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# Copyright (c) 2017 Alex Pliutau
import random
import logging
from rasa_nlu.converters import load_data
from rasa_nlu.config import RasaNLUConfig
from rasa_nlu.model import Trainer, Metadata, Interpreter
from rasa_nlu.components import ComponentBuilder
class RasaNLP(object):
"Sorry, I don't know it",
"Next time I will know, but not now",
"Sorry, can't get what do you mean",
"Try something else"
GREET_MSGS = ["Hola!", "Privet!", "Xin chào!"]
INTENT_GREET = "greet"
INTENTS_QUESTION = ["whatis", "howto", "when", "do"]
ENTITY_QUERY = "query"
def __init__(self, data_provider, config_file, data_file, model_dir):
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
# store unparsed messages, so later we can train bot
self.unparsed_messages = []
self.data_provider = data_provider
self.data_file = data_file
self.model_dir = model_dir
self.rasa_config = RasaNLUConfig(config_file)
def train(self):
training_data = load_data(self.data_file)
trainer = Trainer(self.rasa_config)
self.interpreter = Interpreter.load(trainer.persist(self.model_dir), self.rasa_config)"rasa trained successfully")
def parse(self, msg):
return self.interpreter.parse(msg)
def find_reply(self, msg):
res = self.parse(msg)"rasa parse res: {}".format(res))
if not "intent" in res or res["intent"] is None:
# later we can do something with unparsed messages, probably train bot
return random.choice(self.COULD_NOT_PARSE_MSGS)
if res["intent"]["name"] == self.INTENT_GREET:
return random.choice(self.GREET_MSGS)
# same approach for all questions
if res["intent"]["name"] in self.INTENTS_QUESTION and len(res["entities"]) > 0:
for e in res["entities"]:
if e["entity"] == self.ENTITY_QUERY:
return self.get_short_answer(e["value"])
return random.choice(self.COULD_NOT_PARSE_MSGS)
def get_short_answer(self, query):
return self.data_provider.get_short_answer(query)
# saves unparsed messages into a file
def snapshot_unparsed_messages(self, filename):
with open(filename, "a") as f:
for msg in self.unparsed_messages:
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