dream

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import os
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from deeppavlov.core.commands.utils import parse_config, expand_path
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from deeppavlov import train_model
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CONFIG_NAME = os.environ.get("CONFIG_NAME", None)
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parsed = parse_config(CONFIG_NAME)
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if expand_path(parsed["metadata"]["variables"]["MODEL_PATH"]).exists():
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    # model folder exists, so it is already trained
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    print("Model is already trained.")
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else:
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    print("Model is NOT trained.\nLet's train the model!\n\n")
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    model = train_model(CONFIG_NAME)
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    print("Model is trained.")
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