Home.py

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"churn_predictor.sav")

#Import python scripts
from preprocessing import preprocess
import base64


@st.cache(allow_output_mutation=True)
def get_base64_of_bin_file(bin_file):
    with open(bin_file, 'rb') as f:
        data = f.read()
    return base64.b64encode(data).decode()


def set_png_as_page_bg(png_file):
    bin_str = get_base64_of_bin_file(png_file)
    page_bg_img = '''
    <style>
    .stApp {
    background-image: url("data:image/png;base64,%s");
    min-height: 200px;
    background-attachment: scroll;
    background-position: 300px 1800px;
    background-size: cover;
    }
    </style>
    ''' % bin_str

    st.markdown(page_bg_img, unsafe_allow_html=True)
    return

def main():
    #Setting Application title
    st.title('WillTheyStay.ai')
      #Setting Application description
    st.markdown("""
     :dart:  WillTheyStay.ai is an open source streamlit app made to predict customer churn in a ficitional telecommunication use case.
    The application is functional for both online prediction and batch data prediction.
    """)
    st.markdown("<h3></h3>", unsafe_allow_html=True)

    set_png_as_page_bg('joakim-honkasalo-ssvjJLB6wIw-unsplash.jpg')

    #Setting Application sidebar default
    #image = Image.open('App.jpg')
    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")
        #Based on our optimal features selection
        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 the 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("<h3></h3>", unsafe_allow_html=True)
        st.write('Overview of input is shown below')
        st.markdown("<h3></h3>", unsafe_allow_html=True)
        st.dataframe(features_df)
        #Preprocess inputs
        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.subheader("Dataset upload")
        uploaded_file = st.file_uploader("Choose a file")
        if uploaded_file is not None:
            data = pd.read_csv(uploaded_file)
            #Get overview of data
            st.write(data.head())
            st.markdown("<h3></h3>", unsafe_allow_html=True)
            #Preprocess inputs
            preprocess_df = preprocess(data, "Batch")
            if st.button('Predict'):
            #Get batch prediction
                prediction = model.predict(preprocess_df)
                prediction_df = pd.DataFrame(prediction, columns=["Predictions"])
                prediction_df = prediction_df.replace({1:'Yes, the customer will terminate the service.',
                                                        0:'No, the customer is happy with Telco Services.'})

                st.markdown("<h3></h3>", unsafe_allow_html=True)
                st.subheader('Prediction')
                st.write(prediction_df)

if __name__ == '__main__':
        main()