app.py

import gradio as gr
import torch
import torchvision.transforms as transforms
import numpy as np

from PIL import Image
from model.flol import create_model

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
#define some auxiliary functions
pil_to_tensor = transforms.ToTensor()

# Define a dictionary to map image filenames to weight files
image_to_weights = ['./weights/flolv2_UHDLL.pt','./weights/flolv2_all_111439.pt']

# Initial model setup (without weights)
model = create_model()

def load_img(filename):
    img = Image.open(filename).convert("RGB")
    img_tensor = pil_to_tensor(img)
    return img_tensor

def process_img(image, UHD_LL_model):
    # Select the correct weight file based on the image filename

    # filename = image.name.split("/")[-1]
    # if filename in image_to_weights:
    model_path = image_to_weights[0] if UHD_LL_model else image_to_weights[1]
    checkpoints = torch.load(model_path, map_location=device)
    model.load_state_dict(checkpoints['params'])
    model.to(device)

    img = np.array(image)
    img = img / 255.  # Normalize to [0, 1]
    img = img.astype(np.float32)
    y = torch.tensor(img).permute(2, 0, 1).unsqueeze(0).to(device)

    with torch.no_grad():
        x_hat = model(y)

    restored_img = x_hat.squeeze().permute(1, 2, 0).clamp_(0, 1).cpu().detach().numpy()
    restored_img = np.clip(restored_img, 0., 1.)

    restored_img = (restored_img * 255.0).round().astype(np.uint8)  # Convert to uint8
    return Image.fromarray(restored_img)


title = "Fast Baselines for Real-World Low-Light Enhancement 🌠⚡🎆"
description = ''' ## [Github Repository](https://github.com/cidautai/NAFourNet)
[Juan Carlos Benito](https://github.com/juaben)
Fundación Cidaut
> **Disclaimer:** please remember this is not a product, thus, you will notice some limitations.
**This demo expects an image with some degradations. If the checkbox is selected, the program will load the model related to UHD-LL dataset, if not it will load LOLv2-Real weight file.**
Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K).
<br>
'''

examples = [
            ['images/low00772.png'], 
            ['images/low00723.png'], 
            ['images/425_UHD_LL.JPG'],
            ['images/1778_UHD_LL.JPG'],
            ['images/1791_UHD_LL.JPG']
           ]

css = """
    .image-frame img, .image-container img {
        width: auto;
        height: auto;
        max-width: none;
    }
"""



demo = gr.Interface(
    fn = process_img,
    inputs = [gr.Image(type = 'pil', label = 'input'), 'checkbox'],
    outputs = [gr.Image(type='pil', label = 'output')],
    title = title,
    description = description,
    examples = examples,
    css = css
)

if __name__ == '__main__':
    demo.launch()