cd2e307402
主要内容： - 包含12个产业的完整教务系统前端代码 - 智能启动脚本 (start-industry.sh) - 可视化产业导航页面 (index.html) - 项目文档 (README.md) 优化内容： - 删除所有node_modules和.yoyo文件夹，从7.5GB减少到2.7GB - 添加.gitignore文件避免上传不必要的文件 - 自动依赖管理和智能启动系统 产业列表： 1. 文旅产业 (5150) 2. 智能制造 (5151) 3. 智能开发 (5152) 4. 财经商贸 (5153) 5. 视觉设计 (5154) 6. 交通物流 (5155) 7. 大健康 (5156) 8. 土木水利 (5157) 9. 食品产业 (5158) 10. 化工产业 (5159) 11. 能源产业 (5160) 12. 环保产业 (5161) 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import re
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def deep_clean_markdown(content):
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"""深度清理markdown内容中的所有删除线和加粗符号"""
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if not content:
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return content
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# 1. 删除所有类型的删除线及其内容
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# 标准删除线 ~~text~~
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content = re.sub(r'~~[^~]*~~', '', content)
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# 中文删除线 ～～text～～
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content = re.sub(r'～～[^～]*～～', '', content)
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# 2. 去除加粗符号但保留内容
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# **text** -> text
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content = re.sub(r'\*\*([^*]+)\*\*', r'\1', content)
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# __text__ -> text
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content = re.sub(r'__([^_]+)__', r'\1', content)
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# 3. 清理因删除产生的多余符号和空格
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# 清理多余的逗号和顿号
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content = re.sub(r'，\s*，', '，', content)
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content = re.sub(r'、\s*、', '、', content)
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content = re.sub(r'，\s*。', '。', content)
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content = re.sub(r'、\s*。', '。', content)
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# 清理行首的逗号或顿号
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content = re.sub(r'^[，、]\s*', '', content, flags=re.MULTILINE)
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# 清理多余的空格
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content = re.sub(r' {2,}', ' ', content)
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content = re.sub(r'\n{3,}', '\n\n', content)
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# 清理空的列表项
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content = re.sub(r'^\d+\.\s*$', '', content, flags=re.MULTILINE)
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content = re.sub(r'^\d+\.\s*\n', '', content, flags=re.MULTILINE)
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return content.strip()
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# 读取文件
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with open('src/mocks/resumeInterviewMock.js', 'r', encoding='utf-8') as f:
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content = f.read()
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# 需要清理的岗位列表
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positions_to_clean = [
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"会展策划师",
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"会展讲解员",
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"活动执行",
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"活动策划师",
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"漫展策划师",
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"会展执行助理",
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"旅游规划师",
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"旅游计调专员",
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"景区运营专员",
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"文旅运营总监助理"
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]
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print("开始深度清理修改版简历内容...")
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total_cleaned = 0
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for position in positions_to_clean:
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# 查找该岗位的modified内容
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# 使用更宽松的正则表达式来匹配
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pattern = rf'title:\s*["\']({position})["\'][^}}]*?modified:\s*`([^`]+)`'
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matches = list(re.finditer(pattern, content, re.DOTALL))
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for match in matches:
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original_modified = match.group(2)
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# 统计删除线数量
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strikethrough_count = len(re.findall(r'~~[^~]*~~', original_modified))
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strikethrough_count += len(re.findall(r'～～[^～]*～～', original_modified))
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# 统计加粗数量
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bold_count = len(re.findall(r'\*\*[^*]+\*\*', original_modified))
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bold_count += len(re.findall(r'__[^_]+__', original_modified))
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if strikethrough_count > 0 or bold_count > 0:
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cleaned_modified = deep_clean_markdown(original_modified)
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# 替换内容
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old_text = f"modified: `{original_modified}`"
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new_text = f"modified: `{cleaned_modified}`"
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content = content.replace(old_text, new_text)
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print(f"\n✓ {position}")
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print(f" - 删除了 {strikethrough_count} 处删除线")
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print(f" - 清理了 {bold_count} 处加粗符号")
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total_cleaned += 1
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# 写回文件
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with open('src/mocks/resumeInterviewMock.js', 'w', encoding='utf-8') as f:
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f.write(content)
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print(f"\n✅ 深度清理完成！共处理了 {total_cleaned} 个岗位的修改版内容")
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# 验证是否还有遗漏的删除线
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remaining_strikethrough = len(re.findall(r'~~[^~]*~~', content))
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remaining_strikethrough += len(re.findall(r'～～[^～]*～～', content))
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if remaining_strikethrough > 0:
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print(f"\n⚠️ 警告：文件中仍有 {remaining_strikethrough} 处删除线符号")
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# 查找并显示位置
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for match in re.finditer(r'(~~[^~]*~~|～～[^～]*～～)', content):
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start = max(0, match.start() - 50)
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end = min(len(content), match.end() + 50)
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context = content[start:end]
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print(f" 位置: ...{context}...")
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else:
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print("\n✅ 已确认：所有删除线符号都已清理完毕") |