Python CmdlineInput Examples

Example #1

def run_bank_bot(serve_forever=True):
    from rasa_core.agent import Agent
    from rasa_core.interpreter import RasaNLUInterpreter
    from rasa_core.channels.console import CmdlineInput
    interpreter = RasaNLUInterpreter('models/nlu/default/bank_nlu')
    agent = Agent.load('models/dialogue', interpreter=interpreter)
    if serve_forever:
        agent.handle_channels([CmdlineInput()])
    return agent

Example #2

def run_bot(serve_forever=True):
    interpreter = RasaNLUInterpreter('./models/current/nlu/')
    agent = Agent.load('./models/dialogue/', interpreter=interpreter)
    if serve_forever:
        agent.handle_channels([CmdlineInput()])
    return agent

Example #3

File: online_train.py Project: VijiAsh/Restaurant-Search-Chatbot

                  policies=[MemoizationPolicy(),
                            KerasPolicy()],
                  interpreter=interpreter,
                  generator=None)

    agent.train(training_data_file,
                input_channel=input_channel,
                max_history=2,
                batch_size=50,
                epochs=200,
                max_training_samples=300,
                validation_split=0.2)

    return agent


if __name__ == '__main__':
    logging.basicConfig(level="INFO")
    directory = './models'
    for filename in os.listdir(directory):
        if filename.endswith(".tar.gz"):
            #print('FILE NAME== ',filename)
            tar = tarfile.open(os.path.join(directory, filename))
            tar.extractall()
            tar.close()
            continue
        else:
            continue
    nlu_interpreter = RasaNLUInterpreter('./nlu')
    run_restaurant_online(CmdlineInput(), nlu_interpreter)

Example #4

from rasa_core.agent import Agent
from rasa_core.channels.console import CmdlineInput
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
from rasa_core.training import interactive
from rasa_core.utils import EndpointConfig


def train_agent(input_channel, nlu_interpreter,
                          domain_file="domain.yml",
                          training_data_file='./data/dialogue/stories.md'):

    #endpoints = "endpoints.yml"
    agent = Agent(domain_file,
                  policies=[MemoizationPolicy(max_history=2), KerasPolicy()],
                  interpreter=nlu_interpreter)
    data = agent.load_data(training_data_file)
    agent.train(data, input_channel=input_channel, batch_size=50, epochs=200, max_training_samples=300)
    agent = Agent.load('models/dialogue/default/dialogue_model', interpreter = nlu_interpreter, action_endpoint=EndpointConfig(url = "http://localhost:5055/webhook"))
    interactive.run_interactive_learning(agent, training_data_file)

    return agent


if __name__ == '__main__':
    nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/nlu_model')
    train_agent(CmdlineInput(), nlu_interpreter)

Example #5

from rasa_core.utils import EndpointConfig

logger = logging.getLogger(__name__)


def run_weather_online(input_channel,
                       interpreter,
                       domain_file="weather_domain.yml",
                       training_data_file='data/stories.md'):

    #policies2 = policy_config.load("config.yml")
    action_endpoints = EndpointConfig(url="http://localhost:5055/webhook")
    agent = Agent(
        "weather_domain.yml",
        interpreter=interpreter,
        policies=[MemoizationPolicy(),
                  KerasPolicy(epochs=200, batch_size=50)],
        action_endpoint=action_endpoints)
    #data = asyncio.run(agent.load_data(training_data_file))
    data = agent.load_data(training_data_file)
    agent.train(data)
    interactive.run_interactive_learning(agent, training_data_file)
    return agent


if __name__ == '__main__':
    logging.basicConfig(level="INFO")
    nlu_interpreter = NaturalLanguageInterpreter.create(
        './models/nlu/default/weathernlu')
    run_weather_online(CmdlineInput(), nlu_interpreter)

Example #6

File: train_online.py Project: ashwin-kumar-blur/Projects

import logging
from rasa_core.agent import Agent
from rasa_core.channels.console import CmdlineInput
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__)

def train_online(input_channel , interpreter, 
					domain_file = 'domain.yml',
					training_data = './data/stories.md'):

	agent = Agent(domain_file, policies = [MemoizationPolicy(max_history=2),
			 KerasPolicy(epochs = 500, batch_size=10)], interpreter = interpreter)
	data = agent.load_data(training_data)
	agent.continue_training(data,
		input_channel = input_channel,
		augmentation_factor = 50,
		validation_split = 0.2)
	agent.persist(model_path)
	return agent

if __name__ =="__main__":
	logging.basicConfig(level = 'INFO')
	nlu_interpreter = RasaNLUInterpreter('./models/current/nlu/')
	train_online(CmdlineInput(),nlu_interpreter)

Example #7

File: run_app.py Project: gettingtoknowdata/foodie_bot

from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.channels.slack import SlackInput
from rasa_core.channels.console import CmdlineInput;


nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/restaurantnlu')
agent = Agent.load('./models/dialogue', interpreter = nlu_interpreter)

input_channel = SlackInput('xoxp-359418684578-360311923670-373479391381-faf4e29bddac4cd131221b8cf0ffa627', #app verification token
							#'xoxb-359418684578-372796513233-gHxeV6CHQFl8MPkZVrPHBsnQ', # bot verification token
							#'7uAoXNmNANaqCa9WpALV9EhR', # slack verification token
							True)
cmd_channel = CmdlineInput()

agent.handle_channels(channels=[input_channel, cmd_channel], http_port=5004, serve_forever=True)