Update app.py

app.py

@@ -37,7 +37,7 @@ pipe_depth = StableDiffusionControlNetPipeline.from_pretrained(
 
 # Load a quick, highly accurate MiDaS model pipeline via torch hub
 midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small").to(device).eval()
-midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+
 
 
 @spaces.GPU
@@ -121,6 +121,7 @@ def extract_depth_map(image_path):
     Utilizes a lightweight MiDaS pipeline to extract depth estimation layouts.
     """
     transform_depth = midas_transforms.flag_transform if hasattr(midas_transforms, 'flag_transform') else midas_transforms.dpt_transform
+    midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
     
     pil_image = Image.open(image_path).convert("RGB")
     cv_img = np.array(pil_image)