this should work out there

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
 import torch
 from minicons import cwe
-import pandas as pd
-import numpy as np
+from huggingface_hub import hf_hub_download
+import os
 
 from model import FFNModule, FeatureNormPredictor, FFNParams, TrainingParams
 
@@ -14,16 +14,19 @@ def predict (Word, Sentence, LM, Layer, Norm):
     lm = cwe.CWE(models[LM])
     if Layer not in range (lm.layers): return "invalid input: layer not in lm"
 
+    model_path = hf_hub_download("jwalanthi/bert_layer8_to_binder", model_name+".ckpt", use_auth_token=os.environ['TOKEN'])
+    label_path = hf_hub_download("jwalanthi/bert_layer8_to_binder", model_name+".txt", use_auth_token=os.environ['TOKEN'])
+
     # labels = "These are some fake features".split(" ")
     # vals = np.random.randint(-10,10,(5))
     # return model_name+" \n"+"\n".join([labels[i]+" "+str(vals[i]) for i in range(len(labels)) if vals[i]>0])
     model = FeatureNormPredictor.load_from_checkpoint(
-        checkpoint_path='models/'+model_name+'.ckpt',
+        checkpoint_path=model_path,
         map_location=None
     )
     model.eval()
 
-    with open ('models/'+model_name+'.txt', "r") as file:
+    with open (label_path, "r") as file:
         labels = [line.rstrip() for line in file.readlines()]
 
     data = (Sentence, Word)