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