリクエスト制限を設けています",
-    "gpt写的": "gptで書かれた",
-    "向已打开的频道发送一条文本消息": "既に開いているチャンネルにテキストメッセージを送信する",
-    "缺少ChatGLMFT的依赖": "ChatGLMFTの依存関係が不足しています",
-    "注意目前不能多人同时调用Claude接口": "現在、複数の人が同時にClaudeインターフェースを呼び出すことはできません",
-    "或者不在环境变量PATH中": "または環境変数PATHに存在しません",
-    "提问吧! 但注意": "質問してください！ただし注意してください",
-    "因此选择GenerateImage函数": "したがって、GenerateImage関数を選択します",
-    "无法找到一个主Tex文件": "メインのTexファイルが見つかりません",
-    "转化PDF编译已经成功": "PDF変換コンパイルが成功しました",
-    "因为在同一个频道里存在多人使用时历史消息渗透问题": "同じチャンネルで複数の人が使用する場合、過去のメッセージが漏洩する問題があります",
-    "SlackClient类用于与Slack API进行交互": "SlackClientクラスはSlack APIとのインタラクションに使用されます",
-    "如果存在调试缓存文件": "デバッグキャッシュファイルが存在する場合",
-    "举例": "例を挙げる",
-    "无需填写": "記入する必要はありません",
-    "配置教程&视频教程": "設定チュートリアル＆ビデオチュートリアル",
-    "最后一步处理": "最後のステップの処理",
-    "定位主Latex文件": "メインのLatexファイルを特定する",
-    "暂不提交": "一時的に提出しない",
-    "由于最为关键的转化PDF编译失败": "最も重要なPDF変換コンパイルが失敗したため",
-    "用第二人称": "第二人称を使用する",
-    "例如 RoPlZrM88DnAFkZK": "例えば RoPlZrM88DnAFkZK",
-    "没有设置ANTHROPIC_API_KEY选项": "ANTHROPIC_API_KEYオプションが設定されていません",
-    "找不到任何.tex文件": "テキストの翻訳",
-    "请您不要删除或修改这行警告": "テキストの翻訳",
-    "只有第二步成功": "テキストの翻訳",
-    "调用Claude时": "テキストの翻訳",
-    "输入 clear 以清空对话历史": "テキストの翻訳",
-    "= 2 通过一些Latex模板中常见": "テキストの翻訳",
-    "没给定指令": "テキストの翻訳",
-    "还原原文": "テキストの翻訳",
-    "自定义API KEY格式": "テキストの翻訳",
-    "防止丢失最后一条消息": "テキストの翻訳",
-    "方法": "テキストの翻訳",
-    "压缩包": "テキストの翻訳",
-    "对各个llm模型进行单元测试": "テキストの翻訳",
-    "导入依赖失败": "テキストの翻訳",
-    "详情信息见requirements.txt": "テキストの翻訳",
-    "翻译内容可靠性无保障": "テキストの翻訳",
-    "刷新页面即可以退出KnowledgeBaseQuestionAnswer模式": "テキストの翻訳",
-    "上传本地文件/压缩包供函数插件调用": "テキストの翻訳",
-    "循环监听已打开频道的消息": "テキストの翻訳",
-    "一个包含所有切割音频片段文件路径的列表": "テキストの翻訳",
-    "检测到arxiv文档连接": "テキストの翻訳",
-    "P.S. 顺便把CTEX塞进去以支持中文": "テキストの翻訳",
-    "后面是英文冒号": "テキストの翻訳",
-    "上传文件自动修正路径": "テキストの翻訳",
-    "实现消息发送、接收等功能": "メッセージの送受信などの機能を実現する",
-    "改变输入参数的顺序与结构": "入力パラメータの順序と構造を変更する",
-    "正在精细切分latex文件": "LaTeXファイルを細かく分割しています",
-    "读取文件": "ファイルを読み込んでいます"
 }

docs/translate_traditionalchinese.json

@@ -2213,66 +2213,5 @@
     "“喂狗”": "“喂狗”",
     "第4步": "第4步",
     "退出": "退出",
-    "使用 Unsplash API": "使用 Unsplash API",
+    "使用 Unsplash API": "使用 Unsplash API"
-    "非Openai官方接口返回了错误": "非Openai官方接口返回了错误",
-    "用来描述你的要求": "用來描述你的要求",
-    "自定义API KEY格式": "自定義API KEY格式",
-    "前缀": "前綴",
-    "会被加在你的输入之前": "會被加在你的輸入之前",
-    "api2d等请求源": "api2d等請求源",
-    "高危设置! 常规情况下不要修改! 通过修改此设置": "高危設置！常規情況下不要修改！通過修改此設置",
-    "即将编译PDF": "即將編譯PDF",
-    "默认 secondary": "默認 secondary",
-    "正在从github下载资源": "正在從github下載資源",
-    "响应异常": "響應異常",
-    "我好！": "我好！",
-    "无需填写": "無需填寫",
-    "缺少": "缺少",
-    "请问什么是质子": "請問什麼是質子",
-    "如果要使用": "如果要使用",
-    "重组": "重組",
-    "一个单实例装饰器": "一個單實例裝飾器",
-    "的参数！": "的參數！",
-    "🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行": "🏃‍♂️🏃‍♂️🏃‍♂️ 子進程執行",
-    "失败时": "失敗時",
-    "没有设置ANTHROPIC_API_KEY选项": "沒有設置ANTHROPIC_API_KEY選項",
-    "并设置参数": "並設置參數",
-    "格式": "格式",
-    "按钮是否可见": "按鈕是否可見",
-    "即可见": "即可見",
-    "创建request": "創建request",
-    "的依赖": "的依賴",
-    "⭐主进程执行": "⭐主進程執行",
-    "最后一步处理": "最後一步處理",
-    "没有设置ANTHROPIC_API_KEY": "沒有設置ANTHROPIC_API_KEY",
-    "的参数": "的參數",
-    "逆转出错的段落": "逆轉出錯的段落",
-    "本项目现已支持OpenAI和Azure的api-key": "本項目現已支持OpenAI和Azure的api-key",
-    "前者是API2D的结束条件": "前者是API2D的結束條件",
-    "增强稳健性": "增強穩健性",
-    "消耗大量的内存": "消耗大量的內存",
-    "您的 API_KEY 不满足任何一种已知的密钥格式": "您的API_KEY不滿足任何一種已知的密鑰格式",
-    "⭐单线程方法": "⭐單線程方法",
-    "是否在触发时清除历史": "是否在觸發時清除歷史",
-    "⭐多线程方法": "多線程方法",
-    "不能正常加载": "無法正常加載",
-    "举例": "舉例",
-    "即不处理之前的对话历史": "即不處理之前的對話歷史",
-    "尚未加载": "尚未加載",
-    "防止proxies单独起作用": "防止proxies單獨起作用",
-    "默认 False": "默認 False",
-    "检查USE_PROXY": "檢查USE_PROXY",
-    "响应中": "響應中",
-    "扭转的范围": "扭轉的範圍",
-    "后缀": "後綴",
-    "调用": "調用",
-    "创建AcsClient实例": "創建AcsClient實例",
-    "安装": "安裝",
-    "会被加在你的输入之后": "會被加在你的輸入之後",
-    "配合前缀可以把你的输入内容用引号圈起来": "配合前綴可以把你的輸入內容用引號圈起來",
-    "例如翻译、解释代码、润色等等": "例如翻譯、解釋代碼、潤色等等",
-    "后者是OPENAI的结束条件": "後者是OPENAI的結束條件",
-    "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"
 }

docs/use_audio.md

@@ -28,16 +28,6 @@ ALIYUN_APPKEY = "RoPlZrM88DnAFkZK"   # 此appkey已经失效
 参考 https://help.aliyun.com/document_detail/450255.html
 先有阿里云开发者账号，登录之后，需要开通 智能语音交互 的功能，可以免费获得一个token，然后在 全部项目 中，创建一个项目，可以获得一个appkey.
 
-- 进阶功能
-进一步填写ALIYUN_ACCESSKEY和ALIYUN_SECRET实现自动获取ALIYUN_TOKEN
-```
-ALIYUN_APPKEY = "RoP1ZrM84DnAFkZK"
-ALIYUN_TOKEN = ""
-ALIYUN_ACCESSKEY = "LTAI5q6BrFUzoRXVGUWnekh1"
-ALIYUN_SECRET = "eHmI20AVWIaQZ0CiTD2bGQVsaP9i68"
-```
-
-
 ## 3.启动
 
 启动gpt-academic `python main.py`
@@ -58,7 +48,7 @@ III `[把特殊软件（如腾讯会议）的外放声音用VoiceMeeter截留]`
 
 VI 两种音频监听模式切换时，需要刷新页面才有效。
 
-VII 非localhost运行+非https情况下无法打开录音功能的坑：https://blog.csdn.net/weixin_39461487/article/details/109594434
-
 ## 5.点击函数插件区“实时音频采集” 或者其他音频交互功能
 
+
+

main.py

@@ -22,10 +22,8 @@ def main():
     # 问询记录, python 版本建议3.9+（越新越好）
     import logging, uuid
     os.makedirs("gpt_log", exist_ok=True)
-    try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
-    except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
-    # Disable logging output from the 'httpx' logger
-    logging.getLogger("httpx").setLevel(logging.WARNING)
     print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
 
     # 一些普通功能模块

multi_language.py

@@ -3,18 +3,16 @@
     
     
     Usage:
-        1. modify config.py, set your LLM_MODEL and API_KEY(s) to provide access to OPENAI (or any other LLM model provider)
+        1. modify LANG
-
-        2. modify LANG (below ↓)
             LANG = "English"
 
-        3. modify TransPrompt (below ↓)
+        2. modify TransPrompt
             TransPrompt = f"Replace each json value `#` with translated results in English, e.g., \"原始文本\":\"TranslatedText\". Keep Json format. Do not answer #."
 
-        4. Run `python multi_language.py`. 
+        3. Run `python multi_language.py`. 
             Note: You need to run it multiple times to increase translation coverage because GPT makes mistakes sometimes.
 
-        5. Find the translated program in `multi-language\English\*`
+        4. Find the translated program in `multi-language\English\*`
    
     P.S.
     

request_llm/bridge_all.py

@@ -248,6 +248,7 @@ if "moss" in AVAIL_LLM_MODELS:
 if "stack-claude" in AVAIL_LLM_MODELS:
     from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui
     from .bridge_stackclaude import predict as claude_ui
+    # claude
     model_info.update({
         "stack-claude": {
             "fn_with_ui": claude_ui,
@@ -262,6 +263,7 @@ if "newbing-free" in AVAIL_LLM_MODELS:
     try:
         from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
         from .bridge_newbingfree import predict as newbingfree_ui
+        # claude
         model_info.update({
             "newbing-free": {
                 "fn_with_ui": newbingfree_ui,
@@ -278,6 +280,7 @@ if "newbing" in AVAIL_LLM_MODELS:   # same with newbing-free
     try:
         from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
         from .bridge_newbingfree import predict as newbingfree_ui
+        # claude
         model_info.update({
             "newbing": {
                 "fn_with_ui": newbingfree_ui,
@@ -294,6 +297,7 @@ if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
     try:
         from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
         from .bridge_chatglmft import predict as chatglmft_ui
+        # claude
         model_info.update({
             "chatglmft": {
                 "fn_with_ui": chatglmft_ui,
@@ -306,22 +310,7 @@ if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
         })
     except:
         print(trimmed_format_exc())
-if "internlm" in AVAIL_LLM_MODELS:
+
-    try:
-        from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
-        from .bridge_internlm import predict as internlm_ui
-        model_info.update({
-            "internlm": {
-                "fn_with_ui": internlm_ui,
-                "fn_without_ui": internlm_noui,
-                "endpoint": None,
-                "max_token": 4096,
-                "tokenizer": tokenizer_gpt35,
-                "token_cnt": get_token_num_gpt35,
-            }
-        })
-    except:
-        print(trimmed_format_exc())
 
 def LLM_CATCH_EXCEPTION(f):
     """

request_llm/bridge_chatglm.py

@@ -37,23 +37,15 @@ class GetGLMHandle(Process):
         # 子进程执行
         # 第一次运行，加载参数
         retry = 0
-        LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
-
-        if LOCAL_MODEL_QUANT == "INT4":         # INT4
-            _model_name_ = "THUDM/chatglm2-6b-int4"
-        elif LOCAL_MODEL_QUANT == "INT8":       # INT8
-            _model_name_ = "THUDM/chatglm2-6b-int8"
-        else:
-            _model_name_ = "THUDM/chatglm2-6b"  # FP16
-
         while True:
             try:
                 if self.chatglm_model is None:
-                    self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
+                    self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
+                    device, = get_conf('LOCAL_MODEL_DEVICE')
                     if device=='cpu':
-                        self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
+                        self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).float()
                     else:
-                        self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
+                        self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
                     self.chatglm_model = self.chatglm_model.eval()
                     break
                 else:
@@ -144,8 +136,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_chatglmft.py

@@ -185,8 +185,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_chatgpt.py

@@ -129,8 +129,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     raw_input = inputs
     logging.info(f'[raw_input] {raw_input}')
@@ -171,10 +174,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 chunk = next(stream_response)
             except StopIteration:
                 # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
-                chunk_decoded = chunk.decode()
+                from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
-                error_msg = chunk_decoded
+                chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode())}")
-                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk.decode()) # 刷新界面
-                yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
                 return
             
             # print(chunk.decode()[6:])
@@ -185,7 +187,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             if chunk:
                 try:
                     chunk_decoded = chunk.decode()
-                    # 前者是API2D的结束条件，后者是OPENAI的结束条件
+                    # 前者API2D的
                     if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
                         logging.info(f'[response] {gpt_replying_buffer}')
@@ -198,44 +200,40 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     history[-1] = gpt_replying_buffer
                     chatbot[-1] = (history[-2], history[-1])
                     yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
+
                 except Exception as e:
+                    traceback.print_exc()
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
                     chunk = get_full_error(chunk, stream_response)
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
-                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    if "reduce the length" in error_msg:
-                    print(error_msg)
+                        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
-                    return
+                        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
-
-def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
-    from .bridge_all import model_info
-    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
-    if "reduce the length" in error_msg:
-        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
-        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
                                                max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
                         # history = []    # 清除历史
-    elif "does not exist" in error_msg:
+                    elif "does not exist" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
+                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
-    elif "Incorrect API key" in error_msg:
+                    elif "Incorrect API key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
-    elif "exceeded your current quota" in error_msg:
+                    elif "exceeded your current quota" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
-    elif "account is not active" in error_msg:
+                    elif "account is not active" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "associated with a deactivated account" in error_msg:
+                    elif "associated with a deactivated account" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "bad forward key" in error_msg:
+                    elif "bad forward key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
-    elif "Not enough point" in error_msg:
+                    elif "Not enough point" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
-    else:
+                    else:
-        from toolbox import regular_txt_to_markdown
+                        from toolbox import regular_txt_to_markdown
-        tb_str = '```\n' + trimmed_format_exc() + '```'
+                        tb_str = '```\n' + trimmed_format_exc() + '```'
-        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
+                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
-    return chatbot, history
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    return
 
 def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     """

request_llm/bridge_claude.py

@@ -116,8 +116,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         return
     
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     raw_input = inputs
     logging.info(f'[raw_input] {raw_input}')

request_llm/bridge_internlm.py

@@ -1,312 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf, Singleton
-from multiprocessing import Process, Pipe
-
-model_name = "InternLM"
-cmd_to_install = "`pip install ???`"
-load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-def try_to_import_special_deps():
-    import sentencepiece
-
-user_prompt = "<|User|>:{user}<eoh>\n"
-robot_prompt = "<|Bot|>:{robot}<eoa>\n"
-cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"
-
-
-def combine_history(prompt, hist):
-    messages = hist
-    total_prompt = ""
-    for message in messages:
-        cur_content = message
-        cur_prompt = user_prompt.replace("{user}", cur_content[0])
-        total_prompt += cur_prompt
-        cur_prompt = robot_prompt.replace("{robot}", cur_content[1])
-        total_prompt += cur_prompt
-    total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
-    return total_prompt
-
-
-@Singleton
-class GetInternlmHandle(Process):
-    def __init__(self):
-        # ⭐主进程执行
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self._model = None
-        self._tokenizer = None
-        self.info = ""
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-
-    def ready(self):
-        # ⭐主进程执行
-        return self._model is not None
-
-    def load_model_and_tokenizer(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer
-        device, = get_conf('LOCAL_MODEL_DEVICE')
-        if self._model is None:
-            tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
-            if device=='cpu':
-                model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
-            else:
-                model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
-
-            model = model.eval()
-        return model, tokenizer
-
-    def llm_stream_generator(self, **kwargs):
-        import torch
-        import logging
-        import copy
-        import warnings
-        import torch.nn as nn
-        from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
-
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        def adaptor():
-            model = self._model
-            tokenizer = self._tokenizer
-            prompt = kwargs['query']
-            max_length = kwargs['max_length']
-            top_p = kwargs['top_p']
-            temperature = kwargs['temperature']
-            history = kwargs['history']
-            real_prompt = combine_history(prompt, history)
-            return model, tokenizer, real_prompt, max_length, top_p, temperature
-        
-        model, tokenizer, prompt, max_length, top_p, temperature = adaptor()
-        prefix_allowed_tokens_fn = None
-        logits_processor = None
-        stopping_criteria = None
-        additional_eos_token_id = 103028
-        generation_config = None
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25
-
-        inputs = tokenizer([prompt], padding=True, return_tensors="pt")
-        input_length = len(inputs["input_ids"][0])
-        for k, v in inputs.items():
-            inputs[k] = v.cuda()
-        input_ids = inputs["input_ids"]
-        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
-        if generation_config is None:
-            generation_config = model.generation_config
-        generation_config = copy.deepcopy(generation_config)
-        model_kwargs = generation_config.update(**kwargs)
-        bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-        if additional_eos_token_id is not None:
-            eos_token_id.append(additional_eos_token_id)
-        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None:
-            warnings.warn(
-                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
-                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
-                " recommend using `max_new_tokens` to control the maximum length of the generation.",
-                UserWarning,
-            )
-        elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-            if not has_default_max_length:
-                logging.warn(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
-                    UserWarning,
-                )
-
-        if input_ids_seq_length >= generation_config.max_length:
-            input_ids_string = "input_ids"
-            logging.warning(
-                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
-
-        # 2. Set generation parameters if not already defined
-        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
-        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-
-        logits_processor = model._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
-            encoder_input_ids=input_ids,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-            logits_processor=logits_processor,
-        )
-
-        stopping_criteria = model._get_stopping_criteria(
-            generation_config=generation_config, stopping_criteria=stopping_criteria
-        )
-        logits_warper = model._get_logits_warper(generation_config)
-
-        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
-        scores = None
-        while True:
-            model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
-            # forward pass to get next token
-            outputs = model(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=False,
-                output_hidden_states=False,
-            )
-
-            next_token_logits = outputs.logits[:, -1, :]
-
-            # pre-process distribution
-            next_token_scores = logits_processor(input_ids, next_token_logits)
-            next_token_scores = logits_warper(input_ids, next_token_scores)
-
-            # sample
-            probs = nn.functional.softmax(next_token_scores, dim=-1)
-            if generation_config.do_sample:
-                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
-            else:
-                next_tokens = torch.argmax(probs, dim=-1)
-
-            # update generated ids, model inputs, and length for next step
-            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-            model_kwargs = model._update_model_kwargs_for_generation(
-                outputs, model_kwargs, is_encoder_decoder=False
-            )
-            unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
-            
-            output_token_ids = input_ids[0].cpu().tolist()
-            output_token_ids = output_token_ids[input_length:]
-            for each_eos_token_id in eos_token_id:
-                if output_token_ids[-1] == each_eos_token_id:
-                    output_token_ids = output_token_ids[:-1]
-            response = tokenizer.decode(output_token_ids)
-
-            yield response
-            # stop when each sentence is finished, or if we exceed the maximum length
-            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
-                return
-
-
-
-    def check_dependency(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        try:
-            try_to_import_special_deps()
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            self.info = f"缺少{model_name}的依赖，如果要使用{model_name}，除了基础的pip依赖以外，您还需要运行{cmd_to_install}安装{model_name}的依赖。"
-            self.success = False
-
-    def run(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # 第一次运行，加载参数
-        try:
-            self._model, self._tokenizer = self.load_model_and_tokenizer()
-        except:
-            from toolbox import trimmed_format_exc
-            self.child.send(f'[Local Message] 不能正常加载{model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            raise RuntimeError(f"不能正常加载{model_name}的参数！")
-
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            # 收到消息，开始请求
-            try:
-                for response_full in self.llm_stream_generator(**kwargs):
-                    self.child.send(response_full)
-            except:
-                from toolbox import trimmed_format_exc
-                self.child.send(f'[Local Message] 调用{model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # ⭐主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-    
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 GPT-Academic
-# ------------------------------------------------------------------------------------------------------------------------
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        ⭐多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    _llm_handle = GetInternlmHandle()
-    if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
-    if not _llm_handle.success: 
-        error = _llm_handle.info
-        _llm_handle = None
-        raise RuntimeError(error)
-
-    # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    history_feedin.append(["What can I do?", sys_prompt])
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        ⭐单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    _llm_handle = GetInternlmHandle()
-    chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
-    yield from update_ui(chatbot=chatbot, history=[])
-    if not _llm_handle.success: 
-        _llm_handle = None
-        return
-
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    # 处理历史信息
-    history_feedin = []
-    history_feedin.append(["What can I do?", system_prompt] )
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收chatglm的回复
-    response = f"[Local Message]: 等待{model_name}响应中 ..."
-    for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)

request_llm/bridge_jittorllms_llama.py

@@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_jittorllms_pangualpha.py

@@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_jittorllms_rwkv.py

@@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_moss.py

@@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history)
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     # 处理历史信息
     history_feedin = []

request_llm/bridge_newbingfree.py

@@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     history_feedin = []
     for i in range(len(history)//2):

request_llm/bridge_stackclaude.py

@@ -248,8 +248,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             return
 
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]:
+            inputs = core_functional[additional_fn]["PreProcess"](
+                inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + \
+            inputs + core_functional[additional_fn]["Suffix"]
 
     history_feedin = []
     for i in range(len(history)//2):

request_llm/bridge_tgui.py

@@ -96,8 +96,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         additional_fn代表点击的哪个按钮，按钮见functional.py
     """
     if additional_fn is not None:
-        from core_functional import handle_core_functionality
+        import core_functional
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     raw_input = "What I would like to say is the following: " + inputs
     history.extend([inputs, ""])

request_llm/edge_gpt_free.py

@@ -447,15 +447,6 @@ class _ChatHub:
         """
         Ask a question to the bot
         """
-        req_header = HEADERS
-        if self.cookies is not None:
-            ws_cookies = []
-            for cookie in self.cookies:
-                ws_cookies.append(f"{cookie['name']}={cookie['value']}")
-            req_header.update({
-                'Cookie': ';'.join(ws_cookies),
-            })
-            
         timeout = aiohttp.ClientTimeout(total=30)
         self.session = aiohttp.ClientSession(timeout=timeout)
 
@@ -464,7 +455,7 @@ class _ChatHub:
         # Check if websocket is closed
         self.wss = await self.session.ws_connect(
             wss_link,
-            headers=req_header,
+            headers=HEADERS,
             ssl=ssl_context,
             proxy=self.proxy,
             autoping=False,
@@ -519,11 +510,7 @@ class _ChatHub:
         resp_txt_no_link = ""
         while not final:
             msg = await self.wss.receive()
-            try:
+            objects = msg.data.split(DELIMITER)
-                objects = msg.data.split(DELIMITER)
-            except :
-                continue
-            
             for obj in objects:
                 if obj is None or not obj:
                     continue
@@ -1122,4 +1109,4 @@ class ImageQuery(Query):
 
 
 if __name__ == "__main__":
-    main()
+    main()

request_llm/test_llms.py

@@ -14,8 +14,7 @@ if __name__ == "__main__":
     # from request_llm.bridge_moss import predict_no_ui_long_connection
     # from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
     # from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
-    # from request_llm.bridge_claude import predict_no_ui_long_connection
+    from request_llm.bridge_claude import predict_no_ui_long_connection
-    from request_llm.bridge_internlm import predict_no_ui_long_connection
 
     llm_kwargs = {
         'max_length': 512,
@@ -23,8 +22,45 @@ if __name__ == "__main__":
         'temperature': 1,
     }
 
-    result = predict_no_ui_long_connection( inputs="请问什么是质子？", 
+    result = predict_no_ui_long_connection(inputs="你好", 
-                                            llm_kwargs=llm_kwargs,
+                                        llm_kwargs=llm_kwargs,
-                                            history=["你好", "我好！"],
+                                        history=[],
-                                            sys_prompt="")
+                                        sys_prompt="")
     print('final result:', result)
+
+
+
+    # # print(result)
+    # from multiprocessing import Process, Pipe
+    # class GetGLMHandle(Process):
+    #     def __init__(self):
+    #         super().__init__(daemon=True)
+    #         pass
+    #     def run(self):
+    #         # 子进程执行
+    #         # 第一次运行，加载参数
+    #         def validate_path():
+    #             import os, sys
+    #             dir_name = os.path.dirname(__file__)
+    #             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
+    #             os.chdir(root_dir_assume + '/request_llm/jittorllms')
+    #             sys.path.append(root_dir_assume + '/request_llm/jittorllms')
+    #         validate_path() # validate path so you can run from base directory
+    #         jittorllms_model = None
+    #         import types
+    #         try:
+    #             if jittorllms_model is None:
+    #                 from models import get_model
+    #                 # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
+    #                 args_dict = {'model': 'chatrwkv'}
+    #                 print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
+    #                 jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
+    #                 print('done get model')
+    #         except:
+    #             # self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
+    #             raise RuntimeError("不能正常加载jittorllms的参数！")
+    # x = GetGLMHandle()
+    # x.start()
+
+
+    # input()

requirements.txt

@@ -18,4 +18,3 @@ openai
 numpy
 arxiv
 rich
-pypdf2==2.12.1

toolbox.py

@@ -117,20 +117,20 @@ def CatchException(f):
     """
 
     @wraps(f)
-    def decorated(main_input, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, *args, **kwargs):
+    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT=-1):
         try:
-            yield from f(main_input, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, *args, **kwargs)
+            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
         except Exception as e:
             from check_proxy import check_proxy
             from toolbox import get_conf
             proxies, = get_conf('proxies')
             tb_str = '```\n' + trimmed_format_exc() + '```'
-            if len(chatbot_with_cookie) == 0:
+            if len(chatbot) == 0:
-                chatbot_with_cookie.clear()
+                chatbot.clear()
-                chatbot_with_cookie.append(["插件调度异常", "异常原因"])
+                chatbot.append(["插件调度异常", "异常原因"])
-            chatbot_with_cookie[-1] = (chatbot_with_cookie[-1][0],
+            chatbot[-1] = (chatbot[-1][0],
                            f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
-            yield from update_ui(chatbot=chatbot_with_cookie, history=history, msg=f'异常 {e}') # 刷新界面
+            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
     return decorated
 
 
@@ -196,10 +196,11 @@ def write_results_to_file(history, file_name=None):
     import time
     if file_name is None:
         # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-        file_name = 'GPT-Report-' + gen_time_str() + '.md'
+        file_name = 'chatGPT分析报告' + \
+            time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
     os.makedirs('./gpt_log/', exist_ok=True)
     with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        f.write('# GPT-Academic Report\n')
+        f.write('# chatGPT 分析报告\n')
         for i, content in enumerate(history):
             try:    
                 if type(content) != str: content = str(content)
@@ -218,37 +219,6 @@ def write_results_to_file(history, file_name=None):
     return res
 
 
-def write_history_to_file(history, file_basename=None, file_fullname=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_fullname is None:
-        if file_basename is not None:
-            file_fullname = os.path.join(get_log_folder(), file_basename)
-        else:
-            file_fullname = os.path.join(get_log_folder(), f'GPT-Academic-{gen_time_str()}.md')
-    os.makedirs(os.path.dirname(file_fullname), exist_ok=True)
-    with open(file_fullname, 'w', encoding='utf8') as f:
-        f.write('# GPT-Academic Report\n')
-        for i, content in enumerate(history):
-            try:    
-                if type(content) != str: content = str(content)
-            except:
-                continue
-            if i % 2 == 0:
-                f.write('## ')
-            try:
-                f.write(content)
-            except:
-                # remove everything that cannot be handled by utf8
-                f.write(content.encode('utf-8', 'ignore').decode())
-            f.write('\n\n')
-    res = os.path.abspath(file_fullname)
-    return res
-
-
 def regular_txt_to_markdown(text):
     """
     将普通文本转换为Markdown格式的文本。
@@ -496,7 +466,7 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
     # 将文件复制一份到下载区
     import shutil
     if rename_file is None: rename_file = f'{gen_time_str()}-{os.path.basename(file)}'
-    new_path = os.path.join(get_log_folder(), rename_file)
+    new_path = os.path.join(f'./gpt_log/', rename_file)
     # 如果已经存在，先删除
     if os.path.exists(new_path) and not os.path.samefile(new_path, file): os.remove(new_path)
     # 把文件复制过去
@@ -507,10 +477,6 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
         else: current = []
         chatbot._cookies.update({'file_to_promote': [new_path] + current})
 
-def disable_auto_promotion(chatbot):
-    chatbot._cookies.update({'file_to_promote': []})
-    return
-
 def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
     """
     当文件被上传时的回调函数
@@ -526,7 +492,7 @@ def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
         shutil.rmtree('./private_upload/')
     except:
         pass
-    time_tag = gen_time_str()
+    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
     os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
     err_msg = ''
     for file in files:
@@ -572,11 +538,7 @@ def load_chat_cookies():
     return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
 
 def is_openai_api_key(key):
-    CUSTOM_API_KEY_PATTERN, = get_conf('CUSTOM_API_KEY_PATTERN')
+    API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
-    if len(CUSTOM_API_KEY_PATTERN) != 0:
-        API_MATCH_ORIGINAL = re.match(CUSTOM_API_KEY_PATTERN, key)
-    else:
-        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
     return bool(API_MATCH_ORIGINAL)
 
 def is_azure_api_key(key):
@@ -632,7 +594,7 @@ def select_api_key(keys, llm_model):
             if is_azure_api_key(k): avail_key_list.append(k)
 
     if len(avail_key_list) == 0:
-        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
+        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure和api2d请求源）")
 
     api_key = random.choice(avail_key_list) # 随机负载均衡
     return api_key
@@ -708,14 +670,13 @@ def read_single_conf_with_lru_cache(arg):
 
     # 在读取API_KEY时，检查一下是不是忘了改config
     if arg == 'API_KEY':
-        print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
+        print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,api2d-key3\"")
         print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
         if is_any_api_key(r):
             print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
         else:
-            print亮红( "[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
+            print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。")
     if arg == 'proxies':
-        if not read_single_conf_with_lru_cache('USE_PROXY'): r = None   # 检查USE_PROXY，防止proxies单独起作用
         if r is None:
             print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
         else:
@@ -724,7 +685,6 @@ def read_single_conf_with_lru_cache(arg):
     return r
 
 
-@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
     res = []
@@ -883,7 +843,8 @@ def zip_folder(source_folder, dest_folder, zip_name):
     print(f"Zip file created at {zip_file}")
 
 def zip_result(folder):
-    t = gen_time_str()
+    import time
+    t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
     zip_folder(folder, './gpt_log/', f'{t}-result.zip')
     return pj('./gpt_log/', f'{t}-result.zip')
 
@@ -891,11 +852,6 @@ def gen_time_str():
     import time
     return time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
 
-def get_log_folder(user='default', plugin_name='shared'):
-    _dir = os.path.join(os.path.dirname(__file__), 'gpt_log', user, plugin_name)
-    if not os.path.exists(_dir): os.makedirs(_dir)
-    return _dir
-
 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
@@ -927,114 +883,4 @@ def objload(file='objdump.tmp'):
         return
     with open(file, 'rb') as f:
         return pickle.load(f)
-    
+    
-def Singleton(cls):
-    """
-    一个单实例装饰器
-    """
-    _instance = {}
- 
-    def _singleton(*args, **kargs):
-        if cls not in _instance:
-            _instance[cls] = cls(*args, **kargs)
-        return _instance[cls]
- 
-    return _singleton
-
-"""
-========================================================================
-第四部分
-接驳虚空终端:
-    - set_conf:                     在运行过程中动态地修改配置
-    - set_multi_conf:               在运行过程中动态地修改多个配置
-    - get_plugin_handle:            获取插件的句柄
-    - get_plugin_default_kwargs:    获取插件的默认参数
-    - get_chat_handle:              获取简单聊天的句柄
-    - get_chat_default_kwargs:      获取简单聊天的默认参数
-========================================================================
-"""
-
-def set_conf(key, value):
-    from toolbox import read_single_conf_with_lru_cache, get_conf
-    read_single_conf_with_lru_cache.cache_clear()
-    get_conf.cache_clear()
-    os.environ[key] = str(value)
-    altered, = get_conf(key)
-    return altered
-
-def set_multi_conf(dic):
-    for k, v in dic.items(): set_conf(k, v)
-    return
-
-def get_plugin_handle(plugin_name):
-    """
-    e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'
-    """
-    import importlib
-    assert '->' in plugin_name, \
-        "Example of plugin_name: crazy_functions.批量Markdown翻译->Markdown翻译指定语言"
-    module, fn_name = plugin_name.split('->')
-    f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
-    return f_hot_reload
-
-def get_chat_handle():
-    """
-    """
-    from request_llm.bridge_all import predict_no_ui_long_connection
-    return predict_no_ui_long_connection
-
-def get_plugin_default_kwargs():
-    """
-    """
-    from toolbox import get_conf, ChatBotWithCookies
-
-    WEB_PORT, LLM_MODEL, API_KEY = \
-        get_conf('WEB_PORT', 'LLM_MODEL', 'API_KEY')
-
-    llm_kwargs = {
-        'api_key': API_KEY,
-        'llm_model': LLM_MODEL,
-        'top_p':1.0, 
-        'max_length': None,
-        'temperature':1.0,
-    }
-    chatbot = ChatBotWithCookies(llm_kwargs)
-
-    # txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
-    default_plugin_kwargs = {
-        "main_input": "./README.md",
-        "llm_kwargs": llm_kwargs,
-        "plugin_kwargs": {},
-        "chatbot_with_cookie": chatbot,
-        "history": [],
-        "system_prompt": "You are a good AI.", 
-        "web_port": WEB_PORT
-    }
-    return default_plugin_kwargs
-
-def get_chat_default_kwargs():
-    """
-    """
-    from toolbox import get_conf
-
-    LLM_MODEL, API_KEY = get_conf('LLM_MODEL', 'API_KEY')
-
-    llm_kwargs = {
-        'api_key': API_KEY,
-        'llm_model': LLM_MODEL,
-        'top_p':1.0, 
-        'max_length': None,
-        'temperature':1.0,
-    }
-
-    default_chat_kwargs = {
-        "inputs": "Hello there, are you ready?",
-        "llm_kwargs": llm_kwargs,
-        "history": [],
-        "sys_prompt": "You are AI assistant",
-        "observe_window": None,
-        "console_slience": False,
-    }
-
-    return default_chat_kwargs
-

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.47,
+  "version": 3.46,
   "show_feature": true,
-  "new_feature": "优化一键升级 <-> 提高arxiv翻译速度和成功率 <-> 支持自定义APIKEY格式 <-> 临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持"
+  "new_feature": "临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
-}
+}