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5.4 Serving the churn model with Flask

Slides

Notes

In this session, we talked about implementing the functionality of prediction to our churn web service and how to make it usable in development environment.

  • To make the web service predict the churn value for each customer we must modify the code in session 3 with the code we had in previous chapters. Below we can see how the code works in order to predict the churn value.
  • In order to predict we need to first load the previous saved model and use a prediction function in a special route.
    • To load the previous saved model we use the code below:
    • import pickle

with open('churn-model.bin', 'rb') as f_in:
  dv, model = pickle.load(f_in)
    • As we had earlier to predict a value for a customer we need a function like below:
    • def predict_single(customer, dv, model):
  X = dv.transform([customer])  ## apply the one-hot encoding feature to the customer data 
  y_pred = model.predict_proba(X)[:, 1]
  return y_pred[0]
    • Then at last we make the final function used for creating the web service.
    • @app.route('/predict', methods=['POST'])  ## in order to send the customer information we need to post its data.
def predict():
customer = request.get_json()  ## web services work best with json frame, So after the user post its data in json format we need to access the body of json.

prediction = predict_single(customer, dv, model)
churn = prediction >= 0.5

result = {
    'churn_probability': float(prediction), ## we need to cast numpy float type to python native float type
    'churn': bool(churn),  ## same as the line above, casting the value using bool method
}

return jsonify(result)  ## send back the data in json format to the user
    • The whole code above is available in this link: churn_serving.py
    • Finally, run your code. To see the result we can't use a simple request in web browser, because we are expecting a POST request in our app. We can run the code below to post customer data as json and see the response
    • ## a new customer informations
customer = {
  'customerid': '8879-zkjof',
  'gender': 'female',
  'seniorcitizen': 0,
  'partner': 'no',
  'dependents': 'no',
  'tenure': 41,
  'phoneservice': 'yes',
  'multiplelines': 'no',
  'internetservice': 'dsl',
  'onlinesecurity': 'yes',
  'onlinebackup': 'no',
  'deviceprotection': 'yes',
  'techsupport': 'yes',
  'streamingtv': 'yes',
  'streamingmovies': 'yes',
  'contract': 'one_year',
  'paperlessbilling': 'yes',
  'paymentmethod': 'bank_transfer_(automatic)',
  'monthlycharges': 79.85,
  'totalcharges': 3320.75
}
import requests ## to use the POST method we use a library named requests
url = 'http://localhost:9696/predict' ## this is the route we made for prediction
response = requests.post(url, json=customer) ## post the customer information in json format
result = response.json() ## get the server response
print(result)
  • Until here we saw how we made a simple web server that predicts the churn value for every user. When you run your app you will see a warning that it is not a WGSI server and not suitable for production environmnets. To fix this issue and run this as a production server there are plenty of ways available.
    • One way to create a WSGI server is to use gunicorn. To install it use the command pip install gunicorn, And to run the WGSI server you can simply run it with the command gunicorn --bind 0.0.0.0:9696 churn:app. Note that in churn:app the name churn is the name we set for the file containing the code app = Flask('churn')(for example: churn.py), You may need to change it to whatever you named your Flask app file.
    • Windows users may not be able to use gunicorn library because windows system do not support some dependecies of the library. So to be able to run this on a windows machine, there is an alternative library waitress and to install it, just use the command pip install waitress.
    • to run the waitress wgsi server use the command waitress-serve --listen=0.0.0.0:9696 churn:app.
    • To test it, you can run the code above and the result will be the same.
  • So until here you were able to make a production server that predicts the churn value for new customers. In the next session, we can see how to solve library version conflicts in each machine and manage dependencies for production environments.

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