ok lancer huggingface-cli login dans terminal en1e

src/inference_t5.py

@@ -2,7 +2,6 @@
  Allows to predict the summary for a given entry text
 """
 import torch
-import contractions
 import re
 import string
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@@ -10,13 +9,12 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 def clean_text(texts: str) -> str:
     texts = texts.lower()
-    texts = contractions.fix(texts)
     texts = texts.translate(str.maketrans("", "", string.punctuation))
     texts = re.sub(r'\n', ' ', texts)
     return texts
 
 
-def inferenceAPI(text: str) -> str:
+def inferenceAPI_T5(text: str) -> str:
     """
     Predict the summary for an input text
     --------
@@ -31,10 +29,10 @@ def inferenceAPI(text: str) -> str:
     # On défini les paramètres d'entrée pour le modèle
     text = clean_text(text)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    tokenizer = (AutoTokenizer.from_pretrained("Linggg/t5_summary"))
+    tokenizer = (AutoTokenizer.from_pretrained("Linggg/t5_summary",use_auth_token=True))
     # load local model
     model = (AutoModelForSeq2SeqLM
-             .from_pretrained("Linggg/t5_summary")
+             .from_pretrained("Linggg/t5_summary",use_auth_token=True)
              .to(device))
 
     text_encoding = tokenizer(
@@ -64,4 +62,4 @@ def inferenceAPI(text: str) -> str:
 
 # if __name__ == "__main__":
 #     text = input('Entrez votre phrase à résumer : ')
-#     print('summary:', inferenceAPI(text))
+#     print('summary:', inferenceAPI_T5(text))