app.py

from distutils.command.upload import upload
import streamlit as st
import pandas as pd
import numpy as np
from PIL import Image

#load the model from disk
import joblib
model = joblib.load(r"./notebook/model.sav")

from preprocessing import preprocess

def main():
    st.title("Customer Churn Prediction App")
    st.markdown("""
     :dart:  This Streamlit app is made to predict customer churn in a ficitional telecommunication use case.
    The application is functional for both online prediction and batch data prediction. \n
    """)
    st.markdown('<h3></h3>',unsafe_allow_html=True)

    image = Image.open('customer.png')
    add_selectbox = st.sidebar.selectbox(
        "How would you like to predict?",
        ("Online","Batch")
        )
    st.sidebar.info("This app is created to predict Customer Churn")
    st.sidebar.image(image)

    if add_selectbox == "Online":
        st.info("Input Data below")

        st.subheader("Demographic Data")
        seniorcitizen = st.selectbox('Senior Citizen:',('Yes','No'))
        dependents = st.selectbox('Dependent:',('Yes','No'))

        st.subheader("Payment data")
        tenure = st.slider('Number of Months Customer has Stayed with the company',min_value=0,max_value=72,value=0)
        contract = st.selectbox('Contract', ('Month-to-month', 'One year', 'Two year'))
        paperlessbilling = st.selectbox('Paperless Billing', ('Yes', 'No'))
        PaymentMethod = st.selectbox('PaymentMethod',('Electronic check', 'Mailed check', 'Bank transfer (automatic)','Credit card (automatic)'))
        monthlycharges = st.number_input('The amount charged to the customer monthly', min_value=0, max_value=150, value=0)
        totalcharges = st.number_input('The total amount charged to the customer',min_value=0, max_value=10000, value=0)

        st.subheader("Services signed up for")
        mutliplelines = st.selectbox("Does the customer have multiple lines",('Yes','No','No phone service'))
        phoneservice = st.selectbox('Phone Service:', ('Yes', 'No'))
        internetservice = st.selectbox("Does the customer have internet service", ('DSL', 'Fiber optic', 'No'))
        onlinesecurity = st.selectbox("Does the customer have online security",('Yes','No','No internet service'))
        onlinebackup = st.selectbox("Does the customer have online backup",('Yes','No','No internet service'))
        techsupport = st.selectbox("Does the customer have technology support", ('Yes','No','No internet service'))
        streamingtv = st.selectbox("Does the customer stream TV", ('Yes','No','No internet service'))
        streamingmovies = st.selectbox("Does the customer stream movies", ('Yes','No','No internet service'))

        data = {
                'SeniorCitizen': seniorcitizen,
                'Dependents': dependents,
                'tenure':tenure,
                'PhoneService': phoneservice,
                'MultipleLines': mutliplelines,
                'InternetService': internetservice,
                'OnlineSecurity': onlinesecurity,
                'OnlineBackup': onlinebackup,
                'TechSupport': techsupport,
                'StreamingTV': streamingtv,
                'StreamingMovies': streamingmovies,
                'Contract': contract,
                'PaperlessBilling': paperlessbilling,
                'PaymentMethod':PaymentMethod, 
                'MonthlyCharges': monthlycharges, 
                'TotalCharges': totalcharges
                }
        features_df = pd.DataFrame.from_dict([data])
        st.markdown("<br>", unsafe_allow_html=True)
        st.info('Overview of input is shown below')
        st.markdown("<br>", unsafe_allow_html=True)
        st.dataframe(features_df)

        preprocess_df = preprocess(features_df,'Online')
        prediction = model.predict(preprocess_df)

        if st.button('Predict'):
            if prediction == 1:
                st.warning('Yes, the customer will terminate the service.')
            else:
                st.success('No, the customer is happy with Telco Services.')
    else:
        st.header("Dataset Upload")
        upload_file = st.file_uploader("Choose a file")
        if upload_file is not None:
            data = pd.read_csv(upload_file)
            st.write(data.head())
            st.markdown("<br>", unsafe_allow_html=True)   
            preprocess_df = preprocess(data,"Batch")
            if st.button('Predict'):
                prediction = model.predict(preprocess_df)
                prediction_df = pd.DataFrame(prediction,columns=["Predictions"])
                prediction_df = prediction_df.replace({1:"Yes,The Customer will terminate service",0:'No, the customer is happy with Telco Services.'})
                st.markdown("<br>", unsafe_allow_html=True)
                st.subheader('Prediction')
                st.write(prediction_df)



if __name__ == '__main__':
    main()