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import streamlit as st
import pandas as pd
import numpy as np
from math import ceil
from collections import Counter
from string import punctuation
#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('Submit'):
run_model(runtext)
sentences=runtext.split('.')
def visualize(title, sentence_list, best_sentences):
text = ''
#display(HTML(f'<h1>Summary - {title}</h1>'))
for sentence in sentence_list:
if sentence in best_sentences:
#text += ' ' + str(sentence).replace(sentence, f"<mark>{sentence}</mark>")
text += ' ' + str(sentence).replace(sentence, f"<span class='highlight yellow'>{sentence}</span>")
else:
text += ' ' + sentence
display(HTML(f""" {text} """))
output = ''
best_sentences = []
for sentence in output:
#print(sentence)
best_sentences.append(str(sentence))
return text
t = "<div>Hello there my <span class='highlight blue'>name <span class='bold'>yo</span> </span> is <span class='highlight red'>Fanilo <span class='bold'>Name</span></span></div>"
st.write("<div>Hello there my <span class='highlight blue'>name <span class='bold'>yo</span> </span> is <span class='highlight red'>Fanilo <span class='bold'>Name</span></span></div>")
st.text_area('Reference text', str(reference_text)) |