class NLUDefault:
def __init__(self):
self.sz = SentimentAnalyzer()
with open(os.path.join(os.getcwd(), 'vectorizer.pk'), 'rb') as fin:
self.vectorizer = pickle.load(fin)
with open(os.path.join(os.getcwd(), 'question_detection.sav'),
'rb') as file:
self.model = pickle.load(file)
def parse(self, input_str):
sf = SemanticFrame()
input_str = input_str.lower()
sf.Intent = 'user_statement'
for pizza in PizzaMenu.specialty:
if pizza.lower() in input_str:
sf.Slots['pizza'] = pizza
sf.Intent = "order_pizza"
for topping in PizzaMenu.Toppings:
if topping.lower() in input_str:
if 'topping' not in sf.Slots:
sf.Slots['topping'] = []
sf.Slots['topping'].append(topping)
sf.Intent = "order_pizza"
for size in PizzaMenu.sizes:
if size.lower() in input_str:
sf.Slots['size'] = size
sf.Intent = "order_pizza"
for crust in PizzaMenu.crusts:
if crust.lower() in input_str:
ifcontain = True
if crust.lower() == 'thin':
ifcontain = False
for m in re.finditer('thin', input_str):
if len(input_str) == m.end(
) or input_str[m.start():m.end() + 1] != 'think':
ifcontain = True
break
if ifcontain:
sf.Slots['crust'] = crust
sf.Intent = "order_pizza"
for side in PizzaMenu.sides:
if side.lower() in input_str:
sf.Slots['side'] = side
sf.Intent = "order_extras"
for drink in PizzaMenu.drinks:
if drink.lower() in input_str:
sf.Slots['drink'] = drink
sf.Intent = "order_extras"
phone = re.findall(r"([\dA-Z]{3}-[\dA-Z]{3}-[\dA-Z]{4})", input_str,
re.IGNORECASE)
if phone:
for num in phone:
sf.Slots['phone'] = num
sf.Intent = "provide_contact_information"
other_contact = re.findall(r"(?:this is|it's) ([\S]+)", input_str,
re.IGNORECASE)
if other_contact:
for contact in other_contact:
sf.Slots['contact'] = contact
sf.Intent = "provide_contact_information"
for ele in delivery:
if ele in input_str:
if ele == ' delivery' or ele == 'delivery':
ele = 'delivery'
else:
ele = 'pick-up'
sf.Slots['delivery_type'] = ele
sf.Intent = "inform_delivery"
for ele in change:
if ele in input_str:
sf.Intent = "change_order"
for ele in reorder:
if ele in input_str:
sf.Intent = "reorder_favorite"
ifquestion = self.model.predict(self.vectorizer.transform([input_str
]))[0]
if ifquestion == 'whQuestion' or ifquestion == 'ynQuestion':
if 'recommend' in input_str:
sf.Intent = 'ask_for_recommend'
sf.Slots['recommend'] = []
for item in recommend:
if item in input_str:
sf.Slots['recommend'].append(item)
elif 'order' in input_str:
sf.Intent = "query_pizza_status"
for ele in confirm:
if ele in input_str:
if ele == 'yes' or ele == 'yeah' or ele == 'right' or ele == 'sure' or ele == 'exactly' or ele == 'yep' or ele == 'why not':
sf.Slots['confirm'] = 'yes'
else:
sf.Slots['confirm'] = 'no'
sf.Intent = "confirm_previous"
return sf, self.sz.compound_sentiment_score(input_str)
