Update app.py

app.py

@@ -51,14 +51,17 @@ ENDING = """For search acceleration capabilities, please refer to [Searchium.ai]
             """
 
 
-DATA_PATH = './new_data'
+DATA_PATH = './data'
 
 ft_visual_features_file = DATA_PATH + '/video_dataset_visual_features_database.npy'
+ft_visual_features_file_bin = DATA_PATH + '/video_dataset_visual_features_database_packed.npy'
 
 #load database features:
-ft_visual_features_database = np.load(ft_visual_features_file)
+ft_visual_features_database_bin = np.load(ft_visual_features_file_bin)
+ft_visual_features_database = p.memmap(ft_visual_features_file, dtype='float32', mode='r', 
+                                        shape=(ft_visual_features_database_bin.shape[0], ft_visual_features_database_bin.shape[1]*8))
 
-database_csv_path = os.path.join(DATA_PATH, 'half_video_dataset.csv')
+database_csv_path = os.path.join(DATA_PATH, 'video_dataset.csv')
 database_df = pd.read_csv(database_csv_path)
 
 class NearestNeighbors:
@@ -117,7 +120,7 @@ model = CLIPTextModelWithProjection.from_pretrained("Searchium-ai/clip4clip-webv
 tokenizer = CLIPTokenizer.from_pretrained("Searchium-ai/clip4clip-webvid150k")
 
 nn_search = NearestNeighbors(n_neighbors=5, metric='binary', rerank_from=100)
-nn_search.fit(np.packbits((ft_visual_features_database > 0.0).astype(bool), axis=1), o_data=ft_visual_features_database)
+nn_search.fit(ft_visual_features_database_bin, o_data=ft_visual_features_database)
 
 def search(search_sentence):
     inputs = tokenizer(text=search_sentence , return_tensors="pt")