import tensorflow as tf
import numpy as np
from PIL import Image
from glob import glob 
import pandas as pd
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")
model.summary()

def infer(image):

   nx=image.shape[0]
   ny=image.shape[1]

   img = Image.fromarray(image)
   # img = img.resize((100,100))
   # img = img.crop((0,100,0,100))
   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)

   nxo = out.squeeze().shape[0]
   nyo = out.squeeze().shape[1]

   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"
   )

   out = {}
   out.update( {'input image size': (nx,ny) } )
   out.update( {'output image size': (nxo,nyo) } )

   return (pd.DataFrame(data=out.values(), index=out.keys()).transpose(), 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>"

# examples = [['examples/2000-04-28-18-21-24_L5_rgb.jpg'],['examples/2000-08-02-18-23-18_L5_rgb.jpg'],
#             ['examples/2000-08-18-18-23-46_L5_rgb.jpg'],['examples/2000-09-19-18-24-18_L5_rgb.jpg'],['examples/2000-10-21-18-24-43_L5_rgb.jpg']]

examples= [[l] for l in glob('examples/tiles/*.jpg')]
out1 = gr.outputs.Image(label="Cropped input image")
out2 = gr.outputs.Image(label="Super-resolution x3 image")
out3 = gr.outputs.Dataframe(label='Summary', headers=["Input (px)", "Output (px)"], type='pandas')

iface = gr.Interface(
	fn=infer,
	title = " Satellite 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=[out1,out2,out3],
	examples=examples,
).launch()