app.py

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)