Update src/app.py

src/app.py

@@ -74,10 +74,10 @@ def get_neighbors(query_image, selected_descriptor, selected_distance, top_k=5):
             scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
                 'color_embeddings', qi_np, k=top_k)
         elif selected_distance == "Chi-squared":
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=query_vector, histB=row['color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=qi_embedding, histB=row['color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         else:
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=query_vector, histB=row['color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=qi_embedding, histB=row['color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         images = retrieved_examples['image'] #retrieved images is a dict, with images and embeddings
         return images