Fix the bug in identifying the type of image input.

app.py

@@ -102,15 +102,16 @@ def predict(images, resolution, weights_file):
         resolution = [1024, 1024]
         print('Invalid resolution input. Automatically changed to 1024x1024.')
 
-    print('type(images):', type(images))
     if isinstance(images, list):
+        # For tab_batch
+        save_paths = []
         save_dir = 'preds-BiRefNet'
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)
+        tab_is_batch = True
     else:
-        # For tab_batch
-        save_paths = []
         images = [images]
+        tab_is_batch = False
 
     for idx_image, image_src in enumerate(images):
         if isinstance(image_src, str):
@@ -119,38 +120,38 @@ def predict(images, resolution, weights_file):
             image = np.array(Image.open(image_data))
         else:
             image = image_src
-    
+
         image_shape = image.shape[:2]
         image_pil = array_to_pil_image(image, tuple(resolution))
-    
+
         # Preprocess the image
         image_preprocessor = ImagePreprocessor(resolution=tuple(resolution))
         image_proc = image_preprocessor.proc(image_pil)
         image_proc = image_proc.unsqueeze(0)
-    
+
         # Perform the prediction
         with torch.no_grad():
             scaled_pred_tensor = birefnet(image_proc.to(device))[-1].sigmoid()
-    
+
         if device == 'cuda':
             scaled_pred_tensor = scaled_pred_tensor.cpu()
-        
+
         # Resize the prediction to match the original image shape
         pred = torch.nn.functional.interpolate(scaled_pred_tensor, size=image_shape, mode='bilinear', align_corners=True).squeeze().numpy()
-    
+
         # Apply the prediction mask to the original image
         image_pil = image_pil.resize(pred.shape[::-1])
         pred = np.repeat(np.expand_dims(pred, axis=-1), 3, axis=-1)
         image_pred = (pred * np.array(image_pil)).astype(np.uint8)
-    
+
         torch.cuda.empty_cache()
-        
-        if isinstance(images, list):
+
+        if tab_is_batch:
             save_file_path = os.path.join(save_dir, "{}.png".format(os.path.splitext(os.path.basename(image_src))[0]))
             cv2.imwrite(save_file_path)
             save_paths.append(save_file_path)
 
-    if len(save_paths) > 1:
+    if tab_is_batch:
         zip_file_path = os.path.join(save_dir, "{}.zip".format(save_dir))
         with zipfile.ZipFile(zip_file_path, 'w') as zipf:
             for file in save_paths: