# fix com_zhipuglm.py illegal temperature problem (#1687)

* Update com_zhipuglm.py

# fix 用户在使用 zhipuai 界面时遇到了关于温度参数的非法参数错误

README.md

@@ -1,7 +1,6 @@
 > [!IMPORTANT]
+> 2024.3.11: 恭迎Claude3和Moonshot，全力支持Qwen、GLM、DeepseekCoder等中文大语言模型！  
 > 2024.1.18: 更新3.70版本，支持Mermaid绘图库（让大模型绘制脑图）  
-> 2024.1.17: 恭迎GLM4，全力支持Qwen、GLM、DeepseekCoder等国内中文大语言基座模型！  
-> 2024.1.17: 某些依赖包尚不兼容python 3.12，推荐python 3.11。  
 > 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
 
 <br>
@@ -253,8 +252,7 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 # Advanced Usage
 ### I：自定义新的便捷按钮（学术快捷键）
 
-任意文本编辑器打开`core_functional.py`，添加如下条目，然后重启程序。（如果按钮已存在，那么可以直接修改（前缀、后缀都已支持热修改），无需重启程序即可生效。）
-例如
+现在已可以通过UI中的`界面外观`菜单中的`自定义菜单`添加新的便捷按钮。如果需要在代码中定义，请使用任意文本编辑器打开`core_functional.py`，添加如下条目即可：
 
 ```python
 "超级英译中": {

check_proxy.py

@@ -47,7 +47,7 @@ def backup_and_download(current_version, remote_version):
     shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
     proxies = get_conf('proxies')
     try:    r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
-    except: r = requests.get('https://public.gpt-academic.top/publish/master.zip', proxies=proxies, stream=True)
+    except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
     zip_file_path = backup_dir+'/master.zip'
     with open(zip_file_path, 'wb+') as f:
         f.write(r.content)
@@ -113,7 +113,7 @@ def auto_update(raise_error=False):
         import json
         proxies = get_conf('proxies')
         try:    response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
-        except: response = requests.get("https://public.gpt-academic.top/publish/version", proxies=proxies, timeout=5)
+        except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
         remote_json_data = json.loads(response.text)
         remote_version = remote_json_data['version']
         if remote_json_data["show_feature"]:

config.py

@@ -30,7 +30,33 @@ if USE_PROXY:
 else:
     proxies = None
 
-# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
+# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
+LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
+                    "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
+                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo",
+                    "gemini-pro", "chatglm3"
+                    ]
+# --- --- --- ---
+# P.S. 其他可用的模型还包括
+# AVAIL_LLM_MODELS = [
+#   "qianfan", "deepseekcoder",
+#   "spark", "sparkv2", "sparkv3", "sparkv3.5",
+#   "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
+#   "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
+#   "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125"
+#   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
+#   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
+#   "yi-34b-chat-0205", "yi-34b-chat-200k"
+# ]
+# --- --- --- ---
+# 此外，为了更灵活地接入one-api多模型管理界面，您还可以在接入one-api时，
+# 使用"one-api-*"前缀直接使用非标准方式接入的模型，例如
+# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)"]
+# --- --- --- ---
+
+
+# --------------- 以下配置可以优化体验 ---------------
 
 # 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
 # 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
@@ -85,20 +111,6 @@ MAX_RETRY = 2
 DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
 
 
-# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
-LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-preview",
-                    "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
-                    "gemini-pro", "chatglm3", "claude-2", "zhipuai"]
-# P.S. 其他可用的模型还包括 [
-# "moss", "qwen-turbo", "qwen-plus", "qwen-max"
-# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", 
-# "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
-# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
-# ]
-
-
 # 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
 MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
 
@@ -127,6 +139,7 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
 LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
 LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
 
+
 # 设置gradio的并行线程数（不需要修改）
 CONCURRENT_COUNT = 100
 
@@ -144,7 +157,8 @@ ADD_WAIFU = False
 AUTHENTICATION = []
 
 
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
+# 如果需要在二级路径下运行（常规情况下，不要修改!!）
+# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
 CUSTOM_PATH = "/"
 
 
@@ -172,14 +186,8 @@ AZURE_ENGINE = "填入你亲手写的部署名"            # 读 docs\use_azure.
 AZURE_CFG_ARRAY = {}
 
 
-# 使用Newbing (不推荐使用，未来将删除)
-NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
-NEWBING_COOKIES = """
-put your new bing cookies here
-"""
-
-
-# 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
+# 阿里云实时语音识别 配置难度较高
+# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
 ENABLE_AUDIO = False
 ALIYUN_TOKEN=""     # 例如 f37f30e0f9934c34a992f6f64f7eba4f
 ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
@@ -195,19 +203,26 @@ XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 # 接入智谱大模型
 ZHIPUAI_API_KEY = ""
-ZHIPUAI_MODEL = "glm-4" # 可选 "glm-3-turbo" "glm-4"
-
-
-# # 火山引擎YUNQUE大模型
-# YUNQUE_SECRET_KEY = ""
-# YUNQUE_ACCESS_KEY = ""
-# YUNQUE_MODEL = ""
+ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
 
 
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 
 
+# 月之暗面 API KEY
+MOONSHOT_API_KEY = ""
+
+
+# 零一万物(Yi Model) API KEY
+YIMODEL_API_KEY = ""
+
+
+# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
+MATHPIX_APPID = ""
+MATHPIX_APPKEY = ""
+
+
 # 自定义API KEY格式
 CUSTOM_API_KEY_PATTERN = ""
 
@@ -261,7 +276,11 @@ PLUGIN_HOT_RELOAD = False
 # 自定义按钮的最大数量限制
 NUM_CUSTOM_BASIC_BTN = 4
 
+
+
 """
+--------------- 配置关联关系说明 ---------------
+
 在线大模型配置关联关系示意图
 │
 ├── "gpt-3.5-turbo" 等openai模型
@@ -285,7 +304,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── XFYUN_API_SECRET
 │   └── XFYUN_API_KEY
 │
-├── "claude-1-100k" 等claude模型
+├── "claude-3-opus-20240229" 等claude模型
 │   └── ANTHROPIC_API_KEY
 │
 ├── "stack-claude"
@@ -297,9 +316,11 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── BAIDU_CLOUD_API_KEY
 │   └── BAIDU_CLOUD_SECRET_KEY
 │
-├── "zhipuai" 智谱AI大模型chatglm_turbo
-│   ├── ZHIPUAI_API_KEY
-│   └── ZHIPUAI_MODEL
+├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
+│   └── ZHIPUAI_API_KEY
+│
+├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
+│   └── YIMODEL_API_KEY
 │
 ├── "qwen-turbo" 等通义千问大模型
 │   └──  DASHSCOPE_API_KEY
@@ -307,9 +328,10 @@ NUM_CUSTOM_BASIC_BTN = 4
 ├── "Gemini"
 │   └──  GEMINI_API_KEY
 │
-└── "newbing" Newbing接口不再稳定，不推荐使用
-    ├── NEWBING_STYLE
-    └── NEWBING_COOKIES
+└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
+    ├── AVAIL_LLM_MODELS
+    ├── API_KEY
+    └── API_URL_REDIRECT
 
 
 本地大模型示意图
@@ -351,6 +373,9 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   └── ALIYUN_SECRET
 │
 └── PDF文档精准解析
-    └── GROBID_URLS
+    ├── GROBID_URLS
+    ├── MATHPIX_APPID
+    └── MATHPIX_APPKEY
+
 
 """

core_functional.py

@@ -38,12 +38,12 @@ def get_core_functions():
 
         "总结绘制脑图": {
             # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-            "Prefix":   r"",
+            "Prefix":   '''"""\n\n''',
             # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
             "Suffix":
                 # dedent() 函数用于去除多行字符串的缩进
-                dedent("\n"+r'''
-                    ==============================
+                dedent("\n\n"+r'''
+                    """
 
                     使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
 
@@ -57,7 +57,7 @@ def get_core_functions():
                         C --> |"箭头名2"| F["节点名6"]
                     ```
 
-                    警告：
+                    注意：
                     （1）使用中文
                     （2）节点名字使用引号包裹，如["Laptop"]
                     （3）`|` 和 `"`之间不要存在空格

crazy_functional.py

@@ -70,11 +70,11 @@ def get_crazy_functions():
             "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
             "Function": HotReload(清除缓存),
         },
-        "生成多种Mermaid图表(从当前对话或文件(.pdf/.md)中生产图表）": {
+        "生成多种Mermaid图表(从当前对话或路径(.pdf/.md/.docx)中生产图表）": {
             "Group": "对话",
             "Color": "stop",
             "AsButton": False,
-            "Info" : "基于当前对话或PDF生成多种Mermaid图表,图表类型由模型判断",
+            "Info" : "基于当前对话或文件生成多种Mermaid图表,图表类型由模型判断",
             "Function": HotReload(生成多种Mermaid图表),
             "AdvancedArgs": True,
             "ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
@@ -532,8 +532,9 @@ def get_crazy_functions():
         print("Load function plugin failed")
 
     try:
-        from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
-        from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
+        from crazy_functions.Latex输出PDF import Latex英文纠错加PDF对比
+        from crazy_functions.Latex输出PDF import Latex翻译中文并重新编译PDF
+        from crazy_functions.Latex输出PDF import PDF翻译中文并重新编译PDF
 
         function_plugins.update(
             {
@@ -550,9 +551,9 @@ def get_crazy_functions():
                     "Color": "stop",
                     "AsButton": False,
                     "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
                     "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
                     "Function": HotReload(Latex翻译中文并重新编译PDF),
                 },
@@ -561,11 +562,22 @@ def get_crazy_functions():
                     "Color": "stop",
                     "AsButton": False,
                     "AdvancedArgs": True,
-                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
-                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
-                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
                     "Info": "本地Latex论文精细翻译 | 输入参数是路径",
                     "Function": HotReload(Latex翻译中文并重新编译PDF),
+                },
+                "PDF翻译中文并重新编译PDF（上传PDF）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": r"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                                    r"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                                    r'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
+                    "Function": HotReload(PDF翻译中文并重新编译PDF)
                 }
             }
         )

crazy_functions/CodeInterpreter.py

@@ -1,232 +0,0 @@
-from collections.abc import Callable, Iterable, Mapping
-from typing import Any
-from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc
-from toolbox import promote_file_to_downloadzone, get_log_folder
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import input_clipping, try_install_deps
-from multiprocessing import Process, Pipe
-import os
-import time
-
-templete = """
-```python
-import ...  # Put dependencies here, e.g. import numpy as np
-
-class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
-
-    def run(self, path):    # The name of the function must be `run`, it takes only a positional argument.
-        # rewrite the function you have just written here 
-        ...
-        return generated_file_path
-```
-"""
-
-def inspect_dependency(chatbot, history):
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    return True
-
-def get_code_block(reply):
-    import re
-    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
-    matches = re.findall(pattern, reply) # find all code blocks in text
-    if len(matches) == 1: 
-        return matches[0].strip('python') #  code block
-    for match in matches:
-        if 'class TerminalFunction' in match:
-            return match.strip('python') #  code block
-    raise RuntimeError("GPT is not generating proper code.")
-
-def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
-    # 输入
-    prompt_compose = [
-        f'Your job:\n'
-        f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
-        f"2. You should write this function to perform following task: " + txt + "\n",
-        f"3. Wrap the output python function with markdown codeblock."
-    ]
-    i_say = "".join(prompt_compose)
-    demo = []
-
-    # 第一步
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, 
-        sys_prompt= r"You are a programmer."
-    )
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-    # 第二步
-    prompt_compose = [
-        "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
-        templete
-    ]
-    i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=inputs_show_user, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt= r"You are a programmer."
-    )
-    code_to_return = gpt_say
-    history.extend([i_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-    
-    # # 第三步
-    # i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
-    # i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=inputs_show_user, 
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-    #     sys_prompt= r"You are a programmer."
-    # )
-    # # # 第三步  
-    # i_say = "Show me how to use `pip` to install packages to run the code above. "
-    # i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
-    # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=i_say, inputs_show_user=i_say, 
-    #     llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-    #     sys_prompt= r"You are a programmer."
-    # )
-    installation_advance = ""
-    
-    return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
-
-def make_module(code):
-    module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
-    with open(f'{get_log_folder()}/{module_file}.py', 'w', encoding='utf8') as f:
-        f.write(code)
-
-    def get_class_name(class_string):
-        import re
-        # Use regex to extract the class name
-        class_name = re.search(r'class (\w+)\(', class_string).group(1)
-        return class_name
-
-    class_name = get_class_name(code)
-    return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}"
-
-def init_module_instance(module):
-    import importlib
-    module_, class_ = module.split('->')
-    init_f = getattr(importlib.import_module(module_), class_)
-    return init_f()
-
-def for_immediate_show_off_when_possible(file_type, fp, chatbot):
-    if file_type in ['png', 'jpg']:
-        image_path = os.path.abspath(fp)
-        chatbot.append(['这是一张图片, 展示如下:',  
-            f'本地文件地址: <br/>`{image_path}`<br/>'+
-            f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
-        ])
-    return chatbot
-
-def subprocess_worker(instance, file_path, return_dict):
-    return_dict['result'] = instance.run(file_path)
-
-def have_any_recent_upload_files(chatbot):
-    _5min = 5 * 60
-    if not chatbot: return False    # chatbot is None
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    if not most_recent_uploaded: return False   # most_recent_uploaded is None
-    if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
-    else: return False  # most_recent_uploaded is too old
-
-def get_recent_file_prompt_support(chatbot):
-    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
-    path = most_recent_uploaded['path']
-    return path
-
-@CatchException
-def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    user_request    当前用户的请求信息（IP地址等）
-    """
-    raise NotImplementedError
-
-    # 清空历史，以免输入溢出
-    history = []; clear_file_downloadzone(chatbot)
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..."
-    ])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if have_any_recent_upload_files(chatbot):
-        file_path = get_recent_file_prompt_support(chatbot)
-    else:
-        chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 读取文件
-    if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files")
-    recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None)
-    file_path = recently_uploaded_files[-1]
-    file_type = file_path.split('.')[-1]
-
-    # 粗心检查
-    if is_the_upload_folder(txt):
-        chatbot.append([
-            "...",
-            f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）"
-        ])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # 开始干正事
-    for j in range(5):  # 最多重试5次
-        try:
-            code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
-                yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
-            code = get_code_block(code)
-            res = make_module(code)
-            instance = init_module_instance(res)
-            break
-        except Exception as e:
-            chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 代码生成结束, 开始执行
-    try:
-        import multiprocessing
-        manager = multiprocessing.Manager()
-        return_dict = manager.dict()
-
-        p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict))
-        # only has 10 seconds to run
-        p.start(); p.join(timeout=10)
-        if p.is_alive(): p.terminate(); p.join()
-        p.close()
-        res = return_dict['result']
-        # res = instance.run(file_path)
-    except Exception as e:
-        chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
-        # chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 顺利完成，收尾
-    res = str(res)
-    if os.path.exists(res):
-        chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
-        new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-    else:
-        chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新   
-
-"""
-测试：
-    裁剪图像，保留下半部分
-    交换图像的蓝色通道和红色通道
-    将图像转为灰度图像
-    将csv文件转excel表格
-"""

crazy_functions/Latex全文润色.py

@@ -81,8 +81,8 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
     #  <-------- 多线程润色开始 ---------->
     if language == 'en':
         if mode == 'polish':
-            inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " + 
-                            "improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" + 
+            inputs_array = [r"Below is a section from an academic paper, polish this section to meet the academic standard, " +
+                            r"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         else:
             inputs_array = [r"Below is a section from an academic paper, proofread this section." +
@@ -93,10 +93,10 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
     elif language == 'zh':
         if mode == 'polish':
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+            inputs_array = [r"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         else:
-            inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+            inputs_array = [r"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
                             f"\n\n{frag}" for frag in pfg.sp_file_contents]
         inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
         sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]

crazy_functions/Latex输出PDF.py

@@ -0,0 +1,538 @@
+from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
+from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
+from functools import partial
+import glob, os, requests, time, json, tarfile
+
+pj = os.path.join
+ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
+def switch_prompt(pfg, mode, more_requirement):
+    """
+    Generate prompts and system prompts based on the mode for proofreading or translating.
+    Args:
+    - pfg: Proofreader or Translator instance.
+    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
+
+    Returns:
+    - inputs_array: A list of strings containing prompts for users to respond to.
+    - sys_prompt_array: A list of strings containing prompts for system prompts.
+    """
+    n_split = len(pfg.sp_file_contents)
+    if mode == 'proofread_en':
+        inputs_array = [r"Below is a section from an academic paper, proofread this section." +
+                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
+                        r"Answer me only with the revised text:" +
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
+    elif mode == 'translate_zh':
+        inputs_array = [
+            r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
+            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
+            r"Answer me only with the translated text:" +
+            f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
+    else:
+        assert False, "未知指令"
+    return inputs_array, sys_prompt_array
+
+
+def desend_to_extracted_folder_if_exist(project_folder):
+    """
+    Descend into the extracted folder if it exists, otherwise return the original folder.
+
+    Args:
+    - project_folder: A string specifying the folder path.
+
+    Returns:
+    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
+    """
+    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
+    if len(maybe_dir) == 0: return project_folder
+    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
+    return project_folder
+
+
+def move_project(project_folder, arxiv_id=None):
+    """
+    Create a new work folder and copy the project folder to it.
+
+    Args:
+    - project_folder: A string specifying the folder path of the project.
+
+    Returns:
+    - A string specifying the path to the new work folder.
+    """
+    import shutil, time
+    time.sleep(2)  # avoid time string conflict
+    if arxiv_id is not None:
+        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
+    else:
+        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
+    try:
+        shutil.rmtree(new_workfolder)
+    except:
+        pass
+
+    # align subfolder if there is a folder wrapper
+    items = glob.glob(pj(project_folder, '*'))
+    items = [item for item in items if os.path.basename(item) != '__MACOSX']
+    if len(glob.glob(pj(project_folder, '*.tex'))) == 0 and len(items) == 1:
+        if os.path.isdir(items[0]): project_folder = items[0]
+
+    shutil.copytree(src=project_folder, dst=new_workfolder)
+    return new_workfolder
+
+
+def arxiv_download(chatbot, history, txt, allow_cache=True):
+    def check_cached_translation_pdf(arxiv_id):
+        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
+        if not os.path.exists(translation_dir):
+            os.makedirs(translation_dir)
+        target_file = pj(translation_dir, 'translate_zh.pdf')
+        if os.path.exists(target_file):
+            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
+            target_file_compare = pj(translation_dir, 'comparison.pdf')
+            if os.path.exists(target_file_compare):
+                promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
+            return target_file
+        return False
+
+    def is_float(s):
+        try:
+            float(s)
+            return True
+        except ValueError:
+            return False
+
+    if ('.' in txt) and ('/' not in txt) and is_float(txt):  # is arxiv ID
+        txt = 'https://arxiv.org/abs/' + txt.strip()
+    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]):  # is arxiv ID
+        txt = 'https://arxiv.org/abs/' + txt[:10]
+
+    if not txt.startswith('https://arxiv.org'):
+        return txt, None    # 是本地文件，跳过下载
+
+    # <-------------- inspect format ------------->
+    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
+    yield from update_ui(chatbot=chatbot, history=history)
+    time.sleep(1)  # 刷新界面
+
+    url_ = txt  # https://arxiv.org/abs/1707.06690
+    if not txt.startswith('https://arxiv.org/abs/'):
+        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
+        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history)  # 刷新界面
+        return msg, None
+    # <-------------- set format ------------->
+    arxiv_id = url_.split('/abs/')[-1]
+    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
+    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
+    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
+
+    url_tar = url_.replace('/abs/', '/e-print/')
+    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
+    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
+    os.makedirs(translation_dir, exist_ok=True)
+
+    # <-------------- download arxiv source file ------------->
+    dst = pj(translation_dir, arxiv_id + '.tar')
+    if os.path.exists(dst):
+        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
+    else:
+        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
+        proxies = get_conf('proxies')
+        r = requests.get(url_tar, proxies=proxies)
+        with open(dst, 'wb+') as f:
+            f.write(r.content)
+    # <-------------- extract file ------------->
+    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
+    from toolbox import extract_archive
+    extract_archive(file_path=dst, dest_dir=extract_dst)
+    return extract_dst, arxiv_id
+
+
+def pdf2tex_project(pdf_file_path):
+    # Mathpix API credentials
+    app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
+    headers = {"app_id": app_id, "app_key": app_key}
+
+    # Step 1: Send PDF file for processing
+    options = {
+        "conversion_formats": {"tex.zip": True},
+        "math_inline_delimiters": ["$", "$"],
+        "rm_spaces": True
+    }
+
+    response = requests.post(url="https://api.mathpix.com/v3/pdf",
+                             headers=headers,
+                             data={"options_json": json.dumps(options)},
+                             files={"file": open(pdf_file_path, "rb")})
+
+    if response.ok:
+        pdf_id = response.json()["pdf_id"]
+        print(f"PDF processing initiated. PDF ID: {pdf_id}")
+
+        # Step 2: Check processing status
+        while True:
+            conversion_response = requests.get(f"https://api.mathpix.com/v3/pdf/{pdf_id}", headers=headers)
+            conversion_data = conversion_response.json()
+
+            if conversion_data["status"] == "completed":
+                print("PDF processing completed.")
+                break
+            elif conversion_data["status"] == "error":
+                print("Error occurred during processing.")
+            else:
+                print(f"Processing status: {conversion_data['status']}")
+                time.sleep(5)  # wait for a few seconds before checking again
+
+        # Step 3: Save results to local files
+        output_dir = os.path.join(os.path.dirname(pdf_file_path), 'mathpix_output')
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+        url = f"https://api.mathpix.com/v3/pdf/{pdf_id}.tex"
+        response = requests.get(url, headers=headers)
+        file_name_wo_dot = '_'.join(os.path.basename(pdf_file_path).split('.')[:-1])
+        output_name = f"{file_name_wo_dot}.tex.zip"
+        output_path = os.path.join(output_dir, output_name)
+        with open(output_path, "wb") as output_file:
+            output_file.write(response.content)
+        print(f"tex.zip file saved at: {output_path}")
+
+        import zipfile
+        unzip_dir = os.path.join(output_dir, file_name_wo_dot)
+        with zipfile.ZipFile(output_path, 'r') as zip_ref:
+            zip_ref.extractall(unzip_dir)
+
+        return unzip_dir
+
+    else:
+        print(f"Error sending PDF for processing. Status code: {response.status_code}")
+        return None
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+
+@CatchException
+def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    # <-------------- information about this plugin ------------->
+    chatbot.append(["函数插件功能？",
+                    "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    history = []
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- if is a zip/tar file ------------->
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+    # <-------------- move latex project away from temp folder ------------->
+    project_folder = move_project(project_folder, arxiv_id=None)
+
+    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
+        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                       chatbot, history, system_prompt, mode='proofread_en',
+                                       switch_prompt=_switch_prompt_)
+
+    # <-------------- compile PDF ------------->
+    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                   main_file_modified='merge_proofread_en',
+                                   work_folder_original=project_folder, work_folder_modified=project_folder,
+                                   work_folder=project_folder)
+
+    # <-------------- zip PDF ------------->
+    zip_res = zip_result(project_folder)
+    if success:
+        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    else:
+        chatbot.append((f"失败了",
+                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+    # <-------------- we are done ------------->
+    return success
+
+
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+@CatchException
+def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    # <-------------- information about this plugin ------------->
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    no_cache = more_req.startswith("--no-cache")
+    if no_cache: more_req.lstrip("--no-cache")
+    allow_cache = not no_cache
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    history = []
+    try:
+        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
+    except tarfile.ReadError as e:
+        yield from update_ui_lastest_msg(
+            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
+            chatbot=chatbot, history=history)
+        return
+
+    if txt.endswith('.pdf'):
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现已经存在翻译好的PDF文档")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- if is a zip/tar file ------------->
+    project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+    # <-------------- move latex project away from temp folder ------------->
+    project_folder = move_project(project_folder, arxiv_id)
+
+    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
+        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                       chatbot, history, system_prompt, mode='translate_zh',
+                                       switch_prompt=_switch_prompt_)
+
+    # <-------------- compile PDF ------------->
+    success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                   main_file_modified='merge_translate_zh', mode='translate_zh',
+                                   work_folder_original=project_folder, work_folder_modified=project_folder,
+                                   work_folder=project_folder)
+
+    # <-------------- zip PDF ------------->
+    zip_res = zip_result(project_folder)
+    if success:
+        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+    else:
+        chatbot.append((f"失败了",
+                        '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
+        yield from update_ui(chatbot=chatbot, history=history);
+        time.sleep(1)  # 刷新界面
+        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+    # <-------------- we are done ------------->
+    return success
+
+
+#  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 插件主程序3  =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+@CatchException
+def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # <-------------- information about this plugin ------------->
+    chatbot.append([
+        "函数插件功能？",
+        "将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # <-------------- more requirements ------------->
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    more_req = plugin_kwargs.get("advanced_arg", "")
+    no_cache = more_req.startswith("--no-cache")
+    if no_cache: more_req.lstrip("--no-cache")
+    allow_cache = not no_cache
+    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
+
+    # <-------------- check deps ------------->
+    try:
+        import glob, os, time, subprocess
+        subprocess.Popen(['pdflatex', '-version'])
+        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
+    except Exception as e:
+        chatbot.append([f"解析项目: {txt}",
+                        f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # <-------------- clear history and read input ------------->
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
+    if len(file_manifest) == 0:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    if len(file_manifest) != 1:
+        report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"不支持同时处理多个pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
+    if len(app_id) == 0 or len(app_key) == 0:
+        report_exception(chatbot, history, a="缺失 MATHPIX_APPID 和 MATHPIX_APPKEY。", b=f"请配置 MATHPIX_APPID 和 MATHPIX_APPKEY")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    hash_tag = map_file_to_sha256(file_manifest[0])
+
+    # <-------------- check repeated pdf ------------->
+    chatbot.append([f"检查PDF是否被重复上传", "正在检查..."])
+    yield from update_ui(chatbot=chatbot, history=history)
+    repeat, project_folder = check_repeat_upload(file_manifest[0], hash_tag)
+
+    except_flag = False
+
+    if repeat:
+        yield from update_ui_lastest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
+
+        try:
+            trans_html_file = [f for f in glob.glob(f'{project_folder}/**/*.trans.html', recursive=True)][0]
+            promote_file_to_downloadzone(trans_html_file, rename_file=None, chatbot=chatbot)
+
+            translate_pdf = [f for f in glob.glob(f'{project_folder}/**/merge_translate_zh.pdf', recursive=True)][0]
+            promote_file_to_downloadzone(translate_pdf, rename_file=None, chatbot=chatbot)
+
+            comparison_pdf = [f for f in glob.glob(f'{project_folder}/**/comparison.pdf', recursive=True)][0]
+            promote_file_to_downloadzone(comparison_pdf, rename_file=None, chatbot=chatbot)        
+
+            zip_res = zip_result(project_folder)
+            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+            return True
+        
+        except:
+            report_exception(chatbot, history, b=f"发现重复上传，但是无法找到相关文件")
+            yield from update_ui(chatbot=chatbot, history=history)
+            
+            chatbot.append([f"没有相关文件", '尝试重新翻译PDF...'])
+            yield from update_ui(chatbot=chatbot, history=history)
+
+            except_flag = True
+            
+    
+    elif not repeat or except_flag:
+        yield from update_ui_lastest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
+
+        # <-------------- convert pdf into tex ------------->
+        chatbot.append([f"解析项目: {txt}", "正在将PDF转换为tex项目，请耐心等待..."])
+        yield from update_ui(chatbot=chatbot, history=history)
+        project_folder = pdf2tex_project(file_manifest[0])
+        if project_folder is None:
+            report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"PDF转换为tex项目失败")
+            yield from update_ui(chatbot=chatbot, history=history)
+            return False
+
+        # <-------------- translate latex file into Chinese ------------->
+        yield from update_ui_lastest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+        if len(file_manifest) == 0:
+            report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            return
+
+        # <-------------- if is a zip/tar file ------------->
+        project_folder = desend_to_extracted_folder_if_exist(project_folder)
+
+        # <-------------- move latex project away from temp folder ------------->
+        project_folder = move_project(project_folder)
+
+        # <-------------- set a hash tag for repeat-checking ------------->
+        with open(pj(project_folder, hash_tag + '.tag'), 'w') as f:
+            f.write(hash_tag)
+            f.close()
+
+
+        # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
+        if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
+            yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
+                                        chatbot, history, system_prompt, mode='translate_zh',
+                                        switch_prompt=_switch_prompt_)
+
+        # <-------------- compile PDF ------------->
+        yield from update_ui_lastest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
+        success = yield from 编译Latex(chatbot, history, main_file_original='merge',
+                                    main_file_modified='merge_translate_zh', mode='translate_zh',
+                                    work_folder_original=project_folder, work_folder_modified=project_folder,
+                                    work_folder=project_folder)
+
+        # <-------------- zip PDF ------------->
+        zip_res = zip_result(project_folder)
+        if success:
+            chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
+            yield from update_ui(chatbot=chatbot, history=history);
+            time.sleep(1)  # 刷新界面
+            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+        else:
+            chatbot.append((f"失败了",
+                            '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
+            yield from update_ui(chatbot=chatbot, history=history);
+            time.sleep(1)  # 刷新界面
+            promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
+
+        # <-------------- we are done ------------->
+        return success

crazy_functions/Latex输出PDF结果.py

@@ -1,313 +0,0 @@
-from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
-from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
-from functools import partial
-import glob, os, requests, time, tarfile
-pj = os.path.join
-ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-# 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
-def switch_prompt(pfg, mode, more_requirement):
-    """
-    Generate prompts and system prompts based on the mode for proofreading or translating.
-    Args:
-    - pfg: Proofreader or Translator instance.
-    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
-
-    Returns:
-    - inputs_array: A list of strings containing prompts for users to respond to.
-    - sys_prompt_array: A list of strings containing prompts for system prompts.
-    """
-    n_split = len(pfg.sp_file_contents)
-    if mode == 'proofread_en':
-        inputs_array = [r"Below is a section from an academic paper, proofread this section." + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
-                        r"Answer me only with the revised text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
-    elif mode == 'translate_zh':
-        inputs_array = [r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement + 
-                        r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + 
-                        r"Answer me only with the translated text:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
-    else:
-        assert False, "未知指令"
-    return inputs_array, sys_prompt_array
-
-def desend_to_extracted_folder_if_exist(project_folder):
-    """ 
-    Descend into the extracted folder if it exists, otherwise return the original folder.
-
-    Args:
-    - project_folder: A string specifying the folder path.
-
-    Returns:
-    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
-    """
-    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
-    if len(maybe_dir) == 0: return project_folder
-    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
-    return project_folder
-
-def move_project(project_folder, arxiv_id=None):
-    """ 
-    Create a new work folder and copy the project folder to it.
-
-    Args:
-    - project_folder: A string specifying the folder path of the project.
-
-    Returns:
-    - A string specifying the path to the new work folder.
-    """
-    import shutil, time
-    time.sleep(2)   # avoid time string conflict
-    if arxiv_id is not None:
-        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
-    else:
-        new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
-    try:
-        shutil.rmtree(new_workfolder)
-    except:
-        pass
-
-    # align subfolder if there is a folder wrapper
-    items = glob.glob(pj(project_folder,'*'))
-    items = [item for item in items if os.path.basename(item)!='__MACOSX']
-    if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:
-        if os.path.isdir(items[0]): project_folder = items[0]
-
-    shutil.copytree(src=project_folder, dst=new_workfolder)
-    return new_workfolder
-
-def arxiv_download(chatbot, history, txt, allow_cache=True):
-    def check_cached_translation_pdf(arxiv_id):
-        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
-        if not os.path.exists(translation_dir):
-            os.makedirs(translation_dir)
-        target_file = pj(translation_dir, 'translate_zh.pdf')
-        if os.path.exists(target_file):
-            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
-            target_file_compare = pj(translation_dir, 'comparison.pdf')
-            if os.path.exists(target_file_compare):
-                promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
-            return target_file
-        return False
-    def is_float(s):
-        try:
-            float(s)
-            return True
-        except ValueError:
-            return False
-    if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt.strip()
-    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
-        txt = 'https://arxiv.org/abs/' + txt[:10]
-    if not txt.startswith('https://arxiv.org'): 
-        return txt, None    # 是本地文件，跳过下载
-    
-    # <-------------- inspect format ------------->
-    chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...']) 
-    yield from update_ui(chatbot=chatbot, history=history)
-    time.sleep(1) # 刷新界面
-
-    url_ = txt   # https://arxiv.org/abs/1707.06690
-    if not txt.startswith('https://arxiv.org/abs/'): 
-        msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
-        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
-        return msg, None
-    # <-------------- set format ------------->
-    arxiv_id = url_.split('/abs/')[-1]
-    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
-    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
-    if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
-
-    url_tar = url_.replace('/abs/', '/e-print/')
-    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
-    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
-    os.makedirs(translation_dir, exist_ok=True)
-    
-    # <-------------- download arxiv source file ------------->
-    dst = pj(translation_dir, arxiv_id+'.tar')
-    if os.path.exists(dst):
-        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
-    else:
-        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
-        proxies = get_conf('proxies')
-        r = requests.get(url_tar, proxies=proxies)
-        with open(dst, 'wb+') as f:
-            f.write(r.content)
-    # <-------------- extract file ------------->
-    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
-    from toolbox import extract_archive
-    extract_archive(file_path=dst, dest_dir=extract_dst)
-    return extract_dst, arxiv_id
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
-
-
-@CatchException
-def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([ "函数插件功能？",
-        "对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id=None)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-    
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-    # <-------------- we are done ------------->
-    return success
-
-# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
-
-@CatchException
-def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-    # <-------------- information about this plugin ------------->
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # <-------------- more requirements ------------->
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    more_req = plugin_kwargs.get("advanced_arg", "")
-    no_cache = more_req.startswith("--no-cache")
-    if no_cache: more_req.lstrip("--no-cache")
-    allow_cache = not no_cache
-    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
-
-    # <-------------- check deps ------------->
-    try:
-        import glob, os, time, subprocess
-        subprocess.Popen(['pdflatex', '-version'])
-        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
-    except Exception as e:
-        chatbot.append([ f"解析项目: {txt}",
-            f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- clear history and read input ------------->
-    history = []
-    try:
-        txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
-    except tarfile.ReadError as e:
-        yield from update_ui_lastest_msg(
-            "无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。", 
-            chatbot=chatbot, history=history)
-        return
-
-    if txt.endswith('.pdf'):
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-
-    # <-------------- if is a zip/tar file ------------->
-    project_folder = desend_to_extracted_folder_if_exist(project_folder)
-
-
-    # <-------------- move latex project away from temp folder ------------->
-    project_folder = move_project(project_folder, arxiv_id)
-
-
-    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
-    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
-        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, 
-                                chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)
-
-
-    # <-------------- compile PDF ------------->
-    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh', 
-                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
-
-    # <-------------- zip PDF ------------->
-    zip_res = zip_result(project_folder)
-    if success:
-        chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-    else:
-        chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
-        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
-        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
-
-
-    # <-------------- we are done ------------->
-    return success

crazy_functions/crazy_utils.py

@@ -135,14 +135,26 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
     return final_result
 
-def can_multi_process(llm):
+def can_multi_process(llm) -> bool:
+    from request_llms.bridge_all import model_info
+
+    def default_condition(llm) -> bool:
+        # legacy condition
         if llm.startswith('gpt-'): return True
         if llm.startswith('api2d-'): return True
         if llm.startswith('azure-'): return True
         if llm.startswith('spark'): return True
-    if llm.startswith('zhipuai'): return True
+        if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
         return False
 
+    if llm in model_info:
+        if 'can_multi_thread' in model_info[llm]:
+            return model_info[llm]['can_multi_thread']
+        else:
+            return default_condition(llm)
+    else:
+        return default_condition(llm)
+
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         inputs_array, inputs_show_user_array, llm_kwargs,
         chatbot, history_array, sys_prompt_array,

crazy_functions/pdf_fns/parse_word.py

@@ -0,0 +1,85 @@
+from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
+import os
+import re
+def extract_text_from_files(txt, chatbot, history):
+    """
+    查找pdf/md/word并获取文本内容并返回状态以及文本
+
+    输入参数 Args:
+        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
+        history (list): List of chat history （历史，对话历史列表）
+
+    输出 Returns:
+        文件是否存在(bool)
+        final_result(list):文本内容
+        page_one(list):第一页内容/摘要
+        file_manifest(list):文件路径
+        excption(string):需要用户手动处理的信息,如没出错则保持为空
+    """
+
+    final_result = []
+    page_one = []
+    file_manifest = []
+    excption = ""
+
+    if txt == "":
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    #查找输入区内容中的文件
+    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
+    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
+    file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx')
+    file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc')
+
+    if file_doc:
+        excption = "word"
+        return False, final_result, page_one, file_manifest, excption
+
+    file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
+    if file_num == 0:
+        final_result.append(txt)
+        return False, final_result, page_one, file_manifest, excption   #如输入区内容不是文件则直接返回输入区内容
+
+    if file_pdf:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            import fitz
+        except:
+            excption = "pdf"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(pdf_manifest):
+            file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
+            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+            pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars
+            final_result.append(file_content)
+            page_one.append(pdf_one)
+            file_manifest.append(os.path.relpath(fp, folder_pdf))
+
+    if file_md:
+        for index, fp in enumerate(md_manifest):
+            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+                file_content = f.read()
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE)  #接下来提取md中的一级/二级标题作为摘要
+            if len(headers) > 0:
+                page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
+            else:
+                page_one.append("")
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_md))
+
+    if file_word:
+        try:    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+            from docx import Document
+        except:
+            excption = "word_pip"
+            return False, final_result, page_one, file_manifest, excption
+        for index, fp in enumerate(word_manifest):
+            doc = Document(fp)
+            file_content = '\n'.join([p.text for p in doc.paragraphs])
+            file_content = file_content.encode('utf-8', 'ignore').decode()
+            page_one.append(file_content[:200])
+            final_result.append(file_content)
+            file_manifest.append(os.path.relpath(fp, folder_word))
+
+    return True, final_result, page_one, file_manifest, excption

crazy_functions/生成多种Mermaid图表.py

@@ -1,6 +1,5 @@
 from toolbox import CatchException, update_ui, report_exception
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import read_and_clean_pdf_text
 import datetime
 
 #以下是每类图表的PROMPT
@@ -162,7 +161,7 @@ mindmap
 ```
 """
 
-def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
+def 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs):
     ############################## <第 0 步，切割输入> ##################################
     # 借用PDF切割中的函数对文本进行切割
     TOKEN_LIMIT_PER_FRAGMENT = 2500
@@ -170,8 +169,6 @@ def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
     from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
     txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
     ############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你从历史记录或文件中提取摘要。'; gpt_say = "[Local Message] 收到。"   # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history)    # 更新UI
     results = []
     MAX_WORD_TOTAL = 4096
     n_txt = len(txt)
@@ -179,7 +176,7 @@ def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
     if n_txt >= 20: print('文章极长，不能达到预期效果')
     for i in range(n_txt):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i]}"
+        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
         i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i][:200]} ...."
         gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
                                                                            llm_kwargs, chatbot,
@@ -232,35 +229,11 @@ def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
         inputs=i_say,
         inputs_show_user=i_say_show_user,
         llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
-        sys_prompt="你精通使用mermaid语法来绘制图表,首先确保语法正确,其次避免在mermaid语法中使用不允许的字符,此外也应当分考虑图表的可读性。"
+        sys_prompt=""
     )
     history.append(gpt_say)
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
 
-def 输入区文件处理(txt):
-    if txt == "": return False, txt
-    success = True
-    import glob
-    from .crazy_utils import get_files_from_everything
-    file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
-    file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
-    if len(pdf_manifest) == 0 and len(md_manifest) == 0:
-        return False, txt   #如输入区内容不是文件则直接返回输入区内容
-    
-    final_result = ""
-    if file_pdf:
-        for index, fp in enumerate(pdf_manifest):
-            file_content, page_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
-            file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-            final_result += "\n" + file_content
-    if file_md:
-        for index, fp in enumerate(md_manifest):
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-            file_content = file_content.encode('utf-8', 'ignore').decode()
-            final_result += "\n" + file_content
-    return True, final_result
-    
 @CatchException
 def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
     """
@@ -277,26 +250,47 @@ def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history,
     # 基本信息：功能、贡献者
     chatbot.append([
         "函数插件功能？",
-        "根据当前聊天历史或文件中(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
+        "根据当前聊天历史或指定的路径文件(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
         \n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
+    if os.path.exists(txt):     #如输入区无内容则直接解析历史记录
+        from crazy_functions.pdf_fns.parse_word import extract_text_from_files
+        file_exist, final_result, page_one, file_manifest, excption = extract_text_from_files(txt, chatbot, history)
+    else:
+        file_exist = False
+        excption = ""
+        file_manifest = []
+
+    if excption != "":
+        if excption == "word":
+            report_exception(chatbot, history,
+                a = f"解析项目: {txt}",
+                b = f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。")
+
+        elif excption == "pdf":
             report_exception(chatbot, history,
                 a = f"解析项目: {txt}",
                 b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
+
+        elif excption == "word_pip":
+                report_exception(chatbot, history,
+                    a=f"解析项目: {txt}",
+                    b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
+
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
 
-    if os.path.exists(txt):     #如输入区无内容则直接解析历史记录
-        file_exist, txt = 输入区文件处理(txt)
     else:
-        file_exist = False
-
-    if file_exist : history = []    #如输入区内容为文件则清空历史记录
-    history.append(txt)     #将解析后的txt传递加入到历史中
-    
-    yield from 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs)  
+        if not file_exist:
+            history.append(txt)     #如输入区不是文件则将输入区内容加入历史记录
+            i_say_show_user = f'首先你从历史记录中提取摘要。'; gpt_say = "[Local Message] 收到。"   # 用户提示
+            chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history)    # 更新UI
+            yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs)
+        else:
+            file_num = len(file_manifest)
+            for i in range(file_num):     #依次处理文件
+                i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"; gpt_say = "[Local Message] 收到。"   # 用户提示
+                chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history)    # 更新UI
+                history = []    #如输入区内容为文件则清空历史记录
+                history.append(final_result[i])
+                yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs)

crazy_functions/解析JupyterNotebook.py

@@ -12,6 +12,12 @@ class PaperFileGroup():
         self.sp_file_index = []
         self.sp_file_tag = []
 
+        # count_token
+        from request_llms.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
     def run_file_split(self, max_token_limit=1900):
         """
         将长文本分离开来

crazy_functions/解析项目源代码.py

@@ -345,9 +345,12 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
     pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
     # 将要忽略匹配的文件名(例如: ^README.md)
-    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
+    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", r"\.") # 移除左边通配符，移除右侧逗号，转义点号
+                           for _ in txt_pattern.split(" ") # 以空格分割
+                           if (_ != "" and _.strip().startswith("^") and not _.strip().startswith("^*."))   # ^开始，但不是^*.开始
+                           ]
     # 生成正则表达式
-    pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
+    pattern_except = r'/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
     pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
 
     history.clear()

docker-compose.yml

@@ -4,9 +4,9 @@
 # 1. 请在以下方案中选择任意一种，然后删除其他的方案
 # 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py
 # 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改：
-    # 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置
+    # 「方法1: 适用于Linux，很方便，可惜windows不支持」与宿主的网络融合为一体，这个是默认配置
     # network_mode: "host"
-    # 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
+    # 「方法2: 适用于所有系统包括Windows和MacOS」端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
     # ports:
     #   - "12345:12345"  # 注意！12345必须与WEB_PORT环境变量相互对应
 # 4. 最后`docker-compose up`运行
@@ -25,7 +25,7 @@
 ## ===================================================
 
 ## ===================================================
-## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
+## 「方案零」 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
 ## ===================================================
 version: '3'
 services:
@@ -63,10 +63,10 @@ services:
     #             count: 1
     #             capabilities: [gpu]
 
-    # 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
 
-    # 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
+    # 「WEB_PORT暴露方法2: 适用于所有系统」端口映射
     # ports:
     #   - "12345:12345"  # 12345必须与WEB_PORT相互对应
 
@@ -75,10 +75,8 @@ services:
       bash -c "python3 -u main.py"
 
 
-
-
 ## ===================================================
-## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
+## 「方案一」 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
 ## ===================================================
 version: '3'
 services:
@@ -97,16 +95,16 @@ services:
       # DEFAULT_WORKER_NUM:       '    10                                                                                             '
       # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
 
-    # 与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
 
-    # 不使用代理网络拉取最新代码
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
 
 ### ===================================================
-### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
+### 「方案二」 如果需要运行ChatGLM + Qwen + MOSS等本地模型
 ### ===================================================
 version: '3'
 services:
@@ -130,8 +128,10 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
+
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
@@ -139,8 +139,9 @@ services:
     # command: >
     #   bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
 
+
 ### ===================================================
-### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
+### 「方案三」 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
 ### ===================================================
 version: '3'
 services:
@@ -164,16 +165,16 @@ services:
     devices:
       - /dev/nvidia0:/dev/nvidia0
 
-    # 与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
 
-    # 不使用代理网络拉取最新代码
+    # 启动命令
     command: >
       python3 -u main.py
 
 
 ## ===================================================
-## 【方案四】 ChatGPT + Latex
+## 「方案四」 ChatGPT + Latex
 ## ===================================================
 version: '3'
 services:
@@ -190,16 +191,16 @@ services:
       DEFAULT_WORKER_NUM:       '    10                                                                               '
       WEB_PORT:                 '    12303                                                                            '
 
-    # 与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
 
-    # 不使用代理网络拉取最新代码
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"
 
 
 ## ===================================================
-## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
+## 「方案五」 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
 ## ===================================================
 version: '3'
 services:
@@ -223,9 +224,9 @@ services:
       # (无需填写) ALIYUN_ACCESSKEY:         '    LTAI5q6BrFUzoRXVGUWnekh1                                         '
       # (无需填写) ALIYUN_SECRET:            '    eHmI20AVWIaQZ0CiTD2bGQVsaP9i68                                   '
 
-    # 与宿主的网络融合
+    # 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
     network_mode: "host"
 
-    # 不使用代理网络拉取最新代码
+    # 启动命令
     command: >
       bash -c "python3 -u main.py"

docs/GithubAction+NoLocal+AudioAssistant

@@ -13,7 +13,7 @@ COPY . .
 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
+RUN pip3 install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad 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()'

docs/translate_english.json

@@ -1668,7 +1668,7 @@
     "Markdown翻译指定语言": "TranslateMarkdownToSpecifiedLanguage",
     "Langchain知识库": "LangchainKnowledgeBase",
     "Latex英文纠错加PDF对比": "CorrectEnglishInLatexWithPDFComparison",
-    "Latex输出PDF结果": "OutputPDFFromLatex",
+    "Latex输出PDF": "OutputPDFFromLatex",
     "Latex翻译中文并重新编译PDF": "TranslateChineseToEnglishInLatexAndRecompilePDF",
     "sprint亮靛": "SprintIndigo",
     "寻找Latex主文件": "FindLatexMainFile",
@@ -3004,5 +3004,7 @@
     "1. 上传图片": "TranslatedText",
     "保存状态": "TranslatedText",
     "GPT-Academic对话存档": "TranslatedText",
-    "Arxiv论文精细翻译": "TranslatedText"
+    "Arxiv论文精细翻译": "TranslatedText",
+    "from crazy_functions.AdvancedFunctionTemplate import 测试图表渲染": "from crazy_functions.AdvancedFunctionTemplate import test_chart_rendering",
+    "测试图表渲染": "test_chart_rendering"
 }

docs/translate_japanese.json

@@ -1492,7 +1492,7 @@
     "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输出PDF": "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",

docs/translate_std.json

@@ -16,7 +16,7 @@
     "批量Markdown翻译": "BatchTranslateMarkdown",
     "连接bing搜索回答问题": "ConnectBingSearchAnswerQuestion",
     "Langchain知识库": "LangchainKnowledgeBase",
-    "Latex输出PDF结果": "OutputPDFFromLatex",
+    "Latex输出PDF": "OutputPDFFromLatex",
     "把字符太少的块清除为回车": "ClearBlocksWithTooFewCharactersToNewline",
     "Latex精细分解与转化": "DecomposeAndConvertLatex",
     "解析一个C项目的头文件": "ParseCProjectHeaderFiles",
@@ -97,5 +97,12 @@
     "多智能体": "MultiAgent",
     "图片生成_DALLE2": "ImageGeneration_DALLE2",
     "图片生成_DALLE3": "ImageGeneration_DALLE3",
-    "图片修改_DALLE2": "ImageModification_DALLE2"
+    "图片修改_DALLE2": "ImageModification_DALLE2",
+    "生成多种Mermaid图表": "GenerateMultipleMermaidCharts",
+    "知识库文件注入": "InjectKnowledgeBaseFiles",
+    "PDF翻译中文并重新编译PDF": "TranslatePDFToChineseAndRecompilePDF",
+    "随机小游戏": "RandomMiniGame",
+    "互动小游戏": "InteractiveMiniGame",
+    "解析历史输入": "ParseHistoricalInput",
+    "高阶功能模板函数示意图": "HighOrderFunctionTemplateDiagram"
 }

docs/translate_traditionalchinese.json

@@ -1468,7 +1468,7 @@
     "交互功能模板函数": "InteractiveFunctionTemplateFunctions",
     "交互功能函数模板": "InteractiveFunctionFunctionTemplates",
     "Latex英文纠错加PDF对比": "LatexEnglishCorrectionWithPDFComparison",
-    "Latex输出PDF结果": "OutputPDFFromLatex",
+    "Latex输出PDF": "OutputPDFFromLatex",
     "Latex翻译中文并重新编译PDF": "TranslateLatexToChineseAndRecompilePDF",
     "语音助手": "VoiceAssistant",
     "微调数据集生成": "FineTuneDatasetGeneration",

docs/use_audio.md

@@ -3,7 +3,7 @@
 
 ## 1. 安装额外依赖
 ```
-pip install --upgrade pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+pip install --upgrade pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
 ```
 
 如果因为特色网络问题导致上述命令无法执行：

docs/waifu_plugin/autoload.js

@@ -1,30 +0,0 @@
-try {
-    $("<link>").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head');
-    $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
-    $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
-        $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
-            /* 可直接修改部分参数 */
-            live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
-            live2d_settings['modelId'] = 5;                  // 默认模型 ID
-            live2d_settings['modelTexturesId'] = 1;          // 默认材质 ID
-            live2d_settings['modelStorage'] = false;         // 不储存模型 ID
-            live2d_settings['waifuSize']            = '210x187';
-            live2d_settings['waifuTipsSize']        = '187x52';
-            live2d_settings['canSwitchModel']       = true;
-            live2d_settings['canSwitchTextures']    = true;
-            live2d_settings['canSwitchHitokoto']    = false;
-            live2d_settings['canTakeScreenshot']    = false;
-            live2d_settings['canTurnToHomePage']    = false;
-            live2d_settings['canTurnToAboutPage']   = false;
-            live2d_settings['showHitokoto']         = false;         // 显示一言
-            live2d_settings['showF12Status']        = false;         // 显示加载状态
-            live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-            live2d_settings['showF12OpenMsg']       = false;         // 显示控制台打开提示
-            live2d_settings['showCopyMessage']      = false;         // 显示 复制内容 提示
-            live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-            /* 在 initModel 前添加 */
-            initModel("file=docs/waifu_plugin/waifu-tips.json");
-        }});
-    }});
-} catch(err) { console.log("[Error] JQuery is not defined.") }

main.py

@@ -13,35 +13,40 @@ help_menu_description = \
 </br></br>如何语音对话: 请阅读Wiki
 </br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
 
+def enable_log(PATH_LOGGING):
+    import logging, uuid
+    admin_log_path = os.path.join(PATH_LOGGING, "admin")
+    os.makedirs(admin_log_path, exist_ok=True)
+    log_dir = os.path.join(admin_log_path, "chat_secrets.log")
+    try:logging.basicConfig(filename=log_dir, level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    except:logging.basicConfig(filename=log_dir, level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+    # Disable logging output from the 'httpx' logger
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    print(f"所有对话记录将自动保存在本地目录{log_dir}, 请注意自我隐私保护哦！")
+
 def main():
     import gradio as gr
-    if gr.__version__ not in ['3.32.6', '3.32.7']: 
+    if gr.__version__ not in ['3.32.9']:
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
     from request_llms.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
     # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
-    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
-    DARK_MODE, NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('DARK_MODE', 'NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
-    INIT_SYS_PROMPT = get_conf('INIT_SYS_PROMPT')
+    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME, ADD_WAIFU = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME', 'ADD_WAIFU')
+    NUM_CUSTOM_BASIC_BTN, SSL_KEYFILE, SSL_CERTFILE = get_conf('NUM_CUSTOM_BASIC_BTN', 'SSL_KEYFILE', 'SSL_CERTFILE')
+    DARK_MODE, INIT_SYS_PROMPT, ADD_WAIFU = get_conf('DARK_MODE', 'INIT_SYS_PROMPT', 'ADD_WAIFU')
 
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
-    from themes.theme import adjust_theme, advanced_css, theme_declaration
-    from themes.theme import js_code_for_css_changing, js_code_for_darkmode_init, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
-    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_reset, js_code_show_or_hide, js_code_show_or_hide_group2
+    from themes.theme import js_code_for_css_changing, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
+    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
 
-    # 问询记录, python 版本建议3.9+（越新越好）
-    import logging, uuid
-    os.makedirs(PATH_LOGGING, exist_ok=True)
-    try:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    except:logging.basicConfig(filename=f"{PATH_LOGGING}/chat_secrets.log", level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    # Disable logging output from the 'httpx' logger
-    logging.getLogger("httpx").setLevel(logging.WARNING)
-    print(f"所有问询记录将自动保存在本地目录./{PATH_LOGGING}/chat_secrets.log, 请注意自我隐私保护哦！")
+    # 对话、日志记录
+    enable_log(PATH_LOGGING)
 
     # 一些普通功能模块
     from core_functional import get_core_functions
@@ -65,7 +70,7 @@ def main():
     proxy_info = check_proxy(proxies)
 
     gr_L1 = lambda: gr.Row().style()
-    gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id)
+    gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id, min_width=400)
     if LAYOUT == "TOP-DOWN":
         gr_L1 = lambda: DummyWith()
         gr_L2 = lambda scale, elem_id: gr.Row()
@@ -74,9 +79,9 @@ def main():
     cancel_handles = []
     customize_btns = {}
     predefined_btns = {}
-    with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
+    with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as app_block:
         gr.HTML(title_html)
-        secret_css, dark_mode, persistent_cookie = gr.Textbox(visible=False), gr.Textbox(DARK_MODE, visible=False), gr.Textbox(visible=False)
+        secret_css, web_cookie_cache = gr.Textbox(visible=False), gr.Textbox(visible=False)
         cookies = gr.State(load_chat_cookies())
         with gr_L1():
             with gr_L2(scale=2, elem_id="gpt-chat"):
@@ -98,6 +103,7 @@ def main():
                             audio_mic = gr.Audio(source="microphone", type="numpy", elem_id="elem_audio", streaming=True, show_label=False).style(container=False)
                     with gr.Row():
                         status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}", elem_id="state-panel")
+
                 with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
                     with gr.Row():
                         for k in range(NUM_CUSTOM_BASIC_BTN):
@@ -142,7 +148,6 @@ def main():
                         with gr.Accordion("点击展开“文件下载区”。", open=False) as area_file_up:
                             file_upload = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload")
 
-
         with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden", elem_id="tooltip"):
             with gr.Row():
                 with gr.Tab("上传文件", elem_id="interact-panel"):
@@ -152,16 +157,21 @@ def main():
                 with gr.Tab("更换模型", elem_id="interact-panel"):
                     md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
                     top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
-                    temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
+                    temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature", elem_id="elem_temperature")
                     max_length_sl = gr.Slider(minimum=256, maximum=1024*32, value=4096, step=128, interactive=True, label="Local LLM MaxLength",)
-                    system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=INIT_SYS_PROMPT)
+                    system_prompt = gr.Textbox(show_label=True, lines=2, placeholder=f"System Prompt", label="System prompt", value=INIT_SYS_PROMPT, elem_id="elem_prompt")
+                    temperature.change(None, inputs=[temperature], outputs=None,
+                        _js="""(temperature)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_temperature_cookie", temperature)""")
+                    system_prompt.change(None, inputs=[system_prompt], outputs=None,
+                        _js="""(system_prompt)=>gpt_academic_gradio_saveload("save", "elem_prompt", "js_system_prompt_cookie", system_prompt)""")
 
                 with gr.Tab("界面外观", elem_id="interact-panel"):
                     theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").style(container=False)
-                    checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "浮动输入区", "输入清除键", "插件参数区"], 
-                                                  value=["基础功能区", "函数插件区"], label="显示/隐藏功能区", elem_id='cbs').style(container=False)
-                    checkboxes_2 = gr.CheckboxGroup(["自定义菜单"], 
-                                                  value=[], label="显示/隐藏自定义菜单", elem_id='cbsc').style(container=False)
+                    checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "浮动输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区", elem_id='cbs').style(container=False)
+                    opt = ["自定义菜单"]
+                    value=[]
+                    if ADD_WAIFU: opt += ["添加Live2D形象"]; value += ["添加Live2D形象"]
+                    checkboxes_2 = gr.CheckboxGroup(opt, value=value, label="显示/隐藏自定义菜单", elem_id='cbsc').style(container=False)
                     dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")
                     dark_mode_btn.click(None, None, None, _js=js_code_for_toggle_darkmode)
                 with gr.Tab("帮助", elem_id="interact-panel"):
@@ -178,7 +188,7 @@ def main():
                         submitBtn2 = gr.Button("提交", variant="primary"); submitBtn2.style(size="sm")
                         resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm")
                         stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
-                        clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
+                        clearBtn2 = gr.Button("清除", elem_id="elem_clear2", variant="secondary", visible=False); clearBtn2.style(size="sm")
 
 
         with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_customize:
@@ -192,69 +202,31 @@ def main():
                         basic_fn_suffix = gr.Textbox(show_label=False, placeholder="输入新提示后缀", lines=4).style(container=False)
                     with gr.Column(scale=1, min_width=70):
                         basic_fn_confirm = gr.Button("确认并保存", variant="primary"); basic_fn_confirm.style(size="sm")
-                        basic_fn_load    = gr.Button("加载已保存", variant="primary"); basic_fn_load.style(size="sm")
-                        def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix):
-                            ret = {}
-                            customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
-                            customize_fn_overwrite_.update({
-                                basic_btn_dropdown_:
-                                    {
-                                        "Title":basic_fn_title,
-                                        "Prefix":basic_fn_prefix,
-                                        "Suffix":basic_fn_suffix,
-                                    }
-                                }
-                            )
-                            cookies_.update(customize_fn_overwrite_)
-                            if basic_btn_dropdown_ in customize_btns:
-                                ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
-                            else:
-                                ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
-                            ret.update({cookies: cookies_})
-                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)   # persistent cookie to dict
-                            except: persistent_cookie_ = {}
-                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_      # dict update new value
-                            persistent_cookie_ = to_cookie_str(persistent_cookie_)          # persistent cookie to dict
-                            ret.update({persistent_cookie: persistent_cookie_})             # write persistent cookie
-                            return ret
+                        basic_fn_clean   = gr.Button("恢复默认", variant="primary"); basic_fn_clean.style(size="sm")
 
-                        def reflesh_btn(persistent_cookie_, cookies_):
-                            ret = {}
-                            for k in customize_btns:
-                                ret.update({customize_btns[k]: gr.update(visible=False, value="")})
+                        from shared_utils.cookie_manager import assign_btn__fn_builder
+                        assign_btn = assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)
+                        # update btn
+                        h = basic_fn_confirm.click(assign_btn, [web_cookie_cache, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix],
+                                                   [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h.then(None, [web_cookie_cache], None, _js="""(web_cookie_cache)=>{setCookie("web_cookie_cache", web_cookie_cache, 365);}""")
+                        # clean up btn
+                        h2 = basic_fn_clean.click(assign_btn, [web_cookie_cache, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix, gr.State(True)],
+                                                   [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()])
+                        h2.then(None, [web_cookie_cache], None, _js="""(web_cookie_cache)=>{setCookie("web_cookie_cache", web_cookie_cache, 365);}""")
 
-                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
-                            except: return ret
 
-                            customize_fn_overwrite_ = persistent_cookie_.get("custom_bnt", {})
-                            cookies_['customize_fn_overwrite'] = customize_fn_overwrite_
-                            ret.update({cookies: cookies_})
-
-                            for k,v in persistent_cookie_["custom_bnt"].items():
-                                if v['Title'] == "": continue
-                                if k in customize_btns: ret.update({customize_btns[k]: gr.update(visible=True, value=v['Title'])})
-                                else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
-                            return ret
-                        
-                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies], [cookies, *customize_btns.values(), *predefined_btns.values()])
-                        h = basic_fn_confirm.click(assign_btn, [persistent_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix], 
-                                                   [persistent_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
-                        # save persistent cookie
-                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""")
 
         # 功能区显示开关与功能区的互动
         def fn_area_visibility(a):
             ret = {}
-            ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
-            ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
             ret.update({area_input_primary: gr.update(visible=("浮动输入区" not in a))})
             ret.update({area_input_secondary: gr.update(visible=("浮动输入区" in a))})
-            ret.update({clearBtn: gr.update(visible=("输入清除键" in a))})
-            ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))})
             ret.update({plugin_advanced_arg: gr.update(visible=("插件参数区" in a))})
             if "浮动输入区" in a: ret.update({txt: gr.update(value="")})
             return ret
-        checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2, plugin_advanced_arg] )
+        checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, plugin_advanced_arg] )
+        checkboxes.select(None, [checkboxes], None, _js=js_code_show_or_hide)
 
         # 功能区显示开关与功能区的互动
         def fn_area_visibility_2(a):
@@ -262,6 +234,7 @@ def main():
             ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
             return ret
         checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
+        checkboxes_2.select(None, [checkboxes_2], None, _js=js_code_show_or_hide_group2)
 
         # 整理反复出现的控件句柄组合
         input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
@@ -272,15 +245,17 @@ def main():
         cancel_handles.append(txt2.submit(**predict_args))
         cancel_handles.append(submitBtn.click(**predict_args))
         cancel_handles.append(submitBtn2.click(**predict_args))
-        resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
-        resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
-        clearBtn.click(lambda: ("",""), None, [txt, txt2])
-        clearBtn2.click(lambda: ("",""), None, [txt, txt2])
+        resetBtn.click(None, None, [chatbot, history, status], _js=js_code_reset)   # 先在前端快速清除chatbot&status
+        resetBtn2.click(None, None, [chatbot, history, status], _js=js_code_reset)  # 先在前端快速清除chatbot&status
+        resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])    # 再在后端清除history
+        resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])   # 再在后端清除history
+        clearBtn.click(None, None, [txt, txt2], _js=js_code_clear)
+        clearBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
         if AUTO_CLEAR_TXT:
-            submitBtn.click(lambda: ("",""), None, [txt, txt2])
-            submitBtn2.click(lambda: ("",""), None, [txt, txt2])
-            txt.submit(lambda: ("",""), None, [txt, txt2])
-            txt2.submit(lambda: ("",""), None, [txt, txt2])
+            submitBtn.click(None, None, [txt, txt2], _js=js_code_clear)
+            submitBtn2.click(None, None, [txt, txt2], _js=js_code_clear)
+            txt.submit(None, None, [txt, txt2], _js=js_code_clear)
+            txt2.submit(None, None, [txt, txt2], _js=js_code_clear)
         # 基础功能区的回调函数注册
         for k in functional:
             if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
@@ -360,11 +335,14 @@ def main():
             audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
 
 
-        demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
-        darkmode_js = js_code_for_darkmode_init
-        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=js_code_for_persistent_cookie_init)
-        demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js)    # 配置暗色主题或亮色主题
-        demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
+        app_block.load(assign_user_uuid, inputs=[cookies], outputs=[cookies])
+
+        from shared_utils.cookie_manager import load_web_cookie_cache__fn_builder
+        load_web_cookie_cache = load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)
+        app_block.load(load_web_cookie_cache, inputs = [web_cookie_cache, cookies],
+            outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
+
+        app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}")""")    # 配置暗色主题或亮色主题
 
     # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
     def run_delayed_tasks():
@@ -381,26 +359,13 @@ def main():
         threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面
         threading.Thread(target=warm_up_mods, name="warm-up",      daemon=True).start() # 预热tiktoken模块
 
+    # 运行一些异步任务：自动更新、打开浏览器页面、预热tiktoken模块
     run_delayed_tasks()
-    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-        quiet=True,
-        server_name="0.0.0.0", 
-        ssl_keyfile=None if SSL_KEYFILE == "" else SSL_KEYFILE,
-        ssl_certfile=None if SSL_CERTFILE == "" else SSL_CERTFILE,
-        ssl_verify=False,
-        server_port=PORT,
-        favicon_path=os.path.join(os.path.dirname(__file__), "docs/logo.png"), 
-        auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None,
-        blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
 
-    # 如果需要在二级路径下运行
-    # CUSTOM_PATH = get_conf('CUSTOM_PATH')
-    # if CUSTOM_PATH != "/": 
-    #     from toolbox import run_gradio_in_subpath
-    #     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
-    # else: 
-    #     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png",
-    #                 blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin"])
+    # 最后，正式开始服务
+    from shared_utils.fastapi_server import start_app
+    start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE)
+
 
 if __name__ == "__main__":
     main()

request_llms/bridge_all.py

@@ -8,10 +8,10 @@
     具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
     2. predict_no_ui_long_connection(...)
 """
-import tiktoken, copy
+import tiktoken, copy, re
 from functools import lru_cache
 from concurrent.futures import ThreadPoolExecutor
-from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask
+from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
 
 from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
 from .bridge_chatgpt import predict as chatgpt_ui
@@ -31,6 +31,12 @@ from .bridge_qianfan import predict as qianfan_ui
 from .bridge_google_gemini import predict as genai_ui
 from .bridge_google_gemini import predict_no_ui_long_connection  as genai_noui
 
+from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
+from .bridge_zhipu import predict as zhipu_ui
+
+from .bridge_cohere import predict as cohere_ui
+from .bridge_cohere import predict_no_ui_long_connection as cohere_noui
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
 
 class LazyloadTiktoken(object):
@@ -58,6 +64,11 @@ API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "A
 openai_endpoint = "https://api.openai.com/v1/chat/completions"
 api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
 newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
+gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
+claude_endpoint = "https://api.anthropic.com/v1/messages"
+yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
+cohere_endpoint = 'https://api.cohere.ai/v1/chat'
+
 if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
 azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
 # 兼容旧版的配置
@@ -72,7 +83,10 @@ except:
 if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
 if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
 if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
-
+if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemini_endpoint]
+if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
+if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
+if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
 
 # 获取tokenizer
 tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
@@ -91,7 +105,7 @@ model_info = {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
-        "max_token": 4096,
+        "max_token": 16385,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
@@ -123,7 +137,16 @@ model_info = {
         "token_cnt": get_token_num_gpt35,
     },
 
-    "gpt-3.5-turbo-1106": {#16k
+    "gpt-3.5-turbo-1106": { #16k
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 16385,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+    "gpt-3.5-turbo-0125": { #16k
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
         "endpoint": openai_endpoint,
@@ -215,16 +238,25 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
-    # api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
-    "api2d-gpt-3.5-turbo": {
-        "fn_with_ui": chatgpt_ui,
-        "fn_without_ui": chatgpt_noui,
-        "endpoint": api2d_endpoint,
-        "max_token": 4096,
+    # 智谱AI
+    "glm-4": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "glm-3-turbo": {
+        "fn_with_ui": zhipu_ui,
+        "fn_without_ui": zhipu_noui,
+        "endpoint": None,
+        "max_token": 10124 * 4,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
 
+    # api_2d (此后不需要在此处添加api2d的接口了，因为下面的代码会自动添加)
     "api2d-gpt-4": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -270,7 +302,7 @@ model_info = {
     "gemini-pro": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
-        "endpoint": None,
+        "endpoint": gemini_endpoint,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
@@ -278,13 +310,56 @@ model_info = {
     "gemini-pro-vision": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
+        "endpoint": gemini_endpoint,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+    # cohere
+    "cohere-command-r-plus": {
+        "fn_with_ui": cohere_ui,
+        "fn_without_ui": cohere_noui,
+        "can_multi_thread": True,
+        "endpoint": cohere_endpoint,
+        "max_token": 1024 * 4,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+
+}
+# -=-=-=-=-=-=- 月之暗面 -=-=-=-=-=-=-
+from request_llms.bridge_moonshot import predict as moonshot_ui
+from request_llms.bridge_moonshot import predict_no_ui_long_connection as moonshot_no_ui
+model_info.update({
+    "moonshot-v1-8k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
+        "endpoint": None,
+        "max_token": 1024 * 8,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "moonshot-v1-32k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
         "endpoint": None,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
-}
-
+    "moonshot-v1-128k": {
+        "fn_with_ui": moonshot_ui,
+        "fn_without_ui": moonshot_no_ui,
+        "can_multi_thread": True,
+        "endpoint": None,
+        "max_token": 1024 * 128,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    }
+})
 # -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
 for model in AVAIL_LLM_MODELS:
     if model.startswith('api2d-') and (model.replace('api2d-','') in model_info.keys()):
@@ -300,25 +375,67 @@ for model in AVAIL_LLM_MODELS:
         model_info.update({model: mi})
 
 # -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
-if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
+# claude家族
+claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229"]
+if any(item in claude_models for item in AVAIL_LLM_MODELS):
     from .bridge_claude import predict_no_ui_long_connection as claude_noui
     from .bridge_claude import predict as claude_ui
     model_info.update({
-        "claude-1-100k": {
+        "claude-instant-1.2": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": None,
-            "max_token": 8196,
+            "endpoint": claude_endpoint,
+            "max_token": 100000,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
     })
     model_info.update({
-        "claude-2": {
+        "claude-2.0": {
             "fn_with_ui": claude_ui,
             "fn_without_ui": claude_noui,
-            "endpoint": None,
-            "max_token": 8196,
+            "endpoint": claude_endpoint,
+            "max_token": 100000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-2.1": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-haiku-20240307": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-sonnet-20240229": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-3-opus-20240229": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": claude_endpoint,
+            "max_token": 200000,
             "tokenizer": tokenizer_gpt35,
             "token_cnt": get_token_num_gpt35,
         },
@@ -388,22 +505,6 @@ if "stack-claude" in AVAIL_LLM_MODELS:
             "token_cnt": get_token_num_gpt35,
         }
     })
-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
-        model_info.update({
-            "newbing-free": {
-                "fn_with_ui": newbingfree_ui,
-                "fn_without_ui": newbingfree_noui,
-                "endpoint": newbing_endpoint,
-                "max_token": 4096,
-                "tokenizer": tokenizer_gpt35,
-                "token_cnt": get_token_num_gpt35,
-            }
-        })
-    except:
-        print(trimmed_format_exc())
 if "newbing" in AVAIL_LLM_MODELS:   # same with newbing-free
     try:
         from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui
@@ -436,6 +537,7 @@ if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 上海AI-LAB书生大模型 -=-=-=-=-=-=-
 if "internlm" in AVAIL_LLM_MODELS:
     try:
         from .bridge_internlm import predict_no_ui_long_connection as internlm_noui
@@ -468,6 +570,7 @@ if "chatglm_onnx" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 通义-本地模型 -=-=-=-=-=-=-
 if "qwen-local" in AVAIL_LLM_MODELS:
     try:
         from .bridge_qwen_local import predict_no_ui_long_connection as qwen_local_noui
@@ -476,6 +579,7 @@ if "qwen-local" in AVAIL_LLM_MODELS:
             "qwen-local": {
                 "fn_with_ui": qwen_local_ui,
                 "fn_without_ui": qwen_local_noui,
+                "can_multi_thread": False,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -484,6 +588,7 @@ if "qwen-local" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 通义-在线模型 -=-=-=-=-=-=-
 if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS:   # zhipuai
     try:
         from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
@@ -492,6 +597,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-turbo": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 6144,
                 "tokenizer": tokenizer_gpt35,
@@ -500,6 +606,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-plus": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 30720,
                 "tokenizer": tokenizer_gpt35,
@@ -508,6 +615,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             "qwen-max": {
                 "fn_with_ui": qwen_ui,
                 "fn_without_ui": qwen_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 28672,
                 "tokenizer": tokenizer_gpt35,
@@ -516,7 +624,35 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
         })
     except:
         print(trimmed_format_exc())
-if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+# -=-=-=-=-=-=- 零一万物模型 -=-=-=-=-=-=-
+if "yi-34b-chat-0205" in AVAIL_LLM_MODELS or "yi-34b-chat-200k" in AVAIL_LLM_MODELS:   # zhipuai
+    try:
+        from .bridge_yimodel import predict_no_ui_long_connection as yimodel_noui
+        from .bridge_yimodel import predict as yimodel_ui
+        model_info.update({
+            "yi-34b-chat-0205": {
+                "fn_with_ui": yimodel_ui,
+                "fn_without_ui": yimodel_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 4000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "yi-34b-chat-200k": {
+                "fn_with_ui": yimodel_ui,
+                "fn_without_ui": yimodel_noui,
+                "can_multi_thread": False,  # 目前来说，默认情况下并发量极低，因此禁用
+                "endpoint": yimodel_endpoint,
+                "max_token": 200000,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+        })
+    except:
+        print(trimmed_format_exc())
+# -=-=-=-=-=-=- 讯飞星火认知大模型 -=-=-=-=-=-=-
+if "spark" in AVAIL_LLM_MODELS:
     try:
         from .bridge_spark import predict_no_ui_long_connection as spark_noui
         from .bridge_spark import predict as spark_ui
@@ -524,6 +660,7 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "spark": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -540,6 +677,7 @@ if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "sparkv2": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -548,7 +686,7 @@ if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
         })
     except:
         print(trimmed_format_exc())
-if "sparkv3" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
     try:
         from .bridge_spark import predict_no_ui_long_connection as spark_noui
         from .bridge_spark import predict as spark_ui
@@ -556,6 +694,16 @@ if "sparkv3" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             "sparkv3": {
                 "fn_with_ui": spark_ui,
                 "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "sparkv3.5": {
+                "fn_with_ui": spark_ui,
+                "fn_without_ui": spark_noui,
+                "can_multi_thread": True,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -580,22 +728,22 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
         })
     except:
         print(trimmed_format_exc())
-if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai
+# -=-=-=-=-=-=- 智谱 -=-=-=-=-=-=-
+if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容配置
     try:
-        from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
-        from .bridge_zhipu import predict as zhipu_ui
         model_info.update({
             "zhipuai": {
                 "fn_with_ui": zhipu_ui,
                 "fn_without_ui": zhipu_noui,
                 "endpoint": None,
-                "max_token": 4096,
+                "max_token": 10124 * 8,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
-            }
+            },
         })
     except:
         print(trimmed_format_exc())
+# -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:
         from .bridge_deepseekcoder import predict_no_ui_long_connection as deepseekcoder_noui
@@ -612,26 +760,34 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
         })
     except:
         print(trimmed_format_exc())
-# if "skylark" in AVAIL_LLM_MODELS:
-#     try:
-#         from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
-#         from .bridge_skylark2 import predict as skylark_ui
-#         model_info.update({
-#             "skylark": {
-#                 "fn_with_ui": skylark_ui,
-#                 "fn_without_ui": skylark_noui,
-#                 "endpoint": None,
-#                 "max_token": 4096,
-#                 "tokenizer": tokenizer_gpt35,
-#                 "token_cnt": get_token_num_gpt35,
-#             }
-#         })
-#     except:
-#         print(trimmed_format_exc())
 
 
-# <-- 用于定义和切换多个azure模型 -->
-AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")
+# -=-=-=-=-=-=- one-api 对齐支持 -=-=-=-=-=-=-
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
+    # 为了更灵活地接入one-api多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["one-api-mixtral-8x7b(max_token=6666)"]
+    # 其中
+    #   "one-api-"          是前缀（必要）
+    #   "mixtral-8x7b"      是模型名（必要）
+    #   "(max_token=6666)"  是配置（非必要）
+    try:
+        _, max_token_tmp = read_one_api_model_name(model)
+    except:
+        print(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        continue
+    model_info.update({
+        model: {
+            "fn_with_ui": chatgpt_ui,
+            "fn_without_ui": chatgpt_noui,
+            "endpoint": openai_endpoint,
+            "max_token": max_token_tmp,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+
+
+# -=-=-=-=-=-=- azure模型对齐支持 -=-=-=-=-=-=-
+AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY") # <-- 用于定义和切换多个azure模型 -->
 if len(AZURE_CFG_ARRAY) > 0:
     for azure_model_name, azure_cfg_dict in AZURE_CFG_ARRAY.items():
         # 可能会覆盖之前的配置，但这是意料之中的
@@ -660,7 +816,7 @@ def LLM_CATCH_EXCEPTION(f):
     """
     装饰器函数，将错误显示出来
     """
-    def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
+    def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
         try:
             return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
         except Exception as e:
@@ -670,9 +826,9 @@ def LLM_CATCH_EXCEPTION(f):
     return decorated
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=False):
     """
-    发送至LLM，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）用stream的方法避免中途网线被掐。
     inputs：
         是本次问询的输入
     sys_prompt:
@@ -690,7 +846,6 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
     model = llm_kwargs['llm_model']
     n_model = 1
     if '&' not in model:
-        assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
 
         # 如果只询问1个大语言模型：
         method = model_info[model]["fn_without_ui"]
@@ -725,7 +880,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
                 # 观察窗（window）
                 chat_string = []
                 for i in range(n_model):
-                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
+                    color = colors[i%len(colors)]
+                    chat_string.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {window_mutex[i][0]} </font>" )
                 res = '<br/><br/>\n\n---\n\n'.join(chat_string)
                 # # # # # # # # # # #
                 observe_window[0] = res
@@ -742,22 +898,30 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
             time.sleep(1)
 
         for i, future in enumerate(futures):  # wait and get
-            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
+            color = colors[i%len(colors)]
+            return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{color}\"> {future.result()} </font>" )
 
         window_mutex[-1] = False # stop mutex thread
         res = '<br/><br/>\n\n---\n\n'.join(return_string_collect)
         return res
 
 
-def predict(inputs, llm_kwargs, *args, **kwargs):
+def predict(inputs:str, llm_kwargs:dict, *args, **kwargs):
     """
     发送至LLM，流式获取输出。
     用于基础的对话功能。
-    inputs 是本次问询的输入
-    top_p, temperature是LLM的内部调优参数
-    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-    additional_fn代表点击的哪个按钮，按钮见functional.py
+
+    完整参数列表：
+        predict(
+            inputs:str,                     # 是本次问询的输入
+            llm_kwargs:dict,                # 是LLM的内部调优参数
+            plugin_kwargs:dict,             # 是插件的内部参数
+            chatbot:ChatBotWithCookies,     # 原样传递，负责向用户前端展示对话，兼顾前端状态的功能
+            history:list=[],                # 是之前的对话列表
+            system_prompt:str='',           # 系统静默prompt
+            stream:bool=True,               # 是否流式输出（已弃用）
+            additional_fn:str=None          # 基础功能区按钮的附加功能
+        ):
     """
 
     inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")

request_llms/bridge_chatglmft.py

@@ -137,7 +137,8 @@ class GetGLMFTHandle(Process):
 global glmft_handle
 glmft_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_chatgpt.py

@@ -21,7 +21,9 @@ import random
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
+from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
+from toolbox import ChatBotWithCookies
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
     get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
 
@@ -68,7 +70,7 @@ def verify_endpoint(endpoint):
         raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
     return endpoint
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -113,6 +115,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             error_msg = get_full_error(chunk, stream_response).decode()
             if "reduce the length" in error_msg:
                 raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
+            elif """type":"upstream_error","param":"307""" in error_msg:
+                raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
             else:
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
         if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
@@ -123,8 +127,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
         json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
         if len(delta) == 0: break
-        if "role" in delta: continue
-        if "content" in delta:
+        if (not has_content) and has_role: continue
+        if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
+        if has_content: # has_role = True/False
             result += delta["content"]
             if not console_slience: print(delta["content"], end='')
             if observe_window is not None:
@@ -143,7 +148,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     return result
 
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
     发送至chatGPT，流式获取输出。
     用于基础的对话功能。
@@ -169,7 +175,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
     raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
+    # logging.info(f'[raw_input] {raw_input}')
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
@@ -250,7 +256,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
                     if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['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}')
+                        # logging.info(f'[response] {gpt_replying_buffer}')
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -262,7 +269,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         # 一些第三方接口的出现这样的错误，兼容一下吧
                         continue
                     else:
-                        # 一些垃圾第三方接口的出现这样的错误
+                        # 至此已经超出了正常接口应该进入的范围，一些垃圾第三方接口会出现这样的错误
+                        if chunkjson['choices'][0]["delta"]["content"] is None: continue # 一些垃圾第三方接口出现这样的错误，兼容一下吧
                         gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
 
                     history[-1] = gpt_replying_buffer
@@ -354,6 +362,9 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     model = llm_kwargs['llm_model']
     if llm_kwargs['llm_model'].startswith('api2d-'):
         model = llm_kwargs['llm_model'][len('api2d-'):]
+    if llm_kwargs['llm_model'].startswith('one-api-'):
+        model = llm_kwargs['llm_model'][len('one-api-'):]
+        model, _ = read_one_api_model_name(model)
 
     if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
         model = random.choice([

request_llms/bridge_claude.py

@@ -9,15 +9,15 @@
     具备多线程调用能力的函数
     2. predict_no_ui_long_connection：支持多线程
 """
-
-import os
-import json
-import time
-import gradio as gr
 import logging
+import os
+import time
 import traceback
+import json
 import requests
-import importlib
+from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path, log_chat
+picture_system_prompt = "\n当回复图像时,必须说明正在回复哪张图像。所有图像仅在最后一个问题中提供,即使它们在历史记录中被提及。请使用'这是第X张图像:'的格式来指明您正在描述的是哪张图像。"
+Claude_3_Models = ["claude-3-haiku-20240307", "claude-3-sonnet-20240229", "claude-3-opus-20240229"]
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -39,6 +39,34 @@ def get_full_error(chunk, stream_response):
             break
     return chunk
 
+def decode_chunk(chunk):
+    # 提前读取一些信息（用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    is_last_chunk = False
+    need_to_pass = False
+    if chunk_decoded.startswith('data:'):
+        try:
+            chunkjson = json.loads(chunk_decoded[6:])
+        except:
+            need_to_pass = True
+            pass
+    elif chunk_decoded.startswith('event:'):
+        try:
+            event_type = chunk_decoded.split(':')[1].strip()
+            if event_type == 'content_block_stop' or event_type == 'message_stop':
+                is_last_chunk = True
+            elif event_type == 'content_block_start' or event_type == 'message_start':
+                need_to_pass = True
+                pass
+        except:
+            need_to_pass = True
+            pass
+    else:
+        need_to_pass = True
+        pass
+    return need_to_pass, chunkjson, is_last_chunk
+
 
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
     """
@@ -54,50 +82,67 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     observe_window = None：
         用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
     """
-    from anthropic import Anthropic
     watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
-    retry = 0
     if len(ANTHROPIC_API_KEY) == 0:
         raise RuntimeError("没有设置ANTHROPIC_API_KEY选项")
+    if inputs == "":     inputs = "空空如也的输入栏"
+    headers, message = generate_payload(inputs, llm_kwargs, history, sys_prompt, image_paths=None)
+    retry = 0
+
 
     while True:
         try:
             # make a POST request to the API endpoint, stream=False
             from .bridge_all import model_info
-            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
-            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            # with ProxyNetworkActivate()
-            stream = anthropic.completions.create(
-                prompt=prompt,
-                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
-                model=llm_kwargs['llm_model'],
-                stream=True,
-                temperature = llm_kwargs['temperature']
-            )
-            break
-        except Exception as e:
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, json=message,
+                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
+        except requests.exceptions.ReadTimeout as e:
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
             if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+    stream_response = response.iter_lines()
     result = ''
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        need_to_pass, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if chunk:
             try:
-        for completion in stream:
-            result += completion.completion
-            if not console_slience: print(completion.completion, end='')
+                if need_to_pass:
+                    pass
+                elif is_last_chunk:
+                    # logging.info(f'[response] {result}')
+                    break
+                else:
+                    if chunkjson and chunkjson['type'] == 'content_block_delta':
+                        result += chunkjson['delta']['text']
+                        print(chunkjson['delta']['text'], end='')
                         if observe_window is not None:
                             # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += completion.completion
+                            if len(observe_window) >= 1:
+                                observe_window[0] += chunkjson['delta']['text']
                             # 看门狗，如果超过期限没有喂狗，则终止
                             if len(observe_window) >= 2:
                                 if (time.time()-observe_window[1]) > watch_dog_patience:
                                     raise RuntimeError("用户取消了程序。")
             except Exception as e:
-        traceback.print_exc()
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                print(error_msg)
+                raise RuntimeError("Json解析不合常规")
 
     return result
 
+def make_media_input(history,inputs,image_paths):
+    for image_path in image_paths:
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
+    return inputs
 
 def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
     """
@@ -109,7 +154,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
-    from anthropic import Anthropic
+    if inputs == "":     inputs = "空空如也的输入栏"
     if len(ANTHROPIC_API_KEY) == 0:
         chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY"))
         yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
@@ -119,13 +164,23 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
+    have_recent_file, image_paths = every_image_file_in_path(chatbot)
+    if len(image_paths) > 20:
+        chatbot.append((inputs, "图片数量超过api上限(20张)"))
+        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应")
+        return
+
+    if any([llm_kwargs['llm_model'] == model for model in Claude_3_Models]) and have_recent_file:
+        if inputs == "" or inputs == "空空如也的输入栏":     inputs = "请描述给出的图片"
+        system_prompt += picture_system_prompt  # 由于没有单独的参数保存包含图片的历史，所以只能通过提示词对第几张图片进行定位
+        chatbot.append((make_media_input(history,inputs, image_paths), ""))
+        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+    else:
         chatbot.append((inputs, ""))
         yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
     try:
-        prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+        headers, message = generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths)
     except RuntimeError as e:
         chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
         yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
@@ -138,91 +193,117 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         try:
             # make a POST request to the API endpoint, stream=True
             from .bridge_all import model_info
-            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
-            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            # with ProxyNetworkActivate()
-            stream = anthropic.completions.create(
-                prompt=prompt,
-                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
-                model=llm_kwargs['llm_model'],
-                stream=True,
-                temperature = llm_kwargs['temperature']
-            )
-            
-            break
-        except:
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, json=message,
+                                     proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
+        except requests.exceptions.ReadTimeout as e:
             retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+    stream_response = response.iter_lines()
     gpt_replying_buffer = ""
 
-    for completion in stream:
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        need_to_pass, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if chunk:
             try:
-            gpt_replying_buffer = gpt_replying_buffer + completion.completion
+                if need_to_pass:
+                    pass
+                elif is_last_chunk:
+                    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                    # logging.info(f'[response] {gpt_replying_buffer}')
+                    break
+                else:
+                    if chunkjson and chunkjson['type'] == 'content_block_delta':
+                        gpt_replying_buffer += chunkjson['delta']['text']
                         history[-1] = gpt_replying_buffer
                         chatbot[-1] = (history[-2], history[-1])
                         yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面
 
             except Exception as e:
-            from toolbox import regular_txt_to_markdown
-            tb_str = '```\n' + trimmed_format_exc() + '```'
-            chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str}")
-            yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
-            return
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                print(error_msg)
+                raise RuntimeError("Json解析不合常规")
 
+def multiple_picture_types(image_paths):
+    """
+    根据图片类型返回image/jpeg, image/png, image/gif, image/webp，无法判断则返回image/jpeg
+    """
+    for image_path in image_paths:
+        if image_path.endswith('.jpeg') or image_path.endswith('.jpg'):
+            return 'image/jpeg'
+        elif image_path.endswith('.png'):
+            return 'image/png'
+        elif image_path.endswith('.gif'):
+            return 'image/gif'
+        elif image_path.endswith('.webp'):
+            return 'image/webp'
+    return 'image/jpeg'
 
-
-
-# https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py
-def convert_messages_to_prompt(messages):
-    prompt = ""
-    role_map = {
-        "system": "Human",
-        "user": "Human",
-        "assistant": "Assistant",
-    }
-    for message in messages:
-        role = message["role"]
-        content = message["content"]
-        transformed_role = role_map[role]
-        prompt += f"\n\n{transformed_role.capitalize()}: {content}"
-    prompt += "\n\nAssistant: "
-    return prompt
-
-def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
     """
     整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
     """
-    from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
 
     conversation_cnt = len(history) // 2
 
-    messages = [{"role": "system", "content": system_prompt}]
+    messages = []
+
     if conversation_cnt:
         for index in range(0, 2*conversation_cnt, 2):
             what_i_have_asked = {}
             what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
+            what_i_have_asked["content"] = [{"type": "text", "text": history[index]}]
             what_gpt_answer = {}
             what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
+            what_gpt_answer["content"] = [{"type": "text", "text": history[index+1]}]
+            if what_i_have_asked["content"][0]["text"] != "":
+                if what_i_have_asked["content"][0]["text"] == "": continue
+                if what_i_have_asked["content"][0]["text"] == timeout_bot_msg: continue
                 messages.append(what_i_have_asked)
                 messages.append(what_gpt_answer)
             else:
-                messages[-1]['content'] = what_gpt_answer['content']
+                messages[-1]['content'][0]['text'] = what_gpt_answer['content'][0]['text']
 
+    if any([llm_kwargs['llm_model'] == model for model in Claude_3_Models]) and image_paths:
         what_i_ask_now = {}
         what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
+        what_i_ask_now["content"] = []
+        for image_path in image_paths:
+            what_i_ask_now["content"].append({
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": multiple_picture_types(image_paths),
+                    "data": encode_image(image_path),
+                }
+            })
+        what_i_ask_now["content"].append({"type": "text", "text": inputs})
+    else:
+        what_i_ask_now = {}
+        what_i_ask_now["role"] = "user"
+        what_i_ask_now["content"] = [{"type": "text", "text": inputs}]
     messages.append(what_i_ask_now)
-    prompt = convert_messages_to_prompt(messages)
-
-    return prompt
-
-
+    # 开始整理headers与message
+    headers = {
+        'x-api-key': ANTHROPIC_API_KEY,
+        'anthropic-version': '2023-06-01',
+        'content-type': 'application/json'
+    }
+    payload = {
+        'model': llm_kwargs['llm_model'],
+        'max_tokens': 4096,
+        'messages': messages,
+        'temperature': llm_kwargs['temperature'],
+        'stream': True,
+        'system': system_prompt
+    }
+    return headers, payload

request_llms/bridge_cohere.py

@@ -0,0 +1,328 @@
+# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
+
+"""
+    该文件中主要包含三个函数
+
+    不具备多线程能力的函数：
+    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
+
+    具备多线程调用能力的函数
+    2. predict_no_ui_long_connection：支持多线程
+"""
+
+import json
+import time
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+import random
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
+from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
+from toolbox import ChatBotWithCookies
+proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
+    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
+                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Cohere返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def decode_chunk(chunk):
+    # 提前读取一些信息 （用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    has_choices = False
+    choice_valid = False
+    has_content = False
+    has_role = False
+    try:
+        chunkjson = json.loads(chunk_decoded)
+        has_choices = 'choices' in chunkjson
+        if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
+        if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
+        if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
+        if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
+    except:
+        pass
+    return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
+
+from functools import lru_cache
+@lru_cache(maxsize=32)
+def verify_endpoint(endpoint):
+    """
+        检查endpoint是否可用
+    """
+    if "你亲手写的api名称" in endpoint:
+        raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
+    return endpoint
+
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+    """
+    发送，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    inputs：
+        是本次问询的输入
+    sys_prompt:
+        系统静默prompt
+    llm_kwargs：
+        内部调优参数
+    history：
+        是之前的对话列表
+    observe_window = None：
+        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            from .bridge_all import model_info
+            endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
+        except requests.exceptions.ReadTimeout as e:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    stream_response = response.iter_lines()
+    result = ''
+    json_data = None
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+        if chunkjson['event_type'] == 'stream-start': continue
+        if chunkjson['event_type'] == 'text-generation':
+            result += chunkjson["text"]
+            if not console_slience: print(chunkjson["text"], end='')
+            if observe_window is not None:
+                # 观测窗，把已经获取的数据显示出去
+                if len(observe_window) >= 1:
+                    observe_window[0] += chunkjson["text"]
+                # 看门狗，如果超过期限没有喂狗，则终止
+                if len(observe_window) >= 2:
+                    if (time.time()-observe_window[1]) > watch_dog_patience:
+                        raise RuntimeError("用户取消了程序。")
+        if chunkjson['event_type'] == 'stream-end': break
+    return result
+
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    """
+    发送至chatGPT，流式获取输出。
+    用于基础的对话功能。
+    inputs 是本次问询的输入
+    top_p, temperature是chatGPT的内部调优参数
+    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    # if is_any_api_key(inputs):
+    #     chatbot._cookies['api_key'] = inputs
+    #     chatbot.append(("输入已识别为Cohere的api_key", what_keys(inputs)))
+    #     yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
+    #     return
+    # elif not is_any_api_key(chatbot._cookies['api_key']):
+    #     chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
+    #     yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
+    #     return
+
+    user_input = inputs
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    raw_input = inputs
+    # logging.info(f'[raw_input] {raw_input}')
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
+    try:
+        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+    except RuntimeError as e:
+        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
+        return
+
+    # 检查endpoint是否合法
+    try:
+        from .bridge_all import model_info
+        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+    except:
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (inputs, tb_str)
+        yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
+        return
+
+    history.append(inputs); history.append("")
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            if retry > MAX_RETRY: raise TimeoutError
+
+    gpt_replying_buffer = ""
+
+    is_head_of_the_stream = True
+    if stream:
+        stream_response =  response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                # 非Cohere官方接口的出现这样的报错，Cohere和API2D不会走这里
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                # 其他情况，直接返回报错
+                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                yield from update_ui(chatbot=chatbot, history=history, msg="非Cohere官方接口返回了错误:" + chunk.decode()) # 刷新界面
+                return
+
+            # 提前读取一些信息 （用于判断异常）
+            chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+
+            if chunkjson:
+                try:
+                    if chunkjson['event_type'] == 'stream-start':
+                        continue
+                    if chunkjson['event_type'] == 'text-generation':
+                        gpt_replying_buffer = gpt_replying_buffer + chunkjson["text"]
+                        history[-1] = gpt_replying_buffer
+                        chatbot[-1] = (history[-2], history[-1])
+                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+                    if chunkjson['event_type'] == 'stream-end':
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        history[-1] = gpt_replying_buffer
+                        chatbot[-1] = (history[-2], history[-1])
+                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+                        break
+                except Exception as e:
+                    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)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    print(error_msg)
+                    return
+
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+    from .bridge_all import model_info
+    Cohere_website = ' 请登录Cohere查看详情 https://platform.Cohere.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.)")
+    elif "does not exist" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
+    elif "Incorrect API key" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. Cohere以提供了不正确的API_KEY为由, 拒绝服务. " + Cohere_website)
+    elif "exceeded your current quota" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. Cohere以账户额度不足为由, 拒绝服务." + Cohere_website)
+    elif "account is not active" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
+    elif "associated with a deactivated account" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
+    elif "API key has been deactivated" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
+    elif "bad forward key" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
+    elif "Not enough point" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
+    else:
+        from toolbox import regular_txt_to_markdown
+        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)}")
+    return chatbot, history
+
+def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    # if not is_any_api_key(llm_kwargs['api_key']):
+    #     raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
+
+    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
+    if API_ORG.startswith('org-'): headers.update({"Cohere-Organization": API_ORG})
+    if llm_kwargs['llm_model'].startswith('azure-'):
+        headers.update({"api-key": api_key})
+        if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
+            azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
+            headers.update({"api-key": azure_api_key_unshared})
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "SYSTEM", "message": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "USER"
+            what_i_have_asked["message"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "CHATBOT"
+            what_gpt_answer["message"] = history[index+1]
+            if what_i_have_asked["message"] != "":
+                if what_gpt_answer["message"] == "": continue
+                if what_gpt_answer["message"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['message'] = what_gpt_answer['message']
+
+    model = llm_kwargs['llm_model']
+    if model.startswith('cohere-'): model = model[len('cohere-'):]
+    payload = {
+        "model": model,
+        "message": inputs,
+        "chat_history": messages,
+        "temperature": llm_kwargs['temperature'],  # 1.0,
+        "top_p": llm_kwargs['top_p'],  # 1.0,
+        "n": 1,
+        "stream": stream,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+    }
+
+    return headers,payload
+
+

request_llms/bridge_google_gemini.py

@@ -7,6 +7,7 @@ import re
 import os
 import time
 from request_llms.com_google import GoogleChatInit
+from toolbox import ChatBotWithCookies
 from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
@@ -20,7 +21,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     if get_conf("GEMINI_API_KEY") == "":
         raise ValueError(f"请配置 GEMINI_API_KEY。")
 
-    genai = GoogleChatInit()
+    genai = GoogleChatInit(llm_kwargs)
     watch_dog_patience = 5  # 看门狗的耐心, 设置5秒即可
     gpt_replying_buffer = ''
     stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
@@ -44,7 +45,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     return gpt_replying_buffer
 
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
         yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
@@ -57,6 +59,10 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
     if "vision" in llm_kwargs["llm_model"]:
         have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
+        if not have_recent_file:
+            chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
+            yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
+            return
         def make_media_input(inputs, image_paths):
             for image_path in image_paths:
                 inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
@@ -66,7 +72,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history)
-    genai = GoogleChatInit()
+    genai = GoogleChatInit(llm_kwargs)
     retry = 0
     while True:
         try:

request_llms/bridge_jittorllms_llama.py

@@ -1,10 +1,10 @@
 
-from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
 from toolbox import update_ui, get_conf
 from multiprocessing import Process, Pipe
+from transformers import AutoModel, AutoTokenizer
 
 load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global llama_glm_handle
 llama_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_pangualpha.py

@@ -1,10 +1,10 @@
 
-from transformers import AutoModel, AutoTokenizer
 import time
 import threading
 import importlib
 from toolbox import update_ui, get_conf
 from multiprocessing import Process, Pipe
+from transformers import AutoModel, AutoTokenizer
 
 load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global pangu_glm_handle
 pangu_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_jittorllms_rwkv.py

@@ -106,7 +106,8 @@ class GetGLMHandle(Process):
 global rwkv_glm_handle
 rwkv_glm_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_moonshot.py

@@ -0,0 +1,197 @@
+# encoding: utf-8
+# @Time   : 2024/3/3
+# @Author : Spike
+# @Descr   :
+import json
+import os
+import time
+import logging
+
+from toolbox import get_conf, update_ui, log_chat
+from toolbox import ChatBotWithCookies
+
+import requests
+
+
+class MoonShotInit:
+
+    def __init__(self):
+        self.llm_model = None
+        self.url = 'https://api.moonshot.cn/v1/chat/completions'
+        self.api_key = get_conf('MOONSHOT_API_KEY')
+
+    def __converter_file(self, user_input: str):
+        what_ask = []
+        for f in user_input.splitlines():
+            if os.path.exists(f):
+                files = []
+                if os.path.isdir(f):
+                    file_list = os.listdir(f)
+                    files.extend([os.path.join(f, file) for file in file_list])
+                else:
+                    files.append(f)
+                for file in files:
+                    if file.split('.')[-1] in ['pdf']:
+                        with open(file, 'r') as fp:
+                            from crazy_functions.crazy_utils import read_and_clean_pdf_text
+                            file_content, _ = read_and_clean_pdf_text(fp)
+                        what_ask.append({"role": "system", "content": file_content})
+        return what_ask
+
+    def __converter_user(self, user_input: str):
+        what_i_ask_now = {"role": "user", "content": user_input}
+        return what_i_ask_now
+
+    def __conversation_history(self, history):
+        conversation_cnt = len(history) // 2
+        messages = []
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = {
+                    "role": "user",
+                    "content": str(history[index])
+                }
+                what_gpt_answer = {
+                    "role": "assistant",
+                    "content": str(history[index + 1])
+                }
+                if what_i_have_asked["content"] != "":
+                    if what_gpt_answer["content"] == "": continue
+                    messages.append(what_i_have_asked)
+                    messages.append(what_gpt_answer)
+                else:
+                    messages[-1]['content'] = what_gpt_answer['content']
+        return messages
+
+    def _analysis_content(self, chuck):
+        chunk_decoded = chuck.decode("utf-8")
+        chunk_json = {}
+        content = ""
+        try:
+            chunk_json = json.loads(chunk_decoded[6:])
+            content = chunk_json['choices'][0]["delta"].get("content", "")
+        except:
+            pass
+        return chunk_decoded, chunk_json, content
+
+    def generate_payload(self, inputs, llm_kwargs, history, system_prompt, stream):
+        self.llm_model = llm_kwargs['llm_model']
+        llm_kwargs.update({'use-key': self.api_key})
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.extend(self.__converter_file(inputs))
+        for i in history[0::2]:    # 历史文件继续上传
+            messages.extend(self.__converter_file(i))
+        messages.extend(self.__conversation_history(history))
+        messages.append(self.__converter_user(inputs))
+        header = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+        }
+        payload = {
+            "model": self.llm_model,
+            "messages": messages,
+            "temperature": llm_kwargs.get('temperature', 0.3),  # 1.0,
+            "top_p": llm_kwargs.get('top_p', 1.0),  # 1.0,
+            "n": llm_kwargs.get('n_choices', 1),
+            "stream": stream
+        }
+        return payload, header
+
+    def generate_messages(self, inputs, llm_kwargs, history, system_prompt, stream):
+        payload, headers = self.generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+        response = requests.post(self.url, headers=headers, json=payload, stream=stream)
+
+        chunk_content = ""
+        gpt_bro_result = ""
+        for chuck in response.iter_lines():
+            chunk_decoded, check_json, content = self._analysis_content(chuck)
+            chunk_content += chunk_decoded
+            if content:
+                gpt_bro_result += content
+                yield content, gpt_bro_result, ''
+            else:
+                error_msg = msg_handle_error(llm_kwargs, chunk_decoded)
+                if error_msg:
+                    yield error_msg, gpt_bro_result, error_msg
+                    break
+
+
+def msg_handle_error(llm_kwargs, chunk_decoded):
+    use_ket = llm_kwargs.get('use-key', '')
+    api_key_encryption = use_ket[:8] + '****' + use_ket[-5:]
+    openai_website = f' 请登录OpenAI查看详情 https://platform.openai.com/signup  api-key: `{api_key_encryption}`'
+    error_msg = ''
+    if "does not exist" in chunk_decoded:
+        error_msg = 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 chunk_decoded:
+        error_msg = f"[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 chunk_decoded:
+        error_msg = "[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 chunk_decoded:
+        error_msg = "[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 chunk_decoded:
+        error_msg = "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website
+    elif "API key has been deactivated" in chunk_decoded:
+        error_msg = "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website
+    elif "bad forward key" in chunk_decoded:
+        error_msg = "[Local Message] Bad forward key. API2D账户额度不足."
+    elif "Not enough point" in chunk_decoded:
+        error_msg = "[Local Message] Not enough point. API2D账户点数不足."
+    elif 'error' in str(chunk_decoded).lower():
+        try:
+            error_msg = json.dumps(json.loads(chunk_decoded[:6]), indent=4, ensure_ascii=False)
+        except:
+            error_msg = chunk_decoded
+    return error_msg
+
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    chatbot.append([inputs, ""])
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应")  # 刷新界面
+    gpt_bro_init = MoonShotInit()
+    history.extend([inputs, ''])
+    stream_response = gpt_bro_init.generate_messages(inputs, llm_kwargs, history, system_prompt, stream)
+    for content, gpt_bro_result, error_bro_meg in stream_response:
+        chatbot[-1] = [inputs, gpt_bro_result]
+        history[-1] = gpt_bro_result
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        if error_bro_meg:
+            chatbot[-1] = [inputs, error_bro_meg]
+            history = history[:-2]
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            break
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result)
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
+                                  console_slience=False):
+    gpt_bro_init = MoonShotInit()
+    watch_dog_patience = 60  # 看门狗的耐心, 设置10秒即可
+    stream_response = gpt_bro_init.generate_messages(inputs, llm_kwargs, history, sys_prompt, True)
+    moonshot_bro_result = ''
+    for content, moonshot_bro_result, error_bro_meg in stream_response:
+        moonshot_bro_result = moonshot_bro_result
+        if error_bro_meg:
+            if len(observe_window) >= 3:
+                observe_window[2] = error_bro_meg
+            return f'{moonshot_bro_result} 对话错误'
+            # 观测窗
+        if len(observe_window) >= 1:
+            observe_window[0] = moonshot_bro_result
+        if len(observe_window) >= 2:
+            if (time.time() - observe_window[1]) > watch_dog_patience:
+                observe_window[2] = "请求超时，程序终止。"
+                raise RuntimeError(f"{moonshot_bro_result} 程序终止。")
+    return moonshot_bro_result
+
+if __name__ == '__main__':
+    moon_ai = MoonShotInit()
+    for g in moon_ai.generate_messages('hello', {'llm_model': 'moonshot-v1-8k'},
+                                       [], '', True):
+        print(g)

request_llms/bridge_moss.py

@@ -171,7 +171,8 @@ class GetGLMHandle(Process):
 global moss_handle
 moss_handle = None
 #################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         多线程方法
         函数的说明请见 request_llms/bridge_all.py

request_llms/bridge_qianfan.py

@@ -117,7 +117,8 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
                 raise RuntimeError(dec['error_msg'])
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -146,9 +147,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     yield from update_ui(chatbot=chatbot, history=history)
     # 开始接收回复
     try:
+        response = f"[Local Message] 等待{model_name}响应中 ..."
         for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
             chatbot[-1] = (inputs, response)
             yield from update_ui(chatbot=chatbot, history=history)
+        history.extend([inputs, response])
+        yield from update_ui(chatbot=chatbot, history=history)
     except ConnectionAbortedError as e:
         from .bridge_all import model_info
         if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
@@ -157,10 +161,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
         yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
         return
-    
-    # 总结输出
-    response = f"[Local Message] {model_name}响应异常 ..."
-    if response == f"[Local Message] 等待{model_name}响应中 ...":
-        response = f"[Local Message] {model_name}响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)
+    except RuntimeError as e:
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (chatbot[-1][0], tb_str)
+        yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
+        return

request_llms/bridge_qwen.py

@@ -5,7 +5,8 @@ from toolbox import check_packages, report_exception
 
 model_name = 'Qwen'
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -47,10 +48,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     if additional_fn is not None:
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        chatbot[-1] = (inputs, "")
+        yield from update_ui(chatbot=chatbot, history=history)
 
     # 开始接收回复
     from .com_qwenapi import QwenRequestInstance
     sri = QwenRequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_skylark2.py

@@ -9,7 +9,8 @@ def validate_key():
     if YUNQUE_SECRET_KEY == '': return False
     return True
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐ 多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -56,6 +57,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     # 开始接收回复
     from .com_skylark2api import YUNQUERequestInstance
     sri = YUNQUERequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_spark.py

@@ -13,7 +13,8 @@ def validate_key():
         return False
     return True
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -52,6 +53,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     # 开始接收回复
     from .com_sparkapi import SparkRequestInstance
     sri = SparkRequestInstance()
+    response = f"[Local Message] 等待{model_name}响应中 ..."
     for response in sri.generate(inputs, llm_kwargs, history, system_prompt, use_image_api=True):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_yimodel.py

@@ -0,0 +1,283 @@
+# 借鉴自同目录下的bridge_chatgpt.py
+
+"""
+    该文件中主要包含三个函数
+
+    不具备多线程能力的函数：
+    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
+
+    具备多线程调用能力的函数
+    2. predict_no_ui_long_connection：支持多线程
+"""
+
+import json
+import time
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+import random
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, trimmed_format_exc, is_the_upload_folder, read_one_api_model_name
+proxies, TIMEOUT_SECONDS, MAX_RETRY, YIMODEL_API_KEY = \
+    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'YIMODEL_API_KEY')
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
+                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Openai返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def decode_chunk(chunk):
+    # 提前读取一些信息（用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    is_last_chunk = False
+    try:
+        chunkjson = json.loads(chunk_decoded[6:])
+        is_last_chunk = chunkjson.get("lastOne", False)
+    except:
+        pass
+    return chunk_decoded, chunkjson, is_last_chunk
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+    """
+    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    inputs：
+        是本次问询的输入
+    sys_prompt:
+        系统静默prompt
+    llm_kwargs：
+        chatGPT的内部调优参数
+    history：
+        是之前的对话列表
+    observe_window = None：
+        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+    if inputs == "":     inputs = "空空如也的输入栏"
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            from .bridge_all import model_info
+            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
+        except requests.exceptions.ReadTimeout as e:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    stream_response = response.iter_lines()
+    result = ''
+    is_head_of_the_stream = True
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
+        if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r'"role":"assistant"' in chunk_decoded):
+            # 数据流的第一帧不携带content
+            is_head_of_the_stream = False; continue
+        if chunk:
+            try:
+                if is_last_chunk:
+                    # 判定为数据流的结束，gpt_replying_buffer也写完了
+                    logging.info(f'[response] {result}')
+                    break
+                result += chunkjson['choices'][0]["delta"]["content"]
+                if not console_slience: print(chunkjson['choices'][0]["delta"]["content"], end='')
+                if observe_window is not None:
+                    # 观测窗，把已经获取的数据显示出去
+                    if len(observe_window) >= 1:
+                        observe_window[0] += chunkjson['choices'][0]["delta"]["content"]
+                    # 看门狗，如果超过期限没有喂狗，则终止
+                    if len(observe_window) >= 2:
+                        if (time.time()-observe_window[1]) > watch_dog_patience:
+                            raise RuntimeError("用户取消了程序。")
+            except Exception as e:
+                chunk = get_full_error(chunk, stream_response)
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                print(error_msg)
+                raise RuntimeError("Json解析不合常规")
+    return result
+
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+    发送至chatGPT，流式获取输出。
+    用于基础的对话功能。
+    inputs 是本次问询的输入
+    top_p, temperature是chatGPT的内部调优参数
+    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    if len(YIMODEL_API_KEY) == 0:
+        raise RuntimeError("没有设置YIMODEL_API_KEY选项")
+    if inputs == "":     inputs = "空空如也的输入栏"
+    user_input = inputs
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    raw_input = inputs
+    logging.info(f'[raw_input] {raw_input}')
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+
+    from .bridge_all import model_info
+    endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+
+    history.append(inputs); history.append("")
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            if retry > MAX_RETRY: raise TimeoutError
+
+    gpt_replying_buffer = ""
+
+    is_head_of_the_stream = True
+    if stream:
+        stream_response =  response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                break
+            except requests.exceptions.ConnectionError:
+                chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+
+            # 提前读取一些信息 （用于判断异常）
+            chunk_decoded, chunkjson, is_last_chunk = decode_chunk(chunk)
+
+            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r'"role":"assistant"' in chunk_decoded):
+                # 数据流的第一帧不携带content
+                is_head_of_the_stream = False; continue
+
+            if chunk:
+                try:
+                    if is_last_chunk:
+                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                        logging.info(f'[response] {gpt_replying_buffer}')
+                        break
+                    # 处理数据流的主体
+                    status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
+                    gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
+                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    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:
+                    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)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    print(error_msg)
+                    return
+
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+    from .bridge_all import model_info
+    if "bad_request" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 已经超过了模型的最大上下文或是模型格式错误,请尝试削减单次输入的文本量。")
+    elif "authentication_error" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. 请确保API key有效。")
+    elif "not_found" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] {llm_kwargs['llm_model']} 无效，请确保使用小写的模型名称。")
+    elif "rate_limit" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 遇到了控制请求速率限制，请一分钟后重试。")
+    elif "system_busy" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] 系统繁忙，请一分钟后重试。")
+    else:
+        from toolbox import regular_txt_to_markdown
+        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)}")
+    return chatbot, history
+
+def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    api_key = f"Bearer {YIMODEL_API_KEY}"
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": api_key
+    }
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    model = llm_kwargs['llm_model']
+    if llm_kwargs['llm_model'].startswith('one-api-'):
+        model = llm_kwargs['llm_model'][len('one-api-'):]
+        model, _ = read_one_api_model_name(model)
+    tokens = 600 if llm_kwargs['llm_model'] == 'yi-34b-chat-0205' else 4096    #yi-34b-chat-0205只有4k上下文...
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": llm_kwargs['temperature'],  # 1.0,
+        "stream": stream,
+        "max_tokens": tokens
+    }
+    try:
+        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
+    except:
+        print('输入中可能存在乱码。')
+    return headers,payload

request_llms/bridge_zhipu.py

@@ -1,16 +1,24 @@
-
 import time
-from toolbox import update_ui, get_conf, update_ui_lastest_msg
-from toolbox import check_packages, report_exception
+import os
+from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
+from toolbox import ChatBotWithCookies
 
 model_name = '智谱AI大模型'
+zhipuai_default_model = 'glm-4'
 
 def validate_key():
     ZHIPUAI_API_KEY = get_conf("ZHIPUAI_API_KEY")
     if ZHIPUAI_API_KEY == '': return False
     return True
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+def make_media_input(inputs, image_paths):
+    for image_path in image_paths:
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
+    return inputs
+
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
+                                  observe_window:list=[], console_slience:bool=False):
     """
         ⭐多线程方法
         函数的说明请见 request_llms/bridge_all.py
@@ -18,31 +26,38 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     watch_dog_patience = 5
     response = ""
 
+    if llm_kwargs["llm_model"] == "zhipuai":
+        llm_kwargs["llm_model"] = zhipuai_default_model
+
     if validate_key() is False:
         raise RuntimeError('请配置ZHIPUAI_API_KEY')
 
-    from .com_zhipuapi import ZhipuRequestInstance
-    sri = ZhipuRequestInstance()
-    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+    # 开始接收回复
+    from .com_zhipuglm import ZhipuChatInit
+    zhipu_bro_init = ZhipuChatInit()
+    for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, sys_prompt):
         if len(observe_window) >= 1:
             observe_window[0] = response
         if len(observe_window) >= 2:
-            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+            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):
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
         ⭐单线程方法
         函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, ""))
+    chatbot.append([inputs, ""])
     yield from update_ui(chatbot=chatbot, history=history)
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
     try:
         check_packages(["zhipuai"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install zhipuai==1.0.7```。",
+        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
             chatbot=chatbot, history=history, delay=0)
         return
 
@@ -53,16 +68,30 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     if additional_fn is not None:
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        chatbot[-1] = [inputs, ""]
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    if llm_kwargs["llm_model"] == "zhipuai":
+        llm_kwargs["llm_model"] = zhipuai_default_model
+
+    if llm_kwargs["llm_model"] in ["glm-4v"]:
+        have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
+        if not have_recent_file:
+            chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
+            yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
+            return
+        if have_recent_file:
+            inputs = make_media_input(inputs, image_paths)
+            chatbot[-1] = [inputs, ""]
+            yield from update_ui(chatbot=chatbot, history=history)
+
 
     # 开始接收回复
-    from .com_zhipuapi import ZhipuRequestInstance
-    sri = ZhipuRequestInstance()
-    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
-        chatbot[-1] = (inputs, response)
+    from .com_zhipuglm import ZhipuChatInit
+    zhipu_bro_init = ZhipuChatInit()
+    for chunk, response in zhipu_bro_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
+        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])
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/com_google.py

@@ -7,7 +7,7 @@ import os
 import re
 import requests
 from typing import List, Dict, Tuple
-from toolbox import get_conf, encode_image, get_pictures_list
+from toolbox import get_conf, encode_image, get_pictures_list, to_markdown_tabs
 
 proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS")
 
@@ -112,38 +112,12 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr
     return a
 
 
-def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False):
-    """
-    Args:
-        head: 表头：[]
-        tabs: 表值：[[列1], [列2], [列3], [列4]]
-        alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
-        column: True to keep data in columns, False to keep data in rows (default).
-    Returns:
-        A string representation of the markdown table.
-    """
-    if column:
-        transposed_tabs = list(map(list, zip(*tabs)))
-    else:
-        transposed_tabs = tabs
-    # Find the maximum length among the columns
-    max_len = max(len(column) for column in transposed_tabs)
-
-    tab_format = "| %s "
-    tabs_list = "".join([tab_format % i for i in head]) + "|\n"
-    tabs_list += "".join([tab_format % alignment for i in head]) + "|\n"
-
-    for i in range(max_len):
-        row_data = [tab[i] if i < len(tab) else "" for tab in transposed_tabs]
-        row_data = file_manifest_filter_html(row_data, filter_=None)
-        tabs_list += "".join([tab_format % i for i in row_data]) + "|\n"
-
-    return tabs_list
-
 
 class GoogleChatInit:
-    def __init__(self):
-        self.url_gemini = "https://generativelanguage.googleapis.com/v1beta/models/%m:streamGenerateContent?key=%k"
+    def __init__(self, llm_kwargs):
+        from .bridge_all import model_info
+        endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+        self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k"
 
     def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
         headers, payload = self.generate_message_payload(

request_llms/com_qwenapi.py

@@ -48,6 +48,10 @@ class QwenRequestInstance():
         for response in responses:
             if response.status_code == HTTPStatus.OK:
                 if response.output.choices[0].finish_reason == 'stop':
+                    try:
+                        self.result_buf += response.output.choices[0].message.content
+                    except:
+                        pass
                     yield self.result_buf
                     break
                 elif response.output.choices[0].finish_reason == 'length':

request_llms/com_sparkapi.py

@@ -65,6 +65,7 @@ class SparkRequestInstance():
         self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
         self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
         self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
+        self.gpt_url_v35 = "wss://spark-api.xf-yun.com/v3.5/chat"
         self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
 
         self.time_to_yield_event = threading.Event()
@@ -91,6 +92,8 @@ class SparkRequestInstance():
             gpt_url = self.gpt_url_v2
         elif llm_kwargs['llm_model'] == 'sparkv3':
             gpt_url = self.gpt_url_v3
+        elif llm_kwargs['llm_model'] == 'sparkv3.5':
+            gpt_url = self.gpt_url_v35
         else:
             gpt_url = self.gpt_url
         file_manifest = []
@@ -190,6 +193,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest)
         "spark": "general",
         "sparkv2": "generalv2",
         "sparkv3": "generalv3",
+        "sparkv3.5": "generalv3.5",
     }
     domains_select = domains[llm_kwargs['llm_model']]
     if file_manifest: domains_select = 'image'

request_llms/com_zhipuapi.py

@@ -1,70 +0,0 @@
-from toolbox import get_conf
-import threading
-import logging
-
-timeout_bot_msg = '[Local Message] Request timeout. Network error.'
-
-class ZhipuRequestInstance():
-    def __init__(self):
-
-        self.time_to_yield_event = threading.Event()
-        self.time_to_exit_event = threading.Event()
-
-        self.result_buf = ""
-
-    def generate(self, inputs, llm_kwargs, history, system_prompt):
-        # import _thread as thread
-        import zhipuai
-        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
-        zhipuai.api_key = ZHIPUAI_API_KEY
-        self.result_buf = ""
-        response = zhipuai.model_api.sse_invoke(
-            model=ZHIPUAI_MODEL,
-            prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
-            top_p=llm_kwargs['top_p']*0.7,              # 智谱的API抽风，手动*0.7给做个线性变换
-            temperature=llm_kwargs['temperature']*0.95, # 智谱的API抽风，手动*0.7给做个线性变换
-        )
-        for event in response.events():
-            if event.event == "add":
-                # if self.result_buf == "" and event.data.startswith(" "):
-                #     event.data = event.data.lstrip(" ") # 每次智谱为啥都要带个空格开头呢？
-                self.result_buf += event.data
-                yield self.result_buf
-            elif event.event == "error" or event.event == "interrupted":
-                raise RuntimeError("Unknown error:" + event.data)
-            elif event.event == "finish":
-                yield self.result_buf
-                break
-            else:
-                raise RuntimeError("Unknown error:" + str(event))
-        if self.result_buf == "":
-            yield "智谱没有返回任何数据, 请检查ZHIPUAI_API_KEY和ZHIPUAI_MODEL是否填写正确."
-        logging.info(f'[raw_input] {inputs}')
-        logging.info(f'[response] {self.result_buf}')
-        return self.result_buf
-
-def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
-    conversation_cnt = len(history) // 2
-    messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
-    if conversation_cnt:
-        for index in range(0, 2*conversation_cnt, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "":
-                    continue
-                if what_gpt_answer["content"] == timeout_bot_msg:
-                    continue
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-    return messages

request_llms/com_zhipuglm.py

@@ -0,0 +1,129 @@
+# encoding: utf-8
+# @Time   : 2024/1/22
+# @Author : Kilig947 & binary husky
+# @Descr   : 兼容最新的智谱Ai
+from toolbox import get_conf
+from zhipuai import ZhipuAI
+from toolbox import get_conf, encode_image, get_pictures_list
+import logging, os
+
+
+def input_encode_handler(inputs:str, llm_kwargs:dict):
+    if llm_kwargs["most_recent_uploaded"].get("path"):
+        image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
+    md_encode = []
+    for md_path in image_paths:
+        type_ = os.path.splitext(md_path)[1].replace(".", "")
+        type_ = "jpeg" if type_ == "jpg" else type_
+        md_encode.append({"data": encode_image(md_path), "type": type_})
+    return inputs, md_encode
+
+
+class ZhipuChatInit:
+
+    def __init__(self):
+        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
+        if len(ZHIPUAI_MODEL) > 0:
+            logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
+        self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
+        self.model = ''
+
+    def __conversation_user(self, user_input: str, llm_kwargs:dict):
+        if self.model not in ["glm-4v"]:
+            return {"role": "user", "content": user_input}
+        else:
+            input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
+            what_i_have_asked = {"role": "user", "content": []}
+            what_i_have_asked['content'].append({"type": 'text', "text": user_input})
+            if encode_img:
+                img_d = {"type": "image_url",
+                         "image_url": {'url': encode_img}}
+                what_i_have_asked['content'].append(img_d)
+            return what_i_have_asked
+
+    def __conversation_history(self, history:list, llm_kwargs:dict):
+        messages = []
+        conversation_cnt = len(history) // 2
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
+                what_gpt_answer = {
+                    "role": "assistant",
+                    "content": history[index + 1]
+                }
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+        return messages
+
+    @staticmethod
+    def preprocess_param(param, default=0.95, min_val=0.01, max_val=0.99):
+        """预处理参数，保证其在允许范围内，并处理精度问题"""
+        try:
+            param = float(param)
+        except ValueError:
+            return default
+
+        if param <= min_val:
+            return min_val
+        elif param >= max_val:
+            return max_val
+        else:
+            return round(param, 2)  # 可挑选精度，目前是两位小数
+
+    def __conversation_message_payload(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        self.model = llm_kwargs['llm_model']
+        messages.extend(self.__conversation_history(history, llm_kwargs))  # 处理 history
+        if inputs.strip() == "": # 处理空输入导致报错的问题 https://github.com/binary-husky/gpt_academic/issues/1640 提示 {"error":{"code":"1214","message":"messages[1]:content和tool_calls 字段不能同时为空"}
+            inputs = "."    # 空格、换行、空字符串都会报错，所以用最没有意义的一个点代替
+        messages.append(self.__conversation_user(inputs, llm_kwargs))  # 处理用户对话
+        """
+        采样温度，控制输出的随机性，必须为正数
+        取值范围是：(0.0, 1.0)，不能等于 0，默认值为 0.95，
+        值越大，会使输出更随机，更具创造性；
+        值越小，输出会更加稳定或确定
+        建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数
+        """
+        temperature = self.preprocess_param(
+            param=llm_kwargs.get('temperature', 0.95),
+            default=0.95,
+            min_val=0.01,
+            max_val=0.99
+        )
+        """
+        用温度取样的另一种方法，称为核取样
+        取值范围是：(0.0, 1.0) 开区间，
+        不能等于 0 或 1，默认值为 0.7
+        模型考虑具有 top_p 概率质量 tokens 的结果
+        例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens
+        建议您根据应用场景调整 top_p 或 temperature 参数，
+        但不要同时调整两个参数
+        """
+        top_p = self.preprocess_param(
+            param=llm_kwargs.get('top_p', 0.70),
+            default=0.70,
+            min_val=0.01,
+            max_val=0.99
+        )
+        response = self.zhipu_bro.chat.completions.create(
+            model=self.model, messages=messages, stream=True,
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=llm_kwargs.get('max_tokens', 1024 * 4),
+        )
+        return response
+
+    def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
+        self.model = llm_kwargs['llm_model']
+        response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
+        bro_results = ''
+        for chunk in response:
+            bro_results += chunk.choices[0].delta.content
+            yield chunk.choices[0].delta.content, bro_results
+
+
+if __name__ == '__main__':
+    zhipu = ZhipuChatInit()
+    zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')

request_llms/local_llm_class.py

@@ -1,6 +1,7 @@
 import time
 import threading
 from toolbox import update_ui, Singleton
+from toolbox import ChatBotWithCookies
 from multiprocessing import Process, Pipe
 from contextlib import redirect_stdout
 from request_llms.queued_pipe import create_queue_pipe
@@ -214,7 +215,7 @@ class LocalLLMHandle(Process):
 def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
     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 predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False):
         """
             refer to request_llms/bridge_all.py
         """
@@ -260,7 +261,8 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
                     raise RuntimeError("程序终止。")
         return response
 
-    def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+    def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+                history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
         """
             refer to request_llms/bridge_all.py
         """

requirements.txt

@@ -1,12 +1,14 @@
-https://fastly.jsdelivr.net/gh/binary-husky/gradio-fix@gpt-academic/release/gradio-3.32.7-py3-none-any.whl
+https://public.agent-matrix.com/publish/gradio-3.32.9-py3-none-any.whl
+gradio-client==0.8
 pypdf2==2.12.1
-zhipuai<2
+zhipuai>=2
 tiktoken>=0.3.3
 requests[socks]
-pydantic==1.10.11
+pydantic==2.5.2
 protobuf==3.18
 transformers>=4.27.1
 scipdf_parser>=0.52
+anthropic>=0.18.1
 python-markdown-math
 pymdown-extensions
 websocket-client
@@ -15,7 +17,7 @@ prompt_toolkit
 latex2mathml
 python-docx
 mdtex2html
-anthropic
+dashscope
 pyautogen
 colorama
 Markdown

shared_utils/cookie_manager.py

@@ -0,0 +1,61 @@
+from typing import Callable
+def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)->Callable:
+    def load_web_cookie_cache(persistent_cookie_, cookies_):
+        import gradio as gr
+        from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
+
+        ret = {}
+        for k in customize_btns:
+            ret.update({customize_btns[k]: gr.update(visible=False, value="")})
+
+        try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
+        except: return ret
+
+        customize_fn_overwrite_ = persistent_cookie_.get("custom_bnt", {})
+        cookies_['customize_fn_overwrite'] = customize_fn_overwrite_
+        ret.update({cookies: cookies_})
+
+        for k,v in persistent_cookie_["custom_bnt"].items():
+            if v['Title'] == "": continue
+            if k in customize_btns: ret.update({customize_btns[k]: gr.update(visible=True, value=v['Title'])})
+            else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
+        return ret
+    return load_web_cookie_cache
+
+
+def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)->Callable:
+    def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
+        import gradio as gr
+        from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
+        ret = {}
+        # 读取之前的自定义按钮
+        customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
+        # 更新新的自定义按钮
+        customize_fn_overwrite_.update({
+            basic_btn_dropdown_:
+                {
+                    "Title":basic_fn_title,
+                    "Prefix":basic_fn_prefix,
+                    "Suffix":basic_fn_suffix,
+                }
+            }
+        )
+        if clean_up:
+            customize_fn_overwrite_ = {}
+        cookies_.update(customize_fn_overwrite_)    # 更新cookie
+        visible = (not clean_up) and (basic_fn_title != "")
+        if basic_btn_dropdown_ in customize_btns:
+            # 是自定义按钮，不是预定义按钮
+            ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
+        else:
+            # 是预定义按钮
+            ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
+        ret.update({cookies: cookies_})
+        try: persistent_cookie_ = from_cookie_str(persistent_cookie_)   # persistent cookie to dict
+        except: persistent_cookie_ = {}
+        persistent_cookie_["custom_bnt"] = customize_fn_overwrite_      # dict update new value
+        persistent_cookie_ = to_cookie_str(persistent_cookie_)          # persistent cookie to dict
+        ret.update({web_cookie_cache: persistent_cookie_})             # write persistent cookie
+        return ret
+    return assign_btn
+

shared_utils/fastapi_server.py

@@ -0,0 +1,211 @@
+"""
+Tests:
+
+- custom_path false / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block __pycache__ access(yes)
+        -- rel (yes)
+        -- abs (yes)
+    -- block user access(fail) http://localhost:45013/file=gpt_log/admin/chat_secrets.log
+        -- fix(commit f6bf05048c08f5cd84593f7fdc01e64dec1f584a)-> block successful
+
+- custom_path yes("/cc/gptac") / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block __pycache__ access(yes)
+    -- block user access(yes)
+
+- custom_path yes("/cc/gptac/") / no user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+
+- custom_path yes("/cc/gptac/") / + user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+    -- block user-wise access (yes)
+
+- custom_path no + user auth:
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+    -- block user access(yes)
+    -- block user-wise access (yes)
+
+queue cocurrent effectiveness
+    -- upload file(yes)
+    -- download file(yes)
+    -- websocket(yes)
+"""
+
+import os, requests, threading, time
+import uvicorn
+
+def _authorize_user(path_or_url, request, gradio_app):
+    from toolbox import get_conf, default_user_name
+    PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
+    sensitive_path = None
+    path_or_url = os.path.relpath(path_or_url)
+    if path_or_url.startswith(PATH_LOGGING):
+        sensitive_path = PATH_LOGGING
+    if path_or_url.startswith(PATH_PRIVATE_UPLOAD):
+        sensitive_path = PATH_PRIVATE_UPLOAD
+    if sensitive_path:
+        token = request.cookies.get("access-token") or request.cookies.get("access-token-unsecure")
+        user = gradio_app.tokens.get(token)  # get user
+        allowed_users = [user, 'autogen', default_user_name]  # three user path that can be accessed
+        for user_allowed in allowed_users:
+            # exact match
+            if f"{os.sep}".join(path_or_url.split(os.sep)[:2]) == os.path.join(sensitive_path, user_allowed):
+                return True
+        return False # "越权访问!"
+    return True
+
+
+class Server(uvicorn.Server):
+    # A server that runs in a separate thread
+    def install_signal_handlers(self):
+        pass
+
+    def run_in_thread(self):
+        self.thread = threading.Thread(target=self.run, daemon=True)
+        self.thread.start()
+        while not self.started:
+            time.sleep(1e-3)
+
+    def close(self):
+        self.should_exit = True
+        self.thread.join()
+
+
+def start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE):
+    import uvicorn
+    import fastapi
+    import gradio as gr
+    from fastapi import FastAPI
+    from gradio.routes import App
+    from toolbox import get_conf
+    CUSTOM_PATH, PATH_LOGGING = get_conf('CUSTOM_PATH', 'PATH_LOGGING')
+
+    # --- --- configurate gradio app block --- ---
+    app_block:gr.Blocks
+    app_block.ssl_verify = False
+    app_block.auth_message = '请登录'
+    app_block.favicon_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "docs/logo.png")
+    app_block.auth = AUTHENTICATION if len(AUTHENTICATION) != 0 else None
+    app_block.blocked_paths = ["config.py", "__pycache__", "config_private.py", "docker-compose.yml", "Dockerfile", f"{PATH_LOGGING}/admin"]
+    app_block.dev_mode = False
+    app_block.config = app_block.get_config_file()
+    app_block.enable_queue = True
+    app_block.queue(concurrency_count=CONCURRENT_COUNT)
+    app_block.validate_queue_settings()
+    app_block.show_api = False
+    app_block.config = app_block.get_config_file()
+    max_threads = 40
+    app_block.max_threads = max(
+        app_block._queue.max_thread_count if app_block.enable_queue else 0, max_threads
+    )
+    app_block.is_colab = False
+    app_block.is_kaggle = False
+    app_block.is_sagemaker = False
+
+    gradio_app = App.create_app(app_block)
+
+    # --- --- replace gradio endpoint to forbid access to sensitive files --- ---
+    if len(AUTHENTICATION) > 0:
+        dependencies = []
+        endpoint = None
+        for route in list(gradio_app.router.routes):
+            if route.path == "/file/{path:path}":
+                gradio_app.router.routes.remove(route)
+            if route.path == "/file={path_or_url:path}":
+                dependencies = route.dependencies
+                endpoint = route.endpoint
+                gradio_app.router.routes.remove(route)
+        @gradio_app.get("/file/{path:path}", dependencies=dependencies)
+        @gradio_app.head("/file={path_or_url:path}", dependencies=dependencies)
+        @gradio_app.get("/file={path_or_url:path}", dependencies=dependencies)
+        async def file(path_or_url: str, request: fastapi.Request):
+            if len(AUTHENTICATION) > 0:
+                if not _authorize_user(path_or_url, request, gradio_app):
+                    return "越权访问!"
+            return await endpoint(path_or_url, request)
+
+    # --- --- app_lifespan --- ---
+    from contextlib import asynccontextmanager
+    @asynccontextmanager
+    async def app_lifespan(app):
+        async def startup_gradio_app():
+            if gradio_app.get_blocks().enable_queue:
+                gradio_app.get_blocks().startup_events()
+        async def shutdown_gradio_app():
+            pass
+        await startup_gradio_app() # startup logic here
+        yield  # The application will serve requests after this point
+        await shutdown_gradio_app() # cleanup/shutdown logic here
+
+    # --- --- FastAPI --- ---
+    fastapi_app = FastAPI(lifespan=app_lifespan)
+    fastapi_app.mount(CUSTOM_PATH, gradio_app)
+
+    # --- --- favicon --- ---
+    if CUSTOM_PATH != '/':
+        from fastapi.responses import FileResponse
+        @fastapi_app.get("/favicon.ico")
+        async def favicon():
+            return FileResponse(app_block.favicon_path)
+
+    # --- --- uvicorn.Config --- ---
+    ssl_keyfile = None if SSL_KEYFILE == "" else SSL_KEYFILE
+    ssl_certfile = None if SSL_CERTFILE == "" else SSL_CERTFILE
+    server_name = "0.0.0.0"
+    config = uvicorn.Config(
+        fastapi_app,
+        host=server_name,
+        port=PORT,
+        reload=False,
+        log_level="warning",
+        ssl_keyfile=ssl_keyfile,
+        ssl_certfile=ssl_certfile,
+    )
+    server = Server(config)
+    url_host_name = "localhost" if server_name == "0.0.0.0" else server_name
+    if ssl_keyfile is not None:
+        if ssl_certfile is None:
+            raise ValueError(
+                "ssl_certfile must be provided if ssl_keyfile is provided."
+            )
+        path_to_local_server = f"https://{url_host_name}:{PORT}/"
+    else:
+        path_to_local_server = f"http://{url_host_name}:{PORT}/"
+    if CUSTOM_PATH != '/':
+        path_to_local_server += CUSTOM_PATH.lstrip('/').rstrip('/') + '/'
+    # --- --- begin  --- ---
+    server.run_in_thread()
+
+    # --- --- after server launch --- ---
+    app_block.server = server
+    app_block.server_name = server_name
+    app_block.local_url = path_to_local_server
+    app_block.protocol = (
+        "https"
+        if app_block.local_url.startswith("https") or app_block.is_colab
+        else "http"
+    )
+
+    if app_block.enable_queue:
+        app_block._queue.set_url(path_to_local_server)
+
+    forbid_proxies = {
+        "http": "",
+        "https": "",
+    }
+    requests.get(f"{app_block.local_url}startup-events", verify=app_block.ssl_verify, proxies=forbid_proxies)
+    app_block.is_running = True
+    app_block.block_thread()

shared_utils/handle_upload.py

@@ -0,0 +1,137 @@
+import importlib
+import time
+import inspect
+import re
+import os
+import base64
+import gradio
+import shutil
+import glob
+from shared_utils.config_loader import get_conf
+
+def html_local_file(file):
+    base_path = os.path.dirname(__file__)  # 项目目录
+    if os.path.exists(str(file)):
+        file = f'file={file.replace(base_path, ".")}'
+    return file
+
+
+def html_local_img(__file, layout="left", max_width=None, max_height=None, md=True):
+    style = ""
+    if max_width is not None:
+        style += f"max-width: {max_width};"
+    if max_height is not None:
+        style += f"max-height: {max_height};"
+    __file = html_local_file(__file)
+    a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
+    if md:
+        a = f"![{__file}]({__file})"
+    return a
+
+
+def file_manifest_filter_type(file_list, filter_: list = None):
+    new_list = []
+    if not filter_:
+        filter_ = ["png", "jpg", "jpeg"]
+    for file in file_list:
+        if str(os.path.basename(file)).split(".")[-1] in filter_:
+            new_list.append(html_local_img(file, md=False))
+        else:
+            new_list.append(file)
+    return new_list
+
+
+def zip_extract_member_new(self, member, targetpath, pwd):
+    # 修复中文乱码的问题
+    """Extract the ZipInfo object 'member' to a physical
+        file on the path targetpath.
+    """
+    import zipfile
+    if not isinstance(member, zipfile.ZipInfo):
+        member = self.getinfo(member)
+
+    # build the destination pathname, replacing
+    # forward slashes to platform specific separators.
+    arcname = member.filename.replace('/', os.path.sep)
+    arcname = arcname.encode('cp437', errors='replace').decode('gbk', errors='replace')
+
+    if os.path.altsep:
+        arcname = arcname.replace(os.path.altsep, os.path.sep)
+    # interpret absolute pathname as relative, remove drive letter or
+    # UNC path, redundant separators, "." and ".." components.
+    arcname = os.path.splitdrive(arcname)[1]
+    invalid_path_parts = ('', os.path.curdir, os.path.pardir)
+    arcname = os.path.sep.join(x for x in arcname.split(os.path.sep)
+                                if x not in invalid_path_parts)
+    if os.path.sep == '\\':
+        # filter illegal characters on Windows
+        arcname = self._sanitize_windows_name(arcname, os.path.sep)
+
+    targetpath = os.path.join(targetpath, arcname)
+    targetpath = os.path.normpath(targetpath)
+
+    # Create all upper directories if necessary.
+    upperdirs = os.path.dirname(targetpath)
+    if upperdirs and not os.path.exists(upperdirs):
+        os.makedirs(upperdirs)
+
+    if member.is_dir():
+        if not os.path.isdir(targetpath):
+            os.mkdir(targetpath)
+        return targetpath
+
+    with self.open(member, pwd=pwd) as source, \
+            open(targetpath, "wb") as target:
+        shutil.copyfileobj(source, target)
+
+    return targetpath
+
+
+def extract_archive(file_path, dest_dir):
+    import zipfile
+    import tarfile
+    import os
+
+    # Get the file extension of the input file
+    file_extension = os.path.splitext(file_path)[1]
+
+    # Extract the archive based on its extension
+    if file_extension == ".zip":
+        with zipfile.ZipFile(file_path, "r") as zipobj:
+            zipobj._extract_member = lambda a,b,c: zip_extract_member_new(zipobj, a,b,c)    # 修复中文乱码的问题
+            zipobj.extractall(path=dest_dir)
+            print("Successfully extracted zip archive to {}".format(dest_dir))
+
+    elif file_extension in [".tar", ".gz", ".bz2"]:
+        with tarfile.open(file_path, "r:*") as tarobj:
+            tarobj.extractall(path=dest_dir)
+            print("Successfully extracted tar archive to {}".format(dest_dir))
+
+    # 第三方库，需要预先pip install rarfile
+    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
+    elif file_extension == ".rar":
+        try:
+            import rarfile
+
+            with rarfile.RarFile(file_path) as rf:
+                rf.extractall(path=dest_dir)
+                print("Successfully extracted rar archive to {}".format(dest_dir))
+        except:
+            print("Rar format requires additional dependencies to install")
+            return "\n\n解压失败! 需要安装pip install rarfile来解压rar文件。建议：使用zip压缩格式。"
+
+    # 第三方库，需要预先pip install py7zr
+    elif file_extension == ".7z":
+        try:
+            import py7zr
+
+            with py7zr.SevenZipFile(file_path, mode="r") as f:
+                f.extractall(path=dest_dir)
+                print("Successfully extracted 7z archive to {}".format(dest_dir))
+        except:
+            print("7z format requires additional dependencies to install")
+            return "\n\n解压失败! 需要安装pip install py7zr来解压7z文件"
+    else:
+        return ""
+    return ""
+

shared_utils/key_pattern_manager.py

@@ -14,7 +14,7 @@ def is_openai_api_key(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}$", key)
+        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$|sess-[a-zA-Z0-9]{40}$", key)
     return bool(API_MATCH_ORIGINAL)
 
 
@@ -28,6 +28,11 @@ def is_api2d_key(key):
     return bool(API_MATCH_API2D)
 
 
+def is_cohere_api_key(key):
+    API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{40}$", key)
+    return bool(API_MATCH_AZURE)
+
+
 def is_any_api_key(key):
     if ',' in key:
         keys = key.split(',')
@@ -35,7 +40,7 @@ def is_any_api_key(key):
             if is_any_api_key(k): return True
         return False
     else:
-        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
+        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key) or is_cohere_api_key(key)
 
 
 def what_keys(keys):
@@ -62,7 +67,7 @@ def select_api_key(keys, llm_model):
     avail_key_list = []
     key_list = keys.split(',')
 
-    if llm_model.startswith('gpt-'):
+    if llm_model.startswith('gpt-') or llm_model.startswith('one-api-'):
         for k in key_list:
             if is_openai_api_key(k): avail_key_list.append(k)
 
@@ -74,8 +79,12 @@ def select_api_key(keys, llm_model):
         for k in key_list:
             if is_azure_api_key(k): avail_key_list.append(k)
 
+    if llm_model.startswith('cohere-'):
+        for k in key_list:
+            if is_cohere_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（左上角更换模型菜单中可切换openai,azure,claude,cohere等请求源）。")
 
     api_key = random.choice(avail_key_list) # 随机负载均衡
     return api_key

shared_utils/map_names.py

@@ -0,0 +1,34 @@
+import re
+mapping_dic = {
+    # "qianfan": "qianfan（文心一言大模型）",
+    # "zhipuai": "zhipuai（智谱GLM4超级模型🔥）",
+    # "gpt-4-1106-preview": "gpt-4-1106-preview（新调优版本GPT-4🔥）",
+    # "gpt-4-vision-preview": "gpt-4-vision-preview（识图模型GPT-4V）",
+}
+
+rev_mapping_dic = {}
+for k, v in mapping_dic.items():
+    rev_mapping_dic[v] = k
+
+def map_model_to_friendly_names(m):
+    if m in mapping_dic:
+        return mapping_dic[m]
+    return m
+
+def map_friendly_names_to_model(m):
+    if m in rev_mapping_dic:
+        return rev_mapping_dic[m]
+    return m
+
+def read_one_api_model_name(model: str):
+    """return real model name and max_token.
+    """
+    max_token_pattern = r"\(max_token=(\d+)\)"
+    match = re.search(max_token_pattern, model)
+    if match:
+        max_token_tmp = match.group(1)  # 获取 max_token 的值
+        max_token_tmp = int(max_token_tmp)
+        model = re.sub(max_token_pattern, "", model)  # 从原字符串中删除 "(max_token=...)"
+    else:
+        max_token_tmp = 4096
+    return model, max_token_tmp

tests/test_key_pattern_manager.py

@@ -0,0 +1,41 @@
+import unittest
+
+def validate_path():
+    import os, sys
+
+    os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(os.path.dirname(__file__) + "/..")
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+
+
+validate_path()  # validate path so you can run from base directory
+
+from shared_utils.key_pattern_manager import is_openai_api_key
+
+class TestKeyPatternManager(unittest.TestCase):
+    def test_is_openai_api_key_with_valid_key(self):
+        key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+        self.assertTrue(is_openai_api_key(key))
+
+        key = "sx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+        self.assertFalse(is_openai_api_key(key))
+
+        key = "sess-wg61ZafYHpNz7FFwIH7HGZlbVqUVaeV5tatHCWpl"
+        self.assertTrue(is_openai_api_key(key))
+
+        key = "sess-wg61ZafYHpNz7FFwIH7HGZlbVqUVa5tatHCWpl"
+        self.assertFalse(is_openai_api_key(key))
+
+
+    def test_is_openai_api_key_with_invalid_key(self):
+        key = "invalid_key"
+        self.assertFalse(is_openai_api_key(key))
+
+    def test_is_openai_api_key_with_custom_pattern(self):
+        # Assuming you have set a custom pattern in your configuration
+        key = "custom-pattern-key"
+        self.assertFalse(is_openai_api_key(key))
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_llms.py

@@ -11,7 +11,29 @@ def validate_path():
 
 
 validate_path()  # validate path so you can run from base directory
-if __name__ == "__main__":
+
+if "在线模型":
+    if __name__ == "__main__":
+        from request_llms.bridge_cohere import predict_no_ui_long_connection
+        # from request_llms.bridge_spark import predict_no_ui_long_connection
+        # from request_llms.bridge_zhipu import predict_no_ui_long_connection
+        # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
+        llm_kwargs = {
+            "llm_model": "command-r-plus",
+            "max_length": 4096,
+            "top_p": 1,
+            "temperature": 1,
+        }
+
+        result = predict_no_ui_long_connection(
+            inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt="系统"
+        )
+        print("final result:", result)
+        print("final result:", result)
+
+
+if "本地模型":
+    if __name__ == "__main__":
         # from request_llms.bridge_newbingfree import predict_no_ui_long_connection
         # from request_llms.bridge_moss import predict_no_ui_long_connection
         # from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
@@ -20,19 +42,14 @@ if __name__ == "__main__":
         # from request_llms.bridge_internlm import predict_no_ui_long_connection
         # from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
         # from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
-    from request_llms.bridge_qwen_local import predict_no_ui_long_connection
-
-    # from request_llms.bridge_spark import predict_no_ui_long_connection
-    # from request_llms.bridge_zhipu import predict_no_ui_long_connection
-    # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
-
+        # from request_llms.bridge_qwen_local import predict_no_ui_long_connection
         llm_kwargs = {
             "max_length": 4096,
             "top_p": 1,
             "temperature": 1,
         }
-
         result = predict_no_ui_long_connection(
             inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt=""
         )
         print("final result:", result)
+

tests/test_plugins.py

@@ -20,10 +20,10 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.函数动态生成->函数动态生成', main_input='交换图像的蓝色通道和红色通道', advanced_arg={"file_path_arg": "./build/ants.jpg"})
 
-    # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2307.07522")
+    # plugin_test(plugin='crazy_functions.Latex输出PDF->Latex翻译中文并重新编译PDF', main_input="2307.07522")
 
     plugin_test(
-        plugin="crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF",
+        plugin="crazy_functions.Latex输出PDF->Latex翻译中文并重新编译PDF",
         main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix",
     )
 
@@ -66,7 +66,7 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？")
 
-    # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2210.03629")
+    # plugin_test(plugin='crazy_functions.Latex输出PDF->Latex翻译中文并重新编译PDF', main_input="2210.03629")
 
     # advanced_arg = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''" }
     # plugin_test(plugin='crazy_functions.chatglm微调工具->微调数据集生成', main_input='build/dev.json', advanced_arg=advanced_arg)

themes/base64.mjs

@@ -1,296 +1 @@
-/**
- *  base64.ts
- *
- *  Licensed under the BSD 3-Clause License.
- *    http://opensource.org/licenses/BSD-3-Clause
- *
- *  References:
- *    http://en.wikipedia.org/wiki/Base64
- *
- * @author Dan Kogai (https://github.com/dankogai)
- */
-const version = '3.7.2';
-/**
- * @deprecated use lowercase `version`.
- */
-const VERSION = version;
-const _hasatob = typeof atob === 'function';
-const _hasbtoa = typeof btoa === 'function';
-const _hasBuffer = typeof Buffer === 'function';
-const _TD = typeof TextDecoder === 'function' ? new TextDecoder() : undefined;
-const _TE = typeof TextEncoder === 'function' ? new TextEncoder() : undefined;
-const b64ch = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=';
-const b64chs = Array.prototype.slice.call(b64ch);
-const b64tab = ((a) => {
-    let tab = {};
-    a.forEach((c, i) => tab[c] = i);
-    return tab;
-})(b64chs);
-const b64re = /^(?:[A-Za-z\d+\/]{4})*?(?:[A-Za-z\d+\/]{2}(?:==)?|[A-Za-z\d+\/]{3}=?)?$/;
-const _fromCC = String.fromCharCode.bind(String);
-const _U8Afrom = typeof Uint8Array.from === 'function'
-    ? Uint8Array.from.bind(Uint8Array)
-    : (it, fn = (x) => x) => new Uint8Array(Array.prototype.slice.call(it, 0).map(fn));
-const _mkUriSafe = (src) => src
-    .replace(/=/g, '').replace(/[+\/]/g, (m0) => m0 == '+' ? '-' : '_');
-const _tidyB64 = (s) => s.replace(/[^A-Za-z0-9\+\/]/g, '');
-/**
- * polyfill version of `btoa`
- */
-const btoaPolyfill = (bin) => {
-    // console.log('polyfilled');
-    let u32, c0, c1, c2, asc = '';
-    const pad = bin.length % 3;
-    for (let i = 0; i < bin.length;) {
-        if ((c0 = bin.charCodeAt(i++)) > 255 ||
-            (c1 = bin.charCodeAt(i++)) > 255 ||
-            (c2 = bin.charCodeAt(i++)) > 255)
-            throw new TypeError('invalid character found');
-        u32 = (c0 << 16) | (c1 << 8) | c2;
-        asc += b64chs[u32 >> 18 & 63]
-            + b64chs[u32 >> 12 & 63]
-            + b64chs[u32 >> 6 & 63]
-            + b64chs[u32 & 63];
-    }
-    return pad ? asc.slice(0, pad - 3) + "===".substring(pad) : asc;
-};
-/**
- * does what `window.btoa` of web browsers do.
- * @param {String} bin binary string
- * @returns {string} Base64-encoded string
- */
-const _btoa = _hasbtoa ? (bin) => btoa(bin)
-    : _hasBuffer ? (bin) => Buffer.from(bin, 'binary').toString('base64')
-        : btoaPolyfill;
-const _fromUint8Array = _hasBuffer
-    ? (u8a) => Buffer.from(u8a).toString('base64')
-    : (u8a) => {
-        // cf. https://stackoverflow.com/questions/12710001/how-to-convert-uint8-array-to-base64-encoded-string/12713326#12713326
-        const maxargs = 0x1000;
-        let strs = [];
-        for (let i = 0, l = u8a.length; i < l; i += maxargs) {
-            strs.push(_fromCC.apply(null, u8a.subarray(i, i + maxargs)));
-        }
-        return _btoa(strs.join(''));
-    };
-/**
- * converts a Uint8Array to a Base64 string.
- * @param {boolean} [urlsafe] URL-and-filename-safe a la RFC4648 §5
- * @returns {string} Base64 string
- */
-const fromUint8Array = (u8a, urlsafe = false) => urlsafe ? _mkUriSafe(_fromUint8Array(u8a)) : _fromUint8Array(u8a);
-// This trick is found broken https://github.com/dankogai/js-base64/issues/130
-// const utob = (src: string) => unescape(encodeURIComponent(src));
-// reverting good old fationed regexp
-const cb_utob = (c) => {
-    if (c.length < 2) {
-        var cc = c.charCodeAt(0);
-        return cc < 0x80 ? c
-            : cc < 0x800 ? (_fromCC(0xc0 | (cc >>> 6))
-                + _fromCC(0x80 | (cc & 0x3f)))
-                : (_fromCC(0xe0 | ((cc >>> 12) & 0x0f))
-                    + _fromCC(0x80 | ((cc >>> 6) & 0x3f))
-                    + _fromCC(0x80 | (cc & 0x3f)));
-    }
-    else {
-        var cc = 0x10000
-            + (c.charCodeAt(0) - 0xD800) * 0x400
-            + (c.charCodeAt(1) - 0xDC00);
-        return (_fromCC(0xf0 | ((cc >>> 18) & 0x07))
-            + _fromCC(0x80 | ((cc >>> 12) & 0x3f))
-            + _fromCC(0x80 | ((cc >>> 6) & 0x3f))
-            + _fromCC(0x80 | (cc & 0x3f)));
-    }
-};
-const re_utob = /[\uD800-\uDBFF][\uDC00-\uDFFFF]|[^\x00-\x7F]/g;
-/**
- * @deprecated should have been internal use only.
- * @param {string} src UTF-8 string
- * @returns {string} UTF-16 string
- */
-const utob = (u) => u.replace(re_utob, cb_utob);
-//
-const _encode = _hasBuffer
-    ? (s) => Buffer.from(s, 'utf8').toString('base64')
-    : _TE
-        ? (s) => _fromUint8Array(_TE.encode(s))
-        : (s) => _btoa(utob(s));
-/**
- * converts a UTF-8-encoded string to a Base64 string.
- * @param {boolean} [urlsafe] if `true` make the result URL-safe
- * @returns {string} Base64 string
- */
-const encode = (src, urlsafe = false) => urlsafe
-    ? _mkUriSafe(_encode(src))
-    : _encode(src);
-/**
- * converts a UTF-8-encoded string to URL-safe Base64 RFC4648 §5.
- * @returns {string} Base64 string
- */
-const encodeURI = (src) => encode(src, true);
-// This trick is found broken https://github.com/dankogai/js-base64/issues/130
-// const btou = (src: string) => decodeURIComponent(escape(src));
-// reverting good old fationed regexp
-const re_btou = /[\xC0-\xDF][\x80-\xBF]|[\xE0-\xEF][\x80-\xBF]{2}|[\xF0-\xF7][\x80-\xBF]{3}/g;
-const cb_btou = (cccc) => {
-    switch (cccc.length) {
-        case 4:
-            var cp = ((0x07 & cccc.charCodeAt(0)) << 18)
-                | ((0x3f & cccc.charCodeAt(1)) << 12)
-                | ((0x3f & cccc.charCodeAt(2)) << 6)
-                | (0x3f & cccc.charCodeAt(3)), offset = cp - 0x10000;
-            return (_fromCC((offset >>> 10) + 0xD800)
-                + _fromCC((offset & 0x3FF) + 0xDC00));
-        case 3:
-            return _fromCC(((0x0f & cccc.charCodeAt(0)) << 12)
-                | ((0x3f & cccc.charCodeAt(1)) << 6)
-                | (0x3f & cccc.charCodeAt(2)));
-        default:
-            return _fromCC(((0x1f & cccc.charCodeAt(0)) << 6)
-                | (0x3f & cccc.charCodeAt(1)));
-    }
-};
-/**
- * @deprecated should have been internal use only.
- * @param {string} src UTF-16 string
- * @returns {string} UTF-8 string
- */
-const btou = (b) => b.replace(re_btou, cb_btou);
-/**
- * polyfill version of `atob`
- */
-const atobPolyfill = (asc) => {
-    // console.log('polyfilled');
-    asc = asc.replace(/\s+/g, '');
-    if (!b64re.test(asc))
-        throw new TypeError('malformed base64.');
-    asc += '=='.slice(2 - (asc.length & 3));
-    let u24, bin = '', r1, r2;
-    for (let i = 0; i < asc.length;) {
-        u24 = b64tab[asc.charAt(i++)] << 18
-            | b64tab[asc.charAt(i++)] << 12
-            | (r1 = b64tab[asc.charAt(i++)]) << 6
-            | (r2 = b64tab[asc.charAt(i++)]);
-        bin += r1 === 64 ? _fromCC(u24 >> 16 & 255)
-            : r2 === 64 ? _fromCC(u24 >> 16 & 255, u24 >> 8 & 255)
-                : _fromCC(u24 >> 16 & 255, u24 >> 8 & 255, u24 & 255);
-    }
-    return bin;
-};
-/**
- * does what `window.atob` of web browsers do.
- * @param {String} asc Base64-encoded string
- * @returns {string} binary string
- */
-const _atob = _hasatob ? (asc) => atob(_tidyB64(asc))
-    : _hasBuffer ? (asc) => Buffer.from(asc, 'base64').toString('binary')
-        : atobPolyfill;
-//
-const _toUint8Array = _hasBuffer
-    ? (a) => _U8Afrom(Buffer.from(a, 'base64'))
-    : (a) => _U8Afrom(_atob(a), c => c.charCodeAt(0));
-/**
- * converts a Base64 string to a Uint8Array.
- */
-const toUint8Array = (a) => _toUint8Array(_unURI(a));
-//
-const _decode = _hasBuffer
-    ? (a) => Buffer.from(a, 'base64').toString('utf8')
-    : _TD
-        ? (a) => _TD.decode(_toUint8Array(a))
-        : (a) => btou(_atob(a));
-const _unURI = (a) => _tidyB64(a.replace(/[-_]/g, (m0) => m0 == '-' ? '+' : '/'));
-/**
- * converts a Base64 string to a UTF-8 string.
- * @param {String} src Base64 string.  Both normal and URL-safe are supported
- * @returns {string} UTF-8 string
- */
-const decode = (src) => _decode(_unURI(src));
-/**
- * check if a value is a valid Base64 string
- * @param {String} src a value to check
-  */
-const isValid = (src) => {
-    if (typeof src !== 'string')
-        return false;
-    const s = src.replace(/\s+/g, '').replace(/={0,2}$/, '');
-    return !/[^\s0-9a-zA-Z\+/]/.test(s) || !/[^\s0-9a-zA-Z\-_]/.test(s);
-};
-//
-const _noEnum = (v) => {
-    return {
-        value: v, enumerable: false, writable: true, configurable: true
-    };
-};
-/**
- * extend String.prototype with relevant methods
- */
-const extendString = function () {
-    const _add = (name, body) => Object.defineProperty(String.prototype, name, _noEnum(body));
-    _add('fromBase64', function () { return decode(this); });
-    _add('toBase64', function (urlsafe) { return encode(this, urlsafe); });
-    _add('toBase64URI', function () { return encode(this, true); });
-    _add('toBase64URL', function () { return encode(this, true); });
-    _add('toUint8Array', function () { return toUint8Array(this); });
-};
-/**
- * extend Uint8Array.prototype with relevant methods
- */
-const extendUint8Array = function () {
-    const _add = (name, body) => Object.defineProperty(Uint8Array.prototype, name, _noEnum(body));
-    _add('toBase64', function (urlsafe) { return fromUint8Array(this, urlsafe); });
-    _add('toBase64URI', function () { return fromUint8Array(this, true); });
-    _add('toBase64URL', function () { return fromUint8Array(this, true); });
-};
-/**
- * extend Builtin prototypes with relevant methods
- */
-const extendBuiltins = () => {
-    extendString();
-    extendUint8Array();
-};
-const gBase64 = {
-    version: version,
-    VERSION: VERSION,
-    atob: _atob,
-    atobPolyfill: atobPolyfill,
-    btoa: _btoa,
-    btoaPolyfill: btoaPolyfill,
-    fromBase64: decode,
-    toBase64: encode,
-    encode: encode,
-    encodeURI: encodeURI,
-    encodeURL: encodeURI,
-    utob: utob,
-    btou: btou,
-    decode: decode,
-    isValid: isValid,
-    fromUint8Array: fromUint8Array,
-    toUint8Array: toUint8Array,
-    extendString: extendString,
-    extendUint8Array: extendUint8Array,
-    extendBuiltins: extendBuiltins,
-};
-// makecjs:CUT //
-export { version };
-export { VERSION };
-export { _atob as atob };
-export { atobPolyfill };
-export { _btoa as btoa };
-export { btoaPolyfill };
-export { decode as fromBase64 };
-export { encode as toBase64 };
-export { utob };
-export { encode };
-export { encodeURI };
-export { encodeURI as encodeURL };
-export { btou };
-export { decode };
-export { isValid };
-export { fromUint8Array };
-export { toUint8Array };
-export { extendString };
-export { extendUint8Array };
-export { extendBuiltins };
-// and finally,
-export { gBase64 as Base64 };
+// we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0

themes/common.css

@@ -59,6 +59,7 @@
 
 /* Scrollbar Width */
 ::-webkit-scrollbar {
+    height: 12px;
     width: 12px;
 }
 

themes/common.js

@@ -2,6 +2,76 @@
 //  第 1 部分: 工具函数
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 
+function push_data_to_gradio_component(DAT, ELEM_ID, TYPE) {
+    // type,               // type==="str" / type==="float"
+    if (TYPE == "str") {
+        // convert dat to string: do nothing
+    }
+    else if (TYPE == "no_conversion") {
+        // no nothing
+    }
+    else if (TYPE == "float") {
+        // convert dat to float
+        DAT = parseFloat(DAT);
+    }
+    const myEvent = new CustomEvent('gpt_academic_update_gradio_component', {
+        detail: {
+            data: DAT,
+            elem_id: ELEM_ID,
+        }
+    });
+    window.dispatchEvent(myEvent);
+}
+
+
+async function get_gradio_component(ELEM_ID) {
+    function waitFor(ELEM_ID) {
+        return new Promise((resolve) => {
+            const myEvent = new CustomEvent('gpt_academic_get_gradio_component_value', {
+                detail: {
+                    elem_id: ELEM_ID,
+                    resolve,
+                }
+            });
+            window.dispatchEvent(myEvent);
+        });
+    }
+    result = await waitFor(ELEM_ID);
+    return result;
+}
+
+
+async function get_data_from_gradio_component(ELEM_ID) {
+    let comp = await get_gradio_component(ELEM_ID);
+    return comp.props.value;
+}
+
+
+function update_array(arr, item, mode) {
+    //   // Remove "输入清除键"
+    //   p = updateArray(p, "输入清除键", "remove");
+    //   console.log(p); // Should log: ["基础功能区", "函数插件区"]
+
+    //   // Add "输入清除键"
+    //   p = updateArray(p, "输入清除键", "add");
+    //   console.log(p); // Should log: ["基础功能区", "函数插件区", "输入清除键"]
+
+    const index = arr.indexOf(item);
+    if (mode === "remove") {
+        if (index !== -1) {
+            // Item found, remove it
+            arr.splice(index, 1);
+        }
+    } else if (mode === "add") {
+        if (index === -1) {
+            // Item not found, add it
+            arr.push(item);
+        }
+    }
+    return arr;
+}
+
+
 function gradioApp() {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     const elems = document.getElementsByTagName('gradio-app');
@@ -14,6 +84,7 @@ function gradioApp() {
     return elem.shadowRoot ? elem.shadowRoot : elem;
 }
 
+
 function setCookie(name, value, days) {
     var expires = "";
 
@@ -26,6 +97,7 @@ function setCookie(name, value, days) {
     document.cookie = name + "=" + value + expires + "; path=/";
 }
 
+
 function getCookie(name) {
     var decodedCookie = decodeURIComponent(document.cookie);
     var cookies = decodedCookie.split(';');
@@ -41,6 +113,7 @@ function getCookie(name) {
     return null;
 }
 
+
 let toastCount = 0;
 function toast_push(msg, duration) {
     duration = isNaN(duration) ? 3000 : duration;
@@ -63,6 +136,7 @@ function toast_push(msg, duration) {
     toastCount++;
 }
 
+
 function toast_up(msg) {
     var m = document.getElementById('toast_up');
     if (m) {
@@ -75,6 +149,7 @@ function toast_up(msg) {
     document.body.appendChild(m);
 }
 
+
 function toast_down() {
     var m = document.getElementById('toast_up');
     if (m) {
@@ -82,6 +157,7 @@ function toast_down() {
     }
 }
 
+
 function begin_loading_status() {
     // Create the loader div and add styling
     var loader = document.createElement('div');
@@ -234,7 +310,7 @@ let timeoutID = null;
 let lastInvocationTime = 0;
 let lastArgs = null;
 function do_something_but_not_too_frequently(min_interval, func) {
-    return function(...args) {
+    return function (...args) {
         lastArgs = args;
         const now = Date.now();
         if (!lastInvocationTime || (now - lastInvocationTime) >= min_interval) {
@@ -256,6 +332,7 @@ function do_something_but_not_too_frequently(min_interval, func) {
     }
 }
 
+
 function chatbotContentChanged(attempt = 1, force = false) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     for (var i = 0; i < attempt; i++) {
@@ -263,13 +340,8 @@ function chatbotContentChanged(attempt = 1, force = false) {
             gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
         }, i === 0 ? 0 : 200);
     }
+    // we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0
 
-    const run_mermaid_render = do_something_but_not_too_frequently(1000, function () {
-        const blocks = document.querySelectorAll(`pre.mermaid, diagram-div`);
-        if (blocks.length == 0) { return; }
-        uml("mermaid");
-    });
-    run_mermaid_render();
 }
 
 
@@ -277,7 +349,6 @@ function chatbotContentChanged(attempt = 1, force = false) {
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 //  第 3 部分: chatbot动态高度调整
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
 function chatbotAutoHeight() {
     // 自动调整高度：立即
     function update_height() {
@@ -309,6 +380,7 @@ function chatbotAutoHeight() {
     setInterval(function () { update_height_slow() }, 50); // 每50毫秒执行一次
 }
 
+
 swapped = false;
 function swap_input_area() {
     // Get the elements to be swapped
@@ -328,6 +400,7 @@ function swap_input_area() {
     else { swapped = true; }
 }
 
+
 function get_elements(consider_state_panel = false) {
     var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
     if (!chatbot) {
@@ -349,7 +422,7 @@ function get_elements(consider_state_panel = false) {
     var chatbot_height = chatbot.style.height;
     // 交换输入区位置，使得输入区始终可用
     if (!swapped) {
-        if (panel1.top != 0 && (panel1.bottom + panel1.top) / 2 < 0) { swap_input_area(); }
+        if (panel1.top != 0 && (0.9 * panel1.bottom + 0.1 * panel1.top) < 0) { swap_input_area(); }
     }
     else if (swapped) {
         if (panel2.top != 0 && panel2.top > 0) { swap_input_area(); }
@@ -425,6 +498,7 @@ async function upload_files(files) {
     }
 }
 
+
 function register_func_paste(input) {
     let paste_files = [];
     if (input) {
@@ -451,6 +525,7 @@ function register_func_paste(input) {
     }
 }
 
+
 function register_func_drag(elem) {
     if (elem) {
         const dragEvents = ["dragover"];
@@ -487,6 +562,7 @@ function register_func_drag(elem) {
     }
 }
 
+
 function elem_upload_component_pop_message(elem) {
     if (elem) {
         const dragEvents = ["dragover"];
@@ -516,6 +592,7 @@ function elem_upload_component_pop_message(elem) {
     }
 }
 
+
 function register_upload_event() {
     locate_upload_elems();
     if (elem_upload_float) {
@@ -538,6 +615,7 @@ function register_upload_event() {
     }
 }
 
+
 function monitoring_input_box() {
     register_upload_event();
 
@@ -571,7 +649,6 @@ window.addEventListener("DOMContentLoaded", function () {
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 //  第 5 部分: 音频按钮样式变化
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
 function audio_fn_init() {
     let audio_component = document.getElementById('elem_audio');
     if (audio_component) {
@@ -608,6 +685,7 @@ function audio_fn_init() {
     }
 }
 
+
 function minor_ui_adjustment() {
     let cbsc_area = document.getElementById('cbsc');
     cbsc_area.style.paddingTop = '15px';
@@ -672,9 +750,9 @@ function limit_scroll_position() {
     let scrollableDiv = document.querySelector('#gpt-chatbot > div.wrap');
     scrollableDiv.addEventListener('wheel', function (e) {
         let preventScroll = false;
-        if (e.deltaX != 0) { prevented_offset = 0; return;}
-        if (this.scrollHeight == this.clientHeight) { prevented_offset = 0; return;}
-        if (e.deltaY < 0) { prevented_offset = 0; return;}
+        if (e.deltaX != 0) { prevented_offset = 0; return; }
+        if (this.scrollHeight == this.clientHeight) { prevented_offset = 0; return; }
+        if (e.deltaY < 0) { prevented_offset = 0; return; }
         if (e.deltaY > 0 && this.scrollHeight - this.clientHeight - this.scrollTop <= 1) { preventScroll = true; }
 
         if (preventScroll) {
@@ -700,7 +778,88 @@ function limit_scroll_position() {
 //  第 7 部分: JS初始化函数
 // -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 
-function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
+function loadLive2D() {
+    try {
+        $("<link>").attr({ href: "file=themes/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css" }).appendTo('head');
+        $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
+        $.ajax({
+            url: "file=themes/waifu_plugin/waifu-tips.js", dataType: "script", cache: true, success: function () {
+                $.ajax({
+                    url: "file=themes/waifu_plugin/live2d.js", dataType: "script", cache: true, success: function () {
+                        /* 可直接修改部分参数 */
+                        live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
+                        live2d_settings['modelId'] = 3;                  // 默认模型 ID
+                        live2d_settings['modelTexturesId'] = 44;          // 默认材质 ID
+                        live2d_settings['modelStorage'] = false;         // 不储存模型 ID
+                        live2d_settings['waifuSize'] = '210x187';
+                        live2d_settings['waifuTipsSize'] = '187x52';
+                        live2d_settings['canSwitchModel'] = true;
+                        live2d_settings['canSwitchTextures'] = true;
+                        live2d_settings['canSwitchHitokoto'] = false;
+                        live2d_settings['canTakeScreenshot'] = false;
+                        live2d_settings['canTurnToHomePage'] = false;
+                        live2d_settings['canTurnToAboutPage'] = false;
+                        live2d_settings['showHitokoto'] = false;          // 显示一言
+                        live2d_settings['showF12Status'] = false;         // 显示加载状态
+                        live2d_settings['showF12Message'] = false;        // 显示看板娘消息
+                        live2d_settings['showF12OpenMsg'] = false;        // 显示控制台打开提示
+                        live2d_settings['showCopyMessage'] = false;       // 显示 复制内容 提示
+                        live2d_settings['showWelcomeMessage'] = true;     // 显示进入面页欢迎词
+                        /* 在 initModel 前添加 */
+                        initModel("file=themes/waifu_plugin/waifu-tips.json");
+                    }
+                });
+            }
+        });
+    } catch (err) { console.log("[Error] JQuery is not defined.") }
+}
+
+
+function get_checkbox_selected_items(elem_id) {
+    display_panel_arr = [];
+    document.getElementById(elem_id).querySelector('[data-testid="checkbox-group"]').querySelectorAll('label').forEach(label => {
+        // Get the span text
+        const spanText = label.querySelector('span').textContent;
+        // Get the input value
+        const checked = label.querySelector('input').checked;
+        if (checked) {
+            display_panel_arr.push(spanText)
+        }
+    });
+    return display_panel_arr;
+}
+
+
+function gpt_academic_gradio_saveload(
+    save_or_load,       // save_or_load==="save" / save_or_load==="load"
+    elem_id,            // element id
+    cookie_key,         // cookie key
+    save_value = "",      // save value
+    load_type = "str",  // type==="str" / type==="float"
+    load_default = false, // load default value
+    load_default_value = ""
+) {
+    if (save_or_load === "load") {
+        let value = getCookie(cookie_key);
+        if (value) {
+            console.log('加载cookie', elem_id, value)
+            push_data_to_gradio_component(value, elem_id, load_type);
+        }
+        else {
+            if (load_default) {
+                console.log('加载cookie的默认值', elem_id, load_default_value)
+                push_data_to_gradio_component(load_default_value, elem_id, load_type);
+            }
+        }
+    }
+    if (save_or_load === "save") {
+        setCookie(cookie_key, save_value, 365);
+    }
+}
+
+
+async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
+    // 第一部分，布局初始化
     audio_fn_init();
     minor_ui_adjustment();
     chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
@@ -708,8 +867,90 @@ function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
         chatbotContentChanged(1);
     });
     chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
-    if (LAYOUT === "LEFT-RIGHT") { chatbotAutoHeight(); }
-    if (LAYOUT === "LEFT-RIGHT") { limit_scroll_position(); }
-    // setInterval(function () { uml("mermaid") }, 5000); // 每50毫秒执行一次
+    if (layout === "LEFT-RIGHT") { chatbotAutoHeight(); }
+    if (layout === "LEFT-RIGHT") { limit_scroll_position(); }
+
+    // 第二部分，读取Cookie，初始话界面
+    let searchString = "";
+    let bool_value = "";
+
+    //  darkmode 深色模式
+    if (getCookie("js_darkmode_cookie")) {
+        dark = getCookie("js_darkmode_cookie")
+    }
+    dark = dark == "True";
+    if (document.querySelectorAll('.dark').length) {
+        if (!dark) {
+            document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
+        }
+    } else {
+        if (dark) {
+            document.querySelector('body').classList.add('dark');
+        }
+    }
+
+    // SysPrompt 系统静默提示词
+    gpt_academic_gradio_saveload("load", "elem_prompt", "js_system_prompt_cookie", null, "str");
+
+    // Temperature 大模型温度参数
+    gpt_academic_gradio_saveload("load", "elem_temperature", "js_temperature_cookie", null, "float");
+
+    // clearButton 自动清除按钮
+    if (getCookie("js_clearbtn_show_cookie")) {
+        // have cookie
+        bool_value = getCookie("js_clearbtn_show_cookie")
+        bool_value = bool_value == "True";
+        searchString = "输入清除键";
+
+        if (bool_value) {
+            // make btns appear
+            let clearButton = document.getElementById("elem_clear"); clearButton.style.display = "block";
+            let clearButton2 = document.getElementById("elem_clear2"); clearButton2.style.display = "block";
+            // deal with checkboxes
+            let arr_with_clear_btn = update_array(
+                await get_data_from_gradio_component('cbs'), "输入清除键", "add"
+            )
+            push_data_to_gradio_component(arr_with_clear_btn, "cbs", "no_conversion");
+        } else {
+            // make btns disappear
+            let clearButton = document.getElementById("elem_clear"); clearButton.style.display = "none";
+            let clearButton2 = document.getElementById("elem_clear2"); clearButton2.style.display = "none";
+            // deal with checkboxes
+            let arr_without_clear_btn = update_array(
+                await get_data_from_gradio_component('cbs'), "输入清除键", "remove"
+            )
+            push_data_to_gradio_component(arr_without_clear_btn, "cbs", "no_conversion");
+        }
+    }
+
+    // live2d 显示
+    if (getCookie("js_live2d_show_cookie")) {
+        // have cookie
+        searchString = "添加Live2D形象";
+        bool_value = getCookie("js_live2d_show_cookie");
+        bool_value = bool_value == "True";
+        if (bool_value) {
+            loadLive2D();
+            let arr_with_live2d = update_array(
+                await get_data_from_gradio_component('cbsc'), "添加Live2D形象", "add"
+            )
+            push_data_to_gradio_component(arr_with_live2d, "cbsc", "no_conversion");
+        } else {
+            try {
+                $('.waifu').hide();
+                let arr_without_live2d = update_array(
+                    await get_data_from_gradio_component('cbsc'), "添加Live2D形象", "remove"
+                )
+                push_data_to_gradio_component(arr_without_live2d, "cbsc", "no_conversion");
+            } catch (error) {
+            }
+        }
+    } else {
+        // do not have cookie
+        if (live2d) {
+            loadLive2D();
+        } else {
+        }
+    }
 
 }

themes/common.py

@@ -5,17 +5,14 @@ def get_common_html_javascript_code():
     js = "\n"
     for jsf in [
         "file=themes/common.js",
-        "file=themes/mermaid.min.js",
-        "file=themes/mermaid_loader.js",
     ]:
         js += f"""<script src="{jsf}"></script>\n"""
 
     # 添加Live2D
     if ADD_WAIFU:
         for jsf in [
-            "file=docs/waifu_plugin/jquery.min.js",
-            "file=docs/waifu_plugin/jquery-ui.min.js",
-            "file=docs/waifu_plugin/autoload.js",
+            "file=themes/waifu_plugin/jquery.min.js",
+            "file=themes/waifu_plugin/jquery-ui.min.js",
         ]:
             js += f"""<script src="{jsf}"></script>\n"""
     return js

themes/mermaid_editor.js

@@ -1,55 +1 @@
-import { deflate, inflate } from '/file=themes/pako.esm.mjs';
-import { toUint8Array, fromUint8Array, toBase64, fromBase64 } from '/file=themes/base64.mjs';
-
-const base64Serde = {
-    serialize: (state) => {
-        return toBase64(state, true);
-    },
-    deserialize: (state) => {
-        return fromBase64(state);
-    }
-};
-
-const pakoSerde = {
-    serialize: (state) => {
-        const data = new TextEncoder().encode(state);
-        const compressed = deflate(data, { level: 9 });
-        return fromUint8Array(compressed, true);
-    },
-    deserialize: (state) => {
-        const data = toUint8Array(state);
-        return inflate(data, { to: 'string' });
-    }
-};
-
-const serdes = {
-    base64: base64Serde,
-    pako: pakoSerde
-};
-
-export const serializeState = (state, serde = 'pako') => {
-    if (!(serde in serdes)) {
-        throw new Error(`Unknown serde type: ${serde}`);
-    }
-    const json = JSON.stringify(state);
-    const serialized = serdes[serde].serialize(json);
-    return `${serde}:${serialized}`;
-};
-
-const deserializeState = (state) => {
-    let type, serialized;
-    if (state.includes(':')) {
-        let tempType;
-        [tempType, serialized] = state.split(':');
-        if (tempType in serdes) {
-            type = tempType;
-        } else {
-            throw new Error(`Unknown serde type: ${tempType}`);
-        }
-    } else {
-        type = 'base64';
-        serialized = state;
-    }
-    const json = serdes[type].deserialize(serialized);
-    return JSON.parse(json);
-};
+// we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0

themes/mermaid_loader.js

@@ -1,197 +1 @@
-const uml = async className => {
-
-    // Custom element to encapsulate Mermaid content.
-    class MermaidDiv extends HTMLElement {
-
-        /**
-        * Creates a special Mermaid div shadow DOM.
-        * Works around issues of shared IDs.
-        * @return {void}
-        */
-        constructor() {
-            super()
-
-            // Create the Shadow DOM and attach style
-            const shadow = this.attachShadow({ mode: "open" })
-            const style = document.createElement("style")
-            style.textContent = `
-        :host {
-          display: block;
-          line-height: initial;
-          font-size: 16px;
-        }
-        div.diagram {
-          margin: 0;
-          overflow: visible;
-        }`
-            shadow.appendChild(style)
-        }
-    }
-
-    if (typeof customElements.get("diagram-div") === "undefined") {
-        customElements.define("diagram-div", MermaidDiv)
-    }
-
-    const getFromCode = parent => {
-        // Handles <pre><code> text extraction.
-        let text = ""
-        for (let j = 0; j < parent.childNodes.length; j++) {
-            const subEl = parent.childNodes[j]
-            if (subEl.tagName.toLowerCase() === "code") {
-                for (let k = 0; k < subEl.childNodes.length; k++) {
-                    const child = subEl.childNodes[k]
-                    const whitespace = /^\s*$/
-                    if (child.nodeName === "#text" && !(whitespace.test(child.nodeValue))) {
-                        text = child.nodeValue
-                        break
-                    }
-                }
-            }
-        }
-        return text
-    }
-
-    function createOrUpdateHyperlink(parentElement, linkText, linkHref) {
-        // Search for an existing anchor element within the parentElement
-        let existingAnchor = parentElement.querySelector("a");
-
-        // Check if an anchor element already exists
-        if (existingAnchor) {
-            // Update the hyperlink reference if it's different from the current one
-            if (existingAnchor.href !== linkHref) {
-                existingAnchor.href = linkHref;
-            }
-            // Update the target attribute to ensure it opens in a new tab
-            existingAnchor.target = '_blank';
-
-            // If the text must be dynamic, uncomment and use the following line:
-            // existingAnchor.textContent = linkText;
-        } else {
-            // If no anchor exists, create one and append it to the parentElement
-            let anchorElement = document.createElement("a");
-            anchorElement.href = linkHref; // Set hyperlink reference
-            anchorElement.textContent = linkText; // Set text displayed
-            anchorElement.target = '_blank'; // Ensure it opens in a new tab
-            parentElement.appendChild(anchorElement); // Append the new anchor element to the parent
-        }
-    }
-
-    function removeLastLine(str) {
-        // 将字符串按换行符分割成数组
-        var lines = str.split('\n');
-        lines.pop();
-        // 将数组重新连接成字符串，并按换行符连接
-        var result = lines.join('\n');
-        return result;
-    }
-
-    // 给出配置 Provide a default config in case one is not specified
-    const defaultConfig = {
-        startOnLoad: false,
-        theme: "default",
-        flowchart: {
-            htmlLabels: false
-        },
-        er: {
-            useMaxWidth: false
-        },
-        sequence: {
-            useMaxWidth: false,
-            noteFontWeight: "14px",
-            actorFontSize: "14px",
-            messageFontSize: "16px"
-        }
-    }
-    if (document.body.classList.contains("dark")) {
-        defaultConfig.theme = "dark"
-    }
-    
-    const Module = await import('/file=themes/mermaid_editor.js');
-
-    function do_render(block, code, codeContent, cnt) {
-        var rendered_content = mermaid.render(`_diagram_${cnt}`, code);
-        ////////////////////////////// 记录有哪些代码已经被渲染了 ///////////////////////////////////
-        let codeFinishRenderElement = block.querySelector("code_finish_render"); // 如果block下已存在code_already_rendered元素，则获取它
-        if (codeFinishRenderElement) {                                             // 如果block下已存在code_already_rendered元素
-            codeFinishRenderElement.style.display = "none";
-        } else {
-            // 如果不存在code_finish_render元素，则将code元素中的内容添加到新创建的code_finish_render元素中
-            let codeFinishRenderElementNew = document.createElement("code_finish_render"); // 创建一个新的code_already_rendered元素
-            codeFinishRenderElementNew.style.display = "none";
-            codeFinishRenderElementNew.textContent = "";
-            block.appendChild(codeFinishRenderElementNew); // 将新创建的code_already_rendered元素添加到block中
-            codeFinishRenderElement = codeFinishRenderElementNew;
-        }
-
-        ////////////////////////////// 创建一个用于渲染的容器 ///////////////////////////////////
-        let mermaidRender = block.querySelector(".mermaid_render"); // 尝试获取已存在的<div class='mermaid_render'>
-        if (!mermaidRender) {
-            mermaidRender = document.createElement("div");  // 不存在，创建新的<div class='mermaid_render'>
-            mermaidRender.classList.add("mermaid_render");
-            block.appendChild(mermaidRender);               // 将新创建的元素附加到block
-        }
-        mermaidRender.innerHTML = rendered_content
-        codeFinishRenderElement.textContent = code  // 标记已经渲染的部分
-
-        ////////////////////////////// 创建一个“点击这里编辑脑图” ///////////////////////////////
-        let pako_encode = Module.serializeState({
-            "code": codeContent,
-            "mermaid": "{\n  \"theme\": \"default\"\n}",
-            "autoSync": true,
-            "updateDiagram": false
-        });
-        createOrUpdateHyperlink(block, "点击这里编辑脑图", "https://mermaid.live/edit#" + pako_encode)
-    }
-
-    // 加载配置 Load up the config
-    mermaid.mermaidAPI.globalReset() // 全局复位
-    const config = (typeof mermaidConfig === "undefined") ? defaultConfig : mermaidConfig
-    mermaid.initialize(config)
-    // 查找需要渲染的元素 Find all of our Mermaid sources and render them.
-    const blocks = document.querySelectorAll(`pre.mermaid`);
-
-    for (let i = 0; i < blocks.length; i++) {
-        var block = blocks[i]
-        ////////////////////////////// 如果代码没有发生变化，就不渲染了 ///////////////////////////////////
-        var code = getFromCode(block);
-        let code_elem = block.querySelector("code");
-        let codeContent = code_elem.textContent; // 获取code元素中的文本内容
-
-        // 判断codeContent是否包含'<gpt_academic_hide_mermaid_code>'，如果是，则使code_elem隐藏
-        if (codeContent.indexOf('<gpt_academic_hide_mermaid_code>') !== -1) {
-            code_elem.style.display = "none";
-        }
-
-        // 如果block下已存在code_already_rendered元素，则获取它
-        let codePendingRenderElement = block.querySelector("code_pending_render");
-        if (codePendingRenderElement) {                               // 如果block下已存在code_pending_render元素
-            codePendingRenderElement.style.display = "none";
-            if (codePendingRenderElement.textContent !== codeContent) {
-                codePendingRenderElement.textContent = codeContent; // 如果现有的code_pending_render元素中的内容与code元素中的内容不同，更新code_pending_render元素中的内容
-            }
-            else {
-                continue; // 如果相同，就不处理了
-            }
-        } else { // 如果不存在code_pending_render元素，则将code元素中的内容添加到新创建的code_pending_render元素中
-            let codePendingRenderElementNew = document.createElement("code_pending_render"); // 创建一个新的code_already_rendered元素
-            codePendingRenderElementNew.style.display = "none";
-            codePendingRenderElementNew.textContent = codeContent;
-            block.appendChild(codePendingRenderElementNew); // 将新创建的code_pending_render元素添加到block中
-            codePendingRenderElement = codePendingRenderElementNew;
-        }
-
-        ////////////////////////////// 在这里才真正开始渲染 ///////////////////////////////////
-        try {
-            do_render(block, code, codeContent, i);
-            // console.log("渲染", codeContent);
-        } catch (err) {
-            try {
-                var lines = code.split('\n'); if (lines.length < 2) { continue; }
-                do_render(block, removeLastLine(code), codeContent, i);
-                // console.log("渲染", codeContent);
-            } catch (err) {
-                console.log("以下代码不能渲染", code, removeLastLine(code), err);
-            }
-        }
-    }
-}
+// we have moved mermaid-related code to gradio-fix repository: binary-husky/gradio-fix@32150d0

themes/theme.py

@@ -1,7 +1,10 @@
 import pickle
 import base64
 import uuid
+import json
 from toolbox import get_conf
+import json
+
 
 """
 -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
@@ -45,25 +48,24 @@ adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(get_conf("
 cookie相关工具函数
 -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
-
-
-def init_cookie(cookies, chatbot):
+def assign_user_uuid(cookies):
     # 为每一位访问的用户赋予一个独一无二的uuid编码
     cookies.update({"uuid": uuid.uuid4()})
     return cookies
 
 
 def to_cookie_str(d):
-    # Pickle the dictionary and encode it as a string
-    pickled_dict = pickle.dumps(d)
-    cookie_value = base64.b64encode(pickled_dict).decode("utf-8")
+    # serialize the dictionary and encode it as a string
+    serialized_dict = json.dumps(d)
+    cookie_value = base64.b64encode(serialized_dict.encode('utf8')).decode("utf-8")
     return cookie_value
 
 
 def from_cookie_str(c):
-    # Decode the base64-encoded string and unpickle it into a dictionary
-    pickled_dict = base64.b64decode(c.encode("utf-8"))
-    return pickle.loads(pickled_dict)
+    # Decode the base64-encoded string and unserialize it into a dictionary
+    serialized_dict = base64.b64decode(c.encode("utf-8"))
+    serialized_dict.decode("utf-8")
+    return json.loads(serialized_dict)
 
 
 """
@@ -91,31 +93,103 @@ js_code_for_css_changing = """(css) => {
 }
 """
 
-js_code_for_darkmode_init = """(dark) => {
-    dark = dark == "True";
-    if (document.querySelectorAll('.dark').length) {
-        if (!dark){
-            document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-        }
-    } else {
-        if (dark){
-            document.querySelector('body').classList.add('dark');
-        }
-    }
-}
-"""
 
 js_code_for_toggle_darkmode = """() => {
     if (document.querySelectorAll('.dark').length) {
+        setCookie("js_darkmode_cookie", "False", 365);
         document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
     } else {
+        setCookie("js_darkmode_cookie", "True", 365);
         document.querySelector('body').classList.add('dark');
     }
     document.querySelectorAll('code_pending_render').forEach(code => {code.remove();})
 }"""
 
 
-js_code_for_persistent_cookie_init = """(persistent_cookie) => {
-    return getCookie("persistent_cookie");
+js_code_for_persistent_cookie_init = """(web_cookie_cache, cookie) => {
+    return [getCookie("web_cookie_cache"), cookie];
+}
+"""
+
+
+js_code_reset = """
+(a,b,c)=>{
+    return [[], [], "已重置"];
+}
+"""
+
+
+js_code_clear = """
+(a,b)=>{
+    return ["", ""];
+}
+"""
+
+
+js_code_show_or_hide = """
+(display_panel_arr)=>{
+setTimeout(() => {
+    // get conf
+    display_panel_arr = get_checkbox_selected_items("cbs");
+
+    ////////////////////// 输入清除键 ///////////////////////////
+    let searchString = "输入清除键";
+    let ele = "none";
+    if (display_panel_arr.includes(searchString)) {
+        let clearButton = document.getElementById("elem_clear");
+        let clearButton2 = document.getElementById("elem_clear2");
+        clearButton.style.display = "block";
+        clearButton2.style.display = "block";
+        setCookie("js_clearbtn_show_cookie", "True", 365);
+    } else {
+        let clearButton = document.getElementById("elem_clear");
+        let clearButton2 = document.getElementById("elem_clear2");
+        clearButton.style.display = "none";
+        clearButton2.style.display = "none";
+        setCookie("js_clearbtn_show_cookie", "False", 365);
+    }
+
+    ////////////////////// 基础功能区 ///////////////////////////
+    searchString = "基础功能区";
+    if (display_panel_arr.includes(searchString)) {
+        ele = document.getElementById("basic-panel");
+        ele.style.display = "block";
+    } else {
+        ele = document.getElementById("basic-panel");
+        ele.style.display = "none";
+    }
+
+    ////////////////////// 函数插件区 ///////////////////////////
+    searchString = "函数插件区";
+    if (display_panel_arr.includes(searchString)) {
+        ele = document.getElementById("plugin-panel");
+        ele.style.display = "block";
+    } else {
+        ele = document.getElementById("plugin-panel");
+        ele.style.display = "none";
+    }
+
+}, 50);
+}
+"""
+
+
+
+js_code_show_or_hide_group2 = """
+(display_panel_arr)=>{
+setTimeout(() => {
+    display_panel_arr = get_checkbox_selected_items("cbsc");
+
+    let searchString = "添加Live2D形象";
+    let ele = "none";
+    if (display_panel_arr.includes(searchString)) {
+        setCookie("js_live2d_show_cookie", "True", 365);
+        loadLive2D();
+    } else {
+        setCookie("js_live2d_show_cookie", "False", 365);
+        $('.waifu').hide();
+    }
+
+}, 50);
 }
 """

themes/waifu_plugin/waifu-tips.js

@@ -92,7 +92,7 @@ String.prototype.render = function(context) {
 };
 
 var re = /x/;
-console.log(re);
+// console.log(re);
 
 function empty(obj) {return typeof obj=="undefined"||obj==null||obj==""?true:false}
 function getRandText(text) {return Array.isArray(text) ? text[Math.floor(Math.random() * text.length + 1)-1] : text}
@@ -120,7 +120,7 @@ function hideMessage(timeout) {
 
 function initModel(waifuPath, type) {
     /* console welcome message */
-    eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
+    // eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
 
     /* 判断 JQuery */
     if (typeof($.ajax) != 'function') typeof(jQuery.ajax) == 'function' ? window.$ = jQuery : console.log('[Error] JQuery is not defined.');

themes/waifu_plugin/waifu-tips.json

@@ -44,8 +44,8 @@
         { "selector": ".container a[href^='http']", "text": ["要看看 <span style=\"color:#0099cc;\">{text}</span> 么？"] },
         { "selector": ".fui-home", "text": ["点击前往首页，想回到上一页可以使用浏览器的后退功能哦"] },
         { "selector": ".fui-chat", "text": ["一言一语，一颦一笑。一字一句，一颗赛艇。"] },
-        { "selector": ".fui-eye", "text": ["嗯··· 要切换 看板娘 吗？"] },
-        { "selector": ".fui-user", "text": ["喜欢换装 Play 吗？"] },
+        { "selector": ".fui-eye", "text": ["嗯··· 要切换 Live2D形象 吗？"] },
+        { "selector": ".fui-user", "text": ["喜欢换装吗？"] },
         { "selector": ".fui-photo", "text": ["要拍张纪念照片吗？"] },
         { "selector": ".fui-info-circle", "text": ["这里有关于我的信息呢"] },
         { "selector": ".fui-cross", "text": ["你不喜欢我了吗..."] },
@@ -77,14 +77,28 @@
             "看什么看(*^▽^*)",
             "焦虑时，吃顿大餐心情就好啦^_^",
             "你这个年纪，怎么睡得着觉的你^_^",
-            "修改ADD_WAIFU=False，我就不再打扰你了~",
-            "经常去github看看我们的更新吧，也许有好玩的新功能呢。",
+            "打开“界面外观”菜单，可选择关闭Live2D形象",
+            "经常去Github看看我们的更新吧，也许有好玩的新功能呢。",
             "试试本地大模型吧，有的也很强大的哦。",
             "很多强大的函数插件隐藏在下拉菜单中呢。",
-            "红色的插件，使用之前需要把文件上传进去哦。",
-            "想添加功能按钮吗？读读readme很容易就学会啦。",
+            "插件使用之前，需要把文件上传进去哦。",
+            "上传文件时，可以把文件直接拖进对话中的哦。",
+            "上传文件时，可以文件或图片粘贴到输入区哦。",
+            "想添加基础功能按钮吗？打开“界面外观”菜单进行自定义吧！",
             "敏感或机密的信息，不可以问AI的哦！",
-            "LLM究竟是划时代的创新，还是扼杀创造力的毒药呢？"
+            "LLM究竟是划时代的创新，还是扼杀创造力的毒药呢？",
+            "休息一下，起来走动走动吧！",
+            "今天的阳光也很不错哦，不妨外出晒晒。",
+            "笑一笑，生活更美好！",
+            "遇到难题，深呼吸就能解决一半。",
+            "偶尔换换环境，灵感也许就来了。",
+            "小憩片刻，醒来便是满血复活。",
+            "技术改变生活，让我们共同进步。",
+            "保持好奇心，探索未知的世界。",
+            "遇到困难，记得还有朋友和AI陪在你身边。",
+            "劳逸结合，方能长久。",
+            "偶尔给自己放个假，放松心情。",
+            "不要害怕失败，勇敢尝试才能成功。"
         ] }
     ],
     "click": [

toolbox.py

@@ -7,6 +7,8 @@ import base64
 import gradio
 import shutil
 import glob
+import logging
+import uuid
 from functools import wraps
 from shared_utils.config_loader import get_conf
 from shared_utils.config_loader import set_conf
@@ -25,7 +27,14 @@ from shared_utils.text_mask import apply_gpt_academic_string_mask
 from shared_utils.text_mask import build_gpt_academic_masked_string
 from shared_utils.text_mask import apply_gpt_academic_string_mask_langbased
 from shared_utils.text_mask import build_gpt_academic_masked_string_langbased
-
+from shared_utils.map_names import map_friendly_names_to_model
+from shared_utils.map_names import map_model_to_friendly_names
+from shared_utils.map_names import read_one_api_model_name
+from shared_utils.handle_upload import html_local_file
+from shared_utils.handle_upload import html_local_img
+from shared_utils.handle_upload import file_manifest_filter_type
+from shared_utils.handle_upload import extract_archive
+from typing import List
 pj = os.path.join
 default_user_name = "default_user"
 
@@ -77,7 +86,9 @@ def ArgsGeneralWrapper(f):
     该装饰器是大多数功能调用的入口。
     函数示意图：https://mermaid.live/edit#pako:eNqNVFtPGkEY_StkntoEDQtLoTw0sWqapjQxVWPabmOm7AiEZZcsQ9QiiW012qixqdeqqIn10geBh6ZR8PJnmAWe-hc6l3VhrWnLEzNzzvnO953ZyYOYoSIQAWOaMR5LQBN7hvoU3UN_g5iu7imAXEyT4wUF3Pd0dT3y9KGYYUJsmK8V0GPGs0-QjkyojZgwk0Fm82C2dVghX08U8EaoOHjOfoEMU0XmADRhOksVWnNLjdpM82qFzB6S5Q_WWsUhuqCc3JtAsVR_OoMnhyZwXgHWwbS1d4gnsLVZJp-P6mfVxveqAgqC70Jz_pQCOGDKM5xFdNNPDdilF6uSU_hOYqu4a3MHYDZLDzq5fodrC3PWcEaFGPUaRiqJWK_W9g9rvRITa4dhy_0nw67SiePMp3oSR6PPn41DGgllkvkizYwsrmtaejTFd8V4yekGmT1zqrt4XGlAy8WTuiPULF01LksZvukSajfQQRAxmYi5S0D81sDcyzapVdn6sYFHkjhhGyel3frVQnvsnbR23lEjlhIlaOJiFPWzU5G4tfNJo8ejwp47-TbvJkKKZvmxA6SKo16oaazJysfG6klr9T0pbTW2ZqzlL_XaT8fYbQLXe4mSmvoCZXMaa7FePW6s7jVqK9bujvse3WFjY5_Z4KfsA4oiPY4T7Drvn1tLJTbG1to1qR79ulgk89-oJbvZzbIwJty6u20LOReWa9BvwserUd9s9MIKc3x5TUWEoAhUyJK5y85w_yG-dFu_R9waoU7K581y8W_qLle35-rG9Nxcrz8QHRsc0K-r9NViYRT36KsFvCCNzDRMqvSVyzOKAnACpZECIvSvCs2UAhS9QHEwh43BST0GItjMIS_I8e-sLwnj9A262cxA_ZVh0OUY1LJiDSJ5MAEiUijYLUtBORR6KElyQPaCSRDpksNSd8AfluSgHPaFC17wjrOlbgbzyyFf4IFPDvoD_sJvnkdK-g
     """
-    def decorated(request: gradio.Request, cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
+    def decorated(request: gradio.Request, cookies:dict, max_length:int, llm_model:str,
+                  txt:str, txt2:str, top_p:float, temperature:float, chatbot:list,
+                  history:list, system_prompt:str, plugin_advanced_arg:str, *args):
         txt_passon = txt
         if txt == "" and txt2 != "": txt_passon = txt2
         # 引入一个有cookie的chatbot
@@ -129,7 +140,7 @@ def ArgsGeneralWrapper(f):
     return decorated
 
 
-def update_ui(chatbot, history, msg="正常", **kwargs):  # 刷新界面
+def update_ui(chatbot:ChatBotWithCookies, history, msg="正常", **kwargs):  # 刷新界面
     """
     刷新用户界面
     """
@@ -159,7 +170,7 @@ def update_ui(chatbot, history, msg="正常", **kwargs):  # 刷新界面
     yield cookies, chatbot_gr, history, msg
 
 
-def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1):  # 刷新界面
+def update_ui_lastest_msg(lastmsg:str, chatbot:ChatBotWithCookies, history:list, delay=1):  # 刷新界面
     """
     刷新用户界面
     """
@@ -186,13 +197,12 @@ def CatchException(f):
     """
 
     @wraps(f)
-    def decorated(main_input, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, *args, **kwargs):
+    def decorated(main_input:str, llm_kwargs:dict, plugin_kwargs:dict,
+                  chatbot_with_cookie:ChatBotWithCookies, history:list, *args, **kwargs):
         try:
             yield from f(main_input, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, *args, **kwargs)
         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:
                 chatbot_with_cookie.clear()
@@ -245,7 +255,7 @@ def HotReload(f):
 """
 
 
-def get_reduce_token_percent(text):
+def get_reduce_token_percent(text:str):
     """
     * 此函数未来将被弃用
     """
@@ -264,7 +274,7 @@ def get_reduce_token_percent(text):
 
 
 def write_history_to_file(
-    history, file_basename=None, file_fullname=None, auto_caption=True
+    history:list, file_basename:str=None, file_fullname:str=None, auto_caption:bool=True
 ):
     """
     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。
@@ -298,7 +308,7 @@ def write_history_to_file(
     return res
 
 
-def regular_txt_to_markdown(text):
+def regular_txt_to_markdown(text:str):
     """
     将普通文本转换为Markdown格式的文本。
     """
@@ -308,7 +318,7 @@ def regular_txt_to_markdown(text):
     return text
 
 
-def report_exception(chatbot, history, a, b):
+def report_exception(chatbot:ChatBotWithCookies, history:list, a:str, b:str):
     """
     向chatbot中添加错误信息
     """
@@ -316,7 +326,7 @@ def report_exception(chatbot, history, a, b):
     history.extend([a, b])
 
 
-def find_free_port():
+def find_free_port()->int:
     """
     返回当前系统中可用的未使用端口。
     """
@@ -329,58 +339,9 @@ def find_free_port():
         return s.getsockname()[1]
 
 
-def extract_archive(file_path, dest_dir):
-    import zipfile
-    import tarfile
-    import os
-
-    # Get the file extension of the input file
-    file_extension = os.path.splitext(file_path)[1]
-
-    # Extract the archive based on its extension
-    if file_extension == ".zip":
-        with zipfile.ZipFile(file_path, "r") as zipobj:
-            zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
-
-    elif file_extension in [".tar", ".gz", ".bz2"]:
-        with tarfile.open(file_path, "r:*") as tarobj:
-            tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
-
-    # 第三方库，需要预先pip install rarfile
-    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
-    elif file_extension == ".rar":
-        try:
-            import rarfile
-
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
-        except:
-            print("Rar format requires additional dependencies to install")
-            return "\n\n解压失败! 需要安装pip install rarfile来解压rar文件。建议：使用zip压缩格式。"
-
-    # 第三方库，需要预先pip install py7zr
-    elif file_extension == ".7z":
-        try:
-            import py7zr
-
-            with py7zr.SevenZipFile(file_path, mode="r") as f:
-                f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
-        except:
-            print("7z format requires additional dependencies to install")
-            return "\n\n解压失败! 需要安装pip install py7zr来解压7z文件"
-    else:
-        return ""
-    return ""
-
-
-def find_recent_files(directory):
+def find_recent_files(directory:str)->List[str]:
     """
-    me: find files that is created with in one minutes under a directory with python, write a function
-    gpt: here it is!
+    Find files that is created with in one minutes under a directory with python, write a function
     """
     import os
     import time
@@ -403,7 +364,7 @@ def find_recent_files(directory):
     return recent_files
 
 
-def file_already_in_downloadzone(file, user_path):
+def file_already_in_downloadzone(file:str, user_path:str):
     try:
         parent_path = os.path.abspath(user_path)
         child_path = os.path.abspath(file)
@@ -415,7 +376,7 @@ def file_already_in_downloadzone(file, user_path):
         return False
 
 
-def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
+def promote_file_to_downloadzone(file:str, rename_file:str=None, chatbot:ChatBotWithCookies=None):
     # 将文件复制一份到下载区
     import shutil
 
@@ -450,12 +411,12 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
     return new_path
 
 
-def disable_auto_promotion(chatbot):
+def disable_auto_promotion(chatbot:ChatBotWithCookies):
     chatbot._cookies.update({"files_to_promote": []})
     return
 
 
-def del_outdated_uploads(outdate_time_seconds, target_path_base=None):
+def del_outdated_uploads(outdate_time_seconds:float, target_path_base:str=None):
     if target_path_base is None:
         user_upload_dir = get_conf("PATH_PRIVATE_UPLOAD")
     else:
@@ -474,39 +435,8 @@ def del_outdated_uploads(outdate_time_seconds, target_path_base=None):
     return
 
 
-def html_local_file(file):
-    base_path = os.path.dirname(__file__)  # 项目目录
-    if os.path.exists(str(file)):
-        file = f'file={file.replace(base_path, ".")}'
-    return file
 
-
-def html_local_img(__file, layout="left", max_width=None, max_height=None, md=True):
-    style = ""
-    if max_width is not None:
-        style += f"max-width: {max_width};"
-    if max_height is not None:
-        style += f"max-height: {max_height};"
-    __file = html_local_file(__file)
-    a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
-    if md:
-        a = f"![{__file}]({__file})"
-    return a
-
-
-def file_manifest_filter_type(file_list, filter_: list = None):
-    new_list = []
-    if not filter_:
-        filter_ = ["png", "jpg", "jpeg"]
-    for file in file_list:
-        if str(os.path.basename(file)).split(".")[-1] in filter_:
-            new_list.append(html_local_img(file, md=False))
-        else:
-            new_list.append(file)
-    return new_list
-
-
-def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False):
+def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False, omit_path=None):
     """
     Args:
         head: 表头：[]
@@ -530,13 +460,17 @@ def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False):
     for i in range(max_len):
         row_data = [tab[i] if i < len(tab) else "" for tab in transposed_tabs]
         row_data = file_manifest_filter_type(row_data, filter_=None)
+        # for dat in row_data:
+        #     if (omit_path is not None) and os.path.exists(dat):
+        #         dat = os.path.relpath(dat, omit_path)
         tabs_list += "".join([tab_format % i for i in row_data]) + "|\n"
 
     return tabs_list
 
 
 def on_file_uploaded(
-    request: gradio.Request, files, chatbot, txt, txt2, checkboxes, cookies
+    request: gradio.Request, files:List[str], chatbot:ChatBotWithCookies,
+    txt:str, txt2:str, checkboxes:List[str], cookies:dict
 ):
     """
     当文件被上传时的回调函数
@@ -565,15 +499,21 @@ def on_file_uploaded(
         )
 
     # 整理文件集合 输出消息
-    moved_files = [fp for fp in glob.glob(f"{target_path_base}/**/*", recursive=True)]
-    moved_files_str = to_markdown_tabs(head=["文件"], tabs=[moved_files])
+    files = glob.glob(f"{target_path_base}/**/*", recursive=True)
+    moved_files = [fp for fp in files]
+    max_file_to_show = 10
+    if len(moved_files) > max_file_to_show:
+        moved_files = moved_files[:max_file_to_show//2] + [f'... ( 📌省略{len(moved_files) - max_file_to_show}个文件的显示 ) ...'] + \
+                      moved_files[-max_file_to_show//2:]
+    moved_files_str = to_markdown_tabs(head=["文件"], tabs=[moved_files], omit_path=target_path_base)
     chatbot.append(
         [
             "我上传了文件，请查收",
-            f"[Local Message] 收到以下文件: \n\n{moved_files_str}"
-            + f"\n\n调用路径参数已自动修正到: \n\n{txt}"
-            + f"\n\n现在您点击任意函数插件时，以上文件将被作为输入参数"
-            + upload_msg,
+            f"[Local Message] 收到以下文件 （上传到路径：{target_path_base}）: " +
+            f"\n\n{moved_files_str}" +
+            f"\n\n调用路径参数已自动修正到: \n\n{txt}" +
+            f"\n\n现在您点击任意函数插件时，以上文件将被作为输入参数" +
+            upload_msg,
         ]
     )
 
@@ -594,18 +534,14 @@ def on_file_uploaded(
     return chatbot, txt, txt2, cookies
 
 
-def on_report_generated(cookies, files, chatbot):
-    # from toolbox import find_recent_files
-    # PATH_LOGGING = get_conf('PATH_LOGGING')
+def on_report_generated(cookies:dict, files:List[str], chatbot:ChatBotWithCookies):
     if "files_to_promote" in cookies:
         report_files = cookies["files_to_promote"]
         cookies.pop("files_to_promote")
     else:
         report_files = []
-    #     report_files = find_recent_files(PATH_LOGGING)
     if len(report_files) == 0:
         return cookies, None, chatbot
-    # files.extend(report_files)
     file_links = ""
     for f in report_files:
         file_links += (
@@ -885,7 +821,7 @@ def is_the_upload_folder(string):
         return False
 
 
-def get_user(chatbotwithcookies):
+def get_user(chatbotwithcookies:ChatBotWithCookies):
     return chatbotwithcookies._cookies.get("user_name", default_user_name)
 
 
@@ -968,7 +904,7 @@ def get_pictures_list(path):
     return file_manifest
 
 
-def have_any_recent_upload_image_files(chatbot):
+def have_any_recent_upload_image_files(chatbot:ChatBotWithCookies):
     _5min = 5 * 60
     if chatbot is None:
         return False, None  # chatbot is None
@@ -985,6 +921,18 @@ def have_any_recent_upload_image_files(chatbot):
     else:
         return False, None  # most_recent_uploaded is too old
 
+# Claude3 model supports graphic context dialogue, reads all images
+def every_image_file_in_path(chatbot:ChatBotWithCookies):
+    if chatbot is None:
+        return False, []  # chatbot is None
+    most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
+    if not most_recent_uploaded:
+        return False, []  # most_recent_uploaded is None
+    path = most_recent_uploaded["path"]
+    file_manifest = get_pictures_list(path)
+    if len(file_manifest) == 0:
+        return False, []
+    return True, file_manifest
 
 # Function to encode the image
 def encode_image(image_path):
@@ -1005,3 +953,65 @@ def check_packages(packages=[]):
         spam_spec = importlib.util.find_spec(p)
         if spam_spec is None:
             raise ModuleNotFoundError
+
+
+def map_file_to_sha256(file_path):
+    import hashlib
+
+    with open(file_path, 'rb') as file:
+        content = file.read()
+
+    # Calculate the SHA-256 hash of the file contents
+    sha_hash = hashlib.sha256(content).hexdigest()
+
+    return sha_hash
+
+
+def check_repeat_upload(new_pdf_path, pdf_hash):
+    '''
+    检查历史上传的文件是否与新上传的文件相同，如果相同则返回(True, 重复文件路径)，否则返回(False，None)
+    '''
+    from toolbox import get_conf
+    import PyPDF2
+
+    user_upload_dir = os.path.dirname(os.path.dirname(new_pdf_path))
+    file_name = os.path.basename(new_pdf_path)
+
+    file_manifest = [f for f in glob.glob(f'{user_upload_dir}/**/{file_name}', recursive=True)]
+
+    for saved_file in file_manifest:
+        with open(new_pdf_path, 'rb') as file1, open(saved_file, 'rb') as file2:
+            reader1 = PyPDF2.PdfFileReader(file1)
+            reader2 = PyPDF2.PdfFileReader(file2)
+
+            # 比较页数是否相同
+            if reader1.getNumPages() != reader2.getNumPages():
+                continue
+
+            # 比较每一页的内容是否相同
+            for page_num in range(reader1.getNumPages()):
+                page1 = reader1.getPage(page_num).extractText()
+                page2 = reader2.getPage(page_num).extractText()
+                if page1 != page2:
+                    continue
+
+        maybe_project_dir = glob.glob('{}/**/{}'.format(get_log_folder(), pdf_hash + ".tag"), recursive=True)
+
+
+        if len(maybe_project_dir) > 0:
+            return True, os.path.dirname(maybe_project_dir[0])
+
+    # 如果所有页的内容都相同，返回 True
+    return False, None
+
+def log_chat(llm_model: str, input_str: str, output_str: str):
+    try:
+        if output_str and input_str and llm_model:
+            uid = str(uuid.uuid4().hex)
+            logging.info(f"[Model({uid})] {llm_model}")
+            input_str = input_str.rstrip('\n')
+            logging.info(f"[Query({uid})]\n{input_str}")
+            output_str = output_str.rstrip('\n')
+            logging.info(f"[Response({uid})]\n{output_str}\n\n")
+    except:
+        print(trimmed_format_exc())

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.71,
+  "version": 3.74,
   "show_feature": true,
-  "new_feature": "用绘图功能增强部分插件 <-> 基础功能区支持自动切换中英提示词 <-> 支持Mermaid绘图库（让大模型绘制脑图） <-> 支持Gemini-pro <-> 支持直接拖拽文件到上传区 <-> 支持将图片粘贴到输入区"
+  "new_feature": "增加多用户文件鉴权验证提高安全性 <-> 优化oneapi接入方法 <-> 接入Cohere和月之暗面模型 <-> 简化挂载二级目录的步骤 <-> 支持Mermaid绘图库（让大模型绘制脑图）"
 }