Update app.py

app.py

@@ -56,47 +56,23 @@ ENDING = """For search acceleration capabilities, please refer to [Searchium.ai]
             """
 
 
-
-
 DATA_PATH = '/home/user'
-# 1. 加载模型和处理器（只下载模型权重，约几百MB）
-model = CLIPModel.from_pretrained("Searchium-ai/clip4clip-webvid150k")
-processor = CLIPProcessor.from_pretrained("Searchium-ai/clip4clip-webvid150k")
-
-# 2. 将模型移到GPU（如果可用）或CPU
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
-
-# 3. 如果需要提取文本嵌入，可以这样做：
-def get_text_embedding(text):
-    inputs = processor(text=text, return_tensors="pt", padding=True, truncation=True)
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    with torch.no_grad():
-        text_features = model.get_text_features(**inputs)
-    return text_features.cpu().numpy()
+downloaded_path = huggingface_hub.snapshot_download(
+    repo_id="Searchium-ai/clip4clip-webvid150k",
+    repo_type="model",
+    cache_dir=DATA_PATH)
 
-# 4. 如果需要提取视频帧嵌入，可以这样做：
-def get_video_embedding(frames):
-    # frames: 一个PIL Image列表，每个元素是一帧
-    inputs = processor(images=frames, return_tensors="pt", padding=True)
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    with torch.no_grad():
-        video_features = model.get_image_features(**inputs)
-    # 对于多帧视频，可以对帧特征取平均
-    return video_features.mean(dim=0).cpu().numpy()
-
-ft_visual_features_file = downloaded_path +'/new_data/video_half_dataset_visual_features.npy'
+ft_visual_features_file = downloaded_path + '/new_data/video_half_dataset_visual_features.npy'
 ft_visual_features_file_bin = downloaded_path + '/new_data/video_half_dataset_visual_features_binary_packed.npy'
 
-#load database features:
+# load database features:
 ft_visual_features_database_bin = np.load(ft_visual_features_file_bin)
 ft_visual_features_database = np.load(ft_visual_features_file, mmap_mode='r')
 
-
-
 database_csv_path = downloaded_path + '/new_data/video_half_dataset.csv'
 database_df = pd.read_csv(database_csv_path)
 
+
 class NearestNeighbors:
     """
     Class for NearestNeighbors.