From 3bdb8877a66a6b762441e4a41b28f742719294e4 Mon Sep 17 00:00:00 2001
From: Mann Patel <130435633+MannPatel0@users.noreply.github.com>
Date: Sat, 12 Apr 2025 21:57:53 -0600
Subject: [PATCH] update server for recom...

---
 .../__pycache__/app.cpython-313.pyc           | Bin 5173 -> 5732 bytes
 recommondation-engine/app.py                  |  11 +++--------
 2 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/recommondation-engine/__pycache__/app.cpython-313.pyc b/recommondation-engine/__pycache__/app.cpython-313.pyc
index d0c168f949d69b8bef6a98623ac8c6a21666d8d0..585d78d02bba7ebf7bec25974be353f56330e8cb 100644
GIT binary patch
delta 898
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diff --git a/recommondation-engine/app.py b/recommondation-engine/app.py
index dfd8ff2..39afd3d 100644
--- a/recommondation-engine/app.py
+++ b/recommondation-engine/app.py
@@ -107,19 +107,14 @@ def get_user_history(user_id):
     db_con.close()
     return final
 
-
-def delete_user_recommendations(userId):
-    pass
-
-
 def get_recommendations(user_id, top_n=10):
     try:
         # Get all products and user history with their category vectors
         all_products = get_all_products()
         user_history = get_user_history(user_id)
-        if not user_history:
-            #Cold start: return popular products
-            return get_popular_products(top_n)
+        # if not user_history:
+        #     #Cold start: return popular products
+        #     return get_popular_products(top_n)
         # Calculate similarity between all products and user history
         user_profile = np.mean(user_history, axis=0)  # Average user preferences
         similarities = cosine_similarity([user_profile], all_products)