Update app.py

app.py

@@ -11,7 +11,7 @@ model = EfficientNetV2L(weights="imagenet")
 
 def predict_image(image):
     """
-    Process the uploaded image and return the top 5 predictions.
+    Process the uploaded image and return the top 5 predictions as a list.
     """
     try:
         # Preprocess the image
@@ -24,12 +24,12 @@ def predict_image(image):
         predictions = model.predict(image_array)
         decoded_predictions = decode_predictions(predictions, top=5)[0]
 
-        # Format predictions as a dictionary (label -> confidence)
-        results = {label: float(confidence) for _, label, confidence in decoded_predictions}
+        # Format predictions as a list of tuples (label, confidence)
+        results = [(label, float(confidence)) for _, label, confidence in decoded_predictions]
         return results
 
     except Exception as e:
-        return {"Error": str(e)}
+        return [("Error", str(e))]
 
 # Create the Gradio interface
 interface = gr.Interface(
@@ -38,7 +38,7 @@ interface = gr.Interface(
     outputs=gr.Label(num_top_classes=2),  # Shows top 5 predictions with confidence
     title="Image Classifier",
     description="Upload an image, and the model will predict what's in the image with higher accuracy.",
-    examples=["dog.jpg", "cat.jpg", "building.jpg", "tree.jpg"],  # Example images for testing
+    
 )
 
 # Launch the Gradio app