import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences

# Путь к файлу
file_path = 'vlastelin-kolec.txt'

# Замените 'your_text_file.txt' на путь к вашему файлу с художественным текстом
with open(file_path, 'r', encoding='utf-8') as file:
    text = file.read()

tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
total_words = len(tokenizer.word_index) + 1

input_sequences = []
for line in text.split('\n'):
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)

max_sequence_length = max([len(x) for x in input_sequences])
input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')

X, y = input_sequences[:, :-1], input_sequences[:, -1]
y = tf.keras.utils.to_categorical(y, num_classes=total_words)

model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_length-1))
model.add(LSTM(100))
model.add(Dense(total_words, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(X, y, epochs=100, verbose=1)

next_words = 100
while True:
    seed_text = input('Введите текст: ')
    if seed_text == "0":
        break
    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

    print(seed_text)