"""
Model Module - Placeholder for future PyTorch model integration

This module can be extended to include custom trained deepfake detection models.
Currently, the system relies on Gemini API for analysis, but this structure
allows for easy integration of local ML models.
"""

class DeepfakeModel:
    """
    Placeholder class for custom deepfake detection model
    
    This can be extended to load and run PyTorch or TensorFlow models
    for offline deepfake detection.
    """
    
    def __init__(self, model_path=None):
        """
        Initialize the model
        
        Args:
            model_path: Path to the model weights file (.pt, .pth, .h5, etc.)
        """
        self.model_path = model_path
        self.model = None
        self.is_loaded = False
        
    def load_model(self):
        """
        Load the model from file
        
        This method should be implemented when adding custom models:
        - Load PyTorch model: torch.load(self.model_path)
        - Load TensorFlow model: tf.keras.models.load_model(self.model_path)
        """
        # TODO: Implement model loading
        # Example for PyTorch:
        # import torch
        # self.model = torch.load(self.model_path)
        # self.model.eval()
        # self.is_loaded = True
        pass
    
    def predict_image(self, image_array):
        """
        Predict if an image is a deepfake
        
        Args:
            image_array: Numpy array of the image
            
        Returns:
            dict: Prediction results with confidence score
        """
        # TODO: Implement prediction logic
        # Example structure:
        # preprocessed = self.preprocess(image_array)
        # output = self.model(preprocessed)
        # confidence = output.softmax(dim=1)[0][1].item()
        
        return {
            'is_deepfake': False,
            'confidence': 0.0,
            'note': 'Custom model not implemented yet'
        }
    
    def predict_video(self, frame_arrays):
        """
        Predict if a video is a deepfake based on frames
        
        Args:
            frame_arrays: List of numpy arrays (video frames)
            
        Returns:
            dict: Prediction results with confidence score
        """
        # TODO: Implement video prediction logic
        # Can analyze frames individually or use temporal models
        
        return {
            'is_deepfake': False,
            'confidence': 0.0,
            'note': 'Custom model not implemented yet'
        }
    
    def preprocess(self, image_array):
        """
        Preprocess image for model input
        
        Args:
            image_array: Raw image array
            
        Returns:
            Preprocessed tensor ready for model
        """
        # TODO: Implement preprocessing
        # - Resize to model input size
        # - Normalize pixel values
        # - Convert to tensor
        # - Add batch dimension
        pass
    
    def postprocess(self, model_output):
        """
        Postprocess model output
        
        Args:
            model_output: Raw model output
            
        Returns:
            Formatted prediction results
        """
        # TODO: Implement postprocessing
        # - Apply softmax/sigmoid
        # - Extract class labels
        # - Format output
        pass


# Model configuration
MODEL_CONFIG = {
    'input_size': (224, 224),
    'num_classes': 2,  # Real vs Fake
    'threshold': 0.5,  # Classification threshold
    'device': 'cpu',  # 'cpu' or 'cuda'
}


def get_model(model_path=None):
    """
    Factory function to get model instance
    
    Args:
        model_path: Path to model weights
        
    Returns:
        DeepfakeModel instance
    """
    model = DeepfakeModel(model_path)
    if model_path:
        model.load_model()
    return model