def predict(data):
    try:
        image_input = data.get('image', None)
        if not image_input:
            return json.dumps({"error": "No image provided."})

        print(f"Received image input: {image_input}")

        # Check if the input is a PIL Image type
        if isinstance(image_input, Image.Image):
            print(f"Image is already loaded as PIL Image: {image_input}")
        else:
            # Check if the input contains a base64-encoded string or URL
            if image_input.startswith("http"):  # URL case
                try:
                    response = requests.get(image_input)
                    image = Image.open(BytesIO(response.content))
                    print(f"Fetched image from URL: {image}")
                except Exception as e:
                    print(f"Error fetching image from URL: {e}")
                    return json.dumps({"error": f"Error fetching image from URL: {e}"})
            else:  # Assuming it is base64-encoded image data
                try:
                    image_data = base64.b64decode(image_input)
                    image = Image.open(BytesIO(image_data))
                    print(f"Decoded base64 image: {image}")
                except Exception as e:
                    print(f"Error decoding base64 image: {e}")
                    return json.dumps({"error": f"Error decoding base64 image: {e}"})

        # Apply transformations
        image = transform(image).unsqueeze(0)
        print(f"Transformed image tensor: {image.shape}")

        image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))

        with torch.no_grad():
            outputs = model(image)
            predicted_class = torch.argmax(outputs, dim=1).item()
            print(f"Prediction output: {outputs}, Predicted class: {predicted_class}")

        if predicted_class == 0:
            return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."})
        elif predicted_class == 1:
            return json.dumps({"result": "The photo you've sent is of a healthy maize image."})
        else:
            return json.dumps({"error": "Unexpected class prediction."})
    except Exception as e:
        print(f"Error processing image: {e}")
        return json.dumps({"error": f"Error processing image: {e}"})

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.JSON(label="Input JSON"),
    outputs=gr.Textbox(label="Prediction Result"),
    live=True,
    title="Maize Anomaly Detection",
    description="Upload an image of maize to detect anomalies like disease or pest infestation. You can provide local paths, URLs, or base64-encoded images."
)

# Launch the Gradio interface 
iface.launch(share=True, show_error=True)