inference_code.py

def make_inference_models():
    
    encoder_model = tf.keras.models.Model(encoder_inputs, encoder_states)
    
    decoder_state_input_h = tf.keras.layers.Input(shape=( 200 ,))
    decoder_state_input_c = tf.keras.layers.Input(shape=( 200 ,))
    
    decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
    
    decoder_outputs, state_h, state_c = decoder_lstm(
        decoder_embedding , initial_state=decoder_states_inputs)
    decoder_states = [state_h, state_c]
    decoder_outputs = decoder_dense(decoder_outputs)
    decoder_model = tf.keras.models.Model(
        [decoder_inputs] + decoder_states_inputs,
        [decoder_outputs] + decoder_states)
    
    return encoder_model , decoder_model

def str_to_tokens( sentence : str ):
    words = sentence.lower().split()
    tokens_list = list()
    for word in words:
        tokens_list.append( tokenizer.word_index[ word ] ) 
    return preprocessing.sequence.pad_sequences( [tokens_list] , maxlen=maxlen , padding='post')