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| 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 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 | |
| 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): | |
| 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。" 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) | |