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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
# Load all files
with open('list_num_cols.txt', 'r') as file_1:
list_num_cols = json.load(file_1)
with open('model_scaler.pkl', 'rb') as file_2:
scaler = pickle.load(file_2)
with open('model_rfc.pkl', 'rb') as file_3:
model_rfc = pickle.load(file_3)
with open('model_gbc.pkl', 'rb') as file_4:
model_gbc = pickle.load(file_4)
def run():
st.write('##### Form Prediction of Death Due To Heart Failure')
# Making Form
with st.form(key='Form Form Prediction of Death Due To Heart Failure'):
age = st.number_input('age', min_value=30, max_value=90, value=30)
anaemia = st.selectbox('anaemia', (0,1), index=0, help='0=No, 1=Yes | Decrease of red blood cells or hemogrobin.')
creatinine_phosphokinase = st.number_input('creatinine_phosphokinase', min_value=50, max_value=6000, value=50, step=1, help='Level of the CPK enzyme in the blood (mcg/L).')
diabetes = st.selectbox('diabetes', (0,1), index=0, help='0=No, 1=Yes | If the patient has diabetes.')
ejection_fraction = st.number_input('ejection_fraction', min_value=1, max_value=100, value=1, help='Percentage of blood leaving the heart at each contraction (percentage).')
high_blood_pressure = st.selectbox('high_blood_pressure', (0,1), index=0, help='0=No, 1=Yes | If the patient has hypertention.')
platelets = st.number_input('platelets', min_value=40000, max_value=500000, value=40000, help='Platelets in the blood (kiloplatelets/mL).')
serum_creatinine = st.number_input('serum_creatinine', min_value=0, max_value=10, value=0, help='Level of serum creatinine in the blood (mg/dL).')
serum_sodium = st.number_input('serum_sodium', min_value=100, max_value=150, value=100, help='Level of serum sodium in the blood (mEq/L).')
sex = st.selectbox('sex', (0,1), index=0, help='0=Woman, 1=Man')
smoking = st.selectbox('smoking', (0,1), index=0, help='0=No, 1=Yes | If the patient smokes or not.')
time = st.number_input('time', min_value=1, max_value=300, value=1, help='Follow-up period (days).')
st.markdown('---')
submited_1 = st.form_submit_button('Predict using RFC')
submited_2 = st.form_submit_button('Predict using GBC')
data_inf = {
'age' : age,
'anaemia' : 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,
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submited_1:
# Split between numerical columns and categorical columns
data_inf_num = data_inf[list_num_cols]
data_inf_num
#Feature scaling and feature encoding
data_inf_num_scaled = scaler.transform(data_inf_num)
data_inf_final = data_inf_num_scaled
#Predict using Random Forest Classification
y_pred_inf = model_rfc.predict(data_inf_final)
st.write('# Result Prediction using RFC : ', str(int(y_pred_inf)))
else:
# Split between numerical columns and categorical columns
data_inf_num = data_inf[list_num_cols]
data_inf_num
#Feature scaling and feature encoding
data_inf_num_scaled = scaler.transform(data_inf_num)
data_inf_final = data_inf_num_scaled
#Predict using RFC
y_pred_inf = model_gbc.predict(data_inf_final)
st.write('# Result Prediction using GBC : ', str(int(y_pred_inf)))
if __name__ == '__main__':
run() |