Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Reshape, Flatten
+from tensorflow.keras.layers import Conv2D, Conv2DTranspose
+from tensorflow.keras.layers import LeakyReLU, Dropout
+from tensorflow.keras.optimizers import Adam
+
+np.random.seed(42)
+
+# Загрузка и предобработка данных MNIST
+(X_train, _), (_, _) = mnist.load_data()
+X_train = (X_train.astype(np.float32) - 127.5) / 127.5
+X_train = np.expand_dims(X_train, axis=3)
+
+# Создание и компиляция модели GAN
+generator = Sequential()
+generator.add(Dense(7 * 7 * 256, input_dim=100))
+generator.add(LeakyReLU(alpha=0.2))
+generator.add(Reshape((7, 7, 256)))
+generator.add(Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same'))
+generator.add(LeakyReLU(alpha=0.2))
+generator.add(Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same'))
+generator.add(LeakyReLU(alpha=0.2))
+generator.add(Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same'))
+
+discriminator = Sequential()
+discriminator.add(Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=(28, 28, 1)))
+discriminator.add(LeakyReLU(alpha=0.2))
+discriminator.add(Dropout(0.3))
+discriminator.add(Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
+discriminator.add(LeakyReLU(alpha=0.2))
+discriminator.add(Dropout(0.3))
+discriminator.add(Flatten())
+discriminator.add(Dense(1, activation='sigmoid'))
+
+discriminator.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0002, beta_1=0.5), metrics=['accuracy'])
+discriminator.trainable = False
+gan = Sequential()
+gan.add(generator)
+gan.add(discriminator)
+gan.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0002, beta_1=0.5))
+
+# Функция для генерации изображений на основе запроса
+def generate_images(prompt):
+    num_images = 10
+    prompt_vector = preprocess_prompt(prompt)  # Предобработка запроса, если необходимо
+    noise = np.repeat([prompt_vector], num_images, axis=0)  # Генерация шума на основе запроса
+    generated_images = generator.predict(noise)
+    generated_images = (generated_images * 127.5 + 127.5).astype(np.uint8)
+    return generated_images.reshape((num_images, 28, 28))
+
+# Адаптация функции под Gradio
+def gradio_generate_images(prompt):
+    images = generate_images(prompt)
+    image_list = []
+    for img in images:
+        image_list.append(img)
+    return image_list
+
+# Запуск Gradio приложения
+iface = gr.Interface(
+    fn=gradio_generate_images,
+    inputs="text",
+    outputs="image",
+    interpretation="default",
+    title="GAN Image Generation Demo",
+    description="Enter a prompt and generate images based on the prompt using GAN.",
+    example="smiley face"
+)
+iface.launch()