use token

app.py

@@ -10,6 +10,7 @@ import time
 from annotated_text import annotated_text
 
 ORG_ID = "cornell-authorship"
+TOKEN = st.secrets["token"]
 
 @st.cache
 def preprocess_text(s):
@@ -17,7 +18,7 @@ def preprocess_text(s):
 
 @st.cache
 def get_pairwise_distances(model):
-    dataset = load_dataset(f"{ORG_ID}/{model}_distance", use_auth_token=True)["train"]
+    dataset = load_dataset(f"{ORG_ID}/{model}_distance", use_auth_token=TOKEN)["train"]
     df = pd.DataFrame(dataset).set_index('index')
     return df
 
@@ -32,7 +33,7 @@ def get_pairwise_distances_chunked(model, chunk):
 @st.cache
 def get_query_strings():
     # df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_queries_english.jsonl"), lines = True)
-    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_queries_english", use_auth_token=True)["train"]
+    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_queries_english", use_auth_token=TOKEN)["train"]
     df = pd.DataFrame(dataset)
     df['index'] = df.reset_index().index
     return df
@@ -44,7 +45,7 @@ def get_query_strings():
 @st.cache
 def get_candidate_strings():
     # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
-    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_candidates_english", use_auth_token=True)["train"]
+    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_candidates_english", use_auth_token=TOKEN)["train"]
     df = pd.DataFrame(dataset)
     df['index'] = df.reset_index().index
     return df
@@ -55,7 +56,7 @@ def get_candidate_strings():
 @st.cache
 def get_embedding_dataset(model):
     # data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
-    data = load_dataset(f"{ORG_ID}/{model}_embedding", use_auth_token=True)
+    data = load_dataset(f"{ORG_ID}/{model}_embedding", use_auth_token=TOKEN)
     return data
 
 @st.cache