Update app.py

app.py

@@ -1,25 +1,32 @@
 import gradio as gr
+from PIL import Image
+import numpy as np
 
 def predict(image):
     try:
-        # Placeholder for your actual prediction logic
-        # Ensure the image is processed correctly
-        if isinstance(image, Image.Image):
-            # Perform your prediction here and ensure it returns expected types
-            result = {"Disease": "Predicted Disease", "Confidence": 0.95}
-            return result
-        else:
-            return {"error": "Invalid image format"}
+        # 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="pil"),
+    inputs=gr.Image(type="numpy"),  # Use 'numpy' type for better compatibility
     outputs=gr.Label(num_top_classes=3),
-    examples=[["MoucheB.jpg"], ["verdissement.jpg"]],
+    examples=["MoucheB.jpg", "verdissement.jpg"],
     title="Orange Disease Detector",
-    description="Detect diseases in orange leaves."
+    description="Detect diseases in orange leaves and fruits."
 )
 
 if __name__ == "__main__":