app.py

import pandas as pd
import numpy as np
import joblib
from flask import Flask,request,jsonify

app=Flask(__name__)

model=joblib.load('Churn_model_v1.joblib')

# let us create endpoint for home

@app.get('/')
def home():
  return "Welcome to the Backend API for Customer Churn Prediction"

# let us create endpoint for predict data record

@app.post('/Predict/Data')
def predict_data():
  data=request.get_json()

  user_input={ 'CreditScore':data['CreditScore'],
               'Geography':data['Geography'],
               'Age':data['Age'],
               'Tenure':data['Tenure'],
               'Balance':data['Balance'],
               'NumOfProducts':data['NumOfProducts'],
               'HasCrCard':data['HasCrCard'],
               'IsActiveMember':data['IsActiveMember'],
               'EstimatedSalary':data['EstimatedSalary']
                }

  df_user_input=pd.DataFrame([user_input])
  prediction1=model.predict_proba(df_user_input)[:,1]>0.21
  prediction=prediction1.tolist()[0]
  predict_label='Churn' if prediction==1 else 'Not Churn'

  return jsonify({'predict_label':predict_label})


# let us create endpoint for predicting batch data

@app.post('/Predict/Batch')
def predict_batch():

  file1=request.files['file']
  df_file=pd.read_csv(file1)
  prediction1=model.predict_proba(df_file.drop(['CustomerId','Surname'],axis=1))[:,1]>0.21
  prediction=prediction1.tolist()
  predictionlist=['Churn' if x==1 else 'Not Churn' for x in prediction]
  listofids=df_file.CustomerId.values.tolist()
  dictionary1=dict(zip(listofids,predictionlist))
  return dictionary1

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