import logging
import base64
from io import BytesIO
from typing import List, Dict, Any

logger = logging.getLogger(__name__)


class ImageRecognizer:
    """图像识别器"""
    
    def __init__(self):
        """初始化图像识别器"""
        logger.info("初始化图像识别器")
        # 这里可以加载预训练模型
        # 示例中使用简单的规则识别
    
    def recognize(self, images: List[str], params: Dict[str, Any] = None) -> List[Dict[str, Any]]:
        """识别图像
        
        Args:
            images: 图像列表，每个图像为base64编码字符串
            params: 识别参数
            
        Returns:
            识别结果列表
        """
        if params is None:
            params = {}
        
        threshold = params.get("threshold", 0.5)
        
        results = []
        for image_base64 in images:
            # 简单的规则识别示例
            recognition = self._simple_recognize(image_base64)
            results.append({
                "image": image_base64[:100] + "..." if len(image_base64) > 100 else image_base64,
                "label": recognition["label"],
                "confidence": recognition["confidence"]
            })
        
        return results
    
    def _simple_recognize(self, image_base64: str) -> Dict[str, Any]:
        """简单的图像识别实现
        
        Args:
            image_base64: base64编码的图像
            
        Returns:
            识别结果
        """
        # 简单的规则识别（基于图像大小和内容特征）
        try:
            # 解码base64
            image_data = base64.b64decode(image_base64)
            
            # 计算图像大小特征
            image_size = len(image_data)
            
            # 基于大小的简单分类
            if image_size < 10240:  # 小于10KB
                return {"label": "小图像", "confidence": 0.8}
            elif image_size < 102400:  # 小于100KB
                return {"label": "中等图像", "confidence": 0.85}
            else:  # 大于100KB
                return {"label": "大图像", "confidence": 0.9}
        except Exception as e:
            logger.error(f"Image recognition error: {str(e)}")
            return {"label": "未知", "confidence": 0.5}