File size: 996 Bytes
d35f33e f0372d1 d35f33e f0372d1 d35f33e f0372d1 d35f33e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from constance_data import emotion_track_list, decode_cut_list
from pre_processing_data import preprocessing_data, pre_processing_data_2, text_transform, user_capture
def emotion_predict(sentence: str):
lr = 1e-3
wd = 1e-4 * lr
model = tf.keras.models.load_model("model/nlp_surrey_coursework_hunglenhat")
model.compile(loss='sparse_categorical_crossentropy',
optimizer=tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd), metrics=['accuracy'])
sentence_temp = sentence
sentence = pre_processing_data_2(sentence)
if not sentence:
sentence = preprocessing_data(sentence)
sentence = text_transform(sentence)
try:
sentence = model.predict(sentence)
except Exception as E:
print(E)
index_max = np.argmax(sentence)
result = emotion_track_list[decode_cut_list[index_max]]
user_capture(sentence_temp, result)
return result
|