classification label with scores

app.py

@@ -132,19 +132,15 @@ def predict(filepath):
     input_tensor = tf.expand_dims(tensor, axis=0)
 
     output = model.predict(input_tensor)
-    high_score = max(output[0])
 
-    predicted_breed = dogs_breeds[list(output[0]).index(high_score)]
+    confidences = { dogs_breeds[i]: float(output[0][i]) for i in range(120) }
 
-    return predicted_breed, high_score
+    return confidences
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(label='photo', type='filepath'),
-    outputs=[
-        gr.Label(label="Predicted breed"),
-        gr.Label(label="Accuracy score")
-    ],
+    outputs=gr.Label(label="Predicted breed", num_top_classes=3),
     examples=[
         'imgs/beethoven.jpg',
         'imgs/belle.png',