Update app.py

app.py

@@ -8,27 +8,16 @@ import torch
 from transformers import AutoTokenizer, CLIPTextModelWithProjection
 
 
-DATA_PATH = '/data'
+DATA_PATH = './data'
 
-ft_visual_features_file = DATA_PATH + '/features/features_Cl4Cl_ckpt_webvid_retrieval_looseType_bs26_gpus2_lr7_150k_finalsample/dataset_v1_visual_features_database.npy'
-binary_visual_features_file = DATA_PATH + '/features/features_Cl4Cl_ckpt_webvid_retrieval_looseType_bs26_gpus2_lr7_150k_finalsample_binary20/dataset_v1_visual_features_database_packed.npy'
+ft_visual_features_file = DATA_PATH + '/dataset_v1_visual_features_database.npy'
+binary_visual_features_file = DATA_PATH + '/dataset_v1_visual_features_database_packed.npy'
 ft_visual_features_database = np.load(ft_visual_features_file)
 binary_visual_features = np.load(binary_visual_features_file)
 
 database_csv_path = os.path.join(DATA_PATH, 'dataset_v1.csv')
 database_df = pd.read_csv(database_csv_path)
 
-#Gradio can display URL
-def display_videos(display_df):
-    display_path_list = display_df['contentUrl'].to_list()
-
-    display_text_list = display_df['name'].to_list()
-    html = ''
-    for path, text in zip(display_path_list, display_text_list):
-        html_line = '<video autoplay loop {}> <source src="{}" type="video/mp4"> </video> <div class="caption">{}</div><br/>'.format("muted", path, text)
-        html += html_line
-    return display.HTML(html)
-
 
 class NearestNeighbors:
     """