Update app.py

app.py

@@ -21,7 +21,6 @@ torch.set_float32_matmul_precision('high')
 torch.jit.script = lambda f: f
 
 device = "cuda" if torch.cuda.is_available() else "CPU"
-counter_runtime = 0
 
 
 def array_to_pil_image(image: np.ndarray, size: Tuple[int, int] = (1024, 1024)) -> Image.Image:
@@ -68,8 +67,7 @@ def predict(image, resolution, weights_file):
     global birefnet
     # Load BiRefNet with chosen weights
     _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else usage_to_weights_file['General']))
-    global counter_runtime; counter_runtime += 1
-    print('Using weights: {} in runtime {}'.format(_weights_file, counter_runtime))
+    print('Using weights: {}.'.format(_weights_file))
     birefnet = AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
     birefnet.to(device)
     birefnet.eval()