Update app.py

app.py

@@ -20,3 +20,37 @@ 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():
+        result = model(y)
+            
+
+    restored_img = result.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)
+
+title = "Efficient Hazy Vehicle Detection ✏️[] 🤗"
+description = ''' ## [Efficient Hazy Vehicle Detection](https://github.com/cidautai)
+[Paula Garrido Mellado](https://github.com/paugar5)
+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>
+'''
+
+
+    
+