Fix a bug in loading different types of the input in tab_batch.

app.py

@@ -57,32 +57,6 @@ birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7',
 birefnet.to(device)
 birefnet.eval()
 
-    # for idx, image_path in enumerate(images):
-    #     im = load_img(image_path, output_type="pil")
-    #     if im is None:
-    #         continue
-        
-    #     im = im.convert("RGB")
-    #     image_size = im.size
-    #     input_images = transform_image(im).unsqueeze(0).to("cpu")
-        
-    #     with torch.no_grad():
-    #         preds = birefnet(input_images)[-1].sigmoid().cpu()
-    #     pred = preds[0].squeeze()
-    #     pred_pil = transforms.ToPILImage()(pred)
-    #     mask = pred_pil.resize(image_size)
-        
-    #     im.putalpha(mask)
-    #     output_file_path = os.path.join(save_dir, f"output_image_batch_{idx + 1}.png")
-    #     im.save(output_file_path)
-    #     output_paths.append(output_file_path)
-
-    # zip_file_path = os.path.join(save_dir, "processed_images.zip")
-    # with zipfile.ZipFile(zip_file_path, 'w') as zipf:
-    #     for file in output_paths:
-    #         zipf.write(file, os.path.basename(file))
-
-    # return output_paths, zip_file_path
 
 @spaces.GPU
 def predict(images, resolution, weights_file):
@@ -115,9 +89,13 @@ def predict(images, resolution, weights_file):
 
     for idx_image, image_src in enumerate(images):
         if isinstance(image_src, str):
-            response = requests.get(image_src)
-            image_data = BytesIO(response.content)
-            image = np.array(Image.open(image_data))
+            if os.path.isfile(image_src):
+                image = np.array(Image.open(image_src))
+            else:
+                image = np.array(Image.open(image_src))
+                response = requests.get(image_src)
+                image_data = BytesIO(response.content)
+                image = np.array(Image.open(image_data))
         else:
             image = image_src