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| 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 | |
| def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request): | |
| 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_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit | |
| paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model']) | |
| page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model']) | |
| # 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 | |