Update app.py

app.py

@@ -10,7 +10,7 @@ from sentence_transformers import CrossEncoder
 
 from transformers import pipeline
 
-text2text_generator = pipeline("text2text-generation")
+text2text_generator = pipeline("text2text-generation", model = "bigscience/T0")
 
 sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
 passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
@@ -60,11 +60,10 @@ demo = gr.Interface(
     fn=fetch_answers,
     #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...")],
+    inputs=[gr.Textbox(lines=2, label='Question', show_label=True),
+            gr.Textbox(lines=10, label='Document Text', show_label=True)],
     outputs="markdown",
     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."""
+    title='Document Question Answering System with Evidence from document'
 )
 demo.launch()