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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)
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)
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