using cached_download

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from huggingface_hub import hf_hub_url, cached_download
 from matplotlib import cm 
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1 import make_axes_locatable
@@ -226,7 +227,9 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
     # config = EfficientNetConfig.from_pretrained(model_loading_name)
     
     # model = EfficientNetPreTrained.from_pretrained(model_loading_name)
-    model = AutoModel.from_pretrained(model_loading_name, trust_remote_code=True)
+    # model = AutoModel.from_pretrained(model_loading_name, trust_remote_code=True)
+    
+    model = cached_download(hf_hub_url(model_loading_name, filename="pytorch_model.bin"))
     
     # model = EfficientNetPreTrained(config)
     # config.register_for_auto_class()