refactor

helper_functions.py

@@ -5,14 +5,16 @@ from transformers import BatchEncoding, PreTrainedTokenizerBase
 from typing import Optional
 from torch import Tensor
 
-# Charger le modèle
-model = BertForSequenceClassification.from_pretrained(r"bertModel-20240428T144909Z-001\bertModel\saved_model")
+# Load the model
+model_path = "Sayado/Model_PFE"
+model = BertForSequenceClassification.from_pretrained(model_path)
 
-# Charger le tokenizer
-tokenizer = BertTokenizer.from_pretrained(r"bertModel-20240428T144909Z-001\bertModel\saved_tokenizer")
+# Load the tokenizer
+tokenizer_path = "Sayado/Model_PFE"
+tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
 
 # Charger le label encoder
-with open(r"bertModel-20240428T144909Z-001\bertModel\label_encoder.pkl", "rb") as f:
+with open("label_encoder.pkl", "rb") as f:
     label_encoder = pickle.load(f)
 
 class_labels = {
@@ -28,8 +30,6 @@ class_labels = {
     4: ('Computational_Geometry', 'orange', '#fd7e14')
 }
 
-
-
 def predict_class(text):
     # Tokenisation du texte
     inputs = transform_list_of_texts(text, tokenizer, 510, 510, 1, 2550)