| import sys, os | |
| sys.path.insert(0, os.path.abspath('.')) | |
| from pathlib import Path | |
| from src.resume_parser import parse_resume | |
| from src.matcher import rank_jobs | |
| from src.conversion import attach_conversion_scores | |
| # 模拟 case_13 | |
| resume_text = "赵同学 | 电子信息 | 2026 届硕士\n技能：Python、PyTorch、OpenCV、YOLO、目标检测、图像分类、模型部署、TensorRT。\n项目：1. 基于 YOLO-v8 的工业缺陷检测：自制数据集 mAP@0.5=89.2%。2. ResNet 作物病害识别：PlantVillage 准确率 94.7%。3. 模型边缘部署：使用 TensorRT 优化 YOLO 推理速度，FPS 从 15 提升到 45。" | |
| profile = parse_resume(resume_text) | |
| profile['_city'] = '上海' | |
| profile['_stage'] = '实习' | |
| profile['_target_role'] = '大模型应用算法' | |
| print('profile:', profile) | |
| scored = rank_jobs( | |
| resume_text=resume_text, | |
| profile=profile, | |
| target_role='大模型应用算法', | |
| target_city='上海', | |
| stage='实习', | |
| top_k=3, | |
| jobs_path=Path('data/jobs.json') | |
| ) | |
| for j in scored[:3]: | |
| print('Title:', j['title']) | |
| print(' pass_score:', j.get('pass_score')) | |
| print(' risk_score:', j.get('risk_score')) | |
| print(' growth_score:', j.get('growth_score')) | |
| print(' action would be based on these values') | |
| print() | |