text: import numpy as np
  from tensorflow.python.keras.preprocessing.text import Tokenizer
  from tensorflow.python.keras.preprocessing.sequence import pad_sequences
  from string import punctuation
  from tensorflow.python.keras.models import load_model
  from datapreprocess import load_doc, clean_doc
  import pickle
  
  # load all docs in a directory
  def process_docs(vocab):
  	documents = list()
  	doc = load_doc("my_review.txt")
  	# clean doc
  	tokens = clean_doc(doc, vocab)
  	# add to list
  	documents.append(tokens)
  	return documents
  
  review = open("my_review.txt","w")
  text = input("Enter the film review : ")
  review.write(text)
  review.close()
  
  # load the vocabulary
  vocab_filename = 'vocab.txt'
  vocab = load_doc(vocab_filename)
  vocab = vocab.split()
  vocab = set(vocab)
      
  predict_docs = process_docs(vocab)
  
  with open('tokens.pickle', 'rb') as handle:
      tokenizer = pickle.load(handle)
  
  encoded_docs = tokenizer.texts_to_sequences(predict_docs)
  X = pad_sequences(encoded_docs, maxlen=1317, padding='post')
  
  # load model
  model = load_model('my_modelCNN.h5')
  y=model.predict_classes(np.array(X))
  
  if (y == [[1]]) :
  	print("\n The movie is good \n")
  else :
  	print("\n The movie is bad \n")
  
  