predicao_churn.py

import pickle

import pandas as pd
from flask import Flask, render_template, request

# molezinha, só tem que setar as pastas de template e assets
app = Flask(__name__, template_folder='template', static_folder='template/assets')

# Treina lá, usa cá
modelo_pipeline = pickle.load(open('./models/pipe.pkl', 'rb'))

@app.route('/')
def home():
    return render_template("homepage.html")


@app.route('/dados_cliente')
def dados_cliente():
    return render_template("form.html")


def get_data():
    tenure = request.form.get('tenure')
    MonthlyCharges = request.form.get('MonthlyCharges')
    TotalCharges = request.form.get('TotalCharges')
    gender = request.form.get('gender')
    SeniorCitizen = request.form.get('SeniorCitizen')
    Partner = request.form.get('Partner')
    Dependents = request.form.get('Dependents')
    PhoneService = request.form.get('PhoneService')
    MultipleLines = request.form.get('MultipleLines')
    InternetService = request.form.get('InternetService')
    OnlineSecurity = request.form.get('OnlineSecurity')
    OnlineBackup = request.form.get('OnlineBackup')
    DeviceProtection = request.form.get('DeviceProtection')
    TechSupport = request.form.get('TechSupport')
    StreamingTV = request.form.get('StreamingTV')
    StreamingMovies = request.form.get('StreamingMovies')
    Contract = request.form.get('Contract')
    PaperlessBilling = request.form.get('PaperlessBilling')
    PaymentMethod = request.form.get('PaymentMethod')

    d_dict = {'tenure': [tenure], 'MonthlyCharges': [MonthlyCharges], 'TotalCharges': [TotalCharges],
              'gender': [gender], 'SeniorCitizen': [SeniorCitizen], 'Partner': [Partner],
              'Dependents': [Dependents], 'PhoneService': [PhoneService],
              'MultipleLines': [MultipleLines], 'InternetService': [InternetService],
              'OnlineSecurity': [OnlineSecurity], 'OnlineBackup': [OnlineBackup],
              'DeviceProtection': [DeviceProtection], 'TechSupport': [TechSupport],
              'StreamingTV': [StreamingTV], 'StreamingMovies': [StreamingMovies],
              'Contract': [Contract], 'PaperlessBilling': [PaperlessBilling],
              'PaymentMethod': [PaymentMethod]}

    return pd.DataFrame.from_dict(d_dict, orient='columns')


@app.route('/send', methods=['POST'])
def show_data():
    df = get_data()
    df = df[['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
       'MonthlyCharges', 'TotalCharges']]

    prediction = modelo_pipeline.predict(df)
    outcome = 'DANGER!!! VAI VAZAR! TELEMARKETING NELE!!'
    imagem = 'chefe_brabo.jpg'
    if prediction == 0:
        outcome = 'Ufa... esse cliente vai ficar!! Aproveita pra entubar uns serviços novos!'
        imagem = 'chefe_feliz.jpg'

    return render_template('result.html', tables=[df.to_html(classes='data', header=True, col_space=10)],
                           result=outcome, imagem=imagem)


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