def run(self, dispatcher, tracker, domain):
lemma = nltk.wordnet.WordNetLemmatizer()
Operations = []
# get what you need from slots
course = tracker.get_slot("course")
knowledge = tracker.get_slot("knowledges")
time = tracker.get_slot("time")
# for debugging
print("action_name:", self.name())
latest_message = ("example//latest_message:", tracker.latest_message)
print(latest_message)
# get what you need from user_input if not exist in slot
# if slot exists in the latest msg, it will be autofilled in slotList.
# returnType: tracker.get_latest_entity_values() -> Object<generator> : next( ) or list( )
course = list(tracker.get_latest_entity_values(
"course")) if not course else course
knowledge = list(tracker.get_latest_entity_values(
"knowledge")) if not knowledge else knowledge
time = list(
tracker.get_latest_entity_values("time")) if not time else time
# preprocess
if course:
course = str(course).lower()
if knowledge:
knowledge = [lemma.lemmatize(str.lower(key)) for key in knowledge]
if 'exercise' in knowledge:
knowledge.remove("exercise")
if time:
time = str(time).lower()
time = time.replace(' ', '0')
time = time.strip('[').strip(']')
print('time: ', time)
print('knowledge: ', knowledge)
# If all required info has been stored in slot_set -> respond to question
# otherwise -> send message for requesting what missed.
if not (course and (knowledge or time)):
# sending msg
message = "Can you provide the course code of that knowledge?(e.g compXXXX)" if not course else "About what or time of lecture(e.g week 2)?"
dispatcher.utter_message(message)
# setting slot
if course:
Operations.append(SlotSet('course', course))
if knowledge:
Operations.append(SlotSet('knowledges', knowledge))
if course and (knowledge or time):
#retrieval info in DB
search = Search()
if course in search.db_list:
# search method need imporvement for this: searhing exercises by knowledge or by time.
print("time:", time)
if knowledge:
data = search._search_by_key(db_name=course,
key=knowledge,
intent='example',
time=time)
else:
data = search._search_by_time(db_name=course,
intent='example',
time=time)
if not len(data):
dispatcher.utter_message("Nothing matched :(")
dispatcher.utter_message("Is that what you want? (Yes/No)")
Operations.append(SlotSet('knowledges', None))
Operations.append(SlotSet('time', None))
else:
sim, index = search.tf_idf(knowledge, data)
if not knowledge:
ans_from = f'Here is all the examples in {time}:'
dispatcher.utter_message(ans_from)
title = None
time = None
num_key = 1
for i in index:
if ((not title) or title != data[i][2]) and (
(not time) or time != data[i][1]):
ans_from = f'I got you examples in {data[i][0]} about {data[i][2]}:'
title = data[i][2]
time = data[i][1]
dispatcher.utter_message(str(num_key))
dispatcher.utter_message(ans_from)
num_key += 1
dispatcher.utter_message(data[i][1])
dispatcher.utter_message("Is that what you want? (Yes/No)")
Operations.append(SlotSet('knowledges', None))
Operations.append(SlotSet('time', None))
if course not in search.db_list:
dispatcher.utter_message("Sorry.. I can't find course ")
dispatcher.utter_message("Is that what you want? (Yes/No)")
Operations.append(SlotSet('course', None))
return Operations
