120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
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from sre_parse import Tokenizer
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import numpy as np
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from flask import Flask
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from keras.layers import Dense, LSTM, Embedding
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from keras.models import load_model, Sequential
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from keras_preprocessing.sequence import pad_sequences
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app = Flask(__name__)
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@app.route("/")
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def home():
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return "<html>" \
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"<h1>Жукова Алина ПИбд-41</h1>" \
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"<h1>Лабораторная работа №7</h1>" \
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"<table>" \
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"<td>" \
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"<form Action='http://127.0.0.1:5000/k4_1_task_7' Method=get>" \
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"<input type=submit value='Генерация текста'>" \
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"</form>" \
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"</td>" \
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"</table>" \
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"</html>"
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# Реккурентная нейронная сеть, генерация текста
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# 10 вариант
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@app.route("/k4_1_task_7", methods=['GET'])
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def k4_1_task_7():
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# Загрузка текста из файла
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# Русский текст
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# with open('lab_4_1__7_text_rus.txt', 'r', encoding='utf-8') as file:
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# text = file.read()
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# Анлглийский текст
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with open('lab_4_1__7_text_eng.txt', 'r', encoding='utf-8') as file:
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text = file.read()
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# Создание Tokenizer и токенизация текста
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tokenizer = Tokenizer(char_level=True)
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tokenizer.fit_on_texts(text)
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# Преобразование текста в последовательность чисел
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sequences = tokenizer.texts_to_sequences(text)
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# Подготовка обучающих данных
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seq_length = 100
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dataX, dataY = [], []
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for i in range(0, len(sequences) - seq_length):
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seq_in = sequences[i:i + seq_length]
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seq_out = sequences[i + seq_length]
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dataX.append(seq_in)
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dataY.append(seq_out)
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dataX = np.array(dataX)
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dataY = np.array(dataY)
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# Создание модели
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vocab_size = len(tokenizer.word_index) + 1
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embedding_dim = 256
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rnn_units = 1024
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model = Sequential()
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model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=seq_length))
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model.add(LSTM(units=rnn_units))
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model.add(Dense(units=vocab_size, activation='softmax'))
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model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
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# Загрузка модели
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# РУсская модель
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# model = load_model('lab_4_1__7_model.keras')
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# Английская модель
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model = load_model('lab_4_1__7_model_eng.keras')
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print("Loaded model from disk")
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# Обучение модели
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# batch_size = 64
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# model.fit(dataX, dataY, epochs=15, batch_size=batch_size)
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def generate_text(seed_text, gen_length):
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generated_text = seed_text
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for _ in range(gen_length):
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sequence = tokenizer.texts_to_sequences([seed_text])[0]
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sequence = pad_sequences([sequence], maxlen=seq_length)
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prediction = model.predict(sequence)[0]
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predicted_index = np.argmax(prediction)
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predicted_char = tokenizer.index_word[predicted_index]
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generated_text += predicted_char
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seed_text += predicted_char
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seed_text = seed_text[1:]
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return generated_text
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# Пример использования
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start_phraze = "Black cat"
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# Русский
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# generated_text = generate_text("Невероятный котик", 250)
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# Английский
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generated_text = generate_text(start_phraze, 250)
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i = 10
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# Сохранение модели
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# Русская модель
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# model.save('C:/Users/Alina/PycharmProjects/lab1/lab_4_1__7_model.keras')
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# Английская модель
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# model.save('C:/Users/Alina/PycharmProjects/lab1/lab_4_1__7_model_eng.keras')
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# print("Saved model to disk")
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return "<html>" \
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"<h1></h1>" \
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"<h2>Вариант 10. Задание 7 - Генерация текста</h2>" \
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"<h2> Сгенерированный текст, начальная фраза " + start_phraze + ": " + str(generated_text) + " </h2>" \
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"</html>"
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if __name__ == "__main__":
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app.run(debug=True)
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