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import tensorflow as tf |
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import numpy as np |
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from keras.models import load_model |
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from tensorflow.keras.preprocessing.text import Tokenizer |
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import pickle |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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import os |
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from pathlib import Path |
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import pandas as pd |
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import plotly.express as px |
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import keras |
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import unicodedata as ud |
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from underthesea import word_tokenize |
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from phoBERT import BERT_predict |
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LSTM_model = tf.keras.models.load_model('lstm_model.tf') |
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GRU_model = tf.keras.models.load_model('gru_model.tf') |
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def LSTM_predict(x): |
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pred_proba = LSTM_model.predict([x])[0] |
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pred_proba = [round(i,2) for i in pred_proba] |
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return pred_proba |
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def GRU_predict(x): |
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pred_proba = GRU_model.predict([x])[0] |
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pred_proba = [round(i,2) for i in pred_proba] |
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return pred_proba |
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def tokenize(x): |
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x = ud.normalize('NFKC', x) |
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x = word_tokenize(x, format="text") |
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return x |
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def judge(x): |
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result = [] |
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x = tokenize(x) |
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lstm_pred = LSTM_predict(x) |
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gru_pred = GRU_predict(x) |
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result_lstm = np.round(lstm_pred, 2) |
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result_gru = np.round(gru_pred, 2) |
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for i in range(6): |
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result.append((result_lstm[i]+result_gru[i])/2) |
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return (result) |
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def judgePlus(x): |
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result = [] |
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x = tokenize(x) |
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lstm_pred = LSTM_predict(x) |
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gru_pred = GRU_predict(x) |
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try: |
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bert_pred = BERT_predict(x) |
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except: |
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bert_pred = np.average([lstm_pred, gru_pred], axis=0) |
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result_lstm = np.round(lstm_pred, 2) |
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result_gru = np.round(gru_pred, 2) |
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result_bert = np.round(bert_pred, 2) |
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if((result_lstm[0]+result_gru[0])<(result_bert[0]*2)): |
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for i in range(6): |
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result.append((result_bert[i])/1) |
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else: |
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for i in range(6): |
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result.append((result_lstm[i]+result_gru[i])/2) |
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return (result) |
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def judgeBert(x): |
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result = [] |
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x = tokenize(x) |
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try: |
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bert_pred = BERT_predict(x) |
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except: |
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bert_pred = np.zeros(6, dtype=float) |
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result_bert = np.round(bert_pred, 2) |
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for i in range(6): |
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result.append((result_bert[i])/1) |
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return (result) |
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