diff --git a/kamyshov_danila_lab_7/A.txt b/kamyshov_danila_lab_7/A.txt
new file mode 100644
index 0000000..d3e4749
--- /dev/null
+++ b/kamyshov_danila_lab_7/A.txt
@@ -0,0 +1,15 @@
+Once upon a time in a quaint village nestled between rolling hills and meandering streams, there lived a remarkable cat named Whiskers. Whiskers was no ordinary feline; he was a cat with a penchant for adventure and a flair for fashion. His most prized possession was a pair of sleek, knee-high leather boots that made him the talk of the town.
+
+Whiskers' boots weren't just for show—they were his ticket to exploring the world beyond the cozy village. One day, a call for help echoed through the cobbled streets. The villagers were in distress, plagued by a mischievous band of mice that had taken residence in their barns and pantries.
+
+Our daring cat, armed with his trusty boots and a heart full of courage, stepped forward to offer his services. The villagers were skeptical at first. After all, Whiskers was just a cat. But when he donned his iconic boots, an air of determination surrounded him, and doubts quickly transformed into hope.
+
+With each step, Whiskers displayed an uncanny agility, darting through the fields and alleys in pursuit of the elusive mice. His boots, crafted by a skilled cobbler with a flair for the dramatic, not only protected his paws but also added a touch of sophistication to his every move.
+
+The mischievous mice, unaware of the formidable opponent they were facing, soon found themselves outmatched by Whiskers' cunning strategy and lightning-quick reflexes. The villagers watched in awe as their unlikely hero in boots triumphed over the rodent invaders.
+
+News of Whiskers' heroic deeds spread far and wide, reaching even the neighboring kingdoms. Soon, he became a legend—a cat in boots whose bravery knew no bounds. Whiskers, however, remained humble, always returning to his cozy village and the adoring villagers who had once doubted him.
+
+As time passed, Whiskers continued to embark on daring adventures, his boots becoming a symbol of courage and resilience. The cat in boots became a beloved figure, not just in his village but across the entire realm.
+
+And so, in the heart of that quaint village, Whiskers the cat in boots lived out his days, a living testament to the extraordinary things that can happen when courage and style come together in perfect harmony.
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diff --git a/kamyshov_danila_lab_7/app.py b/kamyshov_danila_lab_7/app.py
new file mode 100644
index 0000000..733601d
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+++ b/kamyshov_danila_lab_7/app.py
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+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)
diff --git a/kamyshov_danila_lab_7/readme.md b/kamyshov_danila_lab_7/readme.md
new file mode 100644
index 0000000..59dd8a0
--- /dev/null
+++ b/kamyshov_danila_lab_7/readme.md
@@ -0,0 +1,28 @@
+Общее задание:
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную нейронную сеть
+для решения задачи генерации. Подобрать архитектуру и параметры так,чтобы приблизиться к максимально осмысленному результату. Далее
+разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом. В завершении подобрать компромиссную архитектуру, справляющуюся достаточно хорошо с обоими видами текстов. 
+
+Задание по вариантам:
+нечетные вариант, художественный англоязычный текст
+
+Чтобы Запустить приложение нужно запустить файл app.py
+
+Технологии:
+
+Python
+TensorFlow (библиотека для машинного обучения)
+Keras (интерфейс высокого уровня для построения нейронных сетей)
+Описание работы программы:
+
+Загружает художественный текст из текстового файла.
+Проводит предобработку текста, создавая последовательности символов для обучения модели.
+Строит рекуррентную нейронную сеть с использованием LSTM (долгой краткосрочной памяти).
+Обучает модель на предоставленных данных.
+Генерирует новый текст, начиная с случайной подстроки обучающего текста.
+Входные данные:
+
+Текстовый файл с художественным текстом (путь к файлу задается переменной file_path).
+Выходные данные:
+
+Сгенерированный текст на основе обученной модели.
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