import os
import time
import spaces
import cv2
import gradio as gr
import torch

from basicsr.archs.srvgg_arch import SRVGGNetCompact
from realesrgan.utils import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet

os.system("pip freeze")

upsampler_map = {}
half = True if torch.cuda.is_available() else False

if not os.path.exists('realesr-general-x4v3.pth'):
    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
    model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
    model_path = 'realesr-general-x4v3.pth'
    upsampler_map['RealESR-General-x4v3'] = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
if not os.path.exists('RealESRGAN_x2plus.pth'):
    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -P .")
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
    model_path = 'RealESRGAN_x2plus.pth'
    upsampler_map['RealESRGAN_x2plus'] = RealESRGANer(scale=2, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)

os.makedirs('output', exist_ok=True)

@spaces.GPU(duration=10)
def enhance(
    img_path:str,
    version:str='x4v3',
    scale:int=2,
):
    run_task_time = 0
    time_cost_str = ''
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    extension = os.path.splitext(os.path.basename(img_path))[1]
    img = cv2.imread(img_path, 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)
    
    if version == 'x4v3':
        upsampler = upsampler_map['RealESR-General-x4v3']
    elif version == 'x4plus':
        upsampler = upsampler_map['RealESRGAN_x4plus']
    elif version == 'x2plus':
        upsampler = upsampler_map['RealESRGAN_x2plus']
    elif version == 'net_x4plus':
        upsampler = upsampler_map['RealESRNet_x4plus']

    output, _ = upsampler.enhance(img, outscale=scale)
    
    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)
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    return output, save_path, time_cost_str


def get_time_cost(run_task_time, time_cost_str):
    now_time = int(time.time()*1000)
    if run_task_time == 0:
        time_cost_str = 'start'
    else:
        if time_cost_str != '': 
            time_cost_str += f'-->'
        time_cost_str += f'{now_time - run_task_time}'
    run_task_time = now_time
    return run_task_time, time_cost_str

def create_demo() -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                version = gr.Radio(['x4v3', 'x4plus', 'x2plus', 'net_x4plus'], label="Version", value='x4v3')
                scale = gr.Number(label="Rescaling factor", value=2)
            with gr.Column():
                g_btn = gr.Button("Enhance")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Input Image", type="filepath")
            with gr.Column():
                restored_image = gr.Image(label="Restored Image", type="numpy", interactive=False)
                download_path = gr.File(label="Download the output image", interactive=False)
                restored_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
        
        g_btn.click(
            fn=enhance,
            inputs=[input_image, version, scale],
            outputs=[restored_image, download_path, restored_cost],
        )

    return demo