gpt-academic3.6 / crazy_functions /批量翻译PDF文档_多线程.py
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from toolbox import CatchException, report_execption, write_results_to_file
from toolbox import update_ui
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
def read_and_clean_pdf_text(fp):
"""
**输入参数说明**
- `fp`:需要读取和清理文本的pdf文件路径
**输出参数说明**
- `meta_txt`:清理后的文本内容字符串
- `page_one_meta`:第一页清理后的文本内容列表
**函数功能**
读取pdf文件并清理其中的文本内容,清理规则包括:
- 提取所有块元的文本信息,并合并为一个字符串
- 去除短块(字符数小于100)并替换为回车符
- 清理多余的空行
- 合并小写字母开头的段落块并替换为空格
- 清除重复的换行
- 将每个换行符替换为两个换行符,使每个段落之间有两个换行符分隔
"""
import fitz
import re
import numpy as np
# file_content = ""
with fitz.open(fp) as doc:
meta_txt = []
meta_font = []
for index, page in enumerate(doc):
# file_content += page.get_text()
text_areas = page.get_text("dict") # 获取页面上的文本信息
# 块元提取 for each word segment with in line for each line cross-line words for each block
meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t])
meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
if index == 0:
page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t]
def 把字符太少的块清除为回车(meta_txt):
for index, block_txt in enumerate(meta_txt):
if len(block_txt) < 100:
meta_txt[index] = '\n'
return meta_txt
meta_txt = 把字符太少的块清除为回车(meta_txt)
def 清理多余的空行(meta_txt):
for index in reversed(range(1, len(meta_txt))):
if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
meta_txt.pop(index)
return meta_txt
meta_txt = 清理多余的空行(meta_txt)
def 合并小写开头的段落块(meta_txt):
def starts_with_lowercase_word(s):
pattern = r"^[a-z]+"
match = re.match(pattern, s)
if match:
return True
else:
return False
for _ in range(100):
for index, block_txt in enumerate(meta_txt):
if starts_with_lowercase_word(block_txt):
if meta_txt[index-1] != '\n':
meta_txt[index-1] += ' '
else:
meta_txt[index-1] = ''
meta_txt[index-1] += meta_txt[index]
meta_txt[index] = '\n'
return meta_txt
meta_txt = 合并小写开头的段落块(meta_txt)
meta_txt = 清理多余的空行(meta_txt)
meta_txt = '\n'.join(meta_txt)
# 清除重复的换行
for _ in range(5):
meta_txt = meta_txt.replace('\n\n', '\n')
# 换行 -> 双换行
meta_txt = meta_txt.replace('\n', '\n\n')
return meta_txt, page_one_meta
@CatchException
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
import glob
import os
# 基本信息:功能、贡献者
chatbot.append([
"函数插件功能?",
"批量总结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 fitz
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 pymupdf 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 = []
# 检测输入参数,如没有给定输入参数,直接退出
if os.path.exists(txt):
project_folder = txt
else:
if txt == "":
txt = '空空如也的输入栏'
report_execption(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 搜索需要处理的文件清单
file_manifest = [f for f in glob.glob(
f'{project_folder}/**/*.pdf', recursive=True)]
# 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(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
import os
import tiktoken
TOKEN_LIMIT_PER_FRAGMENT = 1600
generated_conclusion_files = []
for index, fp in enumerate(file_manifest):
# 读取PDF文件
file_content, page_one = read_and_clean_pdf_text(fp)
# 递归地切割PDF文件
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from toolbox import get_conf
enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
def get_token_num(txt): return len(enc.encode(txt))
# 分解文本
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
# 为了更好的效果,我们剥离Introduction之后的部分
paper_meta = page_one_fragments[0].split('introduction')[0].split(
'Introduction')[0].split('INTRODUCTION')[0]
# 单线,获取文章meta信息
paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=f"以下是一篇学术论文的基础信息,请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出,最后用中文翻译摘要部分。请提取:{paper_meta}",
inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
llm_kwargs=llm_kwargs,
chatbot=chatbot, history=[],
sys_prompt="Your job is to collect information from materials。",
)
# 多线,翻译
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
inputs_array=[
f"以下是你需要翻译的文章段落:\n{frag}" for frag in paper_fragments],
inputs_show_user_array=[f"" for _ in paper_fragments],
llm_kwargs=llm_kwargs,
chatbot=chatbot,
history_array=[[paper_meta] for _ in paper_fragments],
sys_prompt_array=[
"请你作为一个学术翻译,把整个段落翻译成中文,要求语言简洁,禁止重复输出原文。" for _ in paper_fragments],
max_workers=16 # OpenAI所允许的最大并行过载
)
final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
final.extend(gpt_response_collection)
create_report_file_name = f"{os.path.basename(fp)}.trans.md"
res = write_results_to_file(final, file_name=create_report_file_name)
generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
chatbot.append((f"{fp}完成了吗?", res))
yield from update_ui(chatbot=chatbot, history=chatbot) # 刷新界面
# 准备文件的下载
import shutil
for pdf_path in generated_conclusion_files:
# 重命名文件
rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
if os.path.exists(rename_file):
os.remove(rename_file)
shutil.copyfile(pdf_path, rename_file)
if os.path.exists(pdf_path):
os.remove(pdf_path)
chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg) # 刷新界面