a7242f0c69
- 包含4个产业方向的前端项目：智能开发、智能制造、大健康、财经商贸 - 已清理node_modules、.yoyo等大文件，项目大小从2.6GB优化至631MB - 配置完善的.gitignore文件 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
141 lines
5.4 KiB
Python
141 lines
5.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import re
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# 读取大健康岗位简历数据
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with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/网页未导入数据/大健康产业/大健康岗位简历.json', 'r', encoding='utf-8') as f:
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health_data = json.load(f)
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# 获取药物研发的面试题
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drug_questions = []
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for item in health_data:
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if item.get('简历岗位群') == '药物研发':
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interview_content = item.get('面试题内容', '')
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if interview_content and not drug_questions: # 只取第一个
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# 提取问题
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content = interview_content
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# 删除"判断题："前缀
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content = re.sub(r'判断题[：:]?\s*', '', content)
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# 分割成类别
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sections = re.split(r'\n# ([一二三四五六七八九十]+、[^#\n]+)', content)
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questions = []
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if len(sections) > 1:
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for i in range(1, len(sections), 2):
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if i+1 >= len(sections):
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break
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category_title = sections[i].strip()
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category_content = sections[i+1]
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# 提取问题和答案
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sub_questions = []
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lines = category_content.split('\n')
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current_question = None
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current_answer = []
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in_answer = False
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for line in lines:
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line = line.strip()
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# 检查是否是新问题
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question_match = re.match(r'^(\d+)\.\s*(.+)$', line)
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if question_match:
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# 保存上一个问题
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if current_question and current_answer:
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answer_text = ' '.join(current_answer).strip()
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answer_text = re.sub(r'^(示例)?答案[：:]?\s*', '', answer_text)
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if answer_text:
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sub_questions.append({
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"question": current_question,
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"answer": answer_text
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})
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# 开始新问题
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current_question = question_match.group(2).strip()
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current_answer = []
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in_answer = False
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# 检查是否是答案开始
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elif '答案' in line or '示例答案' in line:
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in_answer = True
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answer_part = re.sub(r'^(示例)?答案[：:]?\s*', '', line).strip()
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if answer_part:
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current_answer.append(answer_part)
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# 收集答案内容
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elif in_answer and line and not re.match(r'^#', line):
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current_answer.append(line)
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# 保存最后一个问题
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if current_question and current_answer:
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answer_text = ' '.join(current_answer).strip()
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answer_text = re.sub(r'^(示例)?答案[：:]?\s*', '', answer_text)
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if answer_text:
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sub_questions.append({
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"question": current_question,
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"answer": answer_text
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})
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if sub_questions:
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questions.append({
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"category": category_title,
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"questions": sub_questions
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})
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drug_questions = questions
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break
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# 转换为前端格式
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questions_array = []
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total_q = 0
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for i, cat in enumerate(drug_questions, 1):
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sub_q_array = []
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for j, q in enumerate(cat['questions'], 1):
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total_q += 1
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sub_q_array.append({
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"id": f"q{total_q}",
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"question": q['question'],
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"answer": q['answer']
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})
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questions_array.append({
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"id": f"group_q{i}",
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"question": cat['category'],
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"subQuestions": sub_q_array
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})
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# 生成JSON字符串
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questions_json = json.dumps(questions_array, ensure_ascii=False, indent=2)
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# 读取Mock文件
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with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/src/mocks/resumeInterviewMock.js', 'r', encoding='utf-8') as f:
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content = f.read()
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# 在health_11的positions数组后添加questions
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# 找到最后一个position的结束
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pattern = r'("id":\s*"health_11_4"[^}]*?"requirements":\s*\[[^\]]*?\]\s*\}\]\s*\})'
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match = re.search(pattern, content, re.DOTALL)
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if match:
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end_pos = match.end()
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# 在这个位置插入questions
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# 先找到']'的位置（positions数组的结束）
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positions_end = content.rfind(']', 0, end_pos)
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# 构建新的内容
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new_content = content[:positions_end] + '],\n "questions": ' + questions_json + '\n ' + content[end_pos:]
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# 写回文件
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with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/src/mocks/resumeInterviewMock.js', 'w', encoding='utf-8') as f:
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f.write(new_content)
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print(f"✓ 已添加药物研发的 {len(questions_array)} 个分类，共 {total_q} 个面试题")
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else:
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print("未找到health_11的结构") |