app.py

import streamlit as st 
import numpy as np 
import tensorflow as tf 
from sklearn.preprocessing import StandardScaler,LabelEncoder
from tensorflow.keras.models import load_model
import pickle
import pandas as pd 
 
model = load_model('model.h5')

with open ('label_encoder_gender.pkl','rb') as file :
    label_encoder = pickle.load(file)
with open('scaler.pkl','rb') as file:
    scaler = pickle.load(file)

with open('onehot_encoder_geo.pkl','rb') as file :
    ohe = pickle.load(file)
 
st.title('Customer Churn Production')
geography = st.selectbox('Geography', ohe.categories_[0])
gender = st.selectbox('Gender', label_encoder.classes_)
age = st.slider('Age', 18, 92)
balance = st.number_input('Balance')
credit_score = st.number_input('Credit Score')
estimated_salary = st.number_input('Estimated Salary')
tenure = st.slider('Tenure', 0, 10)
num_of_products = st.slider('Number of Products', 1, 4)
has_cr_card = st.selectbox('Has Credit Card', [0, 1])
is_active_member = st.selectbox('Is Active Member', [0, 1])


input_data = pd.DataFrame({
    'CreditScore': [credit_score],
    'Gender': [label_encoder.transform([gender])[0]],
    'Age': [age],
    'Tenure': [tenure],
    'Balance': [balance],
    'NumOfProducts': [num_of_products],
    'HasCrCard': [has_cr_card],
    'IsActiveMember': [is_active_member],
    'EstimatedSalary': [estimated_salary]
})

geo_encoded = ohe.transform([[geography]]).toarray()
geo_encoded_df = pd.DataFrame(geo_encoded,columns=ohe.get_feature_names_out(['Geography']))

input_df = pd.concat([input_data.reset_index(drop=True),geo_encoded_df],axis = 1)
ip_arr = scaler.transform(input_df)
prediction = model.predict(ip_arr)
st.write(prediction)
if prediction > 0.5 :
    st.write('The customer is likely to churn ')
else:
    st.write('The customer is not likely to churn')