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| import traceback | |
| from toolbox import update_ui, get_conf | |
| def input_clipping(inputs, history, max_token_limit): | |
| import tiktoken | |
| import numpy as np | |
| enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL')) | |
| def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) | |
| 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], disallowed_special=()) | |
| 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, llm_kwargs, | |
| 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, ""]) | |
| yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 | |
| executor = ThreadPoolExecutor(max_workers=16) | |
| mutable = ["", time.time()] | |
| def _req_gpt(inputs, history, sys_prompt): | |
| retry_op = retry_times_at_unknown_error | |
| exceeded_cnt = 0 | |
| while True: | |
| # watchdog error | |
| if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: | |
| raise RuntimeError("检测到程序终止。") | |
| try: | |
| # 【第一种情况】：顺利完成 | |
| result = predict_no_ui_long_connection( | |
| inputs=inputs, llm_kwargs=llm_kwargs, | |
| 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：重试几次 | |
| 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 | |
| # 提交任务 | |
| 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) | |
| # “喂狗”（看门狗） | |
| mutable[1] = time.time() | |
| if future.done(): | |
| break | |
| chatbot[-1] = [chatbot[-1][0], mutable[0]] | |
| yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 | |
| final_result = future.result() | |
| chatbot[-1] = [chatbot[-1][0], final_result] | |
| yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息 | |
| return final_result | |
| def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( | |
| inputs_array, inputs_show_user_array, llm_kwargs, | |
| chatbot, history_array, sys_prompt_array, | |
| refresh_interval=0.2, max_workers=-1, 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） | |
| llm_kwargs: llm_kwargs参数 | |
| 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: see config.py) （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) | |
| assert len(inputs_array) == len(sys_prompt_array) | |
| if max_workers == -1: # 读取配置文件 | |
| try: max_workers, = get_conf('DEFAULT_WORKER_NUM') | |
| except: max_workers = 8 | |
| if max_workers <= 0 or max_workers >= 20: max_workers = 8 | |
| executor = ThreadPoolExecutor(max_workers=max_workers) | |
| n_frag = len(inputs_array) | |
| # 用户反馈 | |
| chatbot.append(["请开始多线程操作。", ""]) | |
| yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 | |
| # 跨线程传递 | |
| mutable = [["", time.time(), "等待中"] for _ in range(n_frag)] | |
| # 子线程任务 | |
| def _req_gpt(index, inputs, history, sys_prompt): | |
| gpt_say = "" | |
| retry_op = retry_times_at_unknown_error | |
| exceeded_cnt = 0 | |
| mutable[index][2] = "执行中" | |
| while True: | |
| # watchdog error | |
| if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: | |
| raise RuntimeError("检测到程序终止。") | |
| 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, llm_kwargs=llm_kwargs, 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)] | |
| 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) | |
| # 在前端打印些好玩的东西 | |
| 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)]) | |
| # 在前端打印些好玩的东西 | |
| 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))] | |
| yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 | |
| # 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]) | |
| # 是否在结束时，在界面上显示结果 | |
| 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 from update_ui(chatbot=chatbot, history=[]) # 刷新界面 | |
| time.sleep(0.3) | |
| return gpt_response_collection | |
| 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: | |
| return [txt_tocut] | |
| else: | |
| lines = txt_tocut.split('\n') | |
| estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) | |
| estimated_line_cut = int(estimated_line_cut) | |
| for cnt in reversed(range(estimated_line_cut)): | |
| if must_break_at_empty_line: | |
| if lines[cnt] != "": | |
| continue | |
| print(cnt) | |
| prev = "\n".join(lines[:cnt]) | |
| post = "\n".join(lines[cnt:]) | |
| if get_token_fn(prev) < limit: | |
| break | |
| if cnt == 0: | |
| print('what the fuck ?') | |
| raise RuntimeError("存在一行极长的文本！") | |
| # print(len(post)) | |
| # 列表递归接龙 | |
| result = [prev] | |
| result.extend(cut(post, must_break_at_empty_line)) | |
| return result | |
| try: | |
| return cut(txt, must_break_at_empty_line=True) | |
| except RuntimeError: | |
| return cut(txt, must_break_at_empty_line=False) | |
| def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): | |
| def cut(txt_tocut, must_break_at_empty_line): # 递归 | |
| if get_token_fn(txt_tocut) <= limit: | |
| return [txt_tocut] | |
| else: | |
| 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 | |
| print(cnt) | |
| prev = "\n".join(lines[:cnt]) | |
| post = "\n".join(lines[cnt:]) | |
| if get_token_fn(prev) < limit: | |
| break | |
| if cnt == 0: | |
| # 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 | |
| result = [prev] | |
| result.extend(cut(post, must_break_at_empty_line)) | |
| return result | |
| 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] | |
| def read_and_clean_pdf_text(fp): | |
| """ | |
| 这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好 | |
| **输入参数说明** | |
| - `fp`：需要读取和清理文本的pdf文件路径 | |
| **输出参数说明** | |
| - `meta_txt`：清理后的文本内容字符串 | |
| - `page_one_meta`：第一页清理后的文本内容列表 | |
| **函数功能** | |
| 读取pdf文件并清理其中的文本内容，清理规则包括： | |
| - 提取所有块元的文本信息，并合并为一个字符串 | |
| - 去除短块（字符数小于100）并替换为回车符 | |
| - 清理多余的空行 | |
| - 合并小写字母开头的段落块并替换为空格 | |
| - 清除重复的换行 | |
| - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔 | |
| """ | |
| import fitz, copy | |
| import re | |
| import numpy as np | |
| from colorful import print亮黄, print亮绿 | |
| fc = 0 # Index 0 文本 | |
| fs = 1 # Index 1 字体 | |
| fb = 2 # Index 2 框框 | |
| REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等） | |
| REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化） | |
| def primary_ffsize(l): | |
| """ | |
| 提取文本块主字体 | |
| """ | |
| fsize_statiscs = {} | |
| for wtf in l['spans']: | |
| if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0 | |
| fsize_statiscs[wtf['size']] += len(wtf['text']) | |
| return max(fsize_statiscs, key=fsize_statiscs.get) | |
| def ffsize_same(a,b): | |
| """ | |
| 提取字体大小是否近似相等 | |
| """ | |
| return abs((a-b)/max(a,b)) < 0.02 | |
| with fitz.open(fp) as doc: | |
| meta_txt = [] | |
| meta_font = [] | |
| meta_line = [] | |
| meta_span = [] | |
| ############################## <第 1 步，搜集初始信息> ################################## | |
| 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 | |
| for t in text_areas['blocks']: | |
| if 'lines' in t: | |
| pf = 998 | |
| for l in t['lines']: | |
| txt_line = "".join([wtf['text'] for wtf in l['spans']]) | |
| pf = primary_ffsize(l) | |
| meta_line.append([txt_line, pf, l['bbox'], l]) | |
| for wtf in l['spans']: # for l in t['lines']: | |
| meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])]) | |
| # meta_line.append(["NEW_BLOCK", pf]) | |
| # 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] | |
| ############################## <第 2 步，获取正文主字体> ################################## | |
| fsize_statiscs = {} | |
| for span in meta_span: | |
| if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0 | |
| fsize_statiscs[span[1]] += span[2] | |
| main_fsize = max(fsize_statiscs, key=fsize_statiscs.get) | |
| if REMOVE_FOOT_NOTE: | |
| give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT | |
| ############################## <第 3 步，切分和重新整合> ################################## | |
| mega_sec = [] | |
| sec = [] | |
| for index, line in enumerate(meta_line): | |
| if index == 0: | |
| sec.append(line[fc]) | |
| continue | |
| if REMOVE_FOOT_NOTE: | |
| if meta_line[index][fs] <= give_up_fize_threshold: | |
| continue | |
| if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]): | |
| # 尝试识别段落 | |
| if meta_line[index][fc].endswith('.') and\ | |
| (meta_line[index-1][fc] != 'NEW_BLOCK') and \ | |
| (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7: | |
| sec[-1] += line[fc] | |
| sec[-1] += "\n\n" | |
| else: | |
| sec[-1] += " " | |
| sec[-1] += line[fc] | |
| else: | |
| if (index+1 < len(meta_line)) and \ | |
| meta_line[index][fs] > main_fsize: | |
| # 单行 + 字体大 | |
| mega_sec.append(copy.deepcopy(sec)) | |
| sec = [] | |
| sec.append("# " + line[fc]) | |
| else: | |
| # 尝试识别section | |
| if meta_line[index-1][fs] > meta_line[index][fs]: | |
| sec.append("\n" + line[fc]) | |
| else: | |
| sec.append(line[fc]) | |
| mega_sec.append(copy.deepcopy(sec)) | |
| finals = [] | |
| for ms in mega_sec: | |
| final = " ".join(ms) | |
| final = final.replace('- ', ' ') | |
| finals.append(final) | |
| meta_txt = finals | |
| ############################## <第 4 步，乱七八糟的后处理> ################################## | |
| 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') | |
| ############################## <第 5 步，展示分割效果> ################################## | |
| for f in finals: | |
| print亮黄(f) | |
| print亮绿('***************************') | |
| return meta_txt, page_one_meta | |