updated helper.py

helper.py

@@ -108,7 +108,7 @@ def search(query, df, limit, offset, scoring_func, search_in_images):
         
         # Calculate the cosine similarity between the query vector and each image vector
         query_vector = query_vector[0, :].detach().numpy()  # Detach and convert to a NumPy array
-        image_vectors = image_vectoßrs.detach().numpy()  # Convert the image vectors to a NumPy array
+        image_vectors = image_vectors.detach().numpy()  # Convert the image vectors to a NumPy array
         cosine_similarities = cosine_similarity([query_vector], image_vectors)
 
         # Get the top K indices of the most similar image vectors