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import streamlit as st
import pandas as pd
import requests
import os
from io import StringIO
import datetime
# Set page configuration
st.set_page_config(page_title="Predict", page_icon="🔮", layout="wide")
st.title("Predict Sepsis")
def select_model():
col1, col2 = st.columns(2)
with col1:
choice = st.selectbox('Select a model', options=['xgboost', 'random_forest'], key='select_model')
with col2:
pass
# if st.session_state['selected_model'] == 'Xgboost':
# pipeline = xgboost_pipeline()
# else:
# pipeline = load_random_forest_pipeline()
# encoder = joblib.load('models/encoder.joblib')
return choice
def make_prediction():
selected_model = st.session_state['select_model']
age = st.session_state['age']
insurance = 1 if st.session_state['insurance'] == 'Yes' else 0
m11 = st.session_state['m11']
pr = st.session_state['pr']
prg = st.session_state['prg']
ts = st.session_state['ts']
pl = st.session_state['pl']
sk = st.session_state['sk']
bd2 = st.session_state['bd2']
base_url = 'https://gabcares-team-curium.hf.space/'
url = base_url + f"{ 'xgboost_prediction' if selected_model=='xgboost' else 'random_forest_prediction'}"
data = {'PRG': prg, 'PL': pl, 'PR': pr,'SK': sk, 'TS': ts, 'M11': m11, 'BD2': bd2, 'Age': age, 'Insurance': insurance}
# Send POST request with JSON data using the json parameter
response_status = requests.get(base_url)
if (response_status.status_code == 200):
response = requests.post(url, json=data,timeout=30)
pred_prob = (response.json()['result'])
prediction = pred_prob['prediction']
probability = pred_prob['probability']
st.session_state['prediction'] = prediction
st.session_state['probability'] = probability
else:
st.write('Unable to connect to the server.')
# Creating the form
def display_form():
select_model()
with st.form('input_features'):
col1, col2 = st.columns(2)
with col1:
st.write('### Patient Demographics')
age = st.number_input('Age', min_value=0, max_value=100, step=1, key = 'age')
insurance = st.selectbox('Insurance', options = ['Yes', 'No'], key = 'insurance')
st.write('### Vital Signs')
m11 = st.number_input('BMI', min_value=10.0, format="%.2f",step = 1.00, key = 'm11')
pr = st.number_input('Blood Pressure', min_value=10.0, format="%.2f",step = 1.00, key = 'pr')
prg = st.number_input('PRG(plasma glucose)', min_value=10.0, format="%.2f",step = 1.00, key = 'prg')
with col2:
st.write('### Blood Work')
pl = st.number_input('PL(Blood Work Result 1)', min_value=10.0, format="%.2f",step = 1.00, key = 'pl')
sk = st.number_input('SK(Blood Work Result 2)', min_value=10.0, format="%.2f",step = 1.00, key = 'sk')
ts = st.number_input('TS(Blood Work Result 3)', min_value=10.0, format="%.2f",step = 1.00, key = 'ts')
bd2 = st.number_input('BD2(Blood Work Result 4)', min_value=10.0, format="%.2f",step = 1.00, key = 'bd2')
st.form_submit_button('Submit', on_click=make_prediction)
if __name__ == '__main__':
display_form()
final_prediction = st.session_state.get('prediction')
final_probability = st.session_state.get('probability')
if final_prediction is None:
st.write('Predictions show here!')
st.divider()
else:
if final_prediction.lower() == 'positive':
st.markdown(f'### Patient is likely to develop sepsis😞.')
st.markdown(f'## Probability: {final_probability:.2f}%')
else:
# st.markdown(f'## Sepsis: {final_prediction}')
st.markdown(f'### Patient is unlikely to develop sepsis😊.')
st.markdown(f'## Probability: {final_probability:.2f}%') |