app.py

import numpy as np
import pandas as pd
import xgboost
import pickle
import sklearn
from flask import Flask, render_template, request

app = Flask(__name__)

model = pickle.load(open('model.pkl', 'rb'))
preprocessor = pickle.load(open('preprocessor.pkl', 'rb'))

@app.route('/')
def Home():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    # Gathering inputs
    account_length = int(request.form.get('account_length'))
    international_plan = request.form.get('international_plan')
    vmail_message = int(request.form.get('vmail_message'))
    day_calls = int(request.form.get('day_calls'))
    day_charge = float(request.form.get('day_charge'))
    eve_charge = float(request.form.get('eve_charge'))
    night_charge = float(request.form.get('night_charge'))
    international_calls = int(request.form.get('international_calls'))
    international_charge = float(request.form.get('international_charge'))
    custServ_calls = int(request.form.get('custServ_calls'))

    inputs = inputs = pd.DataFrame(np.array([account_length, international_plan, vmail_message, day_calls, day_charge, eve_charge, night_charge, international_calls, international_charge, custServ_calls]).reshape(1, -1), 
                                    columns=['account_length', 'international_plan', 'vmail_message', 'day_calls',
                                    'day_charge', 'eve_charge', 'night_charge', 'international_calls','international_charge', 
                                    'custServ_calls'])

    input_processed = preprocessor.transform(inputs)

    prediction = model.predict(input_processed)

    # Generate churn risk scores
    churn_risk_scores = np.round(model.predict_proba(input_processed)[:, 1] * 100,2)

    # Churn flag
    if prediction == 1:
        prediction = 'YES'
    else:
        prediction = 'NO'

    return render_template('predict.html', prediction=prediction, churn_risk_scores=churn_risk_scores, inputs=request.form)

if __name__ == '__main__':
    app.run(debug=True)