Added timeit loggings

handler.py

@@ -103,31 +103,31 @@ 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):
@@ -136,9 +136,9 @@ class EndpointHandler:
             download_start_time = timeit.default_timer()
             video_bytes, video_headers = self.download_video_as_bytes(video_url)
             download_end_time = timeit.default_timer()
-                    self.logger.info(
-                        f"Video downloading took {download_end_time - download_start_time} seconds"
-                    )
+            self.logger.info(
+                f"Video downloading took {download_end_time - download_start_time} seconds"
+            )
             self.logger.info("Extracting frames.")
             processing_start_time = timeit.default_timer()
             frames = self.extract_evenly_spaced_frames_from_bytes(
@@ -146,15 +146,15 @@ class EndpointHandler:
             )
             processing_end_time = timeit.default_timer()
             self.logger.info(
-                        f"Extracting video frames took {processing_end_time - processing_start_time} seconds"
-                    )
+                f"Extracting video frames took {processing_end_time - processing_start_time} seconds"
+            )
             self.logger.info("Embedding frames with Xclip.")
             embedding_start_time = timeit.default_timer()
             frame_embeddings = self.embed_frames_with_xclip_processing(frames)
             embedding_end_time = timeit.default_timer()
             self.logger.info(
-                    f"Embedding calculation took {embedding_end_time - embedding_start_time} seconds"
-                )
+                f"Embedding calculation took {embedding_end_time - embedding_start_time} seconds"
+            )
             video_metadata["url"] = video_url
             self.logger.info("Returning embeddings and metadata.")
             return frame_embeddings, video_metadata