update

crazy_functions/crazy_utils.py

@@ -37,6 +37,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
             lines = txt_tocut.split('\n')
             estimated_line_cut = limit / get_token_fn(txt_tocut)  * len(lines)
             estimated_line_cut = int(estimated_line_cut)
+            cnt = 0
             for cnt in reversed(range(estimated_line_cut)):
                 if must_break_at_empty_line: 
                     if lines[cnt] != "": continue
@@ -45,7 +46,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
                 post = "\n".join(lines[cnt:])
                 if get_token_fn(prev) < limit: break
             if cnt == 0:
-                print('what the fuck ?')
+                # print('what the fuck ? 存在一行极长的文本！')
                 raise RuntimeError("存在一行极长的文本！")
             # print(len(post))
             # 列表递归接龙
@@ -55,4 +56,10 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
     try:
         return cut(txt, must_break_at_empty_line=True)
     except RuntimeError:
+        try:
             return cut(txt, must_break_at_empty_line=False)
+        except RuntimeError:
+            # 这个中文的句号是故意的，作为一个标识而存在
+            res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
+            return [r.replace('。\n', '.') for r in res]
+

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,7 +1,6 @@
 from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
 import re
 import unicodedata
-fast_debug = False
 
 
 def is_paragraph_break(match):
@@ -61,7 +60,6 @@ def clean_text(raw_text):
 
     return final_text.strip()
 
-
 def read_and_clean_pdf_text(fp):
     import fitz, re
     import numpy as np
@@ -69,18 +67,15 @@ def read_and_clean_pdf_text(fp):
     with fitz.open(fp) as doc:
         meta_txt = []
         meta_font = []
-        for page in doc:
+        for index, page in enumerate(doc):
             # file_content += page.get_text()
             text_areas = page.get_text("dict")  # 获取页面上的文本信息
 
-
+            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
-            # # 行元提取                           for each word segment with in line             for each line                                   for each block
+            meta_txt.extend( [   " ".join(["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ]).replace('- ','')               for t in text_areas['blocks'] if 'lines' in t])
-            # meta_txt.extend( [   ["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ]              for t in text_areas['blocks'] if 'lines' in t])
-            # meta_font.extend([   [     np.mean([wtf['size'] for wtf in l['spans'] ])          for l in t['lines']   ]              for t in text_areas['blocks'] if 'lines' in t])
-
-            # 块元提取                           for each word segment with in line             for each line                                   for each block
-            meta_txt.extend( [   " ".join(["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ])              for t in text_areas['blocks'] if 'lines' in t])
             meta_font.extend([   np.mean( [     np.mean([wtf['size'] for wtf in l['spans'] ])          for l in t['lines']   ])                                for t in text_areas['blocks'] if 'lines' in t])
+            if index==0:
+                page_one_meta = [" ".join(["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ]).replace('- ','')               for t in text_areas['blocks'] if 'lines' in t]
 
         def 把字符太少的块清除为回车(meta_txt):
             for index, block_txt in enumerate(meta_txt):
@@ -123,19 +118,17 @@ def read_and_clean_pdf_text(fp):
         # 换行 -> 双换行
         meta_txt = meta_txt.replace('\n', '\n\n')
 
-        # print(meta_txt)
+    return meta_txt, page_one_meta
-
-    return meta_txt
 
 @CatchException
-def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
+def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
     import glob
     import os
 
     # 基本信息：功能、贡献者
     chatbot.append([
         "函数插件功能？",
-        "批量总结PDF文档。函数插件贡献者: Binary-Husky, ValeriaWong, Eralien"])
+        "批量总结PDF文档。函数插件贡献者: Binary-Husky（二进制哈士奇）"])
     yield chatbot, history, '正常'
 
     # 尝试导入依赖，如果缺少依赖，则给出安装建议
@@ -174,82 +167,116 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromp
         return
 
     # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
+    yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt)
 
 
-def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
+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):
+    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
+    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)
+    )
+    while True:
+        # yield一次以刷新前端页面
+        time.sleep(refresh_interval)
+        # “喂狗”（看门狗）
+        mutable[1] = time.time()
+        if future.done(): break
+        chatbot[-1] = [chatbot[-1][0], mutable[0]]; msg = "正常"
+        yield chatbot, [], msg
+    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, max_workers=10, scroller_max_len=30):
+    import time
+    from concurrent.futures import ThreadPoolExecutor
+    from request_llm.bridge_chatgpt import predict_no_ui_long_connection
+    assert len(inputs_array) == len(history_array)
+    assert len(inputs_array) == len(sys_prompt_array)
+    executor = ThreadPoolExecutor(max_workers=max_workers)
+    n_frag = len(inputs_array)
+    # 异步原子
+    mutable = [["", time.time()] for _ in range(n_frag)]
+    def _req_gpt(index, inputs, history, sys_prompt):
+        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]
+        )
+        return gpt_say
+    # 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)]
+    cnt = 0
+    while True:
+        # yield一次以刷新前端页面
+        time.sleep(refresh_interval); cnt += 1
+        worker_done = [h.done() for h in futures]
+        if all(worker_done): executor.shutdown(); break
+        # 更好的UI视觉效果
+        observe_win = []
+        # 每个线程都要“喂狗”（看门狗）
+        for thread_index, _ in enumerate(worker_done): mutable[thread_index][1] = time.time()
+        # 在前端打印些好玩的东西
+        for thread_index, _ in enumerate(worker_done): 
+            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
+                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))]; msg = "正常"
+        yield chatbot, [], msg
+    # 异步任务结束
+    gpt_response_collection = []
+    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])
+    return gpt_response_collection
+
+def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
     import time
     import glob
     import os
     import fitz
     import tiktoken
-    from concurrent.futures import ThreadPoolExecutor
+    TOKEN_LIMIT_PER_FRAGMENT = 1600
-    print('begin analysis on:', file_manifest)
+    
     for index, fp in enumerate(file_manifest):
-        ### 1. 读取PDF文件
+        # 读取PDF文件
-        file_content = read_and_clean_pdf_text(fp)
+        file_content, page_one = read_and_clean_pdf_text(fp)
-        ### 2. 递归地切割PDF文件
+        # 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")
-        TOKEN_LIMIT_PER_FRAGMENT = 2048
         get_token_num = lambda txt: len(enc.encode(txt))
-        # 分解
+        # 分解文本
         paper_fragments    = breakdown_txt_to_satisfy_token_limit_for_pdf(
             txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        print([get_token_num(frag) for frag in paper_fragments])
+        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        ### 3. 逐个段落翻译
+            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-        ## 3.1. 多线程开始
+        # 为了更好的效果，我们剥离Introduction之后的部分
-        from request_llm.bridge_chatgpt import predict_no_ui_long_connection
+        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        n_frag = len(paper_fragments)
+        # 单线，获取文章meta信息
-        # 异步原子
+        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        mutable = [["", time.time()] for _ in range(n_frag)]
+            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}", 
-        # 翻译函数
+            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。", 
-        def translate_(index, fragment, mutable):
+            top_p=top_p, temperature=temperature,
-            i_say = f"以下是你需要翻译的文章段落：{fragment}"
+            chatbot=chatbot, history=[],
-            # 请求gpt，需要一段时间
+            sys_prompt="Your job is to collect information from materials。",
-            gpt_say = predict_no_ui_long_connection(
+        )
-                inputs=i_say, top_p=top_p, temperature=temperature, history=[], # ["请翻译：" if len(previous_result)!=0 else "", previous_result], 
+        # 多线，翻译
-                sys_prompt="请你作为一个学术翻译，负责将给定的文章段落翻译成中文，要求语言简洁、精准、凝练。你只需要给出翻译后的文本，不能重复原文。",
+        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-                observe_window=mutable[index])
+            inputs_array = [f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments],
-            return gpt_say
+            inputs_show_user_array = [f""  for _ in paper_fragments],
-        ### 4. 异步任务开始
+            top_p=top_p, temperature=temperature,
-        executor = ThreadPoolExecutor(max_workers=16)
+            chatbot=chatbot,
-        # Submit tasks to the pool
+            history_array=[[paper_meta] for _ in paper_fragments],
-        futures = [executor.submit(translate_, index, frag, mutable) for index, frag in enumerate(paper_fragments)]
+            sys_prompt_array=["请你作为一个学术翻译，把整个段落翻译成中文，要求语言简洁，禁止重复输出原文。" for _ in paper_fragments],
+            max_workers=16 # OpenAI所允许的最大并行过载
+        )
 
-        ### 5. UI主线程，在任务期间提供实时的前端显示
+        final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n'].extend(gpt_response_collection)
-        cnt = 0
+        res = write_results_to_file(final)
-        while True:
+        chatbot.append((f"{fp}完成了吗？", res)); msg = "完成"
-            cnt += 1
-            time.sleep(1)
-            worker_done = [h.done() for h in futures]
-            if all(worker_done): 
-                executor.shutdown(); break
-            # 更好的UI视觉效果
-            observe_win = []
-            # 每个线程都要喂狗（看门狗）
-            for thread_index, _ in enumerate(worker_done): 
-                mutable[thread_index][1] = time.time()
-            # 在前端打印些好玩的东西
-            for thread_index, _ in enumerate(worker_done): 
-                print_something_really_funny = "[ ...`"+mutable[thread_index][0][-30:].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))]; msg = "正常"
         yield chatbot, history, msg
 
-        # Wait for tasks to complete
-        results = [future.result() for future in futures]
-
-        print(results)
-        # full_result += gpt_say
-        
-        # history.extend([fp, full_result])
-
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res)); msg = "完成"
-    yield chatbot, history, msg
-
-
-# if __name__ == '__main__':
-#     pro()

crazy_functions/高级功能函数模板.py

@@ -5,7 +5,7 @@ import datetime
 @CatchException
 def 高阶功能模板函数(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
     history = []    # 清空历史，以免输入溢出
-    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。为了做到简单易读，该函数只有25行代码，所以不会实时反馈文字流或心跳，请耐心等待程序输出完成。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
+    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板(该函数只有25行代码)。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
     yield chatbot, history, '正常'  # 由于请求gpt需要一段时间，我们先及时地做一次状态显示
 
     for i in range(5):