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from toolbox import CatchException, report_execption, write_results_to_file |
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from toolbox import update_ui |
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive |
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency |
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from .crazy_utils import read_and_clean_pdf_text |
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from colorful import * |
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@CatchException |
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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port): |
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import glob |
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import os |
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chatbot.append([ |
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"函数插件功能？", |
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"批量翻译PDF文档。函数插件贡献者: Binary-Husky"]) |
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yield from update_ui(chatbot=chatbot, history=history) |
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try: |
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import fitz |
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import tiktoken |
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except: |
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report_execption(chatbot, history, |
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a=f"解析项目: {txt}", |
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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```。") |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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history = [] |
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if os.path.exists(txt): |
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project_folder = txt |
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else: |
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if txt == "": |
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txt = '空空如也的输入栏' |
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report_execption(chatbot, history, |
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a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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file_manifest = [f for f in glob.glob( |
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f'{project_folder}/**/*.pdf', recursive=True)] |
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if len(file_manifest) == 0: |
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report_execption(chatbot, history, |
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a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt) |
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def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt): |
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import os |
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import tiktoken |
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TOKEN_LIMIT_PER_FRAGMENT = 1280 |
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generated_conclusion_files = [] |
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for index, fp in enumerate(file_manifest): |
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file_content, page_one = read_and_clean_pdf_text(fp) |
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf |
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from request_llm.bridge_all import model_info |
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enc = model_info["gpt-3.5-turbo"]['tokenizer'] |
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) |
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( |
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txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) |
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page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( |
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txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4) |
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paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] |
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paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( |
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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}", |
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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。", |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, history=[], |
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sys_prompt="Your job is to collect information from materials。", |
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) |
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( |
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inputs_array=[ |
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f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments], |
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inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')} \n---\n 翻译：\n " for frag in paper_fragments], |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, |
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history_array=[[paper_meta] for _ in paper_fragments], |
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sys_prompt_array=[ |
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"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], |
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) |
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for i,k in enumerate(gpt_response_collection): |
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if i%2==0: |
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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 " |
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else: |
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gpt_response_collection[i] = gpt_response_collection[i] |
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final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""] |
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final.extend(gpt_response_collection) |
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create_report_file_name = f"{os.path.basename(fp)}.trans.md" |
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res = write_results_to_file(final, file_name=create_report_file_name) |
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generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') |
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chatbot.append((f"{fp}完成了吗？", res)) |
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yield from update_ui(chatbot=chatbot, history=history) |
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import shutil |
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for pdf_path in generated_conclusion_files: |
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rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}' |
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if os.path.exists(rename_file): |
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os.remove(rename_file) |
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shutil.copyfile(pdf_path, rename_file) |
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if os.path.exists(pdf_path): |
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os.remove(pdf_path) |
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files))) |
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yield from update_ui(chatbot=chatbot, history=history) |
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