Removed some loggings

handler.py

@@ -132,31 +132,31 @@ class EndpointHandler:
     def embed_frames_with_xclip_processing(self, frames):
         # Initialize an empty list to store the frame embeddings
 
-        self.logger.info("Preprocessing frames.")
+        # self.logger.info("Preprocessing frames.")
         frame_preprocessed = self.preprocess_frames(frames)
 
         # Pass the preprocessed frame through the model to get the frame embeddings
-        self.logger.info("Getting video features.")
+        # self.logger.info("Getting video features.")
         frame_embedding = self.model.get_video_features(**frame_preprocessed)
 
         # Check the shape of the tensor
-        self.logger.info(f"Shape of the batch_emb tensor: {frame_embedding.shape}")
+        # self.logger.info(f"Shape of the batch_emb tensor: {frame_embedding.shape}")
 
         # Normalize the embeddings if it's a 2D tensor
         if frame_embedding.dim() == 2:
-            self.logger.info("Normalizing embeddings")
+            # self.logger.info("Normalizing embeddings")
             batch_emb = torch.nn.functional.normalize(frame_embedding, p=2, dim=1)
         else:
-            self.logger.info("Skipping normalization due to tensor shape")
+            # self.logger.info("Skipping normalization due to tensor shape")
             batch_emb = frame_embedding.squeeze(0)
 
-        self.logger.info("Converting into numpy array")
+        # self.logger.info("Converting into numpy array")
         batch_emb = batch_emb.cpu().detach().numpy()
 
-        self.logger.info("Converting to list")
+        # self.logger.info("Converting to list")
         batch_emb = batch_emb.tolist()
 
-        self.logger.info("Returning batch_emb list")
+        # self.logger.info("Returning batch_emb list")
         return batch_emb
 
     def process_video(self, video_url, video_metadata):