from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down import re import unicodedata def is_paragraph_break(match): """ 根据给定的匹配结果来判断换行符是否表示段落分隔。 如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。 也可以根据之前的内容长度来判断段落是否已经足够长。 """ prev_char, next_char = match.groups() # 句子结束标志 sentence_endings = ".!?" # 设定一个最小段落长度阈值 min_paragraph_length = 140 if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length: return "\n\n" else: return " " def normalize_text(text): """ 通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。 例如，将连字 "fi" 转换为 "f" 和 "i"。 """ # 对文本进行归一化处理，分解连字 normalized_text = unicodedata.normalize("NFKD", text) # 替换其他特殊字符 cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text) return cleaned_text def clean_text(raw_text): """ 对从 PDF 提取出的原始文本进行清洗和格式化处理。 1. 对原始文本进行归一化处理。 2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。 3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。 """ # 对文本进行归一化处理 normalized_text = normalize_text(raw_text) # 替换跨行的连词 text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text) # 根据前后相邻字符的特点，找到原文本中的换行符 newlines = re.compile(r'(\S)\n(\S)') # 根据 heuristic 规则，用空格或段落分隔符替换原换行符 final_text = re.sub(newlines, lambda m: m.group( 1) + is_paragraph_break(m) + m.group(2), text) return final_text.strip() def read_and_clean_pdf_text(fp): import fitz, re import numpy as np # file_content = "" with fitz.open(fp) as doc: meta_txt = [] meta_font = [] 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 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_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): if len(block_txt) < 100: meta_txt[index] = '\n' return meta_txt meta_txt = 把字符太少的块清除为回车(meta_txt) def 清理多余的空行(meta_txt): for index in reversed(range(1, len(meta_txt))): if meta_txt[index] == '\n' and meta_txt[index-1] == '\n': meta_txt.pop(index) return meta_txt meta_txt = 清理多余的空行(meta_txt) def 合并小写开头的段落块(meta_txt): def starts_with_lowercase_word(s): pattern = r"^[a-z]+" match = re.match(pattern, s) if match: return True else: return False for _ in range(100): for index, block_txt in enumerate(meta_txt): if starts_with_lowercase_word(block_txt): if meta_txt[index-1]!='\n': meta_txt[index-1] += ' ' else: meta_txt[index-1] = '' meta_txt[index-1] += meta_txt[index] meta_txt[index] = '\n' return meta_txt meta_txt = 合并小写开头的段落块(meta_txt) meta_txt = 清理多余的空行(meta_txt) meta_txt = '\n'.join(meta_txt) # 清除重复的换行 for _ in range(5): meta_txt = meta_txt.replace('\n\n','\n') # 换行 -> 双换行 meta_txt = meta_txt.replace('\n', '\n\n') return meta_txt, page_one_meta @CatchException 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（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 try: import fitz, tiktoken except: report_execption(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 tiktoken```。") yield chatbot, history, '正常' return # 清空历史，以免输入溢出 history = [] # 检测输入参数，如没有给定输入参数，直接退出 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 # 搜索需要处理的文件清单 file_manifest = [f for f in glob.glob( f'{project_folder}/**/*.pdf', recursive=True)] # 如果没找到任何文件 if len(file_manifest) == 0: report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") yield chatbot, history, '正常' return # 开始正式执行任务 yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt) 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=0.2, 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('
','.....').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 TOKEN_LIMIT_PER_FRAGMENT = 1600 for index, fp in enumerate(file_manifest): # 读取PDF文件 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") get_token_num = lambda txt: len(enc.encode(txt)) # 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) page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4) # 为了更好的效果，我们剥离Introduction之后的部分 paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] # 单线，获取文章meta信息 paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( 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}", 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。", top_p=top_p, temperature=temperature, chatbot=chatbot, history=[], sys_prompt="Your job is to collect information from materials。", ) # 多线，翻译 gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( inputs_array = [f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments], inputs_show_user_array = [f"" for _ in paper_fragments], top_p=top_p, temperature=temperature, chatbot=chatbot, history_array=[[paper_meta] for _ in 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 ) final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n'] final.extend(gpt_response_collection) res = write_results_to_file(final) chatbot.append((f"{fp}完成了吗？", res)); msg = "完成" yield chatbot, history, msg