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
import rasa_core
from rasa_core.agent import Agent
from rasa_core.policies.fallback import FallbackPolicy
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
from rasa_core.run import serve_application
from rasa_core import config

logger = logging.getLogger(__name__)

def train_dialogue(domain_file = 'domain.yml',
					model_path = './models/dialogue',
					training_data_file = './data/stories.md'):
	
	fallback = FallbackPolicy(fallback_action_name="utter_action_fallback", 
							  core_threshold=0.60, nlu_threshold=0.60)

	agent = Agent(domain_file, policies = [MemoizationPolicy(), 
										   KerasPolicy(max_history=4, epochs=1000, batch_size=100),
										   fallback
										  ])
	data = agent.load_data(training_data_file, augmentation_factor=20)
	agent.train(data)
				
	agent.persist(model_path)
	return agent
	
def run_weather_bot(serve_forever=True):
	interpreter = RasaNLUInterpreter('./models/nlu/default/weathernlu')
	action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
	agent = Agent.load('./models/dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
	rasa_core.run.serve_application(agent ,channel='cmdline')
		
	return agent
	
if __name__ == '__main__':
	train_dialogue()
	run_weather_bot()