waseoke commited on
Commit
79dc05d
·
verified ·
1 Parent(s): c123a46

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +1 -3
app.py CHANGED
@@ -51,7 +51,6 @@ def embed_product_data(product_data):
51
  combined_embedding = torch.cat((text_embedding, category_embedding, color_embedding), dim=1)
52
  product_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
53
 
54
- print(f"Generated product_embedding shape: {product_embedding.shape}") # Debugging
55
  return product_embedding.detach().numpy()
56
 
57
  # 사용자 타워: 데이터 임베딩
@@ -83,7 +82,6 @@ def embed_user_data(user_data):
83
  combined_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)
84
  user_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
85
 
86
- print(f"Generated user_embedding shape: {user_embedding.shape}")
87
  return user_embedding.detach().numpy()
88
 
89
  # MongoDB Atlas에서 데이터 가져오기
@@ -172,5 +170,5 @@ def recommend_products_for_user(user_id, top_n=1):
172
 
173
  # 사용자 맞춤 추천 실행
174
  user_id_to_recommend = 1 # 추천할 사용자 ID
175
- top_n_recommendations = 1 # 추천 상품 개수
176
  recommended_products = recommend_products_for_user(user_id_to_recommend, top_n=top_n_recommendations)
 
51
  combined_embedding = torch.cat((text_embedding, category_embedding, color_embedding), dim=1)
52
  product_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
53
 
 
54
  return product_embedding.detach().numpy()
55
 
56
  # 사용자 타워: 데이터 임베딩
 
82
  combined_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)
83
  user_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
84
 
 
85
  return user_embedding.detach().numpy()
86
 
87
  # MongoDB Atlas에서 데이터 가져오기
 
170
 
171
  # 사용자 맞춤 추천 실행
172
  user_id_to_recommend = 1 # 추천할 사용자 ID
173
+ top_n_recommendations = 2 # 추천 상품 개수
174
  recommended_products = recommend_products_for_user(user_id_to_recommend, top_n=top_n_recommendations)