from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages 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, translate_pdf from colorful import * import os @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: check_packages(["fitz", "tiktoken", "scipdf"]) except: report_exception(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_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {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, json TOKEN_LIMIT_PER_FRAGMENT = 1024 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" from crazy_functions.pdf_fns.report_gen_html 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) grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json") with open(grobid_json_res, 'w+', encoding='utf8') as f: f.write(json.dumps(article_dict, indent=4, ensure_ascii=False)) promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot) if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG) 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): """ 此函数已经弃用 """ import copy TOKEN_LIMIT_PER_FRAGMENT = 1024 generated_conclusion_files = [] generated_html_files = [] from crazy_functions.pdf_fns.report_gen_html 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_llms.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