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
from spacy.lang.en import English
import en_ner_bc5cdr_md


from streamlit.components.v1 import html


# Store the initial value of widgets in session state
if "visibility" not in st.session_state:
    st.session_state.visibility = "visible"
    st.session_state.disabled = False

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

st.set_page_config(page_title ='Clinical Note Summarization', 
                   #page_icon= "Notes",
                   layout='wide')
st.title('Clinical Note Summarization')
st.markdown(
    """
    <style>
    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
        width: 400px;
    }
    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
        width: 400px;
        margin-left: -230px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)
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} ")
runtext = ''
inputNote  ='Input note here:'
# Query out relevant Clinical notes
original_text =  df.query(
    "Patient_ID  == @patient & Admission_ID == @HospitalAdmission"
)
original_text2 = original_text['Original_Text'].values
reference_text = original_text['Reference_text'].values

##========= Buttons to the 4 tabs ========
col1, col2, col3, col4 = st.columns(4)

with col1:
    if st.button("🏥 Admission"):
        #nav_page('Admission')
        inputNote = "Input Admission Note"
    
with col2:
    if st.button('📆Daily Narrative'):
        #nav_page('Daily Narrative')
        inputNote = "Input Daily Narrative Note"
with col3:
    if st.button('🗒️Discharge Plan'):
        #nav_page('Discharge Plan')  
        inputNote = "Input Discharge Plan"
with col4:
    if st.button('📝Social Notes'):
        #nav_page('Social Notes')
        inputNote = "Input Social Note"

runtext =st.text_area(inputNote, str(original_text2), height=300)


def visualize (run_text,output):
    text =''
    splitruntext = [x for x in runtext.split('.')]
    splitoutput = [x for x in output.split('.')]
#    best_sentences = []
#    for sentence in output:
#       best_sentences.append(str(sentence))

#    text = ''

#    #display(HTML(f'<h1>Summary - {title}</h1>'))
#    for sentence in run_text:
#        if sentence in best_sentences:
#            text += ' ' + str(sentence).replace(sentence, f"<mark>{sentence}</mark>")
#        else:
#            text += ' ' + sentence
    #    display(HTML(f""" {text} """))
    return splitoutput,splitruntext
    

def run_model(input_text):    
    if model == "BertSummarizer":
        output = original_text['BertSummarizer'].values
        st.write('Summary')

    elif model == "BertGPT2":
        output = original_text['BertGPT2'].values
        st.write('Summary')
        
      
    elif model == "t5seq2eq": 
        output = original_text['t5seq2eq'].values
        st.write('Summary')
         
    elif model == "t5":
        output = original_text['t5'].values
        st.write('Summary')
        
    elif model == "gensim": 
        output = original_text['gensim'].values
        st.write('Summary')
        
    elif model == "pysummarizer": 
        output = original_text['pysummarizer'].values
        st.write('Summary')

    #st.text_area(visualize (runtext,output))
    st.success(output)
   # return output
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)

col1, col2 = st.columns([1,1])
with col1:
    st.button('Summarize')
    run_model(runtext)
    sentences=runtext.split('.')
    st.text_area('Reference text', str(reference_text), height=150)
    ##====== Storing the Diseases/Text
    table= {"Entity":[], "Class":[]}
    ent_bc = {}
    for x in doc.ents:
        ent_bc[x.text] = x.label_
    for key in ent_bc:
        table["Entity"].append(key)
        table["Class"].append(ent_bc[key])
    trans_df = pd.DataFrame(table)
    st.table(trans_df)    

with col2:
    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) 
    #st.write(doc.ents)