from functools import lru_cache from toolbox import gen_time_str from toolbox import promote_file_to_downloadzone from toolbox import write_history_to_file, promote_file_to_downloadzone from toolbox import get_conf from toolbox import ProxyNetworkActivate from shared_utils.colorful import * import requests import random import copy import os import math class GROBID_OFFLINE_EXCEPTION(Exception): pass def get_avail_grobid_url(): GROBID_URLS = get_conf('GROBID_URLS') if len(GROBID_URLS) == 0: return None try: _grobid_url = random.choice(GROBID_URLS) # 随机负载均衡 if _grobid_url.endswith('/'): _grobid_url = _grobid_url.rstrip('/') with ProxyNetworkActivate('Connect_Grobid'): res = requests.get(_grobid_url+'/api/isalive') if res.text=='true': return _grobid_url else: return None except: return None @lru_cache(maxsize=32) def parse_pdf(pdf_path, grobid_url): import scipdf # pip install scipdf_parser if grobid_url.endswith('/'): grobid_url = grobid_url.rstrip('/') try: with ProxyNetworkActivate('Connect_Grobid'): article_dict = scipdf.parse_pdf_to_dict(pdf_path, grobid_url=grobid_url) except GROBID_OFFLINE_EXCEPTION: raise GROBID_OFFLINE_EXCEPTION("GROBID服务不可用，请修改config中的GROBID_URL，可修改成本地GROBID服务。") except: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") return article_dict def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files): # -=-=-=-=-=-=-=-= 写出第1个文件：翻译前后混合 -=-=-=-=-=-=-=-= res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=f"{gen_time_str()}translated_and_original.md", file_fullname=None) promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot) generated_conclusion_files.append(res_path) # -=-=-=-=-=-=-=-= 写出第2个文件：仅翻译后的文本 -=-=-=-=-=-=-=-= translated_res_array = [] # 记录当前的大章节标题： last_section_name = "" for index, value in enumerate(gpt_response_collection): # 先挑选偶数序列号： if index % 2 != 0: # 先提取当前英文标题： cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0] # 如果index是1的话，则直接使用first section name： if cur_section_name != last_section_name: cur_value = cur_section_name + '\n' last_section_name = copy.deepcopy(cur_section_name) else: cur_value = "" # 再做一个小修改：重新修改当前part的标题，默认用英文的 cur_value += value translated_res_array.append(cur_value) res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + translated_res_array, file_basename = f"{gen_time_str()}-translated_only.md", file_fullname = None, auto_caption = False) promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot) generated_conclusion_files.append(res_path) return res_path def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs={}): from crazy_functions.pdf_fns.report_gen_html import construct_html from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency 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')[:100]; 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_llms.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=())) 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_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model']) 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。" + plugin_kwargs.get("additional_prompt", "") for _ in inputs_array], ) # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-= produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files) # -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-= 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: # 先提取当前英文标题： cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0] cur_value = cur_section_name + "\n" + gpt_response_collection_html[i] gpt_response_collection_html[i] = cur_value 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_conclusion_files.append(html_file) promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)