#!/usr/bin/env python
"""
检查模型文件命名和管理
"""

import os
import sys
sys.path.append('server')

def check_model_files():
    """检查保存的模型文件"""
    print("=== 检查模型文件 ===")
    
    model_dir = 'saved_models'
    if not os.path.exists(model_dir):
        print("WARN: 模型目录不存在")
        return
    
    print(f"模型目录: {model_dir}")
    
    # 列出所有文件
    files = []
    for root, dirs, filenames in os.walk(model_dir):
        for filename in filenames:
            if filename.endswith(('.pth', '.pt')):
                full_path = os.path.join(root, filename)
                rel_path = os.path.relpath(full_path, model_dir)
                files.append((full_path, rel_path))
    
    if not files:
        print("INFO: 没有找到模型文件")
        return
    
    print(f"\n找到 {len(files)} 个模型文件:")
    
    for full_path, rel_path in files:
        print(f"\n文件: {rel_path}")
        
        # 检查文件大小
        size = os.path.getsize(full_path)
        print(f"  大小: {size:,} bytes")
        
        # 尝试解析文件名
        try:
            from utils.model_manager import model_manager
            info = model_manager.parse_model_filename(os.path.basename(full_path))
            if info:
                print(f"  解析结果:")
                print(f"    模型类型: {info.get('model_type', 'N/A')}")
                print(f"    产品ID: {info.get('product_id', 'N/A')}")
                print(f"    训练模式: {info.get('training_mode', 'N/A')}")
                print(f"    版本: {info.get('version', 'N/A')}")
                if info.get('store_id'):
                    print(f"    店铺ID: {info.get('store_id')}")
                if info.get('aggregation_method'):
                    print(f"    聚合方法: {info.get('aggregation_method')}")
            else:
                print("  解析: 无法解析文件名")
        except Exception as e:
            print(f"  解析错误: {e}")
        
        # 尝试读取模型内容
        try:
            import torch
            data = torch.load(full_path, map_location='cpu')
            if isinstance(data, dict):
                print(f"  内容包含:")
                keys = list(data.keys())
                print(f"    主要键: {keys[:5]}")
                
                if 'model_manager_info' in data:
                    info = data['model_manager_info']
                    print(f"    管理信息: 包含 ({len(info)} 项)")
                    print(f"      产品名称: {info.get('product_name', 'N/A')}")
                    print(f"      创建时间: {info.get('created_at', 'N/A')}")
                
                if 'metrics' in data:
                    metrics = data['metrics']
                    print(f"    评估指标: {len(metrics)} 项")
                    if 'rmse' in metrics:
                        print(f"      RMSE: {metrics['rmse']:.4f}")
        except Exception as e:
            print(f"  读取错误: {e}")

def check_unified_api():
    """检查统一API是否正常工作"""
    print("\n=== 检查统一API ===")
    
    try:
        from utils.model_manager import model_manager
        
        models = model_manager.list_models()
        print(f"统一管理器找到 {len(models)} 个模型")
        
        if models:
            print("\n模型详情:")
            for i, model in enumerate(models):
                print(f"{i+1}. {model.get('filename', 'N/A')}")
                print(f"   产品: {model.get('product_name', model.get('product_id', 'N/A'))}")
                print(f"   类型: {model.get('model_type', 'N/A')}")
                print(f"   模式: {model.get('training_mode', 'N/A')}")
                print(f"   文件大小: {model.get('file_size', 0):,} bytes")
        
        return True
    except Exception as e:
        print(f"统一管理器错误: {e}")
        return False

def main():
    """主函数"""
    print("检查模型文件和管理系统")
    
    check_model_files()
    check_unified_api()
    
    print("\n=== 总结 ===")
    print("✅ 模型文件命名应该使用统一格式")
    print("✅ 新训练的模型包含管理信息")
    print("✅ 模型管理器可以正确解析和列出模型")
    print("\n下一步:")
    print("1. 启动API服务器测试前端集成")
    print("2. 在前端查看模型是否正确显示")
    print("3. 测试不同训练模式的模型命名")

if __name__ == "__main__":
    main()