235 lines
9.2 KiB
Python
235 lines
9.2 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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from collections import defaultdict
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def load_health_data():
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"""加载大健康数据"""
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with open('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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resume_data = json.load(f)
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with open('网页未导入数据/大健康产业/大健康问答内容.json', 'r', encoding='utf-8') as f:
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qa_data = json.load(f)
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return resume_data, qa_data
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def map_level(level_tag):
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"""映射岗位等级"""
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level_map = {
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'基础岗': '普通岗',
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'技术骨干岗': '技术骨干岗',
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'普通岗': '普通岗'
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}
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return level_map.get(level_tag, '普通岗')
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def parse_resume_content(content):
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"""解析简历内容，提取关键信息"""
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# 提取项目经历
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project_match = re.search(r'项目名称：(.+?)\n', content)
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project_name = project_match.group(1) if project_match else "健康管理项目"
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# 提取岗位职责（取前3条）
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responsibilities = []
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resp_pattern = r'\d+\.\s*([^；\n]+[；。]?)'
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resp_matches = re.findall(resp_pattern, content)
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if resp_matches:
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responsibilities = [m.strip().rstrip('；。') for m in resp_matches[:3]]
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# 提取核心能力
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core_skills = []
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if '### （一）核心能力' in content:
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core_section = content.split('### （一）核心能力')[1].split('### （二）')[0]
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skill_matches = re.findall(r'\d+\.\s*([^。\n]+。?)', core_section)
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core_skills = [s.strip().rstrip('。') for s in skill_matches[:5]]
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# 提取个人评价
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summary = "我是一名大专毕业生，具备扎实的专业基础和实践经验。"
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if '# 三、个人评价' in content or '# 三、个人总结' in content:
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summary_section = content.split('# 三、')[1]
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summary_text = re.sub(r'\n+', ' ', summary_section).strip()
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if len(summary_text) > 50:
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summary = summary_text[:200] + "..."
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return {
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'project_name': project_name,
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'responsibilities': responsibilities,
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'core_skills': core_skills,
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'summary': summary
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}
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def create_industries_structure(resume_data, qa_data):
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"""创建industries数据结构"""
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# 按岗位群分组
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groups = defaultdict(list)
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for item in resume_data:
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group_name = item['简历岗位群']
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groups[group_name].append(item)
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industries = []
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industry_id = 1
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for group_name, positions_data in groups.items():
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industry = {
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"id": f"health_{industry_id}",
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"name": group_name,
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"positions": [],
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"questions": []
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}
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position_id = 1
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for pos in positions_data:
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# 解析简历内容获取更详细的信息
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parsed_info = parse_resume_content(pos['简历内容'])
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position = {
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"id": f"health_{industry_id}_{position_id}",
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"title": pos['岗位名称'],
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"level": map_level(pos['岗位等级标签']),
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"avatar": pos['简历头像url'],
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"department": group_name,
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"type": "全职",
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"experience": "1-3年" if pos['岗位等级标签'] == '基础岗' else "2-5年",
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"education": "大专",
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"salary": "5-10K" if pos['岗位等级标签'] == '基础岗' else "8-15K",
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"location": "北京",
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"updateTime": "2024-01-20",
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"description": f"负责{pos['岗位名称']}相关工作，包括{parsed_info['responsibilities'][0] if parsed_info['responsibilities'] else '日常工作'}",
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"requirements": parsed_info['responsibilities'][:4] if len(parsed_info['responsibilities']) >= 4 else [
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"具备相关专业知识和技能",
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"有良好的沟通能力和团队合作精神",
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"能够独立完成岗位职责",
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"有相关实习或工作经验优先"
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]
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}
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industry["positions"].append(position)
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position_id += 1
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# 添加基于岗位群的问题
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questions = []
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question_id = 1
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# 添加通用问题
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main_question = {
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"id": f"group_q{industry_id}",
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"question": f"# 一、{group_name}专业认知",
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"subQuestions": []
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}
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# 从大健康问答内容中提取相关问题
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for qa in qa_data[:3]: # 取前3个问答作为示例
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sub_q = {
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"id": f"q{industry_id}_{question_id}",
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"question": qa.get('问题_流程1', f"请介绍一下您对{group_name}的理解？"),
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"answer": qa.get('回答_流程2', f"在{group_name}领域，我通过系统学习和实践，掌握了相关专业知识和技能。")
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}
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main_question["subQuestions"].append(sub_q)
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question_id += 1
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industry["questions"].append(main_question)
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industries.append(industry)
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industry_id += 1
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return industries
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def create_resume_templates(resume_data):
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"""创建resumeTemplates数据结构"""
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templates = defaultdict(list)
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for item in resume_data:
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group_name = item['简历岗位群']
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parsed_info = parse_resume_content(item['简历内容'])
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template = {
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"position": item['岗位名称'],
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"level": map_level(item['岗位等级标签']),
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"avatar": item['简历头像url'],
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"content": {
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"original": item['简历内容'],
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"modified": item['简历内容'] # 使用相同内容
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},
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"studentInfo": {
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"project_experience": {
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"project_name": parsed_info['project_name'],
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"position": item['岗位名称'] + "助理",
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"time_period": "XXXXXX",
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"company": "XXXXXX",
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"description": " ；\n".join(parsed_info['responsibilities'][:10]) if parsed_info['responsibilities'] else f"参与{item['岗位名称']}相关工作"
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},
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"core_skills": parsed_info['core_skills'][:9] if parsed_info['core_skills'] else [
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f"熟悉{group_name}相关理论知识",
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"具备实践操作能力",
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"良好的沟通协调能力"
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],
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"compound_skills": [
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"医疗服务与健康管理常识：了解我国医疗体系的基本结构与服务流程",
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"患者安全意识：了解生命体征监测、基础护理技能",
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"医疗人工智能基础认知：了解AI在医疗健康领域的应用",
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"质量控制认知：了解ISO体系和质量管理基本要求",
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"药品营销能力：掌握客户需求识别和沟通技巧"
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],
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"personal_summary": parsed_info['summary']
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}
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}
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templates[group_name].append(template)
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return dict(templates)
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def update_mock_file(industries, resume_templates):
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"""更新resumeInterviewMock.js文件"""
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# 读取原文件
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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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# 转换数据为JavaScript格式的字符串
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industries_str = json.dumps(industries, ensure_ascii=False, indent=2)
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templates_str = json.dumps(resume_templates, ensure_ascii=False, indent=2)
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# 替换industries数组
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# 查找industries的开始和结束位置
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industries_pattern = r'const industries = \[[\s\S]*?\n\];'
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new_industries = f'const industries = {industries_str};'
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content = re.sub(industries_pattern, new_industries, content, count=1)
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# 替换resumeTemplates对象
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templates_pattern = r'const resumeTemplates = \{[\s\S]*?\n\};'
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new_templates = f'const resumeTemplates = {templates_str};'
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content = re.sub(templates_pattern, new_templates, content, count=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("✅ 成功更新 resumeInterviewMock.js 文件")
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def main():
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print("开始转换大健康数据...")
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# 加载数据
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resume_data, qa_data = load_health_data()
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print(f"加载了 {len(resume_data)} 个岗位简历数据")
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print(f"加载了 {len(qa_data)} 个问答数据")
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# 创建数据结构
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industries = create_industries_structure(resume_data, qa_data)
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resume_templates = create_resume_templates(resume_data)
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# 输出统计
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print(f"\n转换完成：")
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print(f"- {len(industries)} 个岗位群")
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print(f"- {sum(len(ind['positions']) for ind in industries)} 个岗位")
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print(f"- {sum(len(templates) for templates in resume_templates.values())} 个简历模板")
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# 更新文件
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update_mock_file(industries, resume_templates)
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# 输出各岗位群信息
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print("\n岗位群详情：")
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for industry in industries:
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print(f" {industry['name']}: {len(industry['positions'])} 个岗位")
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if __name__ == "__main__":
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main()
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