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| # import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from keras.models import load_model | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| import pickle | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import os | |
| from pathlib import Path | |
| import pandas as pd | |
| import plotly.express as px | |
| import keras | |
| import unicodedata as ud | |
| from underthesea import word_tokenize | |
| from phoBERT import BERT_predict | |
| # Load tokenizer | |
| # fp = Path(__file__).with_name('tokenizer.pkl') | |
| # with open(fp,mode="rb") as f: | |
| # tokenizer = pickle.load(f) | |
| #Load LSTM | |
| #fp = Path(__file__).with_name('lstm_model.h5') | |
| LSTM_model = tf.keras.models.load_model('lstm_model.tf') | |
| #Load GRU | |
| #fp = Path(__file__).with_name('gru_model.h5') | |
| GRU_model = tf.keras.models.load_model('gru_model.tf') | |
| def tokenizer_pad(tokenizer,comment_text,max_length=200): | |
| comment_text = word_tokenize(comment_text, format="text") | |
| comment_text = [comment_text] | |
| tokenized_text = tokenizer.texts_to_sequences(comment_text) | |
| padded_sequences = pad_sequences(sequences=tokenized_text,maxlen=max_length,padding="post",truncating="post") | |
| return padded_sequences | |
| def LSTM_predict(x): | |
| # x = tokenizer_pad(tokenizer=tokenizer,comment_text=x) | |
| pred_proba = LSTM_model.predict([x])[0] | |
| pred_proba = [round(i,2) for i in pred_proba] | |
| #print(pred_proba) | |
| return pred_proba | |
| def GRU_predict(x): | |
| # x = tokenizer_pad(tokenizer=tokenizer,comment_text=x) | |
| pred_proba = GRU_model.predict([x])[0] | |
| pred_proba = [round(i,2) for i in pred_proba] | |
| #print(pred_proba) | |
| return pred_proba | |
| def plot(result): | |
| label = ['độc hại', 'cực kì độc hại', 'tục tĩu', 'đe dọa', 'xúc phạm', 'thù ghét cá nhân'] | |
| data = pd.DataFrame() | |
| data['Nhãn'] = label | |
| data['Điểm'] = result | |
| #print(data) | |
| p = px.bar(data, x='Nhãn', y='Điểm', color='Nhãn', range_y=[0, 1] ) | |
| return p | |
| pass | |
| def judge(x): | |
| label = ['độc hại', 'cực kì độc hại', 'tục tĩu', 'đe dọa', 'xúc phạm', 'thù ghét cá nhân'] | |
| result = [] | |
| judge_result = [] | |
| x = ud.normalize('NFKC', x) | |
| x = word_tokenize(x, format="text") | |
| lstm_pred = LSTM_predict(x) | |
| gru_pred = GRU_predict(x) | |
| #bert_pred = BERT_predict(x) | |
| #print(result) | |
| return_result = 'Result' | |
| result_lstm = np.round(lstm_pred, 2) | |
| result_gru = np.round(gru_pred, 2) | |
| #result_bert = np.round(bert_pred, 2) | |
| for i in range(6): | |
| result.append((result_lstm[i]+result_gru[i])/2) | |
| return (result) | |
| def judgePlus(x): | |
| label = ['độc hại', 'cực kì độc hại', 'tục tĩu', 'đe dọa', 'xúc phạm', 'thù ghét cá nhân'] | |
| result = [] | |
| judge_result = [] | |
| x = ud.normalize('NFKC', x) | |
| x = word_tokenize(x, format="text") | |
| lstm_pred = LSTM_predict(x) | |
| gru_pred = GRU_predict(x) | |
| bert_pred = BERT_predict(x) | |
| #bert_pred = BERT_predict(x) | |
| #print(result) | |
| return_result = 'Result' | |
| result_lstm = np.round(lstm_pred, 2) | |
| result_gru = np.round(gru_pred, 2) | |
| result_bert = np.round(bert_pred, 2) | |
| #result_bert = np.round(bert_pred, 2) | |
| for i in range(6): | |
| result.append((result_lstm[i]+result_gru[i]+result_bert[i])/3) | |
| return (result) | |