Upload inference_example.py with huggingface_hub

inference_example.py

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+"""
+Example: Using the model for deepfake detection
+"""
+
+import torch
+from torchvision import transforms
+from PIL import Image
+from model import load_model
+import json
+
+# Load model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = load_model('model_best_checkpoint.ckpt', device=device)
+
+# Load calibrated thresholds
+with open('thresholds_calibrated.json', 'r') as f:
+    config = json.load(f)
+    threshold = config['reconstruction_thresholds']['thresholds']['balanced']['value']
+
+print(f"Using threshold: {threshold:.6f}")
+
+# Prepare image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+def detect_deepfake(image_path, model, threshold, device):
+    """
+    Detect if an image is likely a deepfake based on reconstruction error.
+    
+    Args:
+        image_path: Path to image file
+        model: Loaded autoencoder model
+        threshold: MSE threshold for detection
+        device: torch device
+    
+    Returns:
+        is_fake: Boolean indicating if image is likely fake
+        error: Reconstruction error value
+        confidence: Confidence score (0-1)
+    """
+    # Load and preprocess image
+    image = Image.open(image_path).convert('RGB')
+    input_tensor = transform(image).unsqueeze(0).to(device)
+    
+    # Calculate reconstruction error
+    with torch.no_grad():
+        error = model.reconstruction_error(input_tensor, reduction='none')
+    
+    error_value = error.item()
+    is_fake = error_value > threshold
+    
+    # Calculate confidence (normalized error relative to threshold)
+    confidence = min(abs(error_value - threshold) / threshold, 1.0)
+    
+    return is_fake, error_value, confidence
+
+# Example usage
+image_path = "test_image.jpg"
+is_fake, error, confidence = detect_deepfake(image_path, model, threshold, device)
+
+print(f"\nResults for: {image_path}")
+print(f"Reconstruction Error: {error:.6f}")
+print(f"Threshold: {threshold:.6f}")
+print(f"Classification: {'FAKE' if is_fake else 'REAL'}")
+print(f"Confidence: {confidence:.2%}")
+
+# Batch processing example
+def batch_detect(image_paths, model, threshold, device):
+    """Process multiple images at once"""
+    images = []
+    for path in image_paths:
+        img = Image.open(path).convert('RGB')
+        images.append(transform(img))
+    
+    batch = torch.stack(images).to(device)
+    
+    with torch.no_grad():
+        errors = model.reconstruction_error(batch, reduction='none')
+    
+    results = []
+    for i, error in enumerate(errors):
+        is_fake = error.item() > threshold
+        results.append({
+            'path': image_paths[i],
+            'error': error.item(),
+            'is_fake': is_fake
+        })
+    
+    return results
+
+# Example batch processing
+# image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
+# results = batch_detect(image_paths, model, threshold, device)
+# for r in results:
+#     print(f"{r['path']}: {'FAKE' if r['is_fake'] else 'REAL'} (error: {r['error']:.6f})")