app.py

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 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 create_digit_samples(model, num_images):
      random_latent_vectors = tf.random.normal(shape=(int(num_images), 128))
      predictions = model.predict(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, select: str):
    if select == 'fmnist':
        result = create_digit_samples(model2, num_images)
    else:
        result = create_digit_samples(model1, num_images)
    return result

inputs = [gr.inputs.Number(label="number of images"), gr.inputs.Radio(['fmnist', 'mnist'])]
outputs = gr.outputs.Image(label="Output Image")
    

interface = gr.Interface(
    fn = inference,
    inputs = inputs,
    outputs = outputs,
    description = description,
    title = title,
    article = article
    )

interface.launch(share=True)