from __future__ import annotations

import logging
import os
from pathlib import Path

from dotenv import load_dotenv

from core.database import connect
from core.database import upsert_predictions
from core.database.classes import VisualCommunication

if __name__ == '__main__':
    # setup logging
    fmt = (
        '%(asctime)s | '
        '%(levelname)s | '
        '%(filename)s | '
        '%(funcName)s | '
        '%(message)s'
    )
    datefmt = '%Y-%m-%d %H:%M:%S'
    logging.basicConfig(format=fmt, datefmt=datefmt, level=logging.INFO)
    # get list of image paths
    test_dir = Path(__file__).parent
    img_dir = test_dir / 'imgs'
    img_path_list = [path for path in img_dir.glob('*.jpeg') if path.is_file()]
    # instantiate data object
    vis_com_list = [
        VisualCommunication.from_file(path)
        for path
        in img_path_list
    ]
    # generate random predictions
    for vis_com in vis_com_list:
        vis_com.generate_random_prediction()
    # prepare env vars
    env_path = test_dir.parent / 'local.env'
    assert env_path.exists()
    load_dotenv(env_path)
    os.environ['MONGO_HOST'] = 'localhost'
    # connect to database
    collection, db, client = connect()
    # upload visual communication
    for vis_com in vis_com_list:
        if vis_com.prediction is None:
            continue
        upsert_predictions(
            collection=collection,
            vis_com_name=vis_com.name,
            predictions=vis_com.prediction,
        )