Upload app.py

app.py

@@ -143,7 +143,7 @@ def recommend_books(target_book: str, num_recommendations: int = 10) -> str:
 
 
     correlations = compute_correlations_faiss(faiss_index, book_titles, closest_match)
-
+    correlations = correlations.drop_duplicates(subset=['ISBN'])
     recommendations = correlations[correlations['book'] != target_book].head(num_recommendations)
 
     result = f"Top {num_recommendations} recommendations for '{target_book}':\n\n"