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| """ | |
| 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 |