| 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 | |