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| 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 .crazy_utils import read_and_clean_pdf_text | |
| from colorful import * | |
| 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？", | |
| "批量翻译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 = 1280 | |
| 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 request_llm.bridge_all import model_info | |
| enc = model_info["gpt-3.5-turbo"]['tokenizer'] | |
| def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) | |
| 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"你需要翻译以下内容：\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，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments], | |
| # max_workers=5 # 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 | |