Update app.py

app.py

@@ -103,9 +103,8 @@ def segment_image(image_path, client):
         # 원본 이미지를 다시 읽어 반환
         return Image.open(image_path)
 
-# Process database with segmentation
 @st.cache_data
-def process_database(client):
+def process_database_cached(data):
     database_embeddings = []
     database_info = []
     for item in data:
@@ -120,10 +119,6 @@ def process_database(client):
         temp_path = f"temp_{product_id}.jpg"
         image.save(temp_path, 'JPEG')
         
-        segmented_image = segment_image(temp_path, client)
-        embedding = get_image_embedding(segmented_image)
-        
-        database_embeddings.append(embedding)
         database_info.append({
             'id': product_id,
             'category': item['카테고리'],
@@ -131,9 +126,21 @@ def process_database(client):
             'name': item['제품명'],
             'price': item['정가'],
             'discount': item['할인율'],
-            'image_url': image_url
+            'image_url': image_url,
+            'temp_path': temp_path
         })
     
+    return database_info
+
+def process_database(client, data):
+    database_info = process_database_cached(data)
+    database_embeddings = []
+    
+    for item in database_info:
+        segmented_image = segment_image(item['temp_path'], client)
+        embedding = get_image_embedding(segmented_image)
+        database_embeddings.append(embedding)
+    
     return np.vstack(database_embeddings), database_info
 
 # Streamlit app
@@ -146,7 +153,7 @@ if api_key:
     CLIENT = setup_roboflow_client(api_key)
     
     # Initialize database_embeddings and database_info
-    database_embeddings, database_info = process_database(CLIENT)
+    database_embeddings, database_info = process_database(CLIENT, data)
 
     uploaded_file = st.file_uploader("Choose an image...", type="jpg")
     if uploaded_file is not None: