import os
import gradio as gr
from PIL import Image


os.system('wget https://github.com/FanChiMao/HWMNet/releases/download/v0.0/LOL_enhancement_HWMNet.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/HWMNet/releases/download/v0.0/MIT5K_enhancement_HWMNet.pth -P experiments/pretrained_models')

def inference(img, model):
    os.system('mkdir test')
    #basewidth = 256
    #wpercent = (basewidth / float(img.size[0]))
    #hsize = int((float(img.size[1]) * float(wpercent)))
    #img = img.resize((basewidth, hsize), Image.ANTIALIAS)
    img.save("test/1.png", "PNG")
    if model == 'LOL':
        os.system('python main_test_HWMNet.py --input_dir test --weights experiments/pretrained_models/LOL_enhancement_HWMNet.pth')
    elif model == 'MIT-5K':
        os.system('python main_test_HWMNet.py --input_dir test --weights experiments/pretrained_models/MIT5K_enhancement_HWMNet.pth')
        
    return 'result/1.png'


title = "<span style='color: crimson;'>Aiconvert.online</span>"
description = ""
article = ""

examples = [['low-light.png', 'LOL'], ['low-light_2.png', 'MIT-5K']]
gr.Interface(
    inference,
    [gr.inputs.Image(type="pil", label="Input"), gr.inputs.Dropdown(choices=['LOL', 'MIT-5K'], type="value", default='LOL', label="model")],
    gr.Image(type="filepath", label="Output",show_share_button=False),
    title=title,
    description=description,
    article=article,
    css="footer{display:none !important;}",
    allow_flagging=False,
    allow_screenshot=False,
    theme=gr.themes.Base()
).launch(debug=True)