def classify(text):
if detect_language(text) != 'ko':
language = detect_language(text)
text = translater(text)
else:
language = 'ko'
text = spellchecker(text)
word = []
word.append(text)
tokenizer = Twitter()
word = [tokenizer.morphs(row) for row in word]
with open('./model/tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
sequences_test = tokenizer.texts_to_sequences(word)
data_int_t = pad_sequences(sequences_test,
padding='pre',
maxlen=(MAX_SEQUENCE_LENGTH - 5))
data_test = pad_sequences(data_int_t,
padding='post',
maxlen=(MAX_SEQUENCE_LENGTH))
model = load_model('./model/train_model.h5')
y_prob = model.predict(data_test)
for n, prediction in enumerate(y_prob):
pred = y_prob.argmax(axis=-1)[n]
if pred < 2.0:
return ("질문을 이해하지 못했어요. 다시 입력해주세요.")
else:
if language == 'ko':
return (classes[pred])
else:
return (translater(classes[pred], language))
