app.py

import streamlit as st
import pandas as pd
import numpy as np
from math import ceil
from collections import Counter
from string import punctuation

import spacy
from spacy import displacy
import en_ner_bc5cdr_md

nlp = spacy.load("en_ner_bc5cdr_md")
#nlp = en_core_web_lg.load()

st.set_page_config(layout='wide')
st.title('Clinical Note Summarization')
st.sidebar.markdown('Using transformer model')

## Loading in dataset
#df = pd.read_csv('mtsamples_small.csv',index_col=0)
df = pd.read_csv('shpi_w_rouge21Nov.csv')
df['HADM_ID'] = df['HADM_ID'].astype(str).apply(lambda x: x.replace('.0',''))

#Renaming column
df.rename(columns={'SUBJECT_ID':'Patient_ID',
                  'HADM_ID':'Admission_ID',
                  'hpi_input_text':'Original_Text',
                  'hpi_reference_summary':'Reference_text'}, inplace = True)
 
 #data.rename(columns={'gdp':'log(gdp)'}, inplace=True)

#Filter selection 
st.sidebar.header("Search for Patient:")

patientid = df['Patient_ID']
patient = st.sidebar.selectbox('Select Patient ID:', patientid)
admissionid = df['Admission_ID'].loc[df['Patient_ID'] == patient]
HospitalAdmission = st.sidebar.selectbox('', admissionid) 

# List of Model available
model = st.sidebar.selectbox('Select Model', ('BertSummarizer','BertGPT2','t5seq2eq','t5','gensim','pysummarizer'))

col3,col4 = st.columns(2) 
patientid = col3.write(f"Patient ID:  {patient} ")
admissionid =col4.write(f"Admission ID:  {HospitalAdmission} ")
   
#text = st.text_area('Input Clinical Note here')

# Query out relevant Clinical notes
original_text =  df.query(
    "Patient_ID  == @patient & Admission_ID == @HospitalAdmission"
)

original_text2 = original_text['Original_Text'].values

runtext =st.text_area('Input Clinical Note here:', str(original_text2), height=300)

reference_text = original_text['Reference_text'].values

def run_model(input_text):

    if model == "BertSummarizer":
        output = original_text['BertSummarizer'].values
        st.write('Summary')
        st.success(output[0])

    elif model == "BertGPT2":
        output = original_text['BertGPT2'].values
        st.write('Summary')
        st.success(output[0])
        
      
    elif model == "t5seq2eq": 
        output = original_text['t5seq2eq'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "t5":
        output = original_text['t5'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "gensim": 
        output = original_text['gensim'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "pysummarizer": 
        output = original_text['pysummarizer'].values
        st.write('Summary')
        st.success(output)
        
if st.button('Summarize'):
    run_model(runtext)
    
    sentences=runtext.split('.')
    
  
    st.text_area('Reference text', str(reference_text))

if st.button('NER'):
   doc = nlp(str(original_text2))
   colors = { "DISEASE": "pink","CHEMICAL": "orange"}
   options = {"ents": [ "DISEASE", "CHEMICAL"],"colors": colors}
   ent_html = displacy.render(doc, style="ent", options=options)
   st.markdown(ent_html, unsafe_allow_html=True)