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("IMvision12/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))
      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


inputs = gr.inputs.Number(label="number of images")
outputs = gr.outputs.Image(label="Predictions")

examples = [
            [10], 
            [7],
            [1],
            [3], 
            [5]
]


gr.Interface(Predict, inputs, outputs, article=article, title=title, description=description, examples=examples, analytics_enabled=False).launch(enable_queue=True)