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