Update app.py

app.py

@@ -30,7 +30,7 @@ def predict(context,question):
     # dense_duration = (dense_end_time - dense_start_time) * 1000
     # dense_answer = dense_predictions['answer']
 
-    return sparse_answer,sparse_score,sparse_start,sparse_duration #,dense_answer,dense_duration
+    return sparse_answer,sparse_score,sparse_start #,sparse_duration #,dense_answer,dense_duration
 
 md = """This prediction model is designed to answer a question about a given input text--reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, automated reading comprehension can be a valuable task.
 
@@ -61,7 +61,7 @@ iface = gr.Interface(
     fn=predict, 
     inputs=[context,question],
     # outputs=[sparse_answer,sparse_duration,dense_answer,dense_duration],
-    outputs=[sparse_answer,sparse_score,sparse_start,sparse_duration],
+    outputs=[sparse_answer,sparse_score,sparse_start],
     examples=[[apple_context,apple_question]],
     title = "Question & Answer with Sparse BERT using the SQuAD dataset",
     description = md,