app.py

import gradio as gr
import pysbd
from transformers import pipeline
from sentence_transformers import CrossEncoder
from  transformers  import  AutoTokenizer, AutoModelWithLMHead, pipeline

model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelWithLMHead.from_pretrained(model_name)

sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
qa_model = pipeline("question-answering",'a-ware/bart-squadv2')

def fetch_answers(question, clincal_note ):
    clincal_note_paragraphs = clincal_note.splitlines()
    query_paragraph_list = [(question, para) for para in clincal_note_paragraphs if len(para.strip()) > 0 ]
    
    scores = passage_retreival_model.predict(query_paragraph_list)
    top_5_indices = scores.argsort()[-5:]
    top_5_query_paragraph_list = [query_paragraph_list[i] for i in top_5_indices ]
    top_5_query_paragraph_list.reverse()
    
    top_5_query_paragraph_answer_list = ""
    count = 1
    for query, passage in top_5_query_paragraph_list:
     passage_sentences = sentence_segmenter.segment(passage)
     answer = qa_model(question = query, context = passage)['answer']
     evidence_sentence = None
     for i in range(len(passage_sentences)):
         if answer.startswith('.') or answer.startswith(':'):
             answer = answer[1:].strip()
         if answer in passage_sentences[i]:
             evidence_sentence = passage_sentences[i]
             break
     
         model_input = f"question: {query} context: {evidence_sentence}"
         encoded_input = tokenizer([model_input],
                                     return_tensors='pt',
                                     max_length=512,
                                     truncation=True)
                                     
         output = model.generate(input_ids = encoded_input.input_ids,
                                    attention_mask = encoded_input.attention_mask)
         output_answer = tokenizer.decode(output[0], skip_special_tokens=True)

     result_str = "# RESULT NO: "+str(count)+ output_answer +"\n"  
     result_str = result_str + "REFERENCE: "+ evidence_sentence + "\n\n"
     top_5_query_paragraph_answer_list += result_str
     count+=1
     
    return top_5_query_paragraph_answer_list

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...")],
    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."""
)
demo.launch()