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import json
import random
import pickle
import numpy as np
import streamlit as st
from tensorflow import keras
from sklearn.preprocessing import LabelEncoder
# load dataset
with open("intents.json", encoding="utf8") as file:
data = json.load(file)
# load tokenizer object
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
# load label encoder object
with open('label_encoder.pickle', 'rb') as enc:
lbl_encoder = pickle.load(enc)
# load pretrained model
model = keras.models.load_model('model')
# parameters
max_len = 20
question = st.text_area('.... HelBot اسأل')
if question == '':
st.write('')
else:
result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([question]),
truncating='post', maxlen=max_len))
idk = ['عذراً، لا يمكنني الإجابة عن السؤال المطروح', 'عذراً، لم أفهم سؤالك']
if result[0][np.argmax(result)] < 0.65: # if perplexity is high
st.write(np.random.choice(idk))
else:
tag = lbl_encoder.inverse_transform([np.argmax(result)])
for i in data['intents']:
if i['tag'] == tag:
out = np.random.choice(i['responses'])
st.write(out) |