inference.py

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
import torchvision.transforms as transforms

# You'll need to include your model definition
# Copy the AdvancedDeepSVDD class and related code here
# or import from your training script

def load_model(repo_id="ash12321/ai-image-detector-deepsvdd"):
    """Download and load model from HuggingFace"""
    
    model_path = hf_hub_download(
        repo_id=repo_id,
        filename="model.ckpt"
    )
    
    # Load model (requires model definition)
    from model import AdvancedDeepSVDD
    model = AdvancedDeepSVDD.load_from_checkpoint(model_path)
    model.eval()
    
    return model

def predict_image(image_path, model):
    """Predict if image is AI-generated"""
    
    transform = transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.4914, 0.4822, 0.4465],
            std=[0.2470, 0.2435, 0.2616]
        )
    ])
    
    image = Image.open(image_path).convert('RGB')
    image_tensor = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        is_fake, scores, distances = model.predict_anomaly(image_tensor)
    
    return {
        'is_ai_generated': bool(is_fake[0].item()),
        'confidence': float(scores[0].item()),
        'anomaly_score': float(scores[0].item()),
        'distance': float(distances[0].item())
    }

# Example usage
if __name__ == "__main__":
    model = load_model()
    result = predict_image("test_image.jpg", model)
    print(f"AI-Generated: {result['is_ai_generated']}")
    print(f"Confidence: {result['confidence']*100:.1f}%")