import numpy as np
import cv2
from typing import List, Dict, Any, Tuple

class Model:
    """
    Smoke detection model implementation
    
    This is a simple example that could be replaced with an actual
    TensorFlow, PyTorch, or other ML framework implementation.
    """
    
    def __init__(self):
        """Initialize smoke detection model"""
        # In a real implementation, you would load your model here
        print("Smoke detection model initialized")
        
        # Define smoke class IDs
        self.smoke_classes = {
            0: "smoke",
            1: "fire"
        }
    
    def preprocess(self, image: np.ndarray) -> np.ndarray:
        """Preprocess image for model input"""
        # Convert BGR to grayscale for smoke detection
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # Convert back to 3 channels to match model expected input shape
        gray_3ch = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
        
        # In a real implementation, you would do normalization, etc.
        return gray_3ch
    
    def predict(self, image: np.ndarray) -> List[Dict[str, Any]]:
        """
        Run smoke detection on the image
        
        This is a simplified example that uses basic image processing
        In a real implementation, you would use your ML model
        """
        # Convert to grayscale for processing
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # Apply Gaussian blur to reduce noise
        blurred = cv2.GaussianBlur(gray, (15, 15), 0)
        
        # Simple thresholding to find potential smoke regions
        # In a real implementation, you'd use a trained model
        _, thresh = cv2.threshold(blurred, 100, 255, cv2.THRESH_BINARY)
        
        # Find contours in the thresholded image
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        # Process contours to find potential smoke regions
        detections = []
        height, width = image.shape[:2]
        
        for contour in contours:
            # Get bounding box
            x, y, w, h = cv2.boundingRect(contour)
            
            # Filter small regions
            if w > width * 0.05 and h > height * 0.05:
                # Calculate area ratio
                area = cv2.contourArea(contour)
                rect_area = w * h
                fill_ratio = area / rect_area if rect_area > 0 else 0
                
                # Smoke tends to have irregular shapes
                # This is just for demonstration purposes
                if fill_ratio > 0.2 and fill_ratio < 0.8:
                    # Normalize coordinates
                    x_norm = x / width
                    y_norm = y / height
                    w_norm = w / width
                    h_norm = h / height
                    
                    # Determine if it's smoke or fire (just a simple heuristic for demo)
                    # In a real model, this would be determined by the model prediction
                    class_id = 0  # Default to smoke
                    
                    # Check if the region has high red values (fire)
                    roi = image[y:y+h, x:x+w]
                    if roi.size > 0:  # Make sure ROI is not empty
                        avg_color = np.mean(roi, axis=(0, 1))
                        if avg_color[2] > 150 and avg_color[2] > avg_color[0] * 1.5:  # High red, indicating fire
                            class_id = 1  # Fire
                    
                    # Calculate confidence based on fill ratio
                    # This is just for demonstration
                    confidence = 0.5 + fill_ratio * 0.3
                    
                    # Add to detections
                    detections.append({
                        'bbox': (x_norm, y_norm, w_norm, h_norm),
                        'class_id': class_id,
                        'confidence': confidence
                    })
        
        # For demo purposes, if no smoke detected by algorithm,
        # add a small chance of random detection
        if not detections and np.random.random() < 0.1:  # 10% chance
            # Random smoke detection
            x = np.random.random() * 0.7
            y = np.random.random() * 0.7
            w = 0.1 + np.random.random() * 0.2
            h = 0.1 + np.random.random() * 0.2
            confidence = 0.5 + np.random.random() * 0.3
            
            detections.append({
                'bbox': (x, y, w, h),
                'class_id': 0,  # Smoke
                'confidence': confidence
            })
            
        return detections
    
    @property
    def applies_nms(self) -> bool:
        """Model does not apply NMS internally"""
        return False
    
    def close(self):
        """Release resources"""
        # In a real implementation, you would release model resources here
        pass