204 lines
8.8 KiB
Python
204 lines
8.8 KiB
Python
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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# 处理重名的章节
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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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# 基本信息：功能、贡献者
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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) # 刷新界面
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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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) # 刷新界面
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return
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# 清空历史，以免输入溢出
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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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# 检测输入参数，如没有给定输入参数，直接退出
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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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# 如果没找到任何文件
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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) # 刷新界面
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return
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# 开始正式执行任务
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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) # 刷新界面
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fpp = nougat_handle.NOUGAT_parse_pdf(fp)
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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
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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
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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
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abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
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# command
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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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# 单线，获取文章meta信息
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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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# 多线，翻译
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inputs_array = []
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inputs_show_user_array = []
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# get_token_num
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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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# raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
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# find a smooth token limit to achieve even seperation
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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) # 刷新界面
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