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')
#Loading in Admission chief Complaint and diagnosis
df2 = pd.read_csv('cohort_cc_adm_diag.csv')

#combining both data into one 
df = pd.merge(df, df2, on=['HADM_ID','SUBJECT_ID'])

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
AdmissionChiefCom = original_text['Admission_Chief_Complaint'].values
diagnosis =original_text['DIAGNOSIS'].values
reference_text = original_text['Reference_text'].values


##========= Buttons to the 4 tabs ========
col1, col2, col3, col4, col5 = st.columns([1,1,1,1,1])
col6, col7 =st.columns([2,2])
with st.container():
    with col1:
        btnAdmission = st.button("🏥 Admission")
        if btnAdmission:
            #nav_page('Admission')
            inputNote = "Input Admission Note"
    with col2:
        btnDailyNarrative = st.button('📆Daily Narrative')
        if btnDailyNarrative:
            inputNote = "Input Daily Narrative Note"
    with col3:
        btnDischargePlan = st.button('🗒️Discharge Plan')
        if btnDischargePlan:
            inputNote = "Input Discharge Plan"
    with col4:
        btnSocialNotes = st.button('📝Social Notes')
        if btnSocialNotes: 
            inputNote = "Input Social Note"
    with col5:
        btnPastHistory = st.button('📇Past History (6 Mths)')
        if btnPastHistory:          
            inputNote = "Input History records"
            
with st.container(): 
    if btnPastHistory:
        with col6: 
            st.markdown('**No. of admission past 6 months: xx**')
        with col7:
            st.date_input('Select Admission Date')
            
runtext =st.text_area(inputNote, str(original_text2), height=300)


# Extract words associated with each entity
def genEntities(ann, entity):
              # entity colour dict
              #ent_col = {'DISEASE':'#B42D1B', 'CHEMICAL':'#F06292'}
              ent_col = {'DISEASE':'pink', 'CHEMICAL':'orange'}
              # separate into the different entities
              entities = trans_df['Class'].unique()

              if entity in entities:
                             ent = list(trans_df[trans_df['Class']==entity]['Entity'].unique())
                             entlist = ",".join(ent)
                             st.markdown(f'<p style="background-color:{ent_col[entity]};color:#080808;font-size:16px;">{entlist}</p>', unsafe_allow_html=True)
                                                                       

def visualize (run_text,output):
    text =''
    splitruntext = [x for x in runtext.split('.')]
    splitoutput = [x for x in output.split('.')]
    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.success(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)

with col2:
    st.button('NER')
    st.markdown('**CHIEF COMPLAINT:**')
    st.write(str(AdmissionChiefCom))
    st.markdown('**ADMISSION DIAGNOSIS:**')
    st.markdown(str(diagnosis))
    st.markdown('**PROBLEM/ISSUE**')
    genEntities(trans_df, 'DISEASE')
    st.markdown('**MEDICATION**')
    genEntities(trans_df, 'CHEMICAL')
    #st.table(trans_df)   
    st.markdown('**NER**')
    st.markdown(ent_html, unsafe_allow_html=True)