# Copyright (c) Microsoft Corporation.

# Licensed under the MIT license.

import os

import logging

import json

import joblib

from feast import FeatureStore, RepoConfig

from feast.repo_config import RegistryConfig

from feast.infra.offline_stores.contrib.mssql_offline_store.mssql import (

MsSqlServerOfflineStoreConfig,

)

from feast.infra.online_stores.redis import RedisOnlineStoreConfig, RedisOnlineStore

def init():

sql_conn_str = os.getenv("FEAST_SQL_CONN")

redis_conn_str = os.getenv("FEAST_REDIS_CONN")

feast_registry_path = os.getenv("FEAST_REGISTRY_BLOB")

print("connecting to registry...")

reg_config = RegistryConfig(

registry_store_type="azure",

path=feast_registry_path,

)

print("connecting to repo config...")

repo_cfg = RepoConfig(

project="production",

provider="azure",

registry=reg_config,

offline_store=MsSqlServerOfflineStoreConfig(connection_string=sql_conn_str),

online_store=RedisOnlineStoreConfig(connection_string=redis_conn_str),

)

global store

print("connecting to feature store...")

store = FeatureStore(config=repo_cfg)

global model

# AZUREML_MODEL_DIR is an environment variable created during deployment.

# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)

model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "model/model.pkl")

# deserialize the model file back into a sklearn model

model = joblib.load(model_path)

print("read model, init complete")

def run(raw_data):

data = json.loads(raw_data)

feature_vector = store.get_online_features(

features=[

"driver_stats:conv_rate",

"driver_stats:avg_daily_trips",

"driver_stats:acc_rate",

"customer_profile:current_balance",

"customer_profile:avg_passenger_count",

"customer_profile:lifetime_trip_count",

],

entity_rows=[data],

).to_df()

logging.info(feature_vector)

if len(feature_vector.dropna()) > 0:

data = feature_vector[

[

"conv_rate",

"avg_daily_trips",

"acc_rate",

"current_balance",

"avg_passenger_count",

"lifetime_trip_count",

]

]

y_hat = model.predict(data)

return y_hat.tolist()

else:

return 0.0