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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("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>
"""
inputs = gr.inputs.Number(label="number of images")
outputs = gr.outputs.Image(label="Predictions")
def create_digit_samples(n_samples):
latent_dim = 128
random_latent_vectors = tf.random.normal(shape=(int(n_samples), 128))
examples = model.predict(random_latent_vectors)
#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
inputs = gr.inputs.Number(label="number of images")
outputs = gr.outputs.Image(label="Output Image")
examples = [
[1],
[2],
[3],
[4],
[64]
]
gr.Interface(create_digit_samples, inputs, outputs, analytics_enabled=False, examples=examples).launch()
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