app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib
import ast

st.title('Heart Failure Prediction')

with open('model_scaler.pkl', 'rb') as file_1:
  model_scaler = joblib.load(file_1)

with open('rf_best_model.pkl', 'rb') as file_2:
  rf_random_best = joblib.load(file_2)

with open('list_num_col.txt', 'r') as file_3:
  num_col = file_3.read()

with open('list_cat_col.txt', 'r') as file_4:
  cat_col = file_4.read()

with open('boxcox.pkl', 'rb') as file_5:
  box = joblib.load(file_5)

num_col = ast.literal_eval(num_col)
cat_col = ast.literal_eval(cat_col)


# time, ejection_fraction, age, serum creatinine, serum_sodium, creatinine_phosphokinase, diabetes, high_blood_pressure, smoking

time = st.slider('Follow up perriod (days) : ',min_value=1,max_value=365,step=1)
age = st.slider('Age : ', 30,120,step=1)
ejection_fraction = st.number_input('Ejection Fraction : ',min_value=1,max_value=100,step=1)
serum_creatinine = st.number_input('Serum Creatinine : ',min_value=0.0,max_value=10.0,step=0.01)
serum_sodium = st.number_input('Serum Sodium : ',min_value=100,max_value=150,step=1)
anaemia = st.radio('Anemia (1=Yes, 0=NO) : ',(1, 0))
diabetes = st.radio('Diabetes (1=Yes, 0=NO) : ',(1, 0))
smoking = st.radio('Smoking (1=Yes, 0=NO) : ',(1, 0))

if st.button('Predict'):

    data_inf = pd.DataFrame({'time':time,
                         'ejection_fraction':ejection_fraction,
                         'age':age,
                         'serum_creatinine':serum_creatinine,
                         'serum_sodium':serum_sodium,
                         'anaemia':anaemia,
                         'diabetes':diabetes,
                         'smoking':smoking
    },index=[0])

    data_inf_t = box.transform(data_inf)
    data_inf_num = data_inf_t[num_col]
    data_inf_cat = data_inf_t[cat_col]

    data_inf_num_scaled = data_inf_num.copy()
    data_inf_num_scaled[num_col] = model_scaler.transform(data_inf_num[num_col])
    data_inf_cat_encoded = data_inf_cat.copy()
    data_inf_num_scaled

    data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)

    result = rf_random_best.predict(data_inf_final)[0]
    st.write('Predicted Result:')
    st.header(result)