gradio inputs outputs

app.py

@@ -14,7 +14,9 @@ def predict(context="There are seven continents in the world.",question="How man
     '''
     predictions = qa_pipeline(context=context,question=question)
     print(f'predictions={predictions}')
-    return predictions
+    score = predictions['score']
+    answer = predictions['answer']
+    return score,answer
 
 md = """
 If you came looking for chatGPT, sorry to disappoint, but this is different. This prediction model is designed to answer a question about a text. It is designed to do reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, accomplishing accurate reading comprehension can be a very valuable task, especially if you are attempting to get quick answers from a large (and maybe boring!) document.
@@ -26,12 +28,15 @@ Based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained
 """
 
 # predict()
+context=gr.Text(label="Context")
+question=gr.Text(label="Question")
+score=gr.Text(label="Score")
+answer=gr.Text(label="Answer")
 
 iface = gr.Interface(
     fn=predict, 
-    inputs=[gr.TextBox('Context'),gr.TextBox('Question')],
-    outputs="text",
-    # examples = 
+    inputs=[context,question],
+    outputs=[score,answer],
     title = "Question & Answer with Sparse BERT using the SQuAD dataset",
     description = md
     )