import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Загрузка текстового файла
file_path = "A.txt"

with open(file_path, "r", encoding="utf-8") as file:
    text = file.read()

# Предобработка данных
chars = sorted(list(set(text)))
char_indices = {char: i for i, char in enumerate(chars)}
indices_char = {i: char for i, char in enumerate(chars)}

maxlen = 40
step = 3
sentences = []
next_chars = []

for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i : i + maxlen])
    next_chars.append(text[i + maxlen])

x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.uint8)
y = np.zeros((len(sentences), len(chars)), dtype=np.uint8)

for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1

# Определение модели RNN
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars), activation="softmax"))

model.compile(loss="categorical_crossentropy", optimizer="adam")

# Обучение модели
model.fit(x, y, batch_size=128, epochs=20)

# Генерация текста
start_index = np.random.randint(0, len(text) - maxlen - 1)
seed_text = text[start_index : start_index + maxlen]

generated_text = seed_text
for i in range(400):
    x_pred = np.zeros((1, maxlen, len(chars)))
    for t, char in enumerate(seed_text):
        x_pred[0, t, char_indices[char]] = 1

    preds = model.predict(x_pred, verbose=0)[0]
    next_index = np.argmax(preds)
    next_char = indices_char[next_index]

    generated_text += next_char
    seed_text = seed_text[1:] + next_char

print(generated_text)