nlu_model.py

import logging
import pprint
from rasa_nlu.training_data import load_data
from rasa_nlu import config
from rasa_nlu.model import Trainer
from rasa_nlu.model import Interpreter
from rasa_nlu.evaluate import run_evaluation


logfile = 'nlu_model.log'


def train_nlu(data_path, configs, model_path):
    logging.basicConfig(filename=logfile, level=logging.DEBUG)
    training_data = load_data(data_path)
    trainer = Trainer(config.load(configs))
    trainer.train(training_data)
    model_directory = trainer.persist(model_path, project_name='current', fixed_model_name='nlu')
    run_evaluation(data_path, model_directory)


def run_nlu(nlu_path):
    logging.basicConfig(filename=logfile, level=logging.DEBUG)
    interpreter = Interpreter.load(nlu_path)
    pprint.pprint(interpreter.parse("What do I do when I'm sad?"))
    pprint.pprint(interpreter.parse("What do I do when I'm happy"))
    pprint.pprint(interpreter.parse("What do I do when I'm stressed?"))


if __name__ == '__main__':
    # train_nlu('./data/nlu.md', 'nlu_config.yml', './models')
    run_nlu('./models/current/nlu')
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