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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 | |
| model1 = tf.keras.models.load_model("mnist.h5", compile=False) | |
| model2 = from_pretrained_keras("keras-io/WGAN-GP") | |
| title = "WGAN-GP" | |
| description = "Image Generation(Fashion Mnist and Handwritten Digits) 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(model, num_images): | |
| random_latent_vectors = tf.random.normal(shape=(int(num_images), 128)) | |
| predictions = 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] = predictions[n, :, :, 0] | |
| n += 1 | |
| return images | |
| def inference(num_images, Choose: str): | |
| if Choose == 'Fashion_mnist': | |
| result = Predict(model2, num_images) | |
| else: | |
| result = Predict(model1, num_images) | |
| return result | |
| inputs = [gr.inputs.Number(label="number of images"), gr.inputs.Radio(['Fashion_mnist', 'Handwritten_digits_mnist'])] | |
| outputs = gr.outputs.Image(label="Output Image") | |
| examples = [[4,"Handwritten_digits_mnist"], [6,"Handwritten_digits_mnist"],[10,"Fashion_mnist"]] | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |