Upgrade the weights loading method to avoid duplicated loading.

app.py

@@ -45,28 +45,20 @@ usage_to_weights_file = {
 }
 
 from transformers import AutoModelForImageSegmentation
-weights_path = 'General'
-birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file[weights_path])), trust_remote_code=True)
+birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
 birefnet.to(device)
 birefnet.eval()
-weights_path = weights_path
 
 
 @spaces.GPU
 def predict(image, resolution, weights_file):
-    global birefnet
-    global weights_path
-    if weights_path != weights_file:
-        print('*' * 10)
-        print('\t1: ', weights_file, weights_path)
+    if weights_file != 'General':
         # Load BiRefNet with chosen weights
         _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else 'BiRefNet'))
         print('Change weights to:', _weights_file)
         birefnet = birefnet.from_pretrained(_weights_file)
         birefnet.to(device)
         birefnet.eval()
-        weights_path = weights_file
-        print('\t2: ', weights_file, weights_path)
 
     resolution = f"{image.shape[1]}x{image.shape[0]}" if resolution == '' else resolution
     # Image is a RGB numpy array.