app.py

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

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

from tensorflow.keras.models import load_model
model_ann = load_model('churn_model.h5')

st.title("Customer Churn Prediction")

membership_category = st.selectbox('Membership Category',('No Membership',
                                                         'Basic Membership',
                                                         'Silver Membership',
                                                         'Premium Membership',
                                                         'Gold Membership',
                                                         'Platinum Membership'), index=1)
avg_transaction_value = st.number_input('Average Transaction Value :',
                                       min_value = 800.460000,
                                       max_value = 99914.050000,
                                       value = 800.460000)
points_in_wallet = st.number_input('Points In Wallet :',
                                  min_value = 0.000000,
                                  max_value = 2069.069761,
                                  value = 0.000000)
feedback = st.selectbox('Feedback',('Poor Website',
                                   'Poor Customer Service',
                                   'Too many ads',
                                   'Poor Product Quality',
                                   'No reason specified',
                                   'Products always in Stock',
                                   'Reasonable Price',
                                   'Quality Customer Care',
                                   'User Friendly Website'), index=1)

df_inf = pd.DataFrame({
    'membership_category' : [membership_category],
    'avg_transaction_value' : [avg_transaction_value],
    'points_in_wallet' : [points_in_wallet],
    'feedback' : [feedback]
})

if st.button('Predict'):
    data_inf_transform = model_pipeline.transform(df_inf)
    y_pred_inf = model_ann.predict(data_inf_transform)
    y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
    churn_status = np.where(y_pred_inf == 0, "No", "Yes")

    if churn_status == "No":
        st.success(f"The customer is predicted to `not churn`.")
    else:
        st.error(f"The customer is predicted to `churn`.")