237 lines
11 KiB
Python
237 lines
11 KiB
Python
from toolbox import CatchException, report_execption, gen_time_str
|
||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
|
||
from toolbox import write_history_to_file, get_log_folder
|
||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||
from .crazy_utils import read_and_clean_pdf_text
|
||
from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url
|
||
from colorful import *
|
||
import os
|
||
import math
|
||
import logging
|
||
|
||
def markdown_to_dict(article_content):
|
||
import markdown
|
||
from bs4 import BeautifulSoup
|
||
cur_t = ""
|
||
cur_c = ""
|
||
results = {}
|
||
for line in article_content:
|
||
if line.startswith('#'):
|
||
if cur_t!="":
|
||
if cur_t not in results:
|
||
results.update({cur_t:cur_c.lstrip('\n')})
|
||
else:
|
||
# 处理重名的章节
|
||
results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')})
|
||
cur_t = line.rstrip('\n')
|
||
cur_c = ""
|
||
else:
|
||
cur_c += line
|
||
results_final = {}
|
||
for k in list(results.keys()):
|
||
if k.startswith('# '):
|
||
results_final['title'] = k.split('# ')[-1]
|
||
results_final['authors'] = results.pop(k).lstrip('\n')
|
||
if k.startswith('###### Abstract'):
|
||
results_final['abstract'] = results.pop(k).lstrip('\n')
|
||
|
||
results_final_sections = []
|
||
for k,v in results.items():
|
||
results_final_sections.append({
|
||
'heading':k.lstrip("# "),
|
||
'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section."
|
||
})
|
||
results_final['sections'] = results_final_sections
|
||
return results_final
|
||
|
||
|
||
@CatchException
|
||
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True):
|
||
|
||
disable_auto_promotion(chatbot)
|
||
# 基本信息：功能、贡献者
|
||
chatbot.append([
|
||
"函数插件功能？",
|
||
"批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
|
||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||
try:
|
||
import nougat
|
||
import tiktoken
|
||
except:
|
||
report_execption(chatbot, history,
|
||
a=f"解析项目: {txt}",
|
||
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```。")
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# 清空历史，以免输入溢出
|
||
history = []
|
||
|
||
from .crazy_utils import get_files_from_everything
|
||
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
|
||
# 检测输入参数，如没有给定输入参数，直接退出
|
||
if not success:
|
||
if txt == "": txt = '空空如也的输入栏'
|
||
|
||
# 如果没找到任何文件
|
||
if len(file_manifest) == 0:
|
||
report_execption(chatbot, history,
|
||
a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# 开始正式执行任务
|
||
yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese)
|
||
|
||
|
||
|
||
|
||
def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese=True):
|
||
import copy
|
||
import tiktoken
|
||
TOKEN_LIMIT_PER_FRAGMENT = 1280
|
||
generated_conclusion_files = []
|
||
generated_html_files = []
|
||
DST_LANG = "中文"
|
||
from crazy_functions.crazy_utils import nougat_interface, construct_html
|
||
nougat_handle = nougat_interface()
|
||
for index, fp in enumerate(file_manifest):
|
||
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
|
||
fpp = nougat_handle.NOUGAT_parse_pdf(fp)
|
||
|
||
with open(fpp, 'r', encoding='utf8') as f:
|
||
article_content = f.readlines()
|
||
article_dict = markdown_to_dict(article_content)
|
||
logging.info(article_dict)
|
||
|
||
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'); 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_llm.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=()))
|
||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||
|
||
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_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
|
||
|
||
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],
|
||
)
|
||
if only_chinese:
|
||
# 直接提取出翻译的内容，然后保存下去：
|
||
chinese_list = []
|
||
# 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 = ""
|
||
# 再判断翻译是否错误，如果错误，则直接贴原来的英文：
|
||
if "The OpenAI account associated" in value:
|
||
cur_value += gpt_response_collection[index-1]
|
||
else:
|
||
# 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
|
||
|
||
chinese_list.append(cur_value)
|
||
res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + chinese_list, file_basename=None, file_fullname=None)
|
||
promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
|
||
generated_conclusion_files.append(res_path)
|
||
else:
|
||
res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None)
|
||
promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
|
||
generated_conclusion_files.append(res_path)
|
||
|
||
ch = construct_html()
|
||
orig = ""
|
||
trans = ""
|
||
gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
|
||
# 叠加HTML文件
|
||
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_html_files.append(html_file)
|
||
promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
|
||
|
||
chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
|
||
|