提升稳定性

crazy_functional.py

@@ -29,7 +29,7 @@ def get_crazy_functions():
             "Color": "stop",    # 按钮颜色
             "Function": HotReload(解析一个C项目的头文件)
         },
-        "解析整个C++项目（.cpp/.h）": {
+        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
             "Color": "stop",    # 按钮颜色
             "AsButton": False,  # 加入下拉菜单中
             "Function": HotReload(解析一个C项目)

crazy_functions/Latex全文润色.py

@@ -0,0 +1,70 @@
+from request_llm.bridge_chatgpt import predict_no_ui
+from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
+fast_debug = False
+
+
+def 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
+    import time, glob, os
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8') as f:
+            file_content = f.read()
+
+        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
+        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+        print('[1] yield chatbot, history')
+        yield chatbot, history, '正常'
+
+        if not fast_debug: 
+            msg = '正常'
+            # ** gpt request **
+            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[])   # 带超时倒计时
+
+            print('[2] end gpt req')
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.append(i_say_show_user); history.append(gpt_say)
+            print('[3] yield chatbot, history')
+            yield chatbot, history, msg
+            print('[4] next')
+            if not fast_debug: time.sleep(2)
+
+    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
+    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
+    chatbot.append((i_say, "[Local Message] waiting gpt response."))
+    yield chatbot, history, '正常'
+
+    if not fast_debug: 
+        msg = '正常'
+        # ** gpt request **
+        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history)   # 带超时倒计时
+
+        chatbot[-1] = (i_say, gpt_say)
+        history.append(i_say); history.append(gpt_say)
+        yield chatbot, history, msg
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield chatbot, history, msg
+
+
+
+@CatchException
+def 读文章写摘要(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(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 chatbot, history, '正常'
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield chatbot, history, '正常'
+        return
+    yield from 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)

crazy_functions/crazy_utils.py

@@ -1,19 +1,115 @@
 import traceback
+from toolbox import update_ui
 
-def request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, top_p, temperature, chatbot, history, sys_prompt, refresh_interval=0.2):
+def input_clipping(inputs, history, max_token_limit):
+    import tiktoken
+    import numpy as np
+    from toolbox import get_conf
+    enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
+    def get_token_num(txt): return len(enc.encode(txt))
+
+    mode = 'input-and-history'
+    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
+    input_token_num = get_token_num(inputs)
+    if input_token_num < max_token_limit//2: 
+        mode = 'only-history'
+        max_token_limit = max_token_limit - input_token_num
+
+    everything = [inputs] if mode == 'input-and-history' else ['']
+    everything.extend(history)
+    n_token = get_token_num('\n'.join(everything))
+    everything_token = [get_token_num(e) for e in everything]
+    delta = max(everything_token) // 16 # 截断时的颗粒度
+        
+    while n_token > max_token_limit:
+        where = np.argmax(everything_token)
+        encoded = enc.encode(everything[where])
+        clipped_encoded = encoded[:len(encoded)-delta]
+        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
+        everything_token[where] = get_token_num(everything[where])
+        n_token = get_token_num('\n'.join(everything))
+
+    if mode == 'input-and-history':
+        inputs = everything[0]
+    else:
+        pass
+    history = everything[1:]
+    return inputs, history
+
+def request_gpt_model_in_new_thread_with_ui_alive(
+        inputs, inputs_show_user, top_p, temperature, 
+        chatbot, history, sys_prompt, refresh_interval=0.2,
+        handle_token_exceed=True, 
+        retry_times_at_unknown_error=2,
+        ):
+    """
+    Request GPT model，请求GPT模型同时维持用户界面活跃。
+
+    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
+        inputs (string): List of inputs （输入）
+        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
+        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
+        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
+        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）
+        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
+        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
+        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
+        retry_times_at_unknown_error：失败时的重试次数
+
+    输出 Returns:
+        future: 输出，GPT返回的结果
+    """
     import time
     from concurrent.futures import ThreadPoolExecutor
     from request_llm.bridge_chatgpt import predict_no_ui_long_connection
     # 用户反馈
     chatbot.append([inputs_show_user, ""])
     msg = '正常'
-    yield chatbot, [], msg
+    yield from update_ui(chatbot=chatbot, history=[])
     executor = ThreadPoolExecutor(max_workers=16)
     mutable = ["", time.time()]
-    future = executor.submit(lambda:
-                             predict_no_ui_long_connection(
-                                 inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
-                             )
+    def _req_gpt(inputs, history, sys_prompt):
+        retry_op = retry_times_at_unknown_error
+        exceeded_cnt = 0
+        while True:
+            try:
+                # 【第一种情况】：顺利完成
+                result = predict_no_ui_long_connection(
+                    inputs=inputs, top_p=top_p, temperature=temperature, 
+                    history=history, sys_prompt=sys_prompt, observe_window=mutable)
+                return result
+            except ConnectionAbortedError as token_exceeded_error:
+                # 【第二种情况】：Token溢出，
+                if handle_token_exceed:
+                    exceeded_cnt += 1
+                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
+                    from toolbox import get_reduce_token_percent
+                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
+                    MAX_TOKEN = 4096
+                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
+                    continue # 返回重试
+                else:
+                    # 【选择放弃】
+                    tb_str = '```\n' + traceback.format_exc() + '```'
+                    mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                    return mutable[0] # 放弃
+            except:
+                # 【第三种情况】：其他错误
+                tb_str = '```\n' + traceback.format_exc() + '```'
+                mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                if retry_op > 0: 
+                    retry_op -= 1
+                    mutable[0] += f"[Local Message] 重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
+                    time.sleep(5)
+                    continue # 返回重试
+                else:
+                    time.sleep(5)
+                    return mutable[0] # 放弃
+
+    future = executor.submit(_req_gpt, inputs, history, sys_prompt)
     while True:
         # yield一次以刷新前端页面
         time.sleep(refresh_interval)
@@ -27,8 +123,42 @@ def request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, top_
     return future.result()
 
 
-def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inputs_array, inputs_show_user_array, top_p, temperature, chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=10, scroller_max_len=30):
-    import time
+def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array, inputs_show_user_array, top_p, temperature, 
+        chatbot, history_array, sys_prompt_array, 
+        refresh_interval=0.2, max_workers=10, scroller_max_len=30,
+        handle_token_exceed=True, show_user_at_complete=False,
+        retry_times_at_unknown_error=2,
+        ):
+    """
+    Request GPT model using multiple threads with UI and high efficiency
+    请求GPT模型的[多线程]版。
+    具备以下功能：
+        实时在UI上反馈远程数据流
+        使用线程池，可调节线程池的大小避免openai的流量限制错误
+        处理中途中止的情况
+        网络等出问题时，会把traceback和已经接收的数据转入输出
+
+    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
+        inputs_array (list): List of inputs （每个子任务的输入）
+        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
+        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
+        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
+        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
+        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
+        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
+        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
+        max_workers (int, optional): Maximum number of threads (default: 10) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
+        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
+        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
+        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
+        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
+        retry_times_at_unknown_error：子任务失败时的重试次数
+
+    输出 Returns:
+        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
+    """
+    import time, random
     from concurrent.futures import ThreadPoolExecutor
     from request_llm.bridge_chatgpt import predict_no_ui_long_connection
     assert len(inputs_array) == len(history_array)
@@ -40,20 +170,61 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
     msg = '正常'
     yield chatbot, [], msg
     # 异步原子
-    mutable = [["", time.time()] for _ in range(n_frag)]
+    mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
 
     def _req_gpt(index, inputs, history, sys_prompt):
-        try:
-            gpt_say = predict_no_ui_long_connection(
-                inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[index]
-            )
-        except:
-            # 收拾残局
-            tb_str = '```\n' + traceback.format_exc() + '```'
-            gpt_say = f"[Local Message] 线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-            if len(mutable[index][0]) > 0:
-                gpt_say += "此线程失败前收到的回答：" + mutable[index][0]
-        return gpt_say
+        gpt_say = ""
+        retry_op = retry_times_at_unknown_error
+        exceeded_cnt = 0
+        mutable[index][2] = "执行中"
+        while True:
+            try:
+                # 【第一种情况】：顺利完成
+                # time.sleep(10); raise RuntimeError("测试")
+                gpt_say = predict_no_ui_long_connection(
+                    inputs=inputs, top_p=top_p, temperature=temperature, history=history, 
+                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
+                )
+                mutable[index][2] = "已成功"
+                return gpt_say
+            except ConnectionAbortedError as token_exceeded_error:
+                # 【第二种情况】：Token溢出，
+                if handle_token_exceed:
+                    exceeded_cnt += 1
+                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
+                    from toolbox import get_reduce_token_percent
+                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
+                    MAX_TOKEN = 4096
+                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
+                    mutable[index][2] = f"截断重试"
+                    continue # 返回重试
+                else:
+                    # 【选择放弃】
+                    tb_str = '```\n' + traceback.format_exc() + '```'
+                    gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                    mutable[index][2] = "输入过长已放弃"
+                    return gpt_say # 放弃
+            except:
+                # 【第三种情况】：其他错误
+                tb_str = '```\n' + traceback.format_exc() + '```'
+                gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                if retry_op > 0: 
+                    retry_op -= 1
+                    wait = random.randint(5, 20)
+                    for i in range(wait):# 也许等待十几秒后，情况会好转
+                        mutable[index][2] = f"等待重试 {wait-i}"; time.sleep(1)
+                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
+                    continue # 返回重试
+                else:
+                    mutable[index][2] = "已失败"
+                    wait = 5
+                    time.sleep(5)
+                    return gpt_say # 放弃
+
     # 异步任务开始
     futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
         range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
@@ -68,6 +239,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
             break
         # 更好的UI视觉效果
         observe_win = []
+        # print([mutable[thread_index][2] for thread_index, _ in enumerate(worker_done)])
         # 每个线程都要“喂狗”（看门狗）
         for thread_index, _ in enumerate(worker_done):
             mutable[thread_index][1] = time.time()
@@ -77,10 +249,10 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
                 replace('\n', '').replace('```', '...').replace(
                     ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
             observe_win.append(print_something_really_funny)
-        stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(
-            worker_done, observe_win)])
-        chatbot[-1] = [chatbot[-1][0],
-                       f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
+        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' 
+                            if not done else f'`{mutable[thread_index][2]}`\n\n' 
+                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
+        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
         msg = "正常"
         yield chatbot, [], msg
     # 异步任务结束
@@ -88,9 +260,38 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
     for inputs_show_user, f in zip(inputs_show_user_array, futures):
         gpt_res = f.result()
         gpt_response_collection.extend([inputs_show_user, gpt_res])
+
+    if show_user_at_complete:
+        for inputs_show_user, f in zip(inputs_show_user_array, futures):
+            gpt_res = f.result()
+            chatbot.append([inputs_show_user, gpt_res])
+            yield chatbot, [], msg
+            time.sleep(1)
     return gpt_response_collection
 
 
+def WithRetry(f):
+    """
+        装饰器函数，用于自动重试。
+    """
+    def decorated(retry, res_when_fail, *args, **kwargs):
+        assert retry >= 0
+        while True:
+            try:
+                res = yield from f(*args, **kwargs)
+                return res
+            except:
+                retry -= 1
+                if retry<0:
+                    print("达到最大重试次数")
+                    break
+                else:
+                    print("重试中……")
+                    continue
+        return res_when_fail
+    return decorated
+
+
 def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
     def cut(txt_tocut, must_break_at_empty_line):  # 递归
         if get_token_fn(txt_tocut) <= limit:

crazy_functions/代码重写为全英文_多线程.py

@@ -58,11 +58,10 @@ def 全项目切换英文(txt, top_p, temperature, chatbot, history, sys_prompt,
 
     # 第5步：Token限制下的截断与处理
     MAX_TOKEN = 3000
-    from transformers import GPT2TokenizerFast
-    print('加载tokenizer中')
-    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-    get_token_fn = lambda txt: len(tokenizer(txt)["input_ids"])
-    print('加载tokenizer结束')
+    import tiktoken
+    from toolbox import get_conf
+    enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
+    def get_token_fn(txt): return len(enc.encode(txt))
 
 
     # 第6步：任务函数

crazy_functions/批量翻译PDF文档_多线程.py

@@ -148,7 +148,8 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
         file_content, page_one = read_and_clean_pdf_text(fp)
         # 递归地切割PDF文件
         from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        enc = tiktoken.get_encoding("gpt2")
+        from toolbox import get_conf
+        enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
         def get_token_num(txt): return len(enc.encode(txt))
         # 分解文本
         paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(

crazy_functions/解析项目源代码.py

@@ -2,92 +2,96 @@ from request_llm.bridge_chatgpt import predict_no_ui
 from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
 fast_debug = False
 
-def 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
+
+def 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
+    import os, copy
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, WithRetry
+    msg = '正常'
+    inputs_array = []
+    inputs_show_user_array = []
+    history_array = []
+    sys_prompt_array = []
+    report_part_1 = []
+
+    ############################## <第一步，逐个文件分析，多线程> ##################################
     for index, fp in enumerate(file_manifest):
         with open(fp, 'r', encoding='utf-8') as f:
             file_content = f.read()
-
         prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
         i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
         i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            msg = '正常'
-
-            # ** gpt request **
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[])   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, msg
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history)   # 带超时倒计时
-        
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, msg
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
+        # 装载请求内容
+        inputs_array.append(i_say)
+        inputs_show_user_array.append(i_say_show_user)
+        history_array.append([])
+        sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array = inputs_array,
+        inputs_show_user_array = inputs_show_user_array,
+        history_array = history_array,
+        sys_prompt_array = sys_prompt_array,
+        top_p = top_p,
+        temperature = temperature,
+        chatbot = chatbot,
+        show_user_at_complete = True
+    )
+
+    report_part_1 = copy.deepcopy(gpt_response_collection)
+    history_to_return = report_part_1
+    res = write_results_to_file(report_part_1)
+    chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
+    yield chatbot, history_to_return, msg
+
+    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
+    batchsize = 16  # 10个文件为一组
+    report_part_2 = []
+    previous_iteration_files = []
+    while True:
+        if len(file_manifest) == 0: break
+        this_iteration_file_manifest = file_manifest[:batchsize]
+        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
+        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
+        # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
+        for index, content in enumerate(this_iteration_gpt_response_collection):
+            if index%2==0: this_iteration_gpt_response_collection[index] = f"文件名：{file_rel_path[index//2]}"
+        previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
+        previous_iteration_files_string = ', '.join(previous_iteration_files)
+        current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
+        i_say = f'根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括{previous_iteration_files_string}）。'
+        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
+        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection) 
+        this_iteration_history.extend(report_part_2)
+        result = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=i_say, inputs_show_user=inputs_show_user, top_p=top_p, temperature=temperature, chatbot=chatbot, 
+            history=this_iteration_history,   # 迭代之前的分析
+            sys_prompt="你是一个程序架构分析师，正在分析一个源代码项目。")
+        report_part_2.extend([i_say, result])
+
+        file_manifest = file_manifest[batchsize:]
+        gpt_response_collection = gpt_response_collection[batchsize*2:]
+
+    ############################## <END> ##################################
+    history_to_return.extend(report_part_2)
+    res = write_results_to_file(history_to_return)
+    chatbot.append(("完成了吗？", res))
+    yield chatbot, history_to_return, msg
 
 
 @CatchException
 def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
     history = []    # 清空历史，以免输入溢出
-    import time, glob, os
+    import glob
     file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你分析自己的程序构成，别紧张，" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{fp}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            # ** gpt request **
-            # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], long_connection=True)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, '正常'
-            time.sleep(2)
-
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{file_manifest}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        # ** gpt request **
-        # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history, long_connection=True)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, '正常'
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
+                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \
+                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
+    project_folder = './'
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
         yield chatbot, history, '正常'
+        return
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 @CatchException
 def 解析一个Python项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
@@ -105,7 +109,7 @@ def 解析一个Python项目(txt, top_p, temperature, chatbot, history, systemPr
         report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 
 @CatchException
@@ -126,7 +130,7 @@ def 解析一个C项目的头文件(txt, top_p, temperature, chatbot, history, s
         report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 @CatchException
 def 解析一个C项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
@@ -147,7 +151,7 @@ def 解析一个C项目(txt, top_p, temperature, chatbot, history, systemPromptT
         report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 
 @CatchException
@@ -169,7 +173,7 @@ def 解析一个Java项目(txt, top_p, temperature, chatbot, history, systemProm
         report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 
 @CatchException
@@ -192,7 +196,7 @@ def 解析一个Rect项目(txt, top_p, temperature, chatbot, history, systemProm
         report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何Rect文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
 
 
 @CatchException
@@ -211,4 +215,4 @@ def 解析一个Golang项目(txt, top_p, temperature, chatbot, history, systemPr
         report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
         yield chatbot, history, '正常'
         return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析源代码新(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)

request_llm/bridge_chatgpt.py

@@ -72,7 +72,7 @@ def predict_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
         raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
 
 
-def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_prompt="", observe_window=None):
+def predict_no_ui_long_connection(inputs, top_p, temperature, 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：
@@ -121,7 +121,7 @@ def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_pr
         if "role" in delta: continue
         if "content" in delta: 
             result += delta["content"]
-            print(delta["content"], end='')
+            if not console_slience: print(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] += delta["content"]
@@ -264,8 +264,7 @@ def generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
         "presence_penalty": 0,
         "frequency_penalty": 0,
     }
-    
-    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
+    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs[:100]}")
     return headers,payload
 
 

toolbox.py

@@ -21,6 +21,8 @@ def ArgsGeneralWrapper(f):
         yield from f(txt_passon, *args, **kwargs)
     return decorated
 
+def update_ui(chatbot, history, msg='正常', *args, **kwargs):
+    yield chatbot, history, msg
 
 def get_reduce_token_percent(text):
     try:

version

@@ -1,5 +1,5 @@
 {
-  "version": 2.5,
+  "version": 2.6,
   "show_feature": true,
-  "new_feature": "新增一键更新程序<->高亮代码<->高亮公式<->新增垂直布局选项"
+  "new_feature": "增强多线程稳定性（涉及代码解析、PDF翻译等）<->修复Token计数错误（解决PDF翻译的分割不合理的问题）"
 }