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Update app.py
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app.py
CHANGED
@@ -13,7 +13,7 @@ import keras
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from underthesea import word_tokenize
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from phoBERT import BERT_predict
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#Load tokenizer
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# fp = Path(__file__).with_name('tokenizer.pkl')
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@@ -21,12 +21,12 @@ from phoBERT import BERT_predict
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# tokenizer = pickle.load(f)
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#Load LSTM
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LSTM_model = tf.keras.models.load_model(
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#Load GRU
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GRU_model = tf.keras.models.load_model(
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def tokenizer_pad(tokenizer,comment_text,max_length=200):
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@@ -82,15 +82,15 @@ def judge(x):
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lstm_pred = LSTM_predict(x)
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gru_pred = GRU_predict(x)
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bert_pred = BERT_predict(x)
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#print(result)
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return_result = 'Result'
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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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for i in range(6):
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result.append((result_lstm[i]+result_gru[i]
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return (result)
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from underthesea import word_tokenize
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#from phoBERT import BERT_predict
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#Load tokenizer
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# fp = Path(__file__).with_name('tokenizer.pkl')
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# tokenizer = pickle.load(f)
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#Load LSTM
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fp = Path(__file__).with_name('lstm_model.keras')
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LSTM_model = tf.keras.models.load_model(fp)
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#Load GRU
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fp = Path(__file__).with_name('gru_model.keras')
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GRU_model = tf.keras.models.load_model(fp)
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def tokenizer_pad(tokenizer,comment_text,max_length=200):
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lstm_pred = LSTM_predict(x)
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gru_pred = GRU_predict(x)
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# bert_pred = BERT_predict(x)
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#print(result)
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return_result = 'Result'
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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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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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