def train_intent_classifier(intents):
"""
Train intent classifier model
:param intents:
:return:
"""
X = []
y = []
for intent in intents:
training_data = intent.trainingData
for example in training_data:
X.append(example.get("text"))
y.append([str(intent.id)])
PATH = "{}/{}".format(app.config["MODELS_DIR"],
app.config["INTENT_MODEL_NAME"])
intent_classifier = IntentClassifier()
intent_classifier.train(X, y, outpath=PATH, verbose=False)
def train_intent_classifier(intents, botId):
"""
Train intent classifier model
:param intents:
:return:
"""
X = []
y = []
for intent in intents:
training_data = intent.trainingData
for example in training_data:
if example.get("text").strip() == "":
continue
X.append(example.get("text"))
y.append(str(intent.id))
PATH = "{}/{}".format(app.config["MODELS_DIR"], botId + '.model')
print(PATH)
intent_classifier = IntentClassifier()
intent_classifier.train(X, y, outpath=PATH, verbose=False)
elif "DELETE" in type:
response = requests.delete(url)
else:
raise Exception("unsupported request method.")
result = json.loads(response.text)
print(result)
return result
from app.nlu.intent_classifer import IntentClassifier
with app.app_context():
PATH = "{}/{}".format(app.config["MODELS_DIR"],
app.config["INTENT_MODEL_NAME"])
sentence_classifier = IntentClassifier()
sentence_classifier.load(PATH)
print("Intent Model loaded.")
def update_model(app, message, **extra):
sentence_classifier.load(PATH)
print("Intent Model updated")
from app.nlu.tasks import model_updated_signal
model_updated_signal.connect(update_model, app)
from app.agents.models import Bot
# Loading ML Models at app startup
from app.nlu.intent_classifer import IntentClassifier
'''with app.app_context():
PATH = "{}/{}".format(app.config["MODELS_DIR"],
app.config["INTENT_MODEL_NAME"])
sentence_classifier = IntentClassifier()
sentence_classifier.load(PATH)
synonyms = get_synonyms()
entity_extraction = EntityExtractor(synonyms)
app.logger.info("Intent Model loaded.")'''
sentence_classifier = IntentClassifier()
# Request Handler
@endpoint.route('/v1', methods=['POST'])
def api():
"""
Endpoint to converse with chatbot.
Chat context is maintained by exchanging the payload between client and bot.
sample input/output payload =>
{
"currentNode": "",
"complete": false,
"parameters": [],
"extractedParameters": {},