from toolbox import CatchException, report_execption, write_results_to_file from toolbox import update_ui from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency from colorful import * 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，不建议任何人去读这个函数 **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 fc = 0 fs = 1 fb = 2 REMOVE_FOOT_NOTE = True REMOVE_FOOT_FFSIZE_PERCENT = 0.95 def primary_ffsize(l): 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): return abs((a-b)/max(a,b)) < 0.02 # file_content = "" with fitz.open(fp) as doc: meta_txt = [] meta_font = [] meta_line = [] meta_span = [] 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] # 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 # 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 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') for f in finals: print亮黄(f) print亮绿('***************************') return meta_txt, page_one_meta @CatchException def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, 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"]) 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 try: import fitz import 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 from update_ui(chatbot=chatbot, history=history) # 刷新界面 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 from update_ui(chatbot=chatbot, history=history) # 刷新界面 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 from update_ui(chatbot=chatbot, history=history) # 刷新界面 return # 开始正式执行任务 yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt) def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt): import os import tiktoken TOKEN_LIMIT_PER_FRAGMENT = 1600 generated_conclusion_files = [] 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 from toolbox import get_conf enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL')) def get_token_num(txt): return len(enc.encode(txt)) 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（如果有） 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。", llm_kwargs=llm_kwargs, 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论文片段：\n{frag}" for frag in paper_fragments], inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')} \n---\n 翻译：\n " for frag in paper_fragments], llm_kwargs=llm_kwargs, chatbot=chatbot, history_array=[[paper_meta] for _ in paper_fragments], sys_prompt_array=[ "请你作为一个学术翻译，负责把学术论文的片段准确翻译成中文。" for _ in paper_fragments], max_workers=16 # OpenAI所允许的最大并行过载 ) # 整理报告的格式 for i,k in enumerate(gpt_response_collection): if i%2==0: gpt_response_collection[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection)//2}]： \n\n {paper_fragments[i//2].replace('#', '')} \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection)//2}]：\n " else: gpt_response_collection[i] = gpt_response_collection[i] final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""] final.extend(gpt_response_collection) create_report_file_name = f"{os.path.basename(fp)}.trans.md" res = write_results_to_file(final, file_name=create_report_file_name) # 更新UI generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') chatbot.append((f"{fp}完成了吗？", res)) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 准备文件的下载 import shutil for pdf_path in generated_conclusion_files: # 重命名文件 rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}' if os.path.exists(rename_file): os.remove(rename_file) shutil.copyfile(pdf_path, rename_file) if os.path.exists(pdf_path): os.remove(pdf_path) chatbot.append(("给出输出文件清单", str(generated_conclusion_files))) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面