Update app_recon.py

app_recon.py

@@ -59,7 +59,6 @@ vae = AutoencoderKL.from_pretrained("./", subfolder='vae')
 scheduler = DDIMScheduler.from_pretrained("./", subfolder='scheduler')
 image_encoder = CLIPVisionModelWithProjection.from_pretrained("./", subfolder="image_encoder")
 feature_extractor = CLIPImageProcessor.from_pretrained("./", subfolder="feature_extractor")
-
 unet = UNet2DConditionModel.from_pretrained('.', subfolder="unet")
 
 pipe = DepthNormalEstimationPipeline(vae=vae,
@@ -197,9 +196,6 @@ def reconstruction(image_file, files):
 
     masked_image, mask = seg_foreground(image_file)
 
-    # import pdb
-    # pdb.set_trace()
-
     mask = np.array(mask) > 0.5
     depth_np = np.load(files[0])
     normal_np = np.load(files[1])