import gradio as gr
import torch
import torch.nn as nn
import torchvision
import torch.backends.cudnn as cudnn
import torch.optim
import os
import sys
import argparse
import time
import dataloader
import model
import numpy as np
from torchvision import transforms
from PIL import Image
import glob
import time

 
def lowlight(image_path):
	os.environ['CUDA_VISIBLE_DEVICES']=''
	data_lowlight = Image.open(image_path)

 

	data_lowlight = (np.asarray(data_lowlight)/255.0)


	data_lowlight = torch.from_numpy(data_lowlight).float()
	data_lowlight = data_lowlight.permute(2,0,1)
	data_lowlight = data_lowlight.cpu().unsqueeze(0)

	DCE_net = model.enhance_net_nopool().cpu()
	DCE_net.load_state_dict(torch.load('Epoch99.pth'))
	start = time.time()
	_,enhanced_image,_ = DCE_net(data_lowlight)

	end_time = (time.time() - start)
	print(end_time)
	image_path = image_path.replace('test_data','result')
	result_path = image_path
	if not os.path.exists(image_path.replace('/'+image_path.split("/")[-1],'')):
		os.makedirs(image_path.replace('/'+image_path.split("/")[-1],''))

	torchvision.utils.save_image(enhanced_image, result_path)

if __name__ == '__main__':
# test_images
	with torch.no_grad():
		filePath = 'data/test_data/'
	
		file_list = os.listdir(filePath)

		for file_name in file_list:
			test_list = glob.glob(filePath+file_name+"/*") 
			for image in test_list:
				# image = image
				print(image)
				lowlight(image)


title = "Compound Multi-branch Feature Fusion for Image Restoration (Deblur)"
description = "Gradio demo for CMFNet. CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop. Here, we provide a demo for image deblur. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
article = "<p style='text-align: center'><a href='https://' target='_blank'>Compound Multi-branch Feature Fusion for Real Image Restoration</a> | <a href='https://github.com/FanChiMao/CMFNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"

examples = [['data/test_data//01.jpg'], ['data/test_data//02.jpg'], ['data/test_data//03.jpg'],]
gr.Interface(
    inference,
    [gr.inputs.Image(type="pil", label="Input")],
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    allow_flagging=False,
    allow_screenshot=False,
    examples=examples
).launch(debug=True)