text: import nltk
  nltk.download('punkt')
  nltk.download('wordnet')
  from nltk.stem import WordNetLemmatizer
  lemmatizer = WordNetLemmatizer()
  import json
  import pickle
  
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Dropout
  from keras.optimizers import SGD
  import random
  import csv
  words=[]
  classes = []
  documents = []
  
  file = open("stoppingwords.txt")
  csv_reader = csv.reader(file, delimiter = ',')
  content = list(csv_reader)
  file.close()
  ignore_words = [word for word in content[0]]
  data_file = open('intents.json').read()
  intents = json.loads(data_file)
  
  for intent in intents['intents']:
      for pattern in intent['patterns']:
  
          # take each word and tokenize it
          w = nltk.word_tokenize(pattern)
          words.extend(w)
          # adding documents
          documents.append((w, intent['tag']))
  
          # adding classes to our class list
          if intent['tag'] not in classes:
              classes.append(intent['tag'])
  
  