app.py

import streamlit as st
import pandas as pd
import pickle
# import model
model = pickle.load(open("model.pkl", "rb"))
#title
st.title("Predict Death Event")
st.write("Created by Sihar Pangaribuan")

# User imput
age = st.number_input(label='Age', min_value=40, max_value=95, value=40, step=1)
anaemia = st.selectbox(label='Anemia', options=['0', '1'])
creatinine_phosphokinase = st.number_input(label='Creatinine Phosphokinase', min_value=23, max_value=7861, value=23, step=1)
diabetes = st.selectbox(label='Diabetes', options=['0', '1'])
ejection_fraction = st.number_input(label='Ejection Fraction', min_value=14, max_value=80, value=14, step=1)
high_blood_pressure = st.selectbox(label='High Blood Pressure', options=['0', '1'])
platelets = st.number_input(label='Platelets', min_value=25100.0, max_value=850000.0, value=25100.0, step=1.0)
serum_creatinine = st.number_input(label='Serum Creatinine', min_value=0.5, max_value=9.4, value=0.5, step=0.1)
serum_sodium = st.number_input(label='Serum Sodium', min_value=133, max_value=148, value=133, step=1)
sex = st.selectbox(label='Sex', options=['0', '1'])
smoking = st.selectbox(label='Smoking', options=['0', '1'])
time = st.number_input(label='Time', min_value=4, max_value=285, value=4, step=1)

# Convert ke data frame
data = pd.DataFrame({'age': [age],
                'anemia': [anaemia],
                'creatinine_phosphokinase': [creatinine_phosphokinase],
                'diabetes':[diabetes],
                'ejection_fraction': [ejection_fraction],
                'high_blood_pressure': [high_blood_pressure],
                'platelets': [platelets],
                'serum_creatinine': [serum_creatinine],
                'serum_sodium': [serum_sodium],
                'sex': [sex],
                'smoking': [smoking],
                'time': [time]})

# model predict
death = model.predict(data).tolist()[0]

# interpretation
st.write('Predition Result: ')
if death == 0:
    st.text('live')
else:
    st.text('Death')