# This is an example feature definition file
import pandas as pd
from google.protobuf.duration_pb2 import Duration
from feast import Entity, Feature, FeatureView, FileSource, ValueType, FeatureStore

from feast_trino.connectors.upload import upload_pandas_dataframe_to_trino
from feast_trino import TrinoSource
from feast_trino.trino_utils import Trino

store = FeatureStore(repo_path="feature_repo")

client = Trino(
    user="user",
    catalog=store.config.offline_store.catalog,
    host=store.config.offline_store.host,
    port=store.config.offline_store.port,
)
client.execute_query("CREATE SCHEMA IF NOT EXISTS feast")
client.execute_query("DROP TABLE IF EXISTS feast.driver_stats")

input_df = pd.read_parquet("./feature_repo/data/driver_stats.parquet")
upload_pandas_dataframe_to_trino(
    client=client,
    df=input_df,
    table_ref="feast.driver_stats",
    connector_args={"type": "memory"},
)


# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_hourly_stats = TrinoSource(
    event_timestamp_column="event_timestamp",
    table_ref="feast.driver_stats",
    created_timestamp_column="created",
)

# Define an entity for the driver. You can think of entity as a primary key used to
# fetch features.
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_hourly_stats_view = FeatureView(
    name="driver_hourly_stats",
    entities=["driver_id"],
    ttl=Duration(seconds=86400 * 1),
    features=[
        Feature(name="conv_rate", dtype=ValueType.FLOAT),
        Feature(name="acc_rate", dtype=ValueType.FLOAT),
        Feature(name="avg_daily_trips", dtype=ValueType.INT64),
    ],
    online=True,
    batch_source=driver_hourly_stats,
    tags={},
)
store.apply([driver, driver_hourly_stats_view])

# Run an historical retrieval query
output_df = store.get_historical_features(
    entity_df="""
    SELECT
        1004 AS driver_id,
        TIMESTAMP '2021-11-21 15:00:00+00:00' AS event_timestamp
    """,
    features=["driver_hourly_stats:conv_rate"]
).to_df()
print(output_df.head())