import numpy as np
from keras.models import load_model

# Load the model
model = load_model('LithicAI-Education-Model.h5')

# Function to generate text
def generate_text(seed_text, next_words, model, max_sequence_len):
    for _ in range(next_words):
        # Convert seed text to sequence
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        # Pad the sequence
        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        # Predict the next word
        predicted = model.predict_classes(token_list, verbose=0)
        # Convert predicted word index to word
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        # Add the predicted word to the seed text
        seed_text += " "+output_word
    return seed_text.title()

# Generate text
generated_text = generate_text("the quick brown", 5, model, 10)
print(generated_text)