| import numpy as np | |
| from sklearn.neighbors import NearestNeighbors | |
| # Convert the feature vectors to a NumPy array | |
| features_array = np.array(list(features_dict.values())) | |
| # Create a NearestNeighbors object and fit it to the features array | |
| knn = NearestNeighbors(n_neighbors=11, metric='cosine') | |
| knn.fit(features_array) | |
| # Define a function to retrieve the most similar images to a query image | |
| def retrieve_similar_images(query_image_name, knn_model, features_dict): | |
| # Get the features for the query image | |
| query_features = features_dict[query_image_name] | |
| # Reshape the features to match the expected input format for the knn_model | |
| query_features = query_features.reshape(1, - |