Update app.py

app.py

@@ -29,16 +29,16 @@ def load_models():
     from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
 
     if tokenizer is None or model is None:
-        tokenizer = AutoTokenizer.from_pretrained("fatmata/gpt-psybot", use_auth_token=True)
-        model = AutoModelForCausalLM.from_pretrained("fatmata/gpt-psybot", use_auth_token=True)
+        tokenizer = AutoTokenizer.from_pretrained("fatmata/gpt-psybot")
+        model = AutoModelForCausalLM.from_pretrained("fatmata/gpt-psybot")
 
     if bert_tokenizer is None or bert_model is None:
-        bert_tokenizer = AutoTokenizer.from_pretrained("fatmata/bert_model", use_auth_token=True)
-        bert_model = AutoModelForSequenceClassification.from_pretrained("fatmata/bert_model", use_auth_token=True)
+        bert_tokenizer = AutoTokenizer.from_pretrained("fatmata/bert_model")
+        bert_model = AutoModelForSequenceClassification.from_pretrained("fatmata/bert_model")
 
     if tokenizer_c is None or model_c is None:
-        tokenizer_c = AutoTokenizer.from_pretrained("fatmata/mini_bert", use_auth_token=True)
-        model_c = AutoModelForSequenceClassification.from_pretrained("fatmata/mini_bert", use_auth_token=True)
+        tokenizer_c = AutoTokenizer.from_pretrained("fatmata/mini_bert")
+        model_c = AutoModelForSequenceClassification.from_pretrained("fatmata/mini_bert")
 
 # ---------------- Nettoyage des réponses ----------------
 def clean_response(text):