Update recommend.py

recommend.py

@@ -13,7 +13,7 @@ def recommend(query, n=5):
     # Encode the query
     q_embedding = model.encode(query)
     # Calculate the cosine similarity
-    cos_sim = np.array([1 - cosine(q_embedding, emb) for emb in a_embeddings)
+    cos_sim = np.array([1 - cosine(q_embedding, emb) for emb in a_embeddings])
     # Get the top n recommendations
     top_n = np.argsort(cos_sim)[-n:]
     return data.iloc[top_n]['title']