dialogue_management_model.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging

from rasa_core.agent import Agent
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter


logger = logging.getLogger(__name__)

# Training of Final Model

def train_dialogue(domain_file = "weather_domain.yml",model_path="./models/dialogue",training_data_file="./data/stories.md"):
    
    agent = Agent(domain_file,policies=[MemoizationPolicy(),KerasPolicy()])

    agent.train(training_data_file,
    max_history=3,
    epochs=300,
    batch_size=50,
    validation_split=0.2,
    augmentation_factor=50)

    agent.persist(model_path)
    return agent


def run_weather_bot(serve_forever=True):
    interpreter = RasaNLUInterpreter("./models/nlu/default/weathernlu")
    agent = Agent.load("./models/dialogue",interpreter)

    if serve_forever:
        agent.handle_channel(ConsoleInputChannel())

    return agent

if __name__ == "__main__":
    train_dialogue()
    run_weather_bot()