use hf_hub_download

app.py

@@ -15,14 +15,17 @@ from transforms import (
 )
 
 import gradio as gr
+from huggingface_hub import hf_hub_download
 
 # Device on which to run the model
 # Set to cuda to load on GPU
 device = "cpu"
-os.system("wget https://cdn-lfs.huggingface.co/Andy1621/uniformer/d5fd7b0c49ee6a5422ef5d0c884d962c742003bfbd900747485eb99fa269d0db")
+# os.system("wget https://cdn-lfs.huggingface.co/Andy1621/uniformer/d5fd7b0c49ee6a5422ef5d0c884d962c742003bfbd900747485eb99fa269d0db")
+model_path = hf_hub_download(repo_id="Andy1621/uniformer", filename="uniformer_small_k400_16x8.pth")
 # Pick a pretrained model 
 model = uniformer_small()
-state_dict = torch.load('d5fd7b0c49ee6a5422ef5d0c884d962c742003bfbd900747485eb99fa269d0db', map_location='cpu')
+# state_dict = torch.load('d5fd7b0c49ee6a5422ef5d0c884d962c742003bfbd900747485eb99fa269d0db', map_location='cpu')
+state_dict = torch.load(model_path, map_location='cpu')
 model.load_state_dict(state_dict)
 
 # Set to eval mode and move to desired device