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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 |