Classifying a new text by a Keras model saved on disk

I have developed one text classification model using Keras. I also saved the model using the following code:

model.save('E:/New Notes/Python/My BOW Model/my_model.h5')

The file has been successfully saved. Now I want to load the file and classify a new text.

Here is my code:

from keras.models import load_model
from string import punctuation
from nltk.corpus import stopwords
from keras.preprocessing.text import Tokenizer

model = load_model('E:/New Notes/My BOW Model/my_model.h5')
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


# load doc into memory
def load_doc(filename):
    # open the file as read only
    file = open(filename, 'r')
    # read all text
    text = file.read()
    # close the file
    file.close()
    return text

# load the vocabulary
vocab_filename = 'E:/New Notes/My BOW Model/vocab.txt'
vocab = load_doc(vocab_filename)
vocab = vocab.split()
vocab = set(vocab)

# turn a doc into clean tokens
def clean_doc(doc):
    # split into tokens by white space
    tokens = doc.split()
    # remove punctuation from each token
    table = str.maketrans('', '', punctuation)
    tokens = [w.translate(table) for w in tokens]
    # remove remaining tokens that are not alphabetic
    tokens = [word for word in tokens if word.isalpha()]
    # filter out stop words
    stop_words = set(stopwords.words('english'))
    tokens = [w for w in tokens if not w in stop_words]
    # filter out short tokens
    tokens = [word for word in tokens if len(word) > 1]
    return tokens

def predict_sentiment(review, vocab, tokenizer, model):
    # clean
    tokens = clean_doc(review)
    # filter by vocab
    tokens = [w for w in tokens if w in vocab]
    # convert to line
    line = ' '.join(tokens)
    # encode
    encoded = tokenizer.texts_to_matrix([line], mode='freq')
    # prediction
    classname = model.predict(encoded, verbose=0)
    return round(classname[0,0])
# classify a review as negative (0) or positive (1)
# test positive text
text = 'The movie has a good appeal.'
tokenizer = Tokenizer()
print(predict_sentiment(text, vocab, tokenizer, model))
# test negative text
text = 'I dont like the movie at all.'
print(predict_sentiment(text, vocab, tokenizer, model))

After running the above code, I'm getting this error:

AttributeError: 'Tokenizer' object has no attribute 'word_index'. 

What am I doing wrong? Thanks in advance.