app.py

from flask import Flask, request, jsonify
from flask.logging import create_logger
import logging
import traceback

import pandas as pd
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler

app = Flask(__name__)
LOG = create_logger(app)
LOG.setLevel(logging.INFO)


def scale(payload):
    """Scales Payload"""

    LOG.info("Scaling Payload: %s payload")
    scaler = StandardScaler().fit(payload)
    scaled_adhoc_predict = scaler.transform(payload)
    return scaled_adhoc_predict


@app.route("/")
def home():
    html = (
        "<h3>Sklearn Prediction Home: From Azure Pipelines (Continuous Delivery)</h3>"
    )
    return html.format(format)


# TO DO:  Log out the prediction value
@app.route("/predict", methods=["POST"])
def predict():
    """Performs an sklearn prediction

    input looks like:
            {
    "CHAS":{
      "0":0
    },
    "RM":{
      "0":6.575
    },
    "TAX":{
      "0":296.0
    },
    "PTRATIO":{
       "0":15.3
    },
    "B":{
       "0":396.9
    },
    "LSTAT":{
       "0":4.98
    }

    result looks like:
    { "prediction": [ 20.35373177134412 ] }

    """

    try:
        clf = joblib.load("boston_housing_prediction.joblib")
    except Exception as e:
        LOG.error("Error loading model: %s", str(e))
        LOG.error("Exception traceback: %s", traceback.format_exc())
    return "Model not loaded"

    json_payload = request.json
    LOG.info("JSON payload: %s json_payload")
    inference_payload = pd.DataFrame(json_payload)
    LOG.info("inference payload DataFrame: %s inference_payload")
    scaled_payload = scale(inference_payload)
    prediction = list(clf.predict(scaled_payload))
    return jsonify({"prediction": prediction})


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000, debug=True)