Update app.py

app.py

@@ -15,6 +15,12 @@ user_embedding_collection = db["user_embeddings"]  # 사용자 임베딩을 저
 tokenizer = BertTokenizer.from_pretrained("klue/bert-base")
 model = BertModel.from_pretrained("klue/bert-base")
 
+# Height와 Weight 스케일링에 필요한 값 설정
+min_height = 50
+max_height = 250
+min_weight = 30
+max_weight = 200
+
 # 상품 타워: 데이터 임베딩
 def embed_product_data(product_data):
     # 상품명과 상세 정보 임베딩 (BERT)
@@ -48,10 +54,23 @@ def embed_user_data(user_data):
 
     # 예를 들어 성별을 'M'은 0, 'F'는 1로 인코딩
     gender_id = 0 if user_data['gender'] == 'M' else 1
+
+    # 스케일링 적용
+    height = user_data['height']
+    weight = user_data['weight']
+
+    if not (min_height <= height <= max_height):
+        raise ValueError(f"Invalid height value: {height}. Expected range: {min_height}-{max_height}")
+    if not (min_weight <= weight <= max_weight):
+        raise ValueError(f"Invalid weight value: {weight}. Expected range: {min_weight}-{max_weight}")
+
+    scaled_height = (height - min_height) * 99 // (max_height - min_height)
+    scaled_weight = (weight - min_weight) * 99 // (max_weight - min_weight)
+    
     age_embedding = embedding_layer(torch.tensor([user_data['age']]))
     gender_embedding = embedding_layer(torch.tensor([gender_id]))
-    height_embedding = embedding_layer(torch.tensor([user_data['height']]))
-    weight_embedding = embedding_layer(torch.tensor([user_data['weight']]))
+    height_embedding = embedding_layer(torch.tensor([scaled_height]))
+    weight_embedding = embedding_layer(torch.tensor([scaled_weight]))
 
     # 최종 임베딩 벡터 결합
     user_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)