gistfile1.txt

def generate_text_seq(model,tokenizer,text_seq_length,seed_text,n_words):
    text = []
    for _ in range(n_words):
        encoded = tokenizer.texts_to_sequences([seed_text])[0]
        encoded = pad_sequences([encoded],maxlen = text_seq_length,truncating = 'pre')
        
        y_predict = model.predict_classes(encoded)
        
        predicted_words = " "
        for word,index in tokenizer.word_index.items():
            if index == y_predict:
                predicted_word = word
                break
        seed_text = seed_text + " " + predicted_word
        text.append(predicted_word)
    return " ".join(text)
    
generate_text_seq(model,tokenizer,50,seed_text,100)