BankChurn / app.py
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from flask import Flask, render_template, request
import os
import numpy as np
import pandas as pd
from mlProject.pipeline.stage06_prediction import PredictionPipeline
app=Flask(__name__)
@app.route('/',methods=['GET'])
def homepage():
return render_template("index.html")
@app.route('/train',methods=['GET'])
def training():
os.system("python main.py")
return "Training Successful"
@app.route('/predict',methods=['POST','GET']) # route to show the predictions in a web UI
def index():
if request.method == 'POST':
try:
# reading the inputs given by the user
CustomerId =int(request.form['CustomerId'])
CreditScore =int(request.form['CreditScore'])
Gender =int(request.form['Gender'])
Age =int(request.form['Age'])
Tenure =int(request.form['Tenure'])
Balance =float(request.form['Balance'])
NumOfProducts =int(request.form['NumOfProducts'])
HasCrCard =int(request.form['HasCrCard'])
IsActiveMember=int(request.form['IsActiveMember'])
EstimatedSalary =float(request.form['EstimatedSalary'])
Geography_Germany =int(request.form['Geography_Germany'])
Geography_Spain =int(request.form['Geography_Spain'])
data = [CustomerId,CreditScore,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Geography_Germany,Geography_Spain]
data = np.array(data).reshape(1, 12)
obj = PredictionPipeline()
predict = obj.predict(data)
result = "Likely to Churn" if predict == 1 else "Unlikely to Churn"
return result
except Exception as e:
print('The Exception message is: ',e)
return 'something is wrong'
else:
return render_template('index.html')
# Another trial
if __name__=="__main__":
# app.run(host="0.0.0.0", port=8080, debug=True)
app.run(host="0.0.0.0", port=8080)