Upload app.py

app.py

@@ -143,17 +143,22 @@ 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)
+
+    recommendations = correlations[correlations['book'] != target_book].drop_duplicates(subset=['ISBN'])
+
+    recommendations = recommendations.head(num_recommendations)
 
     result = f"Top {num_recommendations} recommendations for '{target_book}':\n\n"
     result += "\n\n".join([
         f"{idx}. Title: {dataset.loc[dataset['Book-Title'] == row['book'], 'Book-Title'].values[0]}, "
+            f"Author: {dataset.loc[dataset['Book-Title'] == row['book'], 'Book-Author'].values[0]}, "
+    f"Year: {dataset.loc[dataset['Book-Title'] == row['book'], 'Year-Of-Publication'].values[0]}, "
+    f"Publisher: {dataset.loc[dataset['Book-Title'] == row['book'], 'Publisher'].values[0]}, "
         f"ISBN: {dataset.loc[dataset['Book-Title'] == row['book'], 'ISBN'].values[0]}, "
         f"Rating: {ratings_by_isbn.loc[ratings_by_isbn['ISBN'] == dataset.loc[dataset['Book-Title'] == row['book'], 'ISBN'].values[0], 'Book-Rating'].values[0]}"
         for idx, (_, row) in enumerate(recommendations.iterrows(), 1)
     ])
-
+    # "ISBN";"Book-Title";"Book-Author";"Year-Of-Publication";"Publisher";"Image-URL-S"
     return result