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