import os
import random
import gradio as gr
from PIL import Image
import torch
from random import randint
import sys
from subprocess import call
import psutil
torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')

import argparse
import cv2
import glob
import numpy as np
import os
import torch
from basicsr.utils import imwrite
from gfpgan import GFPGANer

# background upsampler
if args.bg_upsampler == 'realesrgan':
    if not torch.cuda.is_available():  # CPU
        import warnings
        warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
                      'If you really want to use it, please modify the corresponding codes.')
        bg_upsampler = None
    else:
        from basicsr.archs.rrdbnet_arch import RRDBNet
        from realesrgan import RealESRGANer
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
        bg_upsampler = RealESRGANer(
            scale=2,
            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
            model=model,
            tile=args.bg_tile,
            tile_pad=10,
            pre_pad=0,
            half=True)  # need to set False in CPU mode
else:
    bg_upsampler = None
    
# set up GFPGAN restorer
restorer = GFPGANer(
    model_path=args.model_path,
    upscale=args.upscale,
    arch=args.arch,
    channel_multiplier=args.channel,
    bg_upsampler=bg_upsampler)
img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))



os.system("pip install gfpgan")
os.system("pip freeze")
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")


def inference(img):
    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
    # restore faces and background if necessary
    cropped_faces, restored_faces, restored_img = restorer.enhance(
        input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
    # save faces
    # save cropped face
   
    # save comparison image
    cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
   
    return Image.fromarray(cmp_img)
        
title = "GFP-GAN"
description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
gr.Interface(
    inference, 
    [gr.inputs.Image(type="pil", label="Input")], 
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[
    ['lincoln.jpg'],
    ['einstein.png']
    ],
    enable_queue=True
    ).launch(debug=True)