Update app.py

app.py

@@ -121,8 +121,8 @@ def KNN_predict(train_features, train_labels, test_feature, K):
 ### main function for gradio to call to classify image
 def call_our_KNN(test_image, K=7):
   test_image_flatten = test_image.reshape((-1, 28*28))
-  y_pred_each, image_path = KNN_predict(train_features, train_labels, test_image_flatten, int(K))
-  return y_pred_each, image_path
+  y_pred_each, y_prob_each, image_path = KNN_predict(train_features, train_labels, test_image_flatten, int(K))
+  return y_pred_each, y_prob_each, image_path
 
 
 ### generate several example cases
@@ -133,13 +133,13 @@ set_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
 set_K = gr.inputs.Slider(1, 24, step=1, default=7)
 
 set_label = gr.outputs.Textbox(label="Predicted Digit")
+
 # define output as the single class text
 set_probability = gr.outputs.Label(num_top_classes=10, label="Predicted Class")
 
 set_out_images = gr.outputs.Image(label="Closest Neighbors")
 
 
-
 ### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
 interface = gr.Interface(fn=call_our_KNN, 
                          inputs=[set_image, set_K],