def wordEmbedding(question): #change all questions in the corpora to vectors and store in a list
embeddingList=[]
nlp = en_core_web_lg.load()
for x in range(len(question)):
doc=nlp(preprocess(question[x]))
pre=[doc]
pre.append(prep.findsenti(question[x]))#also include the sentiment
embeddingList.append(pre)
return embeddingList
def generate(intputSen, doc2, answer):
index = 0
nlp = en_core_web_lg.load()
doc1 = nlp(preprocess(intputSen))
inputsenti = prep.findsenti(intputSen)
similarity = 0
bestlist = []
for x in range(len(doc2)):
if doc2[x][0].vector_norm and doc1.vector_norm:
similarity = doc1.similarity(
doc2[x][0]
) #compare the input sentence and questions stored in the list
if similarity > 0.60:
# this is the threshold, so if this value is too high, then your input must
#have a higher degree of similarity to the questions in the corpora
index = x
bestlist.append([similarity, index, doc2[x][1]])
if len(bestlist) == 0:
# at least 5 different reasonable responses when the user enters something outside the two topics
listReply = [
'Sorry your question is not included in my database',
'Sorry, I do not know how to reply that',
'Whoops! my brain is dead, may be next question',
'Pass that bro, I cannot remember',
'This question is too difficult, next question please',
'Your question is hard for me, sorry abour that'
]
replyOutsideTopic = random.choice(listReply)
print(replyOutsideTopic)
return replyOutsideTopic
sortedanswer = sorted(bestlist, key=operator.itemgetter(0))
if len(sortedanswer) == 1:
print(answer[sortedanswer[0][1]])
return answer[sortedanswer[0][1]]
else:
if sortedanswer[-1][0] != sortedanswer[-2][0]:
print(answer[sortedanswer[-1][1]])
return answer[sortedanswer[-1][1]]
else:
if abs(sortedanswer[-1][2] - inputsenti) > abs(
sortedanswer[-2][2] - inputsenti
): #if top 2 answer have same similarity, then check the sentiment.
print(answer[sortedanswer[-2][1]])
return answer[sortedanswer[-2][1]]
else:
print(answer[sortedanswer[-1][1]])
return answer[sortedanswer[-1][1]]
def generate(intputSen, doc2, answer):
index = 0
# get definition from Wikipedia
if intputSen.find('ask wikipedia about') >= 0:
userinput = intputSen.split('ask wikipedia about')[1].strip()
# print('wikipedia api:' + userinput)
try:
r = wikipedia.summary(userinput)
# make sure the length of message is less than socket buffer size
if len(r) > 1024:
r = r[:1024]
while len(r) > 0 and not r.endswith('.'):
r = r[:-1]
except wikipedia.exceptions.DisambiguationError as e:
r = str(e)
except wikipedia.exceptions.PageError as pe:
r = f'{userinput} does not match any wikipedia pages. Try another question!'
except:
r = 'exception occurs, please ask another question'
return r
nlp = en_core_web_lg.load()
doc1 = nlp(preprocess(intputSen))
inputsenti = prep.findsenti(intputSen)
similarity = 0
bestlist = []
for x in range(len(doc2)):
if doc2[x][0].vector_norm and doc1.vector_norm:
similarity = doc1.similarity(
doc2[x][0]
) # compare the input sentence and questions stored in the list
if similarity > 0.60:
# this is the threshold, so if this value is too high, then your input must
# have a higher degree of similarity to the questions in the corpora
index = x
bestlist.append([similarity, index, doc2[x][1]])
# if we can not find answers from given copra, then using wolfram
if len(bestlist) == 0:
try:
# api usage see https://products.wolframalpha.com/api/documentation/
params = {
'input': intputSen,
'appid': WOLFRAM_APP_ID,
'output': 'json'
}
r = requests.get(url='http://api.wolframalpha.com/v2/query',
params=params)
j = json.loads(r.content)
return j['queryresult']['pods'][1]["subpods"][0]["plaintext"]
except:
# at least 5 different reasonable responses when the user enters something outside the two topics
listReply = [
'Sorry your question is not included in my database',
'Sorry, I do not know how to reply that',
'Whoops! my brain is dead, may be next question',
'Pass that bro, I cannot remember',
'This question is too difficult, next question please',
'Your question is hard for me, sorry about that'
]
replyOutsideTopic = random.choice(listReply)
print(replyOutsideTopic)
return replyOutsideTopic
sortedanswer = sorted(bestlist, key=operator.itemgetter(0))
if len(sortedanswer) == 1:
print(answer[sortedanswer[0][1]])
return answer[sortedanswer[0][1]]
else:
if sortedanswer[-1][0] != sortedanswer[-2][0]:
print(answer[sortedanswer[-1][1]])
return answer[sortedanswer[-1][1]]
else:
if abs(sortedanswer[-1][2] - inputsenti) > abs(
sortedanswer[-2][2] - inputsenti
): # if top 2 answer have same similarity, then check the sentiment.
print(answer[sortedanswer[-2][1]])
return answer[sortedanswer[-2][1]]
else:
print(answer[sortedanswer[-1][1]])
return answer[sortedanswer[-1][1]]