app.py

import gradio as gr
from PIL import Image
import numpy as np

def predict(image):
    try:
        # Ensure the input is a PIL Image
        if isinstance(image, np.ndarray):
            # Convert numpy array to PIL Image
            image = Image.fromarray(image)
        
        if not isinstance(image, Image.Image):
            return {"error": "Input is not a valid image"}
        
        # Perform your prediction here
        # Placeholder for actual prediction logic
        result = {"Disease": "Predicted Disease", "Confidence": 0.95}
        return result

    except Exception as e:
        return {"error": str(e)}

interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy"),  # Use 'numpy' type for better compatibility
    outputs=gr.Label(num_top_classes=3),
    examples=[["MoucheB.jpg"], ["verdissement.jpg"]],
    title="Orange Disease Detector",
    description="Detect diseases in orange leaves and fruits."
)

if __name__ == "__main__":
    interface.launch()