import numpy as np
from keras.models import load_model
from keras_preprocessing.sequence import pad_sequences
from keras_preprocessing.text import Tokenizer

from antonov_dmitry_lab_7.lab7 import tokenizer, max_sequence_length

# Step 3: Load the pre-trained model
model = load_model('my_model.h5')  # Replace with the actual path to your model file

# Recreate the Tokenizer and compile the model (in case the model was not compiled before saving)
with open('small.txt', 'r') as file:
    text = file.read()

tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
total_words = len(tokenizer.word_index) + 1
def generate_text(seed_text, next_words, model, max_sequence_length):
    for _ in range(next_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre')
        predicted = np.argmax(model.predict(token_list), axis=-1)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        seed_text += " " + output_word
    return seed_text


# Generate text using the loaded model (same as before)
generated_text = generate_text("Once upon a", 50, model, max_sequence_length)
print(generated_text)