text: import pickle
  import numpy as np
  import tensorflow as tf
  from keras.models import load_model
  from keras.preprocessing.sequence import pad_sequences
  
  tf.logging.set_verbosity(tf.logging.ERROR)
  
  print("Loading Ai Engine ....")
  model = load_model("./Models/big_dataset_model.h5")
  print("Engine Loaded Successfully")
  
  print("Loading Support Dependencies ....")
  with open('./Models/big_dataset_token.pickle', 'rb') as handle:
      tokenizer = pickle.load(handle)
  print("Dependencies loaded successfully")
  
  txt = input("Enter Your Thoughts ")
  twt = [txt]
  
  twt = tokenizer.texts_to_sequences(twt)
  twt = pad_sequences(twt, maxlen=40, dtype='int32', value=0)
  sentiment = model.predict(twt, batch_size=1, verbose=0)[0]
  print(model.predict(twt, batch_size=1, verbose=0)[0])
  print("Negative", sentiment[0])
  print("Positive", sentiment[1])
  
  if np.argmax(sentiment) == 0:
      print("Negative")
  elif np.argmax(sentiment) == 1:
      print("Positive")
  