edit data

app.py

@@ -7,7 +7,7 @@ data = pd.read_csv(r"data_final.csv")
 def product_recommender(customer_id):
     list_predicted = []
     
-    for id in df_ph_final['product_id'].unique():
+    for id in data['product_id'].unique():
         preds = list(algo.predict(customer_id, id))
         product_id = preds[1]
         product_score = preds[3]
@@ -17,11 +17,11 @@ def product_recommender(customer_id):
     top_5_products_raw = sorted(list_predicted, key=lambda x:x[1], reverse=True)[:5]
     top_5_products = [product[0] for product in top_5_products_raw]
 
-    product_1_category = df_ph_final[df_ph_final['product_id']==top_5_products[0]]['category'].values[0]
-    product_2_category = df_ph_final[df_ph_final['product_id']==top_5_products[1]]['category'].values[0]
-    product_3_category = df_ph_final[df_ph_final['product_id']==top_5_products[2]]['category'].values[0]
-    product_4_category = df_ph_final[df_ph_final['product_id']==top_5_products[3]]['category'].values[0]
-    product_5_category = df_ph_final[df_ph_final['product_id']==top_5_products[4]]['category'].values[0]
+    product_1_category = data[data['product_id']==top_5_products[0]]['category'].values[0]
+    product_2_category = data[data['product_id']==top_5_products[1]]['category'].values[0]
+    product_3_category = data[data['product_id']==top_5_products[2]]['category'].values[0]
+    product_4_category = data[data['product_id']==top_5_products[3]]['category'].values[0]
+    product_5_category = data[data['product_id']==top_5_products[4]]['category'].values[0]
 
     result_1 = f"Recommendation Product ID {top_5_products[0]} with Category {product_1_category}"
     result_2 = f"Recommendation Product ID {top_5_products[1]} with Category {product_2_category}"