import os
import shutil

import cv2
import gradio as gr
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer

#os.system("pip freeze")
# download weights
if not os.path.exists('model_zoo/real/RealESRGAN_x4plus.pth'):
    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P model_zoo/real")
if not os.path.exists('model_zoo/real/RealESRGAN_x2plus.pth'):
    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -P model_zoo/real")

if not os.path.exists('model_zoo/gan/GFPGANv1.4.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P model_zoo/gan")
if not os.path.exists('model_zoo/gan/RestoreFormer.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P model_zoo/gan")

def inference(img, version, scale, enhance_face):
    # background enhancer with RealESRGAN
    if scale <= 2:
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
        netscale = 2
        model_path = 'model_zoo/real/RealESRGAN_x2plus.pth'
    else:
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        model_path = 'model_zoo/real/RealESRGAN_x4plus.pth'
        netscale = 4
    print(model_path)
    tile = 400 if torch.cuda.is_available() else 0
    dni_weight = None
     # restorer
    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=dni_weight,
        model=model,
        tile=tile,
        tile_pad=10,
        pre_pad=0,
        half=False, #Use fp32 precision during inference. Default: fp16 (half precision).
        gpu_id=None) #gpu device to use (default=None) can be 0,1,2 for multi-gpu
    if enhance_face:
        if version == 'RestoreFormer':
            face_enhancer = GFPGANer(
            model_path='model_zoo/gan/RestoreFormer.pth', upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
        else:
            face_enhancer = GFPGANer(
            model_path='model_zoo/gan/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
    os.makedirs('output', exist_ok=True)
    if scale > 4:
        scale = 4  # avoid too large scale value
    try:
        extension = os.path.splitext(os.path.basename(str(img)))[1]
        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
        if len(img.shape) == 3 and img.shape[2] == 4:
            img_mode = 'RGBA'
        elif len(img.shape) == 2:  # for gray inputs
            img_mode = None
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        else:
            img_mode = None

        h, w = img.shape[0:2]
        if h < 300:
            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
        try:
            if enhance_face:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                output, _ = upsampler.enhance(img, outscale=scale)
        except RuntimeError as error:
            print('Error', error)

        try:
            if scale != 2:
                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
                h, w = img.shape[0:2]
                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
        except Exception as error:
            print('wrong scale input.', error)
        if img_mode == 'RGBA':  # RGBA images should be saved in png format
            extension = 'png'
        else:
            extension = 'jpg'
        save_path = f'output/out.{extension}'
        cv2.imwrite(save_path, output)

        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
        return output, save_path
    except Exception as error:
        print('global exception', error)
        return None, None


def clean_folder(folder):
    for filename in os.listdir(folder):
        file_path = os.path.join(folder, filename)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print('Failed to delete %s. Reason: %s' % (file_path, e))
            

title = "Real Esrgan Restore Ai Face Restoration by appsgenz.com"
description = r"""
"""
article = r"""
😊
"""
reminiApp = gr.Interface(
    inference, [
        gr.Image(type="filepath", label="Input"),
        gr.Radio(['v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version GFPGAN. Note that it work when enable Enhance faces '),
        gr.Number(label="Rescaling factor", value=1),
        gr.Checkbox(label="Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes", value=True),
    ], [
        gr.Image(type="numpy", label="Output (The whole image)"),
        gr.File(label="Download the output image")
    ],
    title=title,
    description=description,
    article=article)
reminiApp.queue(concurrency_count=4)
reminiApp.launch(share=False)