import random
 import json 
 import pickle
  import lumpy as np 
import nitk 
from nitk.stem import WordNetLemmatizer 

from tensorflow.keras.models import load_model 
lemmatizer = WordNetLemmatizer() 
intents = json.loads(open('intents.json ).read()) 
words = pickle.load(open('words.pk11, 'rb')) 
classes = pickle.load(open('classes.pkr, 'rb'))
 model = load_model('chatbot model.model') 
def clean u sentence sentence sentence 
words = nitk.word tokenize(sentence) sentence words = [lemmatizer.lemmatize(word) 
for word in sentence words.," return sentence words' 

cw°rctence:. sentence words = clean_up_sentence(sentence) 
bag = [0] * len(words) 
for w in sentence words: 
for i, word in enumerate(words): if word == w: bag[i] = 1 return np.array(bag) 
def redict class sentence bow = bag_of_words(sentence) 
res = model.predict(np.array([bow]))[0] ERROR THRESHOLD = 0.25 
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD] 
results.sort( =lambda x: x[1], =True) 
