Upload 2 files

app.py

@@ -33,9 +33,7 @@ num_users = 943
 def recommend(user_id):
 
     ground_truth_items, recommendations = utils.predict(lightGCNModel, device, data, num_users, num_items, user_id, train_edge_label_index, k=5)
-    return ground_truth_items, recommendations
-
-
+    return ' '.join(ground_truth_items['title'].tolist()), ' '.join(recommendations)
 
 iface = gr.Interface(fn=recommend, inputs="number", outputs=["text", "text"])
 iface.launch()

utils.py

@@ -56,6 +56,6 @@ def predict(model, device, data, num_users, num_items, user_id, train_edge_label
         for index in top_ratings:
             asin_of_item = unique_items[index]
             recommended_item = meta_dataframe[meta_dataframe['asin'] == asin_of_item]['title'].values
-            recommended_items.append(recommended_item)
+            recommended_items.append(recommended_item.item())
 
         return ground_truth_items, recommended_items