diff --git a/README.md b/README.md
index f10f45e..29a9b45 100644
--- a/README.md
+++ b/README.md
@@ -19,19 +19,21 @@ and use deepfake operator to predict the probabilities of fake videos.
 
 
 ```python
-import towhee
-(
-    towhee.glob['path']('/home/test_video')
-          .deepfake['path', 'scores']()
-          .select['path', 'scores']()
-          .show()
+from towhee.dc2 import pipe, ops, DataCollection
+
+p = (
+    pipe.input('path')
+        .map('path', 'scores', ops.towhee.deepfake)
+        .output('scores')
 )
+
+DataCollection(p('./deepfake_video/test/aagfhgtpmv.mp4').get_dict()).show()
 ```
 
 <img src="./deepfake.png" height="100px"/>
 
 ```shell
-[0.9893, 0.9097]
+[0.99]
 ```
 
 <br />
@@ -50,4 +52,4 @@ Absolute address of the test videos.
 
 **Returns:** *list*
 
-The probabilities of videos being fake ones.
\ No newline at end of file
+The probabilities of videos being fake ones.
diff --git a/deepfake.py b/deepfake.py
index f93a1fb..cdc133d 100644
--- a/deepfake.py
+++ b/deepfake.py
@@ -29,8 +29,7 @@ class Deepfake(NNOperator):
         weights_dir = os.path.join(str(Path(__file__).parent),"weights/")
         self.model_paths = [os.path.join(weights_dir,model) for model in os.listdir(weights_dir)]
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        
-    def __call__(self, filepath: string) -> list:
+
         from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video
         from classifiers import DeepFakeClassifier
         models = []
@@ -42,17 +41,21 @@ class Deepfake(NNOperator):
             model.load_state_dict({re.sub("^module.", "", k): v for k, v in state_dict.items()}, strict=False)
             model.eval()
             del checkpoint
-            models.append(model.half())
-        frames_per_video = 32
+            models.append(model.float())
+        self.frames_per_video = 32
         video_reader = VideoReader()
-        video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)
-        face_extractor = FaceExtractor(video_read_fn)
-        input_size = 384
-        strategy = confident_strategy
-        #stime = time.time()
-        prediction = predict_on_video(False, face_extractor=face_extractor, video_path=filepath, 
-                                    input_size=input_size, batch_size=frames_per_video, models=models, 
-                                    strategy=strategy, apply_compression=False)
+        video_read_fn = lambda x: video_reader.read_frames(x, num_frames=self.frames_per_video)
+
+        self.face_extractor = FaceExtractor(video_read_fn)
+        self.input_size = 384
+        self.strategy = confident_strategy
+        self.models = models 
+        self.predict_on_video = predict_on_video
+        
+    def __call__(self, filepath: string) -> list:
+        prediction = self.predict_on_video(False, face_extractor=self.face_extractor, video_path=filepath, 
+                                    input_size=self.input_size, batch_size=self.frames_per_video, models=self.models, 
+                                    strategy=self.strategy, apply_compression=False)
         '''
         test_videos = sorted([x for x in os.listdir(filepath) if x[-4:] == ".mp4"])
         print("Predicting {} videos".format(len(test_videos)))