from toolbox import CatchException, report_execption, gen_time_str from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion from toolbox import write_history_to_file, get_log_folder 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 os import math import logging def markdown_to_dict(article_content): import markdown from bs4 import BeautifulSoup cur_t = "" cur_c = "" results = {} for line in article_content: if line.startswith('#'): if cur_t!="": if cur_t not in results: results.update({cur_t:cur_c.lstrip('\n')}) else: # 处理重名的章节 results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')}) cur_t = line.rstrip('\n') cur_c = "" else: cur_c += line results_final = {} for k in list(results.keys()): if k.startswith('# '): results_final['title'] = k.split('# ')[-1] results_final['authors'] = results.pop(k).lstrip('\n') if k.startswith('###### Abstract'): results_final['abstract'] = results.pop(k).lstrip('\n') results_final_sections = [] for k,v in results.items(): results_final_sections.append({ 'heading':k.lstrip("# "), 'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section." }) results_final['sections'] = results_final_sections return results_final @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 nougat 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 nougat-ocr 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 = [] 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 yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): import copy import tiktoken TOKEN_LIMIT_PER_FRAGMENT = 1280 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" from crazy_functions.crazy_utils import nougat_interface, construct_html nougat_handle = nougat_interface() for index, fp in enumerate(file_manifest): chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history) with open(fpp, 'r', encoding='utf8') as f: article_content = f.readlines() article_dict = markdown_to_dict(article_content) logging.info(article_dict) 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