Create app.py

app.py

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+import gradio as gr 
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+import numpy as np
+
+from archs.model import FourNet
+
+
+opt = parse(path_opt)
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+#define some auxiliary functions
+pil_to_tensor = transforms.ToTensor()
+    
+# define some parameters based on the run we want to make
+
+model = FourNet(nf = 16)
+
+checkpoints = torch.load('./models/NAFourNet16_LOLv2Real.pt', map_location=device)
+
+model.load_state_dict(checkpoints['model_state_dict'])
+
+model = model.to(device)
+
+def load_img (filename):
+    img = Image.open(filename).convert("RGB")
+    img_tensor = pil_to_tensor(img)
+    return img_tensor
+
+def process_img(image):
+    img = np.array(image)
+    img = img / 255.
+    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)  # float32 to uint8
+    return Image.fromarray(restored_img) #(image, Image.fromarray(restored_img))
+
+title = "Efficient Low-Light Enhancement ✏️🖼️ 🤗"
+description = ''' ## [Efficient Low-Light Enhancement](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.**
+Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K).
+
+<br>
+'''
+
+examples = [['examples/inputs/0010.png'],
+            ['examples/inputs/0060.png'], 
+            ['examples/inputs/0075.png'], 
+            ["examples/inputs/0087.png"], 
+            ["examples/inputs/0088.png"]]
+
+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')
+    ],
+    outputs = [gr.Image(type='pil', label = 'output')],
+    title = title,
+    description = description,
+    examples = examples,
+    css = css
+)
+
+if __name__ == '__main__':
+    demo.launch()