Update app.py

app.py

@@ -52,7 +52,7 @@ def load_image_from_url(url, max_retries=3):
             else:
                 return None
 
-# 기존의 get_image_embedding 함수도 수정
+# get_image_embedding 함수 수정
 def get_image_embedding(image):
     image_tensor = preprocess_val(image).unsqueeze(0).to(device)
     with torch.no_grad():
@@ -82,7 +82,7 @@ def get_average_embedding(main_image_url, additional_image_urls):
     
     if embeddings:
         avg_embedding = np.mean(embeddings, axis=0)
-        return avg_embedding.squeeze().tolist()  # numpy 배열을 파이썬 리스트로 변환
+        return avg_embedding.tolist()  # numpy 배열을 파이썬 리스트로 변환
     else:
         return None
 
@@ -105,7 +105,7 @@ def update_collection_embeddings():
             try:
                 avg_embedding = get_average_embedding(main_image_url, additional_image_urls)
                 if avg_embedding is not None:
-                    batch_embeddings.append(avg_embedding)  # 이미 리스트 형태로 반환됨
+                    batch_embeddings.append(avg_embedding)  # 이미 리스트 형태임
                     valid_ids.append(id)
                 else:
                     st.warning(f"Failed to generate embedding for item {id}")
@@ -122,11 +122,10 @@ def update_collection_embeddings():
             except Exception as e:
                 st.error(f"Error updating embeddings: {str(e)}")
                 st.error(f"First embedding type: {type(batch_embeddings[0])}")
-                st.error(f"First embedding shape: {len(batch_embeddings[0])}")
+                st.error(f"First embedding length: {len(batch_embeddings[0])}")
         
         # 진행 상황 표시
         st.progress((i + batch_size) / len(all_ids))
-
 def find_similar_images(query_embedding, collection, top_k=5):
     results = collection.query(
         query_embeddings=[query_embedding.squeeze().tolist()],