import gradio as gr import tensorflow as tf from tensorflow import keras 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 = 128 random_vector_labels = generate_latent_points(int(digit), latent_dim, int(n_samples)) examples = model.predict(random_vector_labels) examples = examples * 255.0 size = ceil(sqrt(n_samples)) digit_images = np.zeros((28*size, 28*size), dtype=float) 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 digit_images = (digit_images/127.5) -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 = ["image"], examples = examples, description = description, title = title ) iface.launch()