Upload app.py

app.py

@@ -74,7 +74,7 @@ def build_faiss_index(dataset: pd.DataFrame) -> Tuple[faiss.IndexFlatIP, np.ndar
     return index
 
 
-def compute_correlations_faiss(index: faiss.IndexFlatIP, book_titles: List[str],
+def compute_correlations_faiss(index, book_titles: List[str],
                                target_book, ) -> pd.DataFrame:
     print(target_book, type(target_book))
     emb = create_embedding([target_book[0]])
@@ -84,6 +84,7 @@ def compute_correlations_faiss(index: faiss.IndexFlatIP, book_titles: List[str],
     # Perform the search
     k = len(book_titles)  # Search for all books
     similarities, I = index.search(emb.astype('float16'), k)
+    print(similarities, I)
 
     # # Reduce database and query vectors to 2D for visualization
     # pca = PCA(n_components=2)
@@ -142,7 +143,7 @@ def recommend_books(target_book: str, num_recommendations: int = 10) -> str:
     closest_match = process.extractOne(target_book, book_titles)
 
 
-    correlations = compute_correlations_faiss(faiss_index, book_titles, closest_match)
+    correlations = compute_correlations_faiss(faiss_index, list(dataset["Book-Title"]), closest_match)
 
     recommendations = correlations[correlations['book'] != target_book].head(num_recommendations)