Update app.py

app.py

@@ -24,16 +24,17 @@ def predict(image):
     
     return {'label': label, 'confidence': float(confidence)}  # Convert confidence to float
 
-# Custom post-processing function to sort by confidence
+# Custom post-processing function to format the label and confidence
 def custom_postprocess(output):
-    sorted_output = sorted(output.items(), key=lambda x: x[1], reverse=True)
-    return f"{sorted_output[0][0]} ({sorted_output[0][1] * 100:.2f}%)"
+    label = output['label']
+    confidence = output['confidence'] * 100
+    return f"{label} ({confidence:.2f}%)"
 
 examples = [["benign.jpg"], ["malignant.jpg"]]
 
 # Define input and output components
 image_input = gr.inputs.Image(shape=(150, 150))
-label_output = gr.outputs.Label(postprocess=custom_postprocess)
+label_output = gr.outputs.Label()
 
 # Define a Gradio interface for user interaction
 iface = gr.Interface(