Update app.py

app.py

@@ -1,13 +1,29 @@
 import gradio as gr
 from transformers import pipeline
+from sentence_transformers import CrossEncoder
+import numpy as np
 
+passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
 qa_model = pipeline("question-answering",'a-ware/bart-squadv2')
 
-def fetch_answer(question, context ):
-    return qa_model(question = question, context = context)['answer']
+def fetch_answers(question, clincal_note ):
+    clincal_note_paragraphs = clincal_note.splitlines()
+    query_paragraph_list = [(question, para) for para in clincal_note_paragraphs ]
+    
+    scores = passage_retreival_model.predict(query_paragraph_list)
+    top_5_indices = scores.argsort()[:5]
+    query_paragraph_list = np.array(query_paragraph_list)
+    top_5_query_paragraph_list = query_paragraph_list[top_5_indices]
+    
+    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])
+     
+    return top_5_query_paragraph_answer_list
 
 demo = gr.Interface(
-    fn=fetch_answer,
+    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 ?"),