diff --git a/clipcap.py b/clipcap.py
index f99c8f8..635b5c9 100644
--- a/clipcap.py
+++ b/clipcap.py
@@ -11,11 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+import sys
 import os
-import torch
 from pathlib import Path
+
+import torch
 from torchvision import transforms
+from transformers import GPT2Tokenizer
 
+from towhee.types.arg import arg, to_image_color
 from towhee.types.image_utils import to_pil
 from towhee.operator.base import NNOperator, OperatorFlag
 from towhee import register
@@ -26,11 +31,16 @@ class ClipCap(NNOperator):
     ClipCap image captioning operator
     """
     def __init__(self, model_name: str):
-        super().__init__():
+        super().__init__()
         sys.path.append(str(Path(__file__).parent))
-        from models.clipcap import ClipCaptionModel
+        from models.clipcap import ClipCaptionModel, generate_beam
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.generate_beam = generate_beam
+        self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
         config = self._configs()[model_name]
 
+        self.prefix_length = 10
+
         self.clip_tfms = self.tfms = transforms.Compose([
             transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
             transforms.CenterCrop(224),
@@ -42,38 +52,38 @@ class ClipCap(NNOperator):
         clip_model_type = 'clip_vit_b32'
         self.clip_model = clip.create_model(model_name=clip_model_type, pretrained=True, jit=True)
 
-        self.model = ClipCaptionModel(prefix = 10)
+        self.model = ClipCaptionModel(self.prefix_length)
         model_path = os.path.dirname(__file__) + '/weights/' + config['weights']
-        self.model.load_state_dict(torch.load(model_path, map_location=CPU))
-        self.model = model.eval()
+        self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+        self.model = self.model.eval()
 
         
     @arg(1, to_image_color('RGB'))
-    def __call__(self, data:):
+    def __call__(self, data):
         vec = self._inference_from_image(data)
         return vec
 
     def _preprocess(self, img):
         img = to_pil(img)
-        processed_img = self.self.clip_tfms(img).unsqueeze(0).to(self.device)
+        processed_img = self.clip_tfms(img).unsqueeze(0).to(self.device)
         return processed_img
 
     @arg(1, to_image_color('RGB'))
     def _inference_from_image(self, img):
         img = self._preprocess(img)
-        clip_feat = self.clip_model.encode_image(image)
+        clip_feat = self.clip_model.encode_image(img)
 
-        prefix_length = 10
-        prefix_embed = self.model.clip_project(clip_feat).reshape(1, prefix_length, -1)
+        self.prefix_length = 10
+        prefix_embed = self.model.clip_project(clip_feat).reshape(1, self.prefix_length, -1)
 
-        generated_text_prefix = generate_beam(model, tokenizer, embed=prefix_embed)[0]
+        generated_text_prefix = self.generate_beam(self.model, self.tokenizer, embed=prefix_embed)[0]
         return generated_text_prefix
 
     def _configs(self):
         config = {}
         config['clipcap_coco'] = {}
-        config['clipcap_coco']['weights'] = 'weights/coco_weights.pt'
+        config['clipcap_coco']['weights'] = 'coco_weights.pt'
         config['clipcap_conceptual'] = {}
-        config['clipcap_conceptual']['weights'] = 'weights/conceptual_weights.pt'
+        config['clipcap_conceptual']['weights'] = 'conceptual_weights.pt'
         return config