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import streamlit as st
import requests
from PIL import Image
image_path = 'images/image.jpg'
image = Image.open(image_path)
# Set API Endpoint
URL = 'https://radiant-lowlands-86946.herokuapp.com/predict'
# Create a function to make prediction
def make_prediction(pg: float, bwr1: float, bp : float, bwr2: float, bwr3: float, bmi: float, bwr4: float, age: int, insurance: bool):
parameters={
'plasma_glucose':pg,
'blood_work_result_1':bwr1,
'blood_pressure':bp,
'blood_work_result_2':bwr2,
'blood_work_result_3':bwr3,
'body_mass_index':bmi,
'blood_work_result_4':bwr4,
'age':int(age),
'insurance':bool(insurance)}
response = requests.post(URL, params=parameters)
response_text = response.json()
sepsis_status = response_text['results'][0]['0']['output']['Predicted Label']
return sepsis_status
# set page configuration
st.set_page_config(
page_title='Sepsis Prediction',
page_icon="🤖",
initial_sidebar_state="expanded",
menu_items={
'About': "# This is a Health App. Call it the Covid Vaccine Sepsis Analyzer!"
}
)
# create a sidebar and contents
st.sidebar.markdown("""
## Demo App
This app return sepsis status base on the input parameters
""")
st.markdown('''
<h1 style="color: green; text-align:center">The Sepsis Prediction App</h1>
''', unsafe_allow_html=True)
# insert an image
st.image(image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
# Create app interface
container = st.container()
container.write("Inputs to predict Sepsis")
with container:
col1, col2, col3 = st.columns(3)
age = col1.number_input(label='Age')
pg = col2.number_input(label='Blood Glucose')
bp = col3.number_input(label='Blood Pressure')
with st.expander(label='Blood Parameter', expanded=True, ):
bwr1 = col1.number_input(label='Blood Work Result-1')
bwr2 = col2.number_input(label='Blood Work Result-2')
bwr3 = col1.number_input(label='Blood Work Result-3')
bwr4 = col2.number_input(label='Blood Work Result-4')
ins = col3.selectbox(label='Insurance', options=[True, False])
bmi = col3.number_input(label='Body Mass Index')
button = st.button(label='Predict', type='primary', use_container_width=True)
if button:
response = make_prediction(pg, bwr1, bp, bwr2, bwr3, bmi, bwr4, age, ins)
st.metric(label='Status', value=f'The {response}')
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