h

README.md

@@ -44,7 +44,7 @@ chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
 Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
 [谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
 互联网信息聚合+GPT | [函数插件] 一键[让GPT从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)回答问题，让信息永不过时
-⭐Arxiv论文精细翻译 ([Docker](https://github.com/binary-husky/gpt_academic/pkgs/container/gpt_academic_with_latex)) | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
+⭐Arxiv论文精细翻译 | [函数插件] 一键[以超高质量翻译arxiv论文](https://www.bilibili.com/video/BV1dz4y1v77A/)，目前最好的论文翻译工具
 ⭐[实时语音对话输入](https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md) | [函数插件] 异步[监听音频](https://www.bilibili.com/video/BV1AV4y187Uy/)，自动断句，自动寻找回答时机
 公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
 多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序

cc.json

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

config.py

@@ -32,9 +32,9 @@ else:
 
 # ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
 
-# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
-# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
+# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!! 高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
+# 格式 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
+# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions":"https://reverse-proxy-url/v1/chat/completions"}
 API_URL_REDIRECT = {}
 
 
@@ -71,7 +71,7 @@ MAX_RETRY = 2
 # 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
-# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
 
 
 # ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）

crazy_functional.py

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

crazy_functions/Three场景交互3D.py

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

crazy_functions/crazy_functions_test.py

@@ -17,7 +17,7 @@ validate_path() # validate path so you can run from base directory
 # ==============================================================================================================================
 
 from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
+from toolbox import get_conf, ChatBotWithCookies, load_chat_cookies
 import contextlib
 import os
 import sys
@@ -32,6 +32,7 @@ llm_kwargs = {
     'max_length': None,
     'temperature':1.0,
 }
+llm_kwargs.update(load_chat_cookies())
 plugin_kwargs = { }
 chatbot = ChatBotWithCookies(llm_kwargs)
 history = []
@@ -226,6 +227,15 @@ def test_chatglm_finetune():
         cli_printer.print(cb)
 
 
+def 三维生成():
+    from crazy_functions.Three场景交互3D import 三维生成
+    txt = "Generate 10 boxes to form a triangle formation with random color."
+    plugin_kwargs = {"advanced_arg":""}
+
+    for cookies, cb, hist, msg in (三维生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        cli_printer.print(cb)
+
+
 if __name__ == "__main__":
     # test_解析一个Python项目()
     # test_Latex英文润色()
@@ -241,6 +251,6 @@ if __name__ == "__main__":
     # test_Langchain知识库()
     # test_Langchain知识库读取()
     # test_Latex()
-    test_chatglm_finetune()
+    三维生成()
     input("程序完成，回车退出。")
     print("退出。")

crazy_functions/crazy_utils.py

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

crazy_functions/vhmap_interact/vhmap.py

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

main.py

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

request_llm/bridge_all.py

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

request_llm/bridge_chatgpt.py

@@ -141,7 +141,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
     try:
-        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream=False)
+        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
@@ -156,7 +156,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             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=False, timeout=TIMEOUT_SECONDS);break
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
         except:
             retry += 1
             chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
@@ -174,18 +174,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 chunk = next(stream_response)
             except StopIteration:
                 # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
-                chunk_decoded = chunk.decode()
-                if '"finish_reason":"stop"' in chunk_decoded:
-                    history[-1] = json.loads(chunk_decoded)['choices'][0]['message']['content']
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield from update_ui(chatbot=chatbot, history=history, msg='OK') # 刷新界面
-                    return
-                else:
-                    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="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
-                    return
-                
+                from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
+                chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode())}")
+                yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk.decode()) # 刷新界面
+                return
+            
             # print(chunk.decode()[6:])
             if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
                 # 数据流的第一帧不携带content
@@ -194,7 +187,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             if chunk:
                 try:
                     chunk_decoded = chunk.decode()
-                    # 前者是API2D的结束条件，后者是OPENAI的结束条件
+                    # 前者API2D的
                     if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
                         logging.info(f'[response] {gpt_replying_buffer}')
@@ -207,44 +200,40 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     history[-1] = gpt_replying_buffer
                     chatbot[-1] = (history[-2], history[-1])
                     yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
+
                 except Exception as e:
+                    traceback.print_exc()
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
                     chunk = get_full_error(chunk, stream_response)
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
-                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                    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
-    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
-    if "reduce the length" in error_msg:
-        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
-        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
+                    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
+                    if "reduce the length" in error_msg:
+                        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
+                        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
                                                max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
                         # history = []    # 清除历史
-    elif "does not exist" in error_msg:
-        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. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
-    elif "exceeded your current quota" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
-    elif "account is not active" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "associated with a deactivated account" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "bad forward key" in error_msg:
-        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
+                    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. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
+                    elif "exceeded your current quota" in error_msg:
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
+                    elif "account is not active" in error_msg:
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+                    elif "associated with a deactivated account" in error_msg:
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+                    elif "bad forward key" in error_msg:
+                        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)}")
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
+                    return
 
 def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     """

request_llm/bridge_internlm.py

@@ -1,315 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf, Singleton
-from multiprocessing import Process, Pipe
-
-model_name = "InternLM"
-cmd_to_install = "`pip install ???`"
-load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-def try_to_import_special_deps():
-    import sentencepiece
-
-user_prompt = "<|User|>:{user}<eoh>\n"
-robot_prompt = "<|Bot|>:{robot}<eoa>\n"
-cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"
-
-
-def combine_history(prompt, hist):
-    messages = hist
-    total_prompt = ""
-    for message in messages:
-        cur_content = message
-        cur_prompt = user_prompt.replace("{user}", cur_content[0])
-        total_prompt += cur_prompt
-        cur_prompt = robot_prompt.replace("{robot}", cur_content[1])
-        total_prompt += cur_prompt
-    total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
-    return total_prompt
-
-
-@Singleton
-class GetInternlmHandle(Process):
-    def __init__(self):
-        # ⭐主进程执行
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self._model = None
-        self._tokenizer = None
-        self.info = ""
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-
-    def ready(self):
-        # ⭐主进程执行
-        return self._model is not None
-
-    def load_model_and_tokenizer(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer
-        device, = get_conf('LOCAL_MODEL_DEVICE')
-        if self._model is None:
-            tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
-            if device=='cpu':
-                model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16)
-            else:
-                model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()
-
-            model = model.eval()
-        return model, tokenizer
-
-    def llm_stream_generator(self, **kwargs):
-        import torch
-        import logging
-        import copy
-        import warnings
-        import torch.nn as nn
-        from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
-
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        def adaptor():
-            model = self._model
-            tokenizer = self._tokenizer
-            prompt = kwargs['query']
-            max_length = kwargs['max_length']
-            top_p = kwargs['top_p']
-            temperature = kwargs['temperature']
-            history = kwargs['history']
-            real_prompt = combine_history(prompt, history)
-            return model, tokenizer, real_prompt, max_length, top_p, temperature
-        
-        model, tokenizer, prompt, max_length, top_p, temperature = adaptor()
-        prefix_allowed_tokens_fn = None
-        logits_processor = None
-        stopping_criteria = None
-        additional_eos_token_id = 103028
-        generation_config = None
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ https://github.com/InternLM/InternLM/blob/efbf5335709a8c8faeac6eaf07193973ff1d56a1/web_demo.py#L25
-
-        inputs = tokenizer([prompt], padding=True, return_tensors="pt")
-        input_length = len(inputs["input_ids"][0])
-        for k, v in inputs.items():
-            inputs[k] = v.cuda()
-        input_ids = inputs["input_ids"]
-        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
-        if generation_config is None:
-            generation_config = model.generation_config
-        generation_config = copy.deepcopy(generation_config)
-        model_kwargs = generation_config.update(**kwargs)
-        bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-        if additional_eos_token_id is not None:
-            eos_token_id.append(additional_eos_token_id)
-        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None:
-            warnings.warn(
-                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
-                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
-                " recommend using `max_new_tokens` to control the maximum length of the generation.",
-                UserWarning,
-            )
-        elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-            if not has_default_max_length:
-                logging.warn(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
-                    UserWarning,
-                )
-
-        if input_ids_seq_length >= generation_config.max_length:
-            input_ids_string = "input_ids"
-            logging.warning(
-                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
-
-        # 2. Set generation parameters if not already defined
-        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
-        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-
-        logits_processor = model._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
-            encoder_input_ids=input_ids,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-            logits_processor=logits_processor,
-        )
-
-        stopping_criteria = model._get_stopping_criteria(
-            generation_config=generation_config, stopping_criteria=stopping_criteria
-        )
-        logits_warper = model._get_logits_warper(generation_config)
-
-        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
-        scores = None
-        while True:
-            model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
-            # forward pass to get next token
-            outputs = model(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=False,
-                output_hidden_states=False,
-            )
-
-            next_token_logits = outputs.logits[:, -1, :]
-
-            # pre-process distribution
-            next_token_scores = logits_processor(input_ids, next_token_logits)
-            next_token_scores = logits_warper(input_ids, next_token_scores)
-
-            # sample
-            probs = nn.functional.softmax(next_token_scores, dim=-1)
-            if generation_config.do_sample:
-                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
-            else:
-                next_tokens = torch.argmax(probs, dim=-1)
-
-            # update generated ids, model inputs, and length for next step
-            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-            model_kwargs = model._update_model_kwargs_for_generation(
-                outputs, model_kwargs, is_encoder_decoder=False
-            )
-            unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
-            
-            output_token_ids = input_ids[0].cpu().tolist()
-            output_token_ids = output_token_ids[input_length:]
-            for each_eos_token_id in eos_token_id:
-                if output_token_ids[-1] == each_eos_token_id:
-                    output_token_ids = output_token_ids[:-1]
-            response = tokenizer.decode(output_token_ids)
-
-            yield response
-            # stop when each sentence is finished, or if we exceed the maximum length
-            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
-                return
-
-
-
-    def check_dependency(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        try:
-            try_to_import_special_deps()
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            self.info = f"缺少{model_name}的依赖，如果要使用{model_name}，除了基础的pip依赖以外，您还需要运行{cmd_to_install}安装{model_name}的依赖。"
-            self.success = False
-
-    def run(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # 第一次运行，加载参数
-        try:
-            self._model, self._tokenizer = self.load_model_and_tokenizer()
-        except:
-            from toolbox import trimmed_format_exc
-            self.child.send(f'[Local Message] 不能正常加载{model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            raise RuntimeError(f"不能正常加载{model_name}的参数！")
-
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            # 收到消息，开始请求
-            try:
-                for response_full in self.llm_stream_generator(**kwargs):
-                    self.child.send(response_full)
-            except:
-                from toolbox import trimmed_format_exc
-                self.child.send(f'[Local Message] 调用{model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # ⭐主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-    
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 GPT-Academic
-# ------------------------------------------------------------------------------------------------------------------------
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        ⭐多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    _llm_handle = GetInternlmHandle()
-    if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
-    if not _llm_handle.success: 
-        error = _llm_handle.info
-        _llm_handle = None
-        raise RuntimeError(error)
-
-    # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    history_feedin.append(["What can I do?", sys_prompt])
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        ⭐单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    _llm_handle = GetInternlmHandle()
-    chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
-    yield from update_ui(chatbot=chatbot, history=[])
-    if not _llm_handle.success: 
-        _llm_handle = None
-        return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    # 处理历史信息
-    history_feedin = []
-    history_feedin.append(["What can I do?", system_prompt] )
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收chatglm的回复
-    response = f"[Local Message]: 等待{model_name}响应中 ..."
-    for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)

request_llm/edge_gpt_free.py

@@ -447,15 +447,6 @@ class _ChatHub:
         """
         Ask a question to the bot
         """
-        req_header = HEADERS
-        if self.cookies is not None:
-            ws_cookies = []
-            for cookie in self.cookies:
-                ws_cookies.append(f"{cookie['name']}={cookie['value']}")
-            req_header.update({
-                'Cookie': ';'.join(ws_cookies),
-            })
-            
         timeout = aiohttp.ClientTimeout(total=30)
         self.session = aiohttp.ClientSession(timeout=timeout)
 
@@ -464,7 +455,7 @@ class _ChatHub:
         # Check if websocket is closed
         self.wss = await self.session.ws_connect(
             wss_link,
-            headers=req_header,
+            headers=HEADERS,
             ssl=ssl_context,
             proxy=self.proxy,
             autoping=False,
@@ -1118,4 +1109,4 @@ class ImageQuery(Query):
 
 
 if __name__ == "__main__":
-    main()
+    main()

request_llm/test_llms.py

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

toolbox.py

@@ -883,16 +883,4 @@ def objload(file='objdump.tmp'):
         return
     with open(file, 'rb') as f:
         return pickle.load(f)
-    
-def Singleton(cls):
-    """
-    一个单实例装饰器
-    """
-    _instance = {}
- 
-    def _singleton(*args, **kargs):
-        if cls not in _instance:
-            _instance[cls] = cls(*args, **kargs)
-        return _instance[cls]
- 
-    return _singleton
+