import sys
if sys.version_info < (3,0):
import urllib as decoder
else:
import urllib.parse as decoder
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer, Metadata, Interpreter
from rasa_nlu import config
import intent
class Classification(object):
def __init__(self, training_data_file = "training_data.json",
config_file = "training_config.json"):
training_data = load_data(training_data_file)
trainer = Trainer(config.load(config_file))
self.interpreter = trainer.train(training_data)
self.confidence_threshold = 0.7
# Create supported intents
context = { 'confidence_threshold': self.confidence_threshold }
self.intents = {
"greet" : intent.HelloIntent(self, context),
"get_time" : intent.GetTimeIntent(self, context),
"ask_joke" : intent.JokeIntent(self, context),
"unknown" : intent.UnKnownIntent(self, context)
}
def handle(self, message):
"""
Handles incoming message using trained NLU model and prints response to
the system out
Arguments:
message the message from user to be handled with known intents
(greet, add_item, clear_list, show_items, _num_items)
"""
intent = ""
confidence = ""
message = decoder.unquote(message)
nlu_data = self.interpreter.parse(message)
if 'intent' in nlu_data:
if 'name' in nlu_data['intent']:
intent = nlu_data['intent']['name']
confidence = nlu_data['intent']['confidence']
if confidence < self.confidence_threshold:
return "I'm sorry! Could you please paraphrase?"
if intent in self.intents:
return self.intents[intent].execute(nlu_data)
