import os
import sys
import gradio as gr
from PIL import Image

## environment settup
os.system("git clone https://github.com/codeslake/RefVSR.git")
os.chdir("RefVSR")
os.system("./install/install_cudnn113.sh")


os.mkdir("ckpt")
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_small_MFID_8K.pytorch -O ckpt/RefVSR_small_MFID_8K.pytorch")
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/SPyNet.pytorch -O ckpt/SPyNet.pytorch")

sys.path.append("RefVSR")

## RefVSR
LR_path = "test/RealMCVSR/test/HR/UW/0000"
Ref_path = "test/RealMCVSR/test/HR/W/0000"
Ref_path_T = "test/RealMCVSR/test/HR/T/0000"
os.makedirs(LR_path)
os.makedirs(Ref_path)
os.makedirs(Ref_path_T)
os.makedirs('result')

os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O LR.png")
os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O Ref.png")

def resize(img):
    max_side = 512
    w = img.size[0]
    h = img.size[1]
    if max(h, w) > max_side:
        scale_ratio = max_side / max(h, w)
        wsize=int(w*scale_ratio)
        hsize=int(h*scale_ratio)
        img = img.resize((wsize,hsize), Image.ANTIALIAS)
    return img
  
def inference(LR, Ref):
    LR = resize(LR)
    Ref = resize(Ref)

    LR.save(os.path.join(LR_path, '0000.png'))
    Ref.save(os.path.join(Ref_path, '0000.png'))
    Ref.save(os.path.join(Ref_path_T, '0000.png'))

    os.system("python -B run.py \
                --mode amp_RefVSR_small_MFID_8K \
                --config config_RefVSR_small_MFID_8K \
                --data RealMCVSR \
                --ckpt_abs_name ckpt/RefVSR_small_MFID_8K.pytorch \
                --data_offset ./test \
                --output_offset ./result \
                --qualitative_only \
                --cpu \
                --is_gradio")

    return "result/0000.png"

title="RefVSR"
description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively."

article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is the small-sized model trained with the proposed two-stage training strategy.</p><p style='text-align: center'>The sample frames are in HD resolution (1920x1080) and in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"

#LR = resize(Image.open('LR.png')).save('LR.png')
#Ref = resize(Image.open('Ref.png')).save('Ref.png')

examples=[['LR.png', 'Ref.png']]

gr.Interface(inference,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True)