diff --git a/webcam.py b/webcam.py
index f437483..d381c71 100644
--- a/webcam.py
+++ b/webcam.py
@@ -260,24 +260,20 @@ model = SPOTER_EMBEDDINGS(
 ).to(device)
 
 model.load_state_dict(checkpoint["state_dict"])
+embeddings = df.drop(columns=['labels', 'label_name', 'embeddings'])
+
+# convert embedding from string to list of floats
+embeddings["embeddings"] = embeddings["embeddings2"].apply(lambda x: [float(i) for i in x[1:-1].split(", ")])
+# drop embeddings2
+embeddings = embeddings.drop(columns=['embeddings2'])
+# to list
+embeddings = embeddings["embeddings"].tolist()
 
 def make_prediction(keypoints):
-    embeddings = df.drop(columns=['labels', 'label_name', 'embeddings'])
-
-    # convert embedding from string to list of floats
-    embeddings["embeddings"] = embeddings["embeddings2"].apply(lambda x: [float(i) for i in x[1:-1].split(", ")])
-    # drop embeddings2
-    embeddings = embeddings.drop(columns=['embeddings2'])
-    # to list
-    embeddings = embeddings["embeddings"].tolist()
-
     # run model on frame
     model.eval()
     with torch.no_grad():
         keypoints = torch.from_numpy(np.array([keypoints])).float().to(device)
-        with open('inputs.txt', 'w') as f:
-            for j in range(keypoints.shape[1]):
-                f.write(str(keypoints[0, j, :].cpu().detach().numpy()) + ' ')
         new_embeddings = model(keypoints).cpu().numpy().tolist()[0]
 
     # calculate distance matrix