Upload app.py

app.py

@@ -85,23 +85,9 @@ def compute_correlations_faiss(index: faiss.IndexFlatIP, dataset,
     k = len(dataset)  # Search for all books
     similarities, I = index.search(emb.astype('float16'), k)
 
-    # # Reduce database and query vectors to 2D for visualization
-    # pca = PCA(n_components=2)
-    # reduced_db = pca.fit_transform(data)
-    # reduced_query = pca.transform(target_vector)
-    #
-    # # Scatter plot
-    # plt.scatter(reduced_db[:, 0], reduced_db[:, 1], label='Database Vectors', alpha=0.5)
-    # plt.scatter(reduced_query[:, 0], reduced_query[:, 1], label='Query Vectors', marker='X', color='red')
-    # plt.legend()
-    # plt.title("PCA Projection of IndexFlatIP Vectors")
-    # plt.show()
-
-
-
     corr_df = pd.DataFrame({
-        'book': [dataset.index(i) for i in I[0]],
-        'corr': similarities[0]
+        'book': [[dataset["Book-Title"][j] for j in list(dataset["Book-Title"])[i]] for i in I[0]],
+        'corr': similarities[0],
     })
     return corr_df.sort_values('corr', ascending=False)