775 lines
30 KiB
Python
775 lines
30 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import re
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import docx
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import numpy as np
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import requests
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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from typing import List, Tuple, Dict, Optional
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from docx.shared import Pt
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from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
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from docx.enum.table import WD_TABLE_ALIGNMENT
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import subprocess
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import tempfile
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import json
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from docx.table import Table, _Cell
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from docx.text.paragraph import Paragraph
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from copy import deepcopy
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from docx.oxml import parse_xml
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from docx.oxml.ns import nsdecls
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class DocCleaner:
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def __init__(self, ollama_host: str = "http://192.168.1.18:11434"):
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"""
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初始化文档清理器
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Args:
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ollama_host: Ollama服务器地址
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"""
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# 页眉页脚模式
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self.header_footer_patterns = [
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r'页码\s*\d+-\d+', # 页码格式：页码1-1, 页码2-1等
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r'第\s*\d+\s*页\s*共\s*\d+\s*页', # 中文页码（第X页共Y页）
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r'Page\s*\d+\s*of\s*\d+', # 英文页码
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]
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# 特殊符号模式
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self.special_char_patterns = [
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r'©\s*\d{4}.*?版权所有', # 版权信息
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r'confidential', # 机密标记
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r'draft|草稿', # 草稿标记
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r'watermark', # 水印标记
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]
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# 附录和参考文献标题模式
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self.appendix_patterns = [
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r'^附录\s*[A-Za-z]?[\s:：]',
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r'^Appendix\s*[A-Za-z]?[\s:：]',
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r'^参考文献$',
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r'^References$',
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r'^Bibliography$'
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]
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# 初始化TF-IDF向量化器
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self.vectorizer = TfidfVectorizer(
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min_df=1,
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stop_words='english'
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)
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self.ollama_host = ollama_host
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self.embedding_model = "bge-m3:latest" # 使用nomic-embed-text模型进行文本嵌入
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def _convert_doc_to_docx(self, doc_path: str) -> str:
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"""
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将doc格式转换为docx格式
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Args:
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doc_path: doc文件路径
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Returns:
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str: 转换后的docx文件路径
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"""
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# 创建临时文件路径
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temp_dir = tempfile.mkdtemp()
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temp_docx = os.path.join(temp_dir, 'temp.docx')
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try:
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# 使用soffice（LibreOffice）进行转换
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cmd = ['soffice', '--headless', '--convert-to', 'docx', '--outdir', temp_dir, doc_path]
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subprocess.run(cmd, check=True, capture_output=True)
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# 返回转换后的文件路径
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return temp_docx
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except subprocess.CalledProcessError as e:
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raise Exception(f"转换doc文件失败: {str(e)}")
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def clean_doc(self, file_path: str) -> Tuple[List[str], List[str], List[Table]]:
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"""
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清理文档并返回处理后的正文、附录和表格
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Args:
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file_path: 文档文件路径
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Returns:
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Tuple[List[str], List[str], List[Table]]: (清理后的正文段落列表, 附录段落列表, 表格列表)
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"""
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print(f"\n开始处理文档: {file_path}")
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# 检测文件类型
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_, file_extension = os.path.splitext(file_path)
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file_extension = file_extension.lower()
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# 如果是doc格式，先转换为docx
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if file_extension == '.doc':
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temp_docx = self._convert_doc_to_docx(file_path)
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doc = docx.Document(temp_docx)
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# 清理临时文件
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os.remove(temp_docx)
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os.rmdir(os.path.dirname(temp_docx))
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else:
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doc = docx.Document(file_path)
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# 提取所有内容（段落和表格）
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content = []
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tables = []
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table_count = 0
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try:
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print("\n开始解析文档结构...")
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# 遍历文档体中的所有元素
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for element in doc._element.body:
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if element.tag.endswith('p'):
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try:
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paragraph = docx.text.paragraph.Paragraph(element, doc)
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text = paragraph.text.strip()
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# 只添加非空段落
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if text:
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# 检查是否是附录标题
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is_appendix = any(re.match(pattern, text, re.IGNORECASE)
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for pattern in self.appendix_patterns)
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content.append({
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'type': 'paragraph',
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'content': text,
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'is_appendix_start': is_appendix
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})
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if is_appendix:
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print(f"发现附录标题: {text}")
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except Exception as e:
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print(f"警告：处理段落时出错: {str(e)}")
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continue
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elif element.tag.endswith('tbl'):
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try:
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table = docx.table.Table(element, doc)
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# 验证表格是否有效
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if hasattr(table, 'rows') and hasattr(table, 'columns'):
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tables.append(table)
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content.append({
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'type': 'table',
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'index': table_count
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})
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print(f"发现表格 {table_count}: {len(table.rows)}行 x {len(table.columns)}列")
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table_count += 1
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except Exception as e:
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print(f"警告：处理表格时出错: {str(e)}")
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continue
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except Exception as e:
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print(f"警告：遍历文档内容时出错: {str(e)}")
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print(f"\n文档结构解析完成:")
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print(f"- 总元素数: {len(content)}")
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print(f"- 表格数量: {len(tables)}")
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# 分离正文和附录
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main_content = []
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appendix = []
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is_appendix = False
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print("\n开始分离正文和附录...")
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for item in content:
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if item['type'] == 'paragraph':
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if item['is_appendix_start']:
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is_appendix = True
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print("进入附录部分")
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if is_appendix:
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appendix.append(item['content'])
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else:
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main_content.append(item['content'])
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elif item['type'] == 'table':
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table_placeholder = f'TABLE_PLACEHOLDER_{item["index"]}'
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if is_appendix:
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appendix.append(table_placeholder)
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print(f"添加表格到附录: {table_placeholder}")
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else:
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main_content.append(table_placeholder)
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print(f"添加表格到正文: {table_placeholder}")
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print(f"\n分离完成:")
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print(f"- 正文元素数: {len(main_content)}")
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print(f"- 附录元素数: {len(appendix)}")
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# 清理正文（保留表格标记）
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cleaned_content = []
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print("\n开始清理正文...")
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for item in main_content:
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if item.startswith('TABLE_PLACEHOLDER_'):
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cleaned_content.append(item)
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print(f"保留表格标记: {item}")
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else:
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cleaned_text = self._clean_text([item])[0]
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if cleaned_text:
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cleaned_content.append(cleaned_text)
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print(f"\n清理完成:")
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print(f"- 清理后元素数: {len(cleaned_content)}")
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print("- 表格标记位置:")
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for i, item in enumerate(cleaned_content):
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if item.startswith('TABLE_PLACEHOLDER_'):
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print(f" 位置 {i}: {item}")
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return cleaned_content, appendix, tables
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def _clean_text(self, text: List[str]) -> List[str]:
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"""
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清理文本内容
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Args:
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text: 待清理的文本段落列表
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Returns:
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List[str]: 清理后的文本段落列表
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"""
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cleaned = []
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for paragraph in text:
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# 如果是表格标记，直接保留
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if paragraph.startswith('TABLE_PLACEHOLDER_'):
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cleaned.append(paragraph)
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continue
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# 跳过空段落
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if not paragraph.strip():
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continue
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# 检查是否是目录项（包含数字序号的行）
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is_toc_item = bool(re.match(r'^\s*(?:\d+\.)*\d+\s+.*', paragraph))
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if not is_toc_item:
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# 移除页眉页脚
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for pattern in self.header_footer_patterns:
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paragraph = re.sub(pattern, '', paragraph, flags=re.IGNORECASE)
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# 移除特殊符号
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for pattern in self.special_char_patterns:
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paragraph = re.sub(pattern, '', paragraph, flags=re.IGNORECASE)
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# 如果段落不为空，添加到结果中
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if paragraph.strip():
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cleaned.append(paragraph.strip())
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return cleaned
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def _split_content(self, paragraphs: List[str]) -> Tuple[List[str], List[str]]:
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"""
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分离正文与附录/参考文献
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Args:
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paragraphs: 文档段落列表
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Returns:
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Tuple[List[str], List[str]]: (正文段落列表, 附录段落列表)
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"""
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main_content = []
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appendix = []
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is_appendix = False
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for p in paragraphs:
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# 检查是否是附录开始
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if any(re.match(pattern, p, re.IGNORECASE) for pattern in self.appendix_patterns):
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is_appendix = True
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if is_appendix:
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appendix.append(p)
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else:
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main_content.append(p)
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return main_content, appendix
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def _get_embeddings(self, texts: List[str]) -> np.ndarray:
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"""
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使用Ollama获取文本嵌入向量
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Args:
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texts: 文本列表
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Returns:
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np.ndarray: 嵌入向量矩阵
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"""
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embeddings = []
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for text in texts:
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try:
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response = requests.post(
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f"{self.ollama_host}/api/embeddings",
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json={
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"model": self.embedding_model,
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"prompt": text
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}
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)
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response.raise_for_status()
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embedding = response.json()["embedding"]
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embeddings.append(embedding)
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except Exception as e:
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print(f"获取文本嵌入失败: {str(e)}")
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# 如果获取嵌入失败，使用零向量
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embeddings.append([0.0] * 768) # nomic-embed-text 模型输出维度为768
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return np.array(embeddings)
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def _remove_duplicates(self, paragraphs: List[str], similarity_threshold: float = 0.92) -> List[str]:
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"""
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删除重复段落，保持表格占位符的位置不变
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Args:
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paragraphs: 段落列表
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similarity_threshold: 相似度阈值，使用嵌入模型后可以设置更高的阈值
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Returns:
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List[str]: 去重后的段落列表
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"""
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if not paragraphs:
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return []
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# 分离表格占位符和普通段落
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table_placeholders = {}
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text_paragraphs = []
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for i, p in enumerate(paragraphs):
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if p.startswith('TABLE_PLACEHOLDER_'):
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table_placeholders[i] = p
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else:
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text_paragraphs.append((i, p))
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try:
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# 只对非表格段落进行去重
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if text_paragraphs:
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# 获取文本嵌入
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text_only = [p[1] for p in text_paragraphs]
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embeddings = self._get_embeddings(text_only)
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# 计算余弦相似度矩阵
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similarity_matrix = cosine_similarity(embeddings)
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# 标记要保留的段落
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keep_indices = []
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for i in range(len(text_paragraphs)):
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# 如果当前段落没有与之前的段落高度相似，则保留
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if not any(similarity_matrix[i][j] > similarity_threshold for j in keep_indices):
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keep_indices.append(i)
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# 保留的非表格段落
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kept_paragraphs = [(text_paragraphs[i][0], text_only[i]) for i in keep_indices]
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else:
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kept_paragraphs = []
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# 合并表格占位符和保留的段落，按原始位置排序
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all_kept = list(table_placeholders.items()) + kept_paragraphs
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all_kept.sort(key=lambda x: x[0])
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return [p[1] for p in all_kept]
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except Exception as e:
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print(f"使用Ollama嵌入模型失败，回退到TF-IDF方法: {str(e)}")
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# 如果使用Ollama失败，回退到原来的TF-IDF方法
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return self._remove_duplicates_tfidf(paragraphs)
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def _remove_duplicates_tfidf(self, paragraphs: List[str], similarity_threshold: float = 0.85) -> List[str]:
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"""
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使用TF-IDF方法删除重复段落（作为备选方案）
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Args:
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paragraphs: 段落列表
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similarity_threshold: 相似度阈值
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Returns:
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List[str]: 去重后的段落列表
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"""
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if not paragraphs:
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return []
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# 分离表格占位符和普通段落
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table_placeholders = {}
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text_paragraphs = []
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for i, p in enumerate(paragraphs):
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if p.startswith('TABLE_PLACEHOLDER_'):
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table_placeholders[i] = p
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else:
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text_paragraphs.append((i, p))
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if text_paragraphs:
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# 计算TF-IDF矩阵
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text_only = [p[1] for p in text_paragraphs]
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tfidf_matrix = self.vectorizer.fit_transform(text_only)
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# 计算余弦相似度矩阵
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similarity_matrix = cosine_similarity(tfidf_matrix)
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# 标记要保留的段落
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keep_indices = []
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for i in range(len(text_paragraphs)):
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# 如果当前段落没有与之前的段落高度相似，则保留
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if not any(similarity_matrix[i][j] > similarity_threshold for j in keep_indices):
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keep_indices.append(i)
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# 保留的非表格段落
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kept_paragraphs = [(text_paragraphs[i][0], text_only[i]) for i in keep_indices]
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else:
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kept_paragraphs = []
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# 合并表格占位符和保留的段落，按原始位置排序
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all_kept = list(table_placeholders.items()) + kept_paragraphs
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all_kept.sort(key=lambda x: x[0])
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return [p[1] for p in all_kept]
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def save_as_docx(self, cleaned_content: List[str], appendix: List[str], tables: List[Table], output_path: str):
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"""
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将清理后的内容保存为docx格式
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Args:
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cleaned_content: 清理后的正文段落列表
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appendix: 附录段落列表
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tables: 表格列表
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output_path: 输出文件路径
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"""
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print(f"\n开始保存文档: {output_path}")
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print(f"- 正文元素数: {len(cleaned_content)}")
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print(f"- 附录元素数: {len(appendix)}")
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print(f"- 表格总数: {len(tables)}")
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# 创建新文档
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doc = docx.Document()
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# 添加正文内容和表格，保持它们的相对位置
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print("\n处理正文内容...")
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# 创建一个列表来存储所有要插入的元素
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elements_to_insert = []
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for i, content in enumerate(cleaned_content):
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try:
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# 检查是否是表格占位符
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table_match = re.match(r'TABLE_PLACEHOLDER_(\d+)', content)
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if table_match:
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table_index = int(table_match.group(1))
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print(f"正在处理表格占位符: {content} (索引: {table_index})")
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if table_index < len(tables):
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table = tables[table_index]
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try:
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# 直接在XML级别复制表格
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new_tbl = deepcopy(table._element)
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# 确保新表格有正确的命名空间
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new_tbl.tbl = parse_xml(new_tbl.xml)
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elements_to_insert.append(('table', new_tbl))
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print(f"准备插入表格 {table_index} 在位置 {i}")
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# 添加表格后的空行
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elements_to_insert.append(('paragraph', doc.add_paragraph()._element))
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except Exception as e:
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print(f"警告：复制表格时出错: {str(e)}")
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try:
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print("尝试使用备用方法...")
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p = doc.add_paragraph()
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self._copy_table_fallback(p._parent, table)
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elements_to_insert.append(('paragraph', p._element))
|
||
elements_to_insert.append(('paragraph', doc.add_paragraph()._element))
|
||
print("备用方法成功")
|
||
except Exception as e2:
|
||
print(f"警告：备用方法也失败: {str(e2)}")
|
||
elements_to_insert.append(('paragraph', doc.add_paragraph("【表格处理失败】")._element))
|
||
else:
|
||
# 添加普通段落
|
||
p = doc.add_paragraph(content)
|
||
p.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY
|
||
elements_to_insert.append(('paragraph', p._element))
|
||
except Exception as e:
|
||
print(f"警告：处理段落或表格时出错: {str(e)}")
|
||
continue
|
||
|
||
# 按顺序将所有元素插入文档
|
||
for element_type, element in elements_to_insert:
|
||
doc._body._element.append(element)
|
||
|
||
# 如果有附录，添加分隔符和附录内容
|
||
if appendix:
|
||
print("\n处理附录内容...")
|
||
try:
|
||
# 添加分页符
|
||
doc.add_page_break()
|
||
|
||
# 添加附录标题
|
||
title = doc.add_paragraph("附录")
|
||
title.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
||
|
||
# 添加附录内容
|
||
appendix_elements = []
|
||
for content in appendix:
|
||
# 检查是否是表格占位符
|
||
table_match = re.match(r'TABLE_PLACEHOLDER_(\d+)', content)
|
||
if table_match:
|
||
table_index = int(table_match.group(1))
|
||
print(f"正在处理附录中的表格占位符: {content} (索引: {table_index})")
|
||
if table_index < len(tables):
|
||
table = tables[table_index]
|
||
try:
|
||
# 直接在XML级别复制表格
|
||
new_tbl = deepcopy(table._element)
|
||
new_tbl.tbl = parse_xml(new_tbl.xml)
|
||
appendix_elements.append(('table', new_tbl))
|
||
print(f"准备插入附录表格 {table_index}")
|
||
appendix_elements.append(('paragraph', doc.add_paragraph()._element))
|
||
except Exception as e:
|
||
print(f"警告：复制附录中的表格时出错: {str(e)}")
|
||
try:
|
||
p = doc.add_paragraph()
|
||
self._copy_table_fallback(p._parent, table)
|
||
appendix_elements.append(('paragraph', p._element))
|
||
appendix_elements.append(('paragraph', doc.add_paragraph()._element))
|
||
print("备用方法成功")
|
||
except Exception as e2:
|
||
print(f"警告：附录表格的备用方法也失败: {str(e2)}")
|
||
appendix_elements.append(('paragraph', doc.add_paragraph("【表格处理失败】")._element))
|
||
else:
|
||
p = doc.add_paragraph(content)
|
||
p.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY
|
||
appendix_elements.append(('paragraph', p._element))
|
||
|
||
# 按顺序将附录元素插入文档
|
||
for element_type, element in appendix_elements:
|
||
doc._body._element.append(element)
|
||
|
||
except Exception as e:
|
||
print(f"警告：处理附录时出错: {str(e)}")
|
||
|
||
# 保存文档
|
||
try:
|
||
doc.save(output_path)
|
||
print("\n文档保存成功!")
|
||
except Exception as e:
|
||
print(f"错误：保存文档时出错: {str(e)}")
|
||
raise
|
||
|
||
def _copy_table_fallback(self, doc: docx.Document, table: Table):
|
||
"""
|
||
表格复制的备用方法
|
||
|
||
Args:
|
||
doc: 目标文档
|
||
table: 源表格
|
||
"""
|
||
# 获取表格的行数和列数
|
||
rows = len(table.rows)
|
||
cols = len(table.columns)
|
||
|
||
# 创建新表格
|
||
new_table = doc.add_table(rows=rows, cols=cols)
|
||
|
||
# 复制表格样式
|
||
if table.style:
|
||
new_table.style = table.style
|
||
|
||
# 复制表格属性
|
||
new_table._element.tblPr = deepcopy(table._element.tblPr)
|
||
|
||
# 复制网格信息
|
||
new_table._element.tblGrid = deepcopy(table._element.tblGrid)
|
||
|
||
# 创建单元格映射以跟踪合并
|
||
cell_map = {}
|
||
|
||
# 第一遍：标记合并的单元格
|
||
for i in range(rows):
|
||
for j in range(cols):
|
||
try:
|
||
src_cell = table.cell(i, j)
|
||
# 检查是否是合并单元格的一部分
|
||
if src_cell._element.tcPr is not None:
|
||
# 检查垂直合并
|
||
vmerge = src_cell._element.tcPr.xpath('.//w:vMerge')
|
||
if vmerge:
|
||
val = vmerge[0].get(qn('w:val'), 'continue')
|
||
if val == 'restart':
|
||
# 这是合并的起始单元格
|
||
span = self._get_vertical_span(table, i, j)
|
||
cell_map[(i, j)] = ('vmerge', span)
|
||
|
||
# 检查水平合并
|
||
gridspan = src_cell._element.tcPr.xpath('.//w:gridSpan')
|
||
if gridspan:
|
||
span = int(gridspan[0].get(qn('w:val')))
|
||
if span > 1:
|
||
cell_map[(i, j)] = ('hmerge', span)
|
||
except Exception as e:
|
||
print(f"警告：处理合并单元格时出错 [{i},{j}]: {str(e)}")
|
||
|
||
# 第二遍：复制内容并执行合并
|
||
for i in range(rows):
|
||
for j in range(cols):
|
||
try:
|
||
src_cell = table.cell(i, j)
|
||
dst_cell = new_table.cell(i, j)
|
||
|
||
# 检查是否需要合并
|
||
if (i, j) in cell_map:
|
||
merge_type, span = cell_map[(i, j)]
|
||
if merge_type == 'vmerge':
|
||
# 垂直合并
|
||
for k in range(1, span):
|
||
if i + k < rows:
|
||
dst_cell.merge(new_table.cell(i + k, j))
|
||
elif merge_type == 'hmerge':
|
||
# 水平合并
|
||
for k in range(1, span):
|
||
if j + k < cols:
|
||
dst_cell.merge(new_table.cell(i, j + k))
|
||
|
||
# 复制单元格属性
|
||
if src_cell._element.tcPr is not None:
|
||
dst_cell._element.tcPr = deepcopy(src_cell._element.tcPr)
|
||
|
||
# 复制单元格内容
|
||
dst_cell.text = "" # 清除默认内容
|
||
for src_paragraph in src_cell.paragraphs:
|
||
dst_paragraph = dst_cell.add_paragraph()
|
||
# 复制段落属性
|
||
if src_paragraph._element.pPr is not None:
|
||
dst_paragraph._element.pPr = deepcopy(src_paragraph._element.pPr)
|
||
|
||
# 复制文本和格式
|
||
for src_run in src_paragraph.runs:
|
||
dst_run = dst_paragraph.add_run(src_run.text)
|
||
# 复制运行属性
|
||
if src_run._element.rPr is not None:
|
||
dst_run._element.rPr = deepcopy(src_run._element.rPr)
|
||
|
||
except Exception as e:
|
||
print(f"警告：复制单元格时出错 [{i},{j}]: {str(e)}")
|
||
continue
|
||
|
||
def _get_vmerge_value(self, cell_element) -> str:
|
||
"""
|
||
获取单元格的垂直合并属性
|
||
|
||
Args:
|
||
cell_element: 单元格元素
|
||
|
||
Returns:
|
||
str: 垂直合并属性值
|
||
"""
|
||
vmerge = cell_element.xpath('.//w:vMerge')
|
||
if vmerge:
|
||
return vmerge[0].get(qn('w:val'), 'continue')
|
||
return None
|
||
|
||
def _get_gridspan_value(self, cell_element) -> int:
|
||
"""
|
||
获取单元格的水平合并数量
|
||
|
||
Args:
|
||
cell_element: 单元格元素
|
||
|
||
Returns:
|
||
int: 水平合并的列数
|
||
"""
|
||
gridspan = cell_element.xpath('.//w:gridSpan')
|
||
if gridspan:
|
||
return int(gridspan[0].get(qn('w:val'), '1'))
|
||
return 1
|
||
|
||
def _get_vertical_span(self, table: Table, start_row: int, col: int) -> int:
|
||
"""
|
||
计算垂直合并的行数
|
||
|
||
Args:
|
||
table: 表格对象
|
||
start_row: 起始行
|
||
col: 列号
|
||
|
||
Returns:
|
||
int: 垂直合并的行数
|
||
"""
|
||
span = 1
|
||
for i in range(start_row + 1, len(table.rows)):
|
||
cell = table.cell(i, col)
|
||
if self._get_vmerge_value(cell._element) == 'continue':
|
||
span += 1
|
||
else:
|
||
break
|
||
return span
|
||
|
||
def _extract_table_text(self, table: Table) -> str:
|
||
"""
|
||
提取表格中的文本内容
|
||
|
||
Args:
|
||
table: docx表格对象
|
||
|
||
Returns:
|
||
str: 表格内容的文本表示
|
||
"""
|
||
table_text = []
|
||
for row in table.rows:
|
||
for cell in row.cells:
|
||
cell_text = cell.text.strip()
|
||
if cell_text:
|
||
table_text.append(cell_text)
|
||
return ' '.join(table_text)
|
||
|
||
def process_directory(input_dir: str, output_dir: str = None):
|
||
"""
|
||
处理指定目录下的所有文档文件
|
||
|
||
Args:
|
||
input_dir: 输入目录路径
|
||
output_dir: 输出目录路径，如果为None则使用输入目录
|
||
"""
|
||
# 如果未指定输出目录，使用输入目录
|
||
if output_dir is None:
|
||
output_dir = input_dir
|
||
|
||
if not os.path.exists(output_dir):
|
||
os.makedirs(output_dir)
|
||
|
||
cleaner = DocCleaner()
|
||
|
||
for root, _, files in os.walk(input_dir):
|
||
for file in files:
|
||
if file.endswith(('.doc', '.docx')):
|
||
input_path = os.path.join(root, file)
|
||
|
||
try:
|
||
# 清理文档
|
||
main_content, appendix, tables = cleaner.clean_doc(input_path)
|
||
|
||
# 创建输出文件名（统一使用docx扩展名）
|
||
base_name = os.path.splitext(file)[0]
|
||
output_path = os.path.join(output_dir, f"{base_name}_cleaned.docx")
|
||
|
||
# 保存为docx格式
|
||
cleaner.save_as_docx(main_content, appendix, tables, output_path)
|
||
|
||
except Exception as e:
|
||
print(f"处理文件 {file} 时出错: {str(e)}")
|
||
# 添加更详细的错误信息
|
||
if isinstance(e, subprocess.CalledProcessError):
|
||
print(f"命令执行错误: {e.output}")
|
||
elif isinstance(e, FileNotFoundError):
|
||
print("请确保已安装LibreOffice并将其添加到系统PATH中")
|
||
|
||
def qn(tag: str) -> str:
|
||
"""
|
||
将标签转换为带命名空间的格式
|
||
|
||
Args:
|
||
tag: 原始标签
|
||
|
||
Returns:
|
||
str: 带命名空间的标签
|
||
"""
|
||
prefix = "{http://schemas.openxmlformats.org/wordprocessingml/2006/main}"
|
||
return prefix + tag
|
||
|
||
if __name__ == '__main__':
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description='文档清理工具')
|
||
parser.add_argument('input_dir', help='输入目录路径')
|
||
parser.add_argument('--output_dir', help='输出目录路径（可选，默认为输入目录）', default=None)
|
||
|
||
args = parser.parse_args()
|
||
|
||
process_directory(args.input_dir, args.output_dir) |