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import numpy as np
import tensorflow as tf
import gradio as gr
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("keras-io/conv_autoencoder")
examples = [
['./example_0.jpeg'],
['./example_1.jpeg'],
['./example_2.jpeg'],
['./example_3.jpeg'],
['./example_4.jpeg']
]
def infer(original_image):
image = tf.keras.utils.img_to_array(original_image)
image = image.astype("float32") / 255.0
image = np.reshape(image, (1, 28, 28, 1))
output = model.predict(image)
output = np.reshape(output, (28, 28, 1))
output_image = tf.keras.preprocessing.image.array_to_img(output)
return output_image
iface = gr.Interface(
fn = infer,
title = "Image Denoising using Convolutional AutoEncoders",
description = "Keras Implementation of a deep convolutional autoencoder for image denoising",
inputs = gr.inputs.Image(image_mode='L', shape=(28, 28)),
outputs = gr.outputs.Image(type = 'pil'),
examples = examples,
article = "Author: <a href=\"https://huggingface.co/Blazer007\">Vivek Rai</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/autoencoder/\">Santiago L. Valdarrama</a>",
).launch(enable_queue=True, debug = True) |