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| from huggingface_hub import from_pretrained_keras | |
| import matplotlib.pyplot as plt | |
| from math import sqrt, ceil | |
| import tensorflow as tf | |
| import gradio as gr | |
| import numpy as np | |
| model = from_pretrained_keras("keras-io/WGAN-GP") | |
| title = "WGAN-GP" | |
| description = "Image Generation Using WGAN" | |
| article = """ | |
| <p style='text-align: center'> | |
| <a href='https://keras.io/examples/generative/wgan_gp/' target='_blank'>Keras Example given by A_K_Nain</a> | |
| <br> | |
| Space by Gitesh Chawda | |
| </p> | |
| """ | |
| def Predict(num_images): | |
| random_latent_vectors = tf.random.normal(shape=(int(num_images), 128)) | |
| preds = model(random_latent_vectors) | |
| num = ceil(sqrt(num_images)) | |
| images = np.zeros((28*num, 28*num), dtype=float) | |
| n = 0 | |
| for i in range(num): | |
| for j in range(num): | |
| if n == num_images: | |
| break | |
| images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = preds[n, :, :, 0] | |
| n += 1 | |
| return images | |
| examples = [[5],[8],[2],[3]] | |
| interface = gr.Interface( | |
| fn = Predict, | |
| inputs = ["number"], | |
| outputs = ["image"], | |
| examples = examples, | |
| description = description, | |
| title = title, | |
| article = article | |
| ) | |
| interface.launch(share=True) |