231 lines
8.5 KiB
Python
231 lines
8.5 KiB
Python
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#!/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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def parse_all_interview_questions(content):
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"""解析所有面试题内容，包括所有问题"""
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questions = []
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question_id = 1
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# 删除"判断题："等前缀
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content = re.sub(r'判断题：\s*', '', content)
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# 分割成不同的问题类别（一、二、三等）
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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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# 如果没有找到类别标记，尝试直接查找所有问题
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all_questions = []
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if len(sections) > 1:
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# 有类别的情况
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for i in range(1, len(sections), 2):
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if i >= len(sections):
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break
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section_title = sections[i].strip()
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section_content = sections[i + 1] if i + 1 < len(sections) else ""
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# 提取该类别下的所有问题
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category_questions = extract_questions_from_section(section_content, question_id)
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question_id += len(category_questions)
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if category_questions:
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all_questions.append({
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"category": section_title,
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"questions": category_questions
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})
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else:
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# 没有类别的情况，直接提取所有问题
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category_questions = extract_questions_from_section(content, question_id)
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if category_questions:
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all_questions.append({
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"category": "综合面试题",
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"questions": category_questions
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})
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return all_questions
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def extract_questions_from_section(content, start_id):
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"""从内容中提取所有问题和答案"""
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questions = []
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question_id = start_id
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# 使用更宽松的模式匹配问题
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# 模式1: 数字. 问题
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pattern1 = r'\n(\d+)\.\s*([^\n]+?)[\n\s]+((?:示例)?答案[：:]\s*[^\n]+(?:\n(?!\d+\.).*)*)'
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# 模式2: 问题后跟答案段落
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pattern2 = r'\n(\d+)\.\s*([^\n]+)\n\s*\n\s*((?:示例)?答案[：:])?\s*\n\s*([^\n]+(?:\n(?!\d+\.|示例答案).*)*)'
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# 先尝试模式1
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matches = re.findall(pattern1, content, re.MULTILINE)
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if not matches:
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# 尝试模式2
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matches = re.findall(pattern2, content, re.MULTILINE)
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matches = [(m[0], m[1], m[3]) for m in matches] # 调整格式
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# 如果还是没有匹配，使用更简单的模式
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if not matches:
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lines = 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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if answer_text:
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questions.append({
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"id": f"q{question_id}",
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"question": current_question,
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"answer": answer_text
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})
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question_id += 1
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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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# 可能答案就在同一行
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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:
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# 检查是否是下一个问题的开始
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if not re.match(r'^\d+\.', line):
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current_answer.append(line)
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else:
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in_answer = False
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# 如果没有明确的答案标记，但有内容，也收集
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elif current_question and not in_answer and line and not re.match(r'^\d+\.', 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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if answer_text:
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questions.append({
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"id": f"q{question_id}",
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"question": current_question,
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"answer": answer_text
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})
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else:
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# 处理正则匹配的结果
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for match in matches:
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question_text = match[1].strip()
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answer_text = match[2].strip()
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# 清理答案文本
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answer_text = re.sub(r'^(示例)?答案[：:]?\s*', '', answer_text).strip()
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answer_text = re.sub(r'\s+', ' ', answer_text) # 合并多余空格
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if question_text and answer_text:
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questions.append({
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"id": f"q{question_id}",
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"question": question_text,
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"answer": answer_text
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})
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question_id += 1
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return questions
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def main():
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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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# 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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# 创建岗位群到面试题的映射
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industry_questions_map = {}
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for item in health_data:
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industry = item.get('简历岗位群', '')
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interview_content = item.get('面试题内容', '')
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if industry and interview_content and industry not in industry_questions_map:
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all_categories = parse_all_interview_questions(interview_content)
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# 转换为前端期望的格式
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questions_array = []
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cat_id = 1
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for category_data in all_categories:
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if category_data['questions']:
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questions_array.append({
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"id": f"group_q{cat_id}",
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"question": category_data['category'],
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"subQuestions": category_data['questions']
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})
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cat_id += 1
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if questions_array:
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industry_questions_map[industry] = questions_array
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total_questions = sum(len(q['subQuestions']) for q in questions_array)
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print(f"✓ {industry}: 提取了 {len(questions_array)} 个分类，共 {total_questions} 个面试题")
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# 映射岗位群名称到ID
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industry_mapping = {
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'健康管理': 'health_1',
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'健康检查': 'health_2',
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'康复治疗': 'health_3',
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'慢性病管理': 'health_4',
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'轻医美': 'health_5',
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'心理健康': 'health_6',
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'社群运营': 'health_7',
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'药品供应链管理': 'health_8',
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'药品生产': 'health_9',
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'药品质量检测': 'health_10',
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'药物研发': 'health_11'
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}
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# 更新Mock文件
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updates = 0
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for orig_name, industry_id in industry_mapping.items():
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if orig_name in industry_questions_map:
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questions = industry_questions_map[orig_name]
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# 生成questions的JSON字符串
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questions_json = json.dumps(questions, ensure_ascii=False, indent=2)
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# 查找并替换questions字段
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# 先删除旧的questions字段
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pattern1 = rf'("id":\s*"{industry_id}"[^{{]*?"positions":\s*\[[^\]]*?\]),\s*"questions":\s*\[[^\]]*?\](\s*\}})'
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replacement1 = rf'\1\2'
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content = re.sub(pattern1, replacement1, content, flags=re.DOTALL)
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# 再添加新的questions字段
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pattern2 = rf'("id":\s*"{industry_id}"[^{{]*?"positions":\s*\[[^\]]*?\])(\s*\}})'
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replacement2 = rf'\1,\n "questions": {questions_json}\2'
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new_content, count = re.subn(pattern2, replacement2, content, flags=re.DOTALL)
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if count > 0:
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content = new_content
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updates += 1
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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(content)
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print(f"\n✅ 完成！更新了 {updates} 个岗位群的完整面试题数据")
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if __name__ == "__main__":
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main()
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