import logging

from waitress import serve

from image_prediction.config import CONFIG
from image_prediction.flask import make_prediction_server
from image_prediction.predictor import Predictor
from image_prediction.response import build_response
from image_prediction.utils import get_logger, show_banner

logger = get_logger()


def main():
    def predict(pdf):
        # Keras service_estimator.predict stalls when service_estimator was loaded in different process
        # https://stackoverflow.com/questions/42504669/keras-tensorflow-and-multiprocessing-in-python
        predictor = Predictor()
        predictions, metadata = predictor.predict_pdf(pdf, verbose=CONFIG.service.progressbar)
        response = build_response(predictions, metadata)
        return response

    logger.info("Predictor ready.")

    prediction_server = make_prediction_server(predict)

    run_prediction_server(prediction_server, mode=CONFIG.webserver.mode)


def run_prediction_server(app, mode="development"):
    if mode == "development":
        app.run(host=CONFIG.webserver.host, port=CONFIG.webserver.port, debug=True)
    elif mode == "production":
        serve(app, host=CONFIG.webserver.host, port=CONFIG.webserver.port)


if __name__ == "__main__":
    logging_level = CONFIG.service.logging_level
    logging.basicConfig(level=logging_level)
    logging.getLogger("flask").setLevel(logging.ERROR)
    logging.getLogger("urllib3").setLevel(logging.ERROR)
    logging.getLogger("werkzeug").setLevel(logging.ERROR)
    logging.getLogger("waitress").setLevel(logging.ERROR)
    logging.getLogger("PIL").setLevel(logging.ERROR)
    logging.getLogger("h5py").setLevel(logging.ERROR)

    show_banner()

    main()