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Update app.py
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app.py
CHANGED
@@ -51,7 +51,6 @@ def embed_product_data(product_data):
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combined_embedding = torch.cat((text_embedding, category_embedding, color_embedding), dim=1)
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product_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
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print(f"Generated product_embedding shape: {product_embedding.shape}") # Debugging
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return product_embedding.detach().numpy()
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# 사용자 타워: 데이터 임베딩
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@@ -83,7 +82,6 @@ def embed_user_data(user_data):
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combined_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)
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user_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
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print(f"Generated user_embedding shape: {user_embedding.shape}")
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return user_embedding.detach().numpy()
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# MongoDB Atlas에서 데이터 가져오기
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@@ -172,5 +170,5 @@ def recommend_products_for_user(user_id, top_n=1):
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# 사용자 맞춤 추천 실행
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user_id_to_recommend = 1 # 추천할 사용자 ID
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top_n_recommendations =
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recommended_products = recommend_products_for_user(user_id_to_recommend, top_n=top_n_recommendations)
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combined_embedding = torch.cat((text_embedding, category_embedding, color_embedding), dim=1)
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product_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
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return product_embedding.detach().numpy()
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# 사용자 타워: 데이터 임베딩
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combined_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)
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user_embedding = torch.nn.functional.adaptive_avg_pool1d(combined_embedding.unsqueeze(0), 512).squeeze(0)
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return user_embedding.detach().numpy()
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# MongoDB Atlas에서 데이터 가져오기
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# 사용자 맞춤 추천 실행
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user_id_to_recommend = 1 # 추천할 사용자 ID
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top_n_recommendations = 2 # 추천 상품 개수
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recommended_products = recommend_products_for_user(user_id_to_recommend, top_n=top_n_recommendations)
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