text: from keras.preprocessing.text import Tokenizer
  from keras.preprocessing.sequence import pad_sequences
  from keras.applications.xception import Xception
  from keras.models import load_model
  from pickle import load
  import numpy as np
  from PIL import Image
  import matplotlib.pyplot as plt
  import argparse
  
  
  ap = argparse.ArgumentParser()
  ap.add_argument('-i', '--image', required=True, help="Image Path")
  args = vars(ap.parse_args())
  img_path = args['image']
  
  def extract_features(filename, model):
          try:
              image = Image.open(filename)
              
          except:
              print("ERROR: Couldn't open image! Make sure the image path and extension is correct")
          image = image.resize((299,299))
          image = np.array(image)
          # for images that has 4 channels, we convert them into 3 channels
          if image.shape[2] == 4: 
              image = image[..., :3]
          image = np.expand_dims(image, axis=0)
          image = image/127.5
          image = image - 1.0
          feature = model.predict(image)
          return feature
  
  def word_for_id(integer, tokenizer):
   for word, index in tokenizer.word_index.items():
       if index == integer:
           return word
   return None
  
  
  def generate_desc(model, tokenizer, photo, max_length):
      in_text = 'start'
      for i in range(max_length):
          sequence = tokenizer.texts_to_sequences([in_text])[0]
          sequence = pad_sequences([sequence], maxlen=max_length)
          pred = model.predict([photo,sequence], verbose=0)
          pred = np.argmax(pred)
          word = word_for_id(pred, tokenizer)
          if word is None:
              break
          in_text += ' ' + word
          if word == 'end':
              break
      return in_text
  
  
  #path = 'Flicker8k_Dataset/111537222_07e56d5a30.jpg'
  max_length = 32
  tokenizer = load(open("tokenizer.p","rb"))
  model = load_model('models/model_9.h5')
  xception_model = Xception(include_top=False, pooling="avg")
  
  photo = extract_features(img_path, xception_model)
  img = Image.open(img_path)
  
  description = generate_desc(model, tokenizer, photo, max_length)
  print("\n\n")
  print(description)
  plt.imshow(img)
  
  
  