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| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| import matplotlib.pyplot as plt | |
| from math import sqrt, ceil | |
| from huggingface_hub import from_pretrained_keras | |
| import numpy as np | |
| model = from_pretrained_keras("keras-io/conditional-gan") | |
| latent_dim = 128 | |
| def generate_latent_points(digit, latent_dim, n_samples, n_classes=10): | |
| # generate points in the latent space | |
| random_latent_vectors = tf.random.normal(shape=(n_samples, latent_dim)) | |
| labels = tf.keras.utils.to_categorical([digit for _ in range(n_samples)], n_classes) | |
| return tf.concat([random_latent_vectors, labels], 1) | |
| def create_digit_samples(digit, n_samples, latent_dim=latent_dim): | |
| random_vector_labels = generate_latent_points(digit, latent_dim, n_samples) | |
| examples = cgan_generator.predict(random_vector_labels) | |
| examples = examples * 255.0 | |
| size = ceil(sqrt(n_samples)) | |
| digit_images = np.zeros((28*size, 28*size)) | |
| n = 0 | |
| for i in range(size): | |
| for j in range(size): | |
| if n == n_samples: | |
| break | |
| digit_images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = examples[n, :, :, 0] | |
| n += 1 | |
| return digit_images | |
| description = "This model is based on the example created here: https://keras.io/examples/generative/conditional_gan/" | |
| title = "Conditional GAN for MNIST" | |
| examples = [[1, 10], [3, 5], [5, 15]] | |
| iface = gr.Interface( | |
| fn = create_digit_samples, | |
| inputs = ["number", "number"], | |
| outputs = [gradio.outputs.Image(invert_colors=True, type="numpy", label="Samples for given digit")], | |
| examples = examples, | |
| description = description, | |
| title = title | |
| ) | |
| iface.launch() |