azure_function_prediction_function.py

import logging

import azure.functions as func
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib


def main(req: func.HttpRequest) -> func.HttpResponse:
    logging.info('Python HTTP trigger function processed a request.')
    if req.body:
        try:
            logging.info("Converting Request to DataFrame")
            req_body = req.get_json()
            df_body  = pd.DataFrame([req_body])

            logging.info("Loadding the Prediction Model")
            filename = "model.pckl"
            loaded_model = joblib.load(filename)
            # Features names need to have been added to the pickled model
            feature_names = loaded_model.feature_names
            # subselect only the feature names 
            
            logging.info("Subselecting the dataframe")
            df_subselect = df_body[feature_names]
            
            logging.info("Predicting the Probability")
            result = loaded_model.predict_proba(df_subselect)
            # We are looking at the probba prediction for class 1
            prediction = result[0][1]

            return func.HttpResponse("{prediction}".format(prediction=prediction), status_code=200)

        except ValueError:
            pass
    else:
        return func.HttpResponse(
             "Please pass a name on the query string or in the request body",
             status_code=400
        )