def run(serve_forever=True):
interpreter = RasaNLUInterpreter(CHAT)
agent = Agent.load(MODEL_DIALOGUE, interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_bot(serve_forever=True):
agent = Agent.load('./models/dialogue/default/dialogue_model', RasaNLUInterpreter('./models/nlu/default/nlu_model'))
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def train_dialogue():
interpreter = RasaNLUInterpreter(CHAT)
agent = Agent(CONFIG, policies=[MemoizationPolicy(max_history=3), KerasPolicy()], interpreter=interpreter)
training_data = STORIES ###TODO
agent.train(
training_data,
epochs=500,
batch_size=100,
validation_split=0.3
)
agent.persist(MODEL_DIALOGUE)
# input_channel = TelegramInput(
# access_token="577522303:AAG6_5NcdBVRq-ndzThybnOh7SHL9I2ylKo", # you get this when setting up a bot
# verify="chatmoviedomainbot", # this is your bots username
# webhook_url="https://07958fff.ngrok.io" # the url your bot should listen for messages
# )
input_channel = ConsoleInputChannel()
agent.train_online(
training_data,
input_channel=input_channel,
epochs=400,
batch_size=100
)
return agent
def run_concerts(serve_forever=True):
agent = Agent.load("examples/concerts/models/policy/init",
interpreter=RegexInterpreter())
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def console(serve_forever=True):
interpreter = RasaNLUInterpreter("models/current/nlu")
agent = Agent.load("models/current/dialogues", interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run(server_forver=True):
interpreter = RasaNLUInterpreter('models/nlu/default/restaurant_finder')
agent = Agent.load('models/dialogue', interpreter=interpreter)
if server_forver:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_online_training(self,
domain, # type: Domain
interpreter, # type: NaturalLanguageInterpreter
input_channel=None # type: Optional[InputChannel]
):
# type: (...) -> None
# from rasa_core.agent import Agent
from lib.my_agent import CustomAgent
if interpreter is None:
interpreter = RegexInterpreter()
# domain = Agent._create_domain("domain.yml")
# tracker_store = RedisTrackerStore(domain, host="redis")
#host="redis:alpine://redis", port="6379"
# tracker_store = RedisTrackerStore(domain)
tracker_store = None
# tracker = tracker_store.get_or_create_tracker("cliniciansID")
bot = CustomAgent(domain, self,
interpreter=interpreter, tracker_store = tracker_store)
bot.toggle_memoization(False)
try:
bot.handle_channel(
input_channel if input_channel else ConsoleInputChannel())
except TrainingFinishedException:
pass # training has finished
def test_dialog():
agent = Agent.load(output_path,
interpreter=NaturalLanguageInterpreter.create(None))
agent.handle_channel(ConsoleInputChannel())
return agent
def run_hello_world(max_training_samples=10, serve_forever=True):
training_data = '../mom/data/stories.md'
default_domain = TemplateDomain.load("../mom/domain.yml")
agent = Agent(
default_domain,
# policies=[SimplePolicy()],
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=HelloInterpreter(),
tracker_store=InMemoryTrackerStore(default_domain))
logger.info("Starting to train policy")
# agent = Agent(default_domain,
# policies=[SimplePolicy()],
# interpreter=HelloInterpreter(),
# tracker_store=InMemoryTrackerStore(default_domain))
# if serve_forever:
# # Attach the commandline input to the controller to handle all
# # incoming messages from that channel
# agent.handle_channel(ConsoleInputChannel())
agent.train_online(training_data,
input_channel=ConsoleInputChannel(),
epochs=1,
max_training_samples=max_training_samples)
return agent
def run(serve_forever=True):
agent = Agent.load("models/dialogue",
interpreter=RasaNLUInterpreter("models/ivr/demo"))
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_chatbot(serve_forever=True):
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/doctornlu')
agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def runRasaTrainOnline(self):
try:
input_channel = ConsoleInputChannel()
interpreter = RasaNLUInterpreter(
os.path.realpath(self.config.get('nluModel',
'model_location')))
domain_file = os.path.realpath(
self.config.get('inputData', 'coreyml'))
training_data_file = os.path.realpath(
self.config.get('inputData', 'stories'))
logger.info(
"nluModel = %s, domain_file = %s, train_data_file = %s" %
(str(
os.path.realpath(
self.config.get('nluModel', 'model_location'))),
str(domain_file), str(training_data_file)))
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent, "Rasa Train Online completed successfully"
except Exception as e:
logger.error("Unable to run Rasa Train Online, exception : %s" %
(str(e)))
raise (e)
def train_dialogue_model(domain_file,
stories_file,
output_path,
use_online_learning=False,
nlu_model_path=None,
max_history=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=max_history),
KerasPolicy()])
data_load_args, kwargs = utils.extract_args(
kwargs, {
"use_story_concatenation", "unique_last_num_states",
"augmentation_factor", "remove_duplicates", "debug_plots"
})
training_data = agent.load_data(stories_file, **data_load_args)
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(training_data,
input_channel=ConsoleInputChannel(),
model_path=output_path,
**kwargs)
else:
agent.train(training_data, **kwargs)
agent.persist(output_path)
def run(serve_forever=True):
interpreter = RasaNLUInterpreter(r".\models\NLU\model_20180601-022829")
agent = Agent.load("models/current/dialogue", interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_server(serve_forever=True):
agent = Agent.load("models/policy/current",
interpreter=RasaNLUInterpreter(nlu_model_path))
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run(serve_forever=True):
interpreter = RasaNLUInterpreter("models/nlu/default/current")
agent = Agent.load("models/dialogue", interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def train_dialogue_model(domain_file, stories_file, output_path,
use_online_learning=False,
nlu_model_path=None,
max_history=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(domain_file, policies=[
MemoizationPolicy(max_history=max_history),
KerasPolicy()])
training_data = agent.load_data(stories_file)
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(
training_data,
input_channel=ConsoleInputChannel(),
model_path=output_path,
**kwargs)
else:
agent.train(training_data, **kwargs)
agent.persist(output_path)
def run_jarvis(serve_forever=True):
agent = Agent.load("models/dialogue",
interpreter=RegexInterpreter())
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def train_dialogue_model(domain_file, stories_file, output_path,
use_online_learning=False, nlu_model_path=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(
stories_file,
input_channel=ConsoleInputChannel(),
epochs=10,
model_path=output_path)
else:
agent.train(
stories_file,
validation_split=0.1,
**kwargs
)
agent.persist(output_path)
def run_weather_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/ABCinsurance')
agent = Agent.load('./models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_faqbot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/current')
agent = Agent.load('models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_cricscore_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/assistantnlu')
agent = Agent.load('./models/dialogue',interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_customer_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('C:/Murali/Testing/hackathon2018/customer_bot-master/models/nlu/current/')
agent = Agent.load('C:/Murali/Testing/hackathon2018/customer_bot-master/models/dialogue/', interpreter = interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run_sonatel_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/vocalbot_nlu')
agent = Agent.load('./models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run(serve_forever=True):
print("loading in interpreter")
interpreter = RasaNLUInterpreter(INTEPRETER_PATH)
print("loading in agent")
agent = Agent.load(MODEL_PATH, interpreter=interpreter)
if serve_forever:
print("handlling channel")
agent.handle_channel(ConsoleInputChannel())
def run(serve_forever=True):
agent = Agent.load(
"./models/dialogue",
interpreter="./models/current/nlu/default/model_20180911-134739")
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
return agent
def run(serve_forever=True):
interpreter = RasaNLUInterpreter(
"models/nlu_v13/default/model_20180518-152807")
agent = Agent.load("models/dialogue", interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
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
def run_restaurant_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/Restaurant_NPSR')
agent = Agent.load('./models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channel(ConsoleInputChannel())
logger.debug("return agent")
return agent
def run_restaurant_bot(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/restaurantnlu')
agent = Agent.load('./models/dialogue', interpreter=interpreter)
if serve_forever:
agent.handle_channels([ConsoleInputChannel()])
return agent
