app.py

import streamlit as st
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
import pandas as pd
import pickle


#Load the trained models
model=tf.keras.models.load_model('model.h5')

# Load the encoders and scaler
with open('label_encoder_gender.pkl', 'rb') as file:
    label_encoder_gender=pickle.load(file)
    
with open('onehot_encoder_geo.pkl', 'rb') as file:
    onehot_encoder_geo=pickle.load(file)
    
with open('scaler.pkl', 'rb') as file:
    scaler=pickle.load(file)
    
    
    
## Streamlit app
st.title("Customer churn Prediction")

## user input
geography=st.selectbox('Geography', onehot_encoder_geo.categories_[0])
gender=st.selectbox('Gender',label_encoder_gender.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])

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

# one-hot encode 'Geography'
geo_encoded=onehot_encoder_geo.transform([[geography]]).toarray()
geo_encoder_df=pd.DataFrame(geo_encoded, columns=onehot_encoder_geo.get_feature_names_out(['Geography']))


# input_data=pd.DataFrame([input_data])
input_data=pd.concat([input_data.reset_index(drop=True),geo_encoder_df], axis=1)

# Scale the input data
input_scaled=scaler.transform(input_data)

## Predict churn
prediction=model.predict(input_scaled)
prediction

prediction_probabillity=prediction[0][0]
st.write(f'Probability churn: {prediction_probabillity} and {balance}')
if prediction_probabillity>0.5:
    st.write("The Cutomer is likely to churn")
else:
    st.write("The customer is not likely to churn")