from toolbox import CatchException, report_execption, write_results_to_file 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 def read_and_clean_pdf_text(fp): """ **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 import 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 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 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 解析PDF(file_manifest, project_folder, top_p, temperature, 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)) # 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) create_report_file_name = f"{os.path.basename(fp)}.trans.md" res = write_results_to_file(final, file_name=create_report_file_name) generated_conclusion_files.append( f'./gpt_log/{create_report_file_name}') chatbot.append((f"{fp}完成了吗？", res)) msg = "完成" yield chatbot, history, msg # 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 chatbot, history, msg