Spaces:
Build error
Build error
File size: 4,111 Bytes
7a7a355 848aa9e ef39de3 2b534d2 0538599 2b534d2 0538599 dd83707 2b534d2 7a7a355 c40f512 7a7a355 5117447 7a7a355 d5549f8 7a7a355 538934b fbe0fb3 d1bb5a0 4900bce 3dfbb45 d1bb5a0 3dfbb45 d1bb5a0 b4adba1 d1bb5a0 3dfbb45 7a7a355 df85b28 7a7a355 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
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
# 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} ")
##========= Buttons to the 4 tabs ========
col1, col2, col3, col4 = st.columns(4)
with col1:
# st.button('Admission')
st.button("🏥 Admission")
with col2:
st.button('📆Daily Narrative')
with col3:
st.button('Discharge Plan')
with col4:
st.button('📝Social Notes')
# 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)
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))#,label_visibility="hidden")
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)
|