from toolbox import CatchException, report_execption, get_log_folder from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion from toolbox import write_history_to_file, promote_file_to_downloadzone 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 .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url from colorful import * import glob import os import math @CatchException def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): disable_auto_promotion(chatbot) # 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 import scipdf 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 scipdf_parser```。") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return # 清空历史，以免输入溢出 history = [] from .crazy_utils import get_files_from_everything success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf') # 检测输入参数，如没有给定输入参数，直接退出 if not success: if txt == "": txt = '空空如也的输入栏' # 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 grobid_url = get_avail_grobid_url() if grobid_url is not None: yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url) else: yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3) yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url): import copy TOKEN_LIMIT_PER_FRAGMENT = 1280 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" from crazy_functions.crazy_utils import construct_html for index, fp in enumerate(file_manifest): chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 article_dict = parse_pdf(fp, grobid_url) if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") prompt = "以下是一篇学术论文的基本信息:\n" # title title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n' # authors authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n' # abstract abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n' # command prompt += f"请将题目和摘要翻译为{DST_LANG}。" meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ] # 单线，获取文章meta信息 paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( inputs=prompt, inputs_show_user=prompt, llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], sys_prompt="You are an academic paper reader。", ) # 多线，翻译 inputs_array = [] inputs_show_user_array = [] # get_token_num from request_llm.bridge_all import model_info enc = model_info[llm_kwargs['llm_model']]['tokenizer'] def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf def break_down(txt): raw_token_num = get_token_num(txt) if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT: return [txt] else: # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT # find a smooth token limit to achieve even seperation count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT)) token_limit_smooth = raw_token_num // count + count return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth) for section in article_dict.get('sections'): if len(section['text']) == 0: continue section_frags = break_down(section['text']) for i, fragment in enumerate(section_frags): heading = section['heading'] if len(section_frags) > 1: heading += f' Part-{i+1}' inputs_array.append( f"你需要翻译{heading}章节，内容如下: \n\n{fragment}" ) inputs_show_user_array.append( f"# {heading}\n\n{fragment}" ) gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( inputs_array=inputs_array, inputs_show_user_array=inputs_show_user_array, llm_kwargs=llm_kwargs, chatbot=chatbot, history_array=[meta for _ in inputs_array], 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 inputs_array], ) res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None) promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot) generated_conclusion_files.append(res_path) ch = construct_html() orig = "" trans = "" gpt_response_collection_html = copy.deepcopy(gpt_response_collection) for i,k in enumerate(gpt_response_collection_html): if i%2==0: gpt_response_collection_html[i] = inputs_show_user_array[i//2] else: gpt_response_collection_html[i] = gpt_response_collection_html[i] final = ["", "", "一、论文概况", "", "Abstract", paper_meta_info, "二、论文翻译", ""] final.extend(gpt_response_collection_html) for i, k in enumerate(final): if i%2==0: orig = k if i%2==1: trans = k ch.add_row(a=orig, b=trans) create_report_file_name = f"{os.path.basename(fp)}.trans.html" html_file = ch.save_file(create_report_file_name) generated_html_files.append(html_file) promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot) chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): import copy TOKEN_LIMIT_PER_FRAGMENT = 1280 generated_conclusion_files = [] generated_html_files = [] from crazy_functions.crazy_utils import construct_html for index, fp in enumerate(file_manifest): # 读取PDF文件 file_content, page_one = read_and_clean_pdf_text(fp) file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars # 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=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 ) gpt_response_collection_md = copy.deepcopy(gpt_response_collection) # 整理报告的格式 for i,k in enumerate(gpt_response_collection_md): if i%2==0: gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')} \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n " else: gpt_response_collection_md[i] = gpt_response_collection_md[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_md) create_report_file_name = f"{os.path.basename(fp)}.trans.md" res = write_history_to_file(final, create_report_file_name) promote_file_to_downloadzone(res, chatbot=chatbot) # 更新UI generated_conclusion_files.append(f'{get_log_folder()}/{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 # write html try: ch = construct_html() orig = "" trans = "" gpt_response_collection_html = copy.deepcopy(gpt_response_collection) for i,k in enumerate(gpt_response_collection_html): if i%2==0: gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '') else: gpt_response_collection_html[i] = gpt_response_collection_html[i] final = ["论文概况", paper_meta_info.replace('# ', '### '), "二、论文翻译", ""] final.extend(gpt_response_collection_html) for i, k in enumerate(final): if i%2==0: orig = k if i%2==1: trans = k ch.add_row(a=orig, b=trans) create_report_file_name = f"{os.path.basename(fp)}.trans.html" generated_html_files.append(ch.save_file(create_report_file_name)) except: from toolbox import trimmed_format_exc print('writing html result failed:', trimmed_format_exc()) # 准备文件的下载 for pdf_path in generated_conclusion_files: # 重命名文件 rename_file = f'翻译-{os.path.basename(pdf_path)}' promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot) for html_path in generated_html_files: # 重命名文件 rename_file = f'翻译-{os.path.basename(html_path)}' promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot) chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面