Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import pandas as pd
 from transformers import pipeline
 from sentence_transformers import CrossEncoder
 
@@ -17,9 +18,9 @@ def fetch_answers(question, clincal_note ):
     top_5_query_paragraph_answer_list = []
     for query, passage in top_5_query_paragraph_list:
      answer = qa_model(question = query, context = passage)['answer']
-     top_5_query_paragraph_answer_list.append([query, passage, answer])
+     top_5_query_paragraph_answer_list.append([passage, answer])
      
-    return top_5_query_paragraph_answer_list
+    return pd.DataFrame(data = top_5_query_paragraph_answer_list, columns=['Relevant Paragraph', 'Extracted Answer'])
 
 demo = gr.Interface(
     fn=fetch_answers,
@@ -27,7 +28,7 @@ demo = gr.Interface(
     #clinical note upload as file (.This is an example of simple text. or doc/docx file)
     inputs=[gr.Textbox(lines=2, label='Question', show_label=True, placeholder="What is age of patient ?"),
             gr.Textbox(lines=10, label='Clinical Note', show_label=True, placeholder="The patient is a 71 year old male...")],
-    outputs="text",
+    outputs="dataframe",
     examples='.',
     title='Question Answering System from Clinical Notes for Physicians',
     description="""Physicians frequently seek answers to questions from a patient’s EHR to support clinical decision-making.​ It is not too hard to imagine a future where a physician interacts with an EHR system and asks it complex questions and expects precise answers with adequate context from a patient’s past clinical notes. ​Central to such a world is a medical question answering system that processes natural language questions asked by physicians and finds answers to the questions from all sources in a patient’s record."""