import os
import sys
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, 
                   format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def test_imports():
    """测试所有必需的模块都可以导入"""
    try:
        import torch
        logger.info(f"PyTorch版本: {torch.__version__}")
        
        import transformers
        logger.info(f"Transformers版本: {transformers.__version__}")
        
        import numpy as np
        logger.info(f"NumPy版本: {np.__version__}")
        
        import PIL
        logger.info(f"PIL版本: {PIL.__version__}")
        
        import scipy
        logger.info(f"SciPy版本: {scipy.__version__}")
        
        logger.info("所有导入成功")
        return True
    except ImportError as e:
        logger.error(f"导入错误: {str(e)}")
        return False

def test_model_loading():
    """测试模型可以加载"""
    try:
        from model import RadarDetectionModel
        
        # 检查是否设置了HF_TOKEN环境变量
        hf_token = os.environ.get("HF_TOKEN")
        if not hf_token:
            logger.warning("未设置HF_TOKEN环境变量，使用公共模型进行测试")
        
        # 尝试初始化模型，使用较小的公共模型
        logger.info("尝试初始化模型(使用较小的公共模型)")
        model = RadarDetectionModel(model_name="google/siglip-base-patch16-224")
        logger.info("模型初始化成功")
        return True
    except Exception as e:
        logger.error(f"模型加载错误: {str(e)}")
        return False

def test_feature_extraction():
    """测试特征提取功能"""
    try:
        import numpy as np
        from PIL import Image
        from feature_extraction import extract_features
        
        # 创建一个虚拟图像和检测结果
        logger.info("创建虚拟测试数据")
        dummy_image = Image.fromarray(np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8))
        dummy_detection = {
            'boxes': [[50, 50, 100, 100]],
            'scores': [0.9],
            'labels': ['测试']
        }
        
        # 提取特征
        logger.info("提取特征")
        features = extract_features(dummy_image, dummy_detection)
        logger.info(f"提取的特征: {features}")
        return True
    except Exception as e:
        logger.error(f"特征提取错误: {str(e)}")
        return False

def test_app_initialization():
    """测试应用程序初始化但不加载模型"""
    try:
        logger.info("测试应用程序初始化")
        import app
        
        # 检查应用程序是否已初始化但没有加载模型
        logger.info("检查应用程序全局变量")
        assert app.model is None, "模型不应该在导入时加载"
        assert app.MODEL_INIT_ATTEMPTED is False, "模型初始化尝试标志应为False"
        
        logger.info("应用程序初始化测试通过")
        return True
    except Exception as e:
        logger.error(f"应用程序初始化错误: {str(e)}")
        return False

def run_tests():
    """运行所有测试"""
    tests = [
        ("导入测试", test_imports),
        ("应用程序初始化测试", test_app_initialization),
        ("模型加载测试", test_model_loading),
        ("特征提取测试", test_feature_extraction)
    ]
    
    results = []
    for name, test_func in tests:
        logger.info(f"运行{name}...")
        try:
            result = test_func()
            results.append((name, result))
            logger.info(f"{name}: {'通过' if result else '失败'}")
        except Exception as e:
            logger.error(f"{name}失败，错误: {str(e)}")
            results.append((name, False))
    
    # 打印摘要
    logger.info("\n--- 测试摘要 ---")
    passed = sum(1 for _, result in results if result)
    total = len(results)
    logger.info(f"通过: {passed}/{total} 测试")
    
    for name, result in results:
        status = "通过" if result else "失败"
        logger.info(f"{name}: {status}")
    
    return passed == total

if __name__ == "__main__":
    success = run_tests()
    sys.exit(0 if success else 1)