import gradio as gr

def classify_image(image):
    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")

    # Predict
    outputs = model(**inputs)
    predictions = outputs.logits.softmax(dim=-1)

    # Assuming your model returns two probabilities: [real, AI-generated]
    probs = predictions.detach().numpy()[0]
    labels = ['Real', 'AI-generated']
    result = {labels[i]: probs[i] for i in range(len(labels))}

    return result

# Create the Gradio interface
iface = gr.Interface(fn=classify_image, inputs=gr.inputs.Image(shape=(224, 224)), outputs=gr.outputs.Label(num_top_classes=2))

iface.launch()