Update app.py

app.py

@@ -7,14 +7,14 @@ def fetch_answer(question, context ):
     return qa_model(question = question, context = context)['answer']
 
 demo = gr.Interface(
-    title='Question Answering System from Clinical Notes',
-    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.""",
     fn=fetch_answer,
     #take input as real time audio and use OPENAPI whisper for S2T
     #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",
-    examples='.'
+    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."""
 )
 demo.launch()