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from toolbox import CatchException, report_execption, gen_time_str |
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from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion |
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from toolbox import write_history_to_file, get_log_folder |
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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 .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url |
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from colorful import * |
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import os |
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import math |
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import logging |
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def markdown_to_dict(article_content): |
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import markdown |
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from bs4 import BeautifulSoup |
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cur_t = "" |
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cur_c = "" |
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results = {} |
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for line in article_content: |
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if line.startswith('#'): |
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if cur_t!="": |
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if cur_t not in results: |
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results.update({cur_t:cur_c.lstrip('\n')}) |
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else: |
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results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')}) |
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cur_t = line.rstrip('\n') |
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cur_c = "" |
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else: |
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cur_c += line |
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results_final = {} |
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for k in list(results.keys()): |
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if k.startswith('# '): |
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results_final['title'] = k.split('# ')[-1] |
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results_final['authors'] = results.pop(k).lstrip('\n') |
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if k.startswith('###### Abstract'): |
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results_final['abstract'] = results.pop(k).lstrip('\n') |
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results_final_sections = [] |
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for k,v in results.items(): |
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results_final_sections.append({ |
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'heading':k.lstrip("# "), |
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'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section." |
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}) |
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results_final['sections'] = results_final_sections |
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return results_final |
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@CatchException |
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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): |
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disable_auto_promotion(chatbot) |
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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 nougat |
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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 nougat-ocr 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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from .crazy_utils import get_files_from_everything |
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success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf') |
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if not success: |
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if txt == "": txt = '空空如也的输入栏' |
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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_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) |
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def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): |
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import copy |
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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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generated_html_files = [] |
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DST_LANG = "中文" |
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from crazy_functions.crazy_utils import nougat_interface, construct_html |
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nougat_handle = nougat_interface() |
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for index, fp in enumerate(file_manifest): |
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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) |
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fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history) |
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with open(fpp, 'r', encoding='utf8') as f: |
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article_content = f.readlines() |
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article_dict = markdown_to_dict(article_content) |
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logging.info(article_dict) |
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prompt = "以下是一篇学术论文的基本信息:\n" |
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title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n' |
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authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n' |
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abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n' |
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prompt += f"请将题目和摘要翻译为{DST_LANG}。" |
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meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ] |
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paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( |
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inputs=prompt, |
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inputs_show_user=prompt, |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, history=[], |
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sys_prompt="You are an academic paper reader。", |
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) |
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inputs_array = [] |
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inputs_show_user_array = [] |
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from request_llm.bridge_all import model_info |
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enc = model_info[llm_kwargs['llm_model']]['tokenizer'] |
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) |
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf |
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def break_down(txt): |
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raw_token_num = get_token_num(txt) |
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if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT: |
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return [txt] |
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else: |
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count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT)) |
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token_limit_smooth = raw_token_num // count + count |
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return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth) |
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for section in article_dict.get('sections'): |
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if len(section['text']) == 0: continue |
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section_frags = break_down(section['text']) |
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for i, fragment in enumerate(section_frags): |
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heading = section['heading'] |
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if len(section_frags) > 1: heading += f' Part-{i+1}' |
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inputs_array.append( |
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f"你需要翻译{heading}章节,内容如下: \n\n{fragment}" |
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) |
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inputs_show_user_array.append( |
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f"# {heading}\n\n{fragment}" |
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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=inputs_array, |
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inputs_show_user_array=inputs_show_user_array, |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, |
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history_array=[meta for _ in inputs_array], |
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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 inputs_array], |
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) |
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res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None) |
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promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot) |
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generated_conclusion_files.append(res_path) |
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ch = construct_html() |
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orig = "" |
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trans = "" |
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gpt_response_collection_html = copy.deepcopy(gpt_response_collection) |
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for i,k in enumerate(gpt_response_collection_html): |
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if i%2==0: |
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gpt_response_collection_html[i] = inputs_show_user_array[i//2] |
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else: |
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gpt_response_collection_html[i] = gpt_response_collection_html[i] |
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final = ["", "", "一、论文概况", "", "Abstract", paper_meta_info, "二、论文翻译", ""] |
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final.extend(gpt_response_collection_html) |
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for i, k in enumerate(final): |
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if i%2==0: |
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orig = k |
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if i%2==1: |
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trans = k |
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ch.add_row(a=orig, b=trans) |
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create_report_file_name = f"{os.path.basename(fp)}.trans.html" |
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html_file = ch.save_file(create_report_file_name) |
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generated_html_files.append(html_file) |
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promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot) |
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) |
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yield from update_ui(chatbot=chatbot, history=history) |
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