Update app.py

app.py

@@ -120,7 +120,7 @@ try:
         if user_choice == "Predict":
             # Call the model with the training mode argument
             # Use a dictionary to map the training mode to the corresponding boolean value
-            prediction = model.predict_on_batch(image[np.newaxis, ...], training={"None": False, "Dropout": True, "Batch Normalization": True}[training_mode])
+            prediction = model.predict(image[np.newaxis, ...], training={"None": False, "Dropout": True, "Batch Normalization": True}[training_mode])
             st.success(f'Prediction: {labels[np.argmax(prediction[0], axis=-1)]}')
         elif user_choice == "Train":
             # Generate some dummy target data for demonstration