delta_lake_bring_your_own_container_local_training_and_serving/container/catboost_regressor/predictor.py [46:79]:
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app = flask.Flask(__name__)

@app.route('/ping', methods=['GET'])
def ping():
    """Determine if the container is working and healthy. In this sample container, we declare
    it healthy if we can load the model successfully."""
    health = ScoringService.get_model() is not None  # You can insert a health check here

    status = 200 if health else 404
    return flask.Response(response='\n', status=status, mimetype='application/json')

@app.route('/invocations', methods=['POST'])
def transformation():
    """Do an inference on a single batch of data. In this sample server, we take data as CSV, convert
    it to a pandas data frame for internal use and then convert the predictions back to CSV (which really
    just means one prediction per line, since there's a single column.
    """
    data = None

    # Convert from CSV to pandas
    if flask.request.content_type == 'text/csv':
        data = flask.request.data.decode('utf-8')
        s = StringIO(data)
        data = pd.read_csv(s, header=None)
    else:
        return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')

    print('Invoked with {} records'.format(data.shape[0]))

    # Do the prediction
    predictions = ScoringService.predict(data)

    # Convert from numpy back to CSV
    out = StringIO()
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prophet_bring_your_own_container_local_training_and_serving/container/prophet/predictor.py [54:89]:
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app = flask.Flask(__name__)


@app.route('/ping', methods=['GET'])
def ping():
    """Determine if the container is working and healthy. In this sample container, we declare
    it healthy if we can load the model successfully."""
    health = ScoringService.get_model() is not None  # You can insert a health check here

    status = 200 if health else 404
    return flask.Response(response='\n', status=status, mimetype='application/json')


@app.route('/invocations', methods=['POST'])
def transformation():
    """Do an inference on a single batch of data. In this sample server, we take data as CSV, convert
    it to a pandas data frame for internal use and then convert the predictions back to CSV (which really
    just means one prediction per line, since there's a single column.
    """
    data = None

    # Convert from CSV to pandas
    if flask.request.content_type == 'text/csv':
        data = flask.request.data.decode('utf-8')
        s = StringIO(data)
        data = pd.read_csv(s, header=None)
    else:
        return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')

    print('Invoked with {} records'.format(data.shape[0]))

    # Do the prediction
    predictions = ScoringService.predict(data)

    # Convert from numpy back to CSV
    out = StringIO()
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