import tensorflow as tf

import math
import numpy as np
from PIL import Image

from tensorflow.keras.preprocessing.image import img_to_array
from huggingface_hub import from_pretrained_keras
import gradio as gr

model = from_pretrained_keras("keras-io/super-resolution")

def infer(image):
	img = Image.fromarray(image)
	img = img.resize((100,100))
	ycbcr = img.convert("YCbCr")
	y, cb, cr = ycbcr.split()
	y = img_to_array(y)
	y = y.astype("float32") / 255.0

	input = np.expand_dims(y, axis=0)
	out = model.predict(input)

	out_img_y = out[0]
	out_img_y *= 255.0

	# Restore the image in RGB color space.
	out_img_y = out_img_y.clip(0, 255)
	out_img_y = out_img_y.reshape((np.shape(out_img_y)[0], np.shape(out_img_y)[1]))
	out_img_y = Image.fromarray(np.uint8(out_img_y), mode="L")
	out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
	out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
	out_img = Image.merge("YCbCr", (out_img_y, out_img_cb, out_img_cr)).convert(
		"RGB"
	)
	return (img,out_img)
	
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.05158' target='_blank'>Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network</a></p><center> <a href='https://keras.io/examples/vision/super_resolution_sub_pixel/' target='_blank'>Image Super-Resolution using an Efficient Sub-Pixel CNN</a></p> <center>Contributors: <a href='https://twitter.com/Cr0wley_zz'>Devjyoti Chakraborty</a>|<a href='https://twitter.com/ritwik_raha'>Ritwik Raha</a>|<a href='https://twitter.com/ariG23498'>Aritra Roy Gosthipaty</a></center>"

iface = gr.Interface(
	fn=infer,
	title = " Image Super-resolution",
	description = "This space is a demo of the keras tutorial 'Image Super-Resolution using an Efficient Sub-Pixel CNN' based on the paper 'Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network' 👀",
	article = article,
	inputs=gr.inputs.Image(label="Input Image"),
	outputs=[gr.outputs.Image(label="Resized 100x100 image"),
	gr.outputs.Image(label="Super-resolution 300x300 image")
	],
	examples=[["camel.jpg"], ["pokemon.jpg"]],
).launch()