import os
import urllib.parse
from predictions import prediction

RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "dev")


def handler(event, context):
    """
    Take in event and trigger the prediction pipeline
    """

    # Assuming a file in a bucket landing for now?
    # Assuming we have a model to use

    # bucket = event["Records"][0]["s3"]["bucket"]["name"]
    # key = urllib.parse.unquote_plus(
    #     event["Records"][0]["s3"]["bucket"]["key"], encoding="utf-8"
    # )
    payload = event["body"]
    data_path = payload["file_location"]
    property_id = payload["property_id"]
    portfolio_id = payload["portfolio_id"]
    created_at = payload["created_at"]

    # prediction_file = bucket + "/" + key

    # TODO: put a model into s3, both locally and in aws
    # model_path = os.environ.get("MODEL_PATH", "http://minio:9000/data/model_directory/")
    model_path = os.environ.get(
        "MODEL_PATH",
        f"s3://retrofit-model-directory-{RUNTIME_ENVIRONMENT}/RDSAP_CHANGE/autogluon/rdsap_change-medium_quality-30"
        "-2023-08-30_11-43-41/deployment/",
    )

    try:
        outputs = prediction(model_path=model_path, data_path=data_path)
        # Store into s3, with key of {portfolio_id}-{property_id}
        outputs.to_csv(
            f"s3://retrofit-sap-prediction-{RUNTIME_ENVIRONMENT}/{portfolio_id}/{property_id}/{created_at}.csv"
        )

    except (Exception, KeyError, ValueError):
        print("Prediction failed")


if __name__ == "__main__":
    handler()