Update app.py

app.py

@@ -20,11 +20,16 @@ except ImportError:
 
 # --- Configuration ---
 # Model path is set to sojka
-MODEL_PATH = os.getenv("MODEL_PATH", "AndromedaPL/sojka") 
+MODEL_PATH = os.getenv("MODEL_PATH", "AndromedaPL/sojka")
+TOKENIZER_PATH = os.getenv("MODEL_PATH", "sdadas/mmlw-roberta-base") 
+
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 LABELS = ["self-harm", "hate", "vulgar", "sex", "crime"]
 MAX_SEQ_LENGTH = 512
 
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+
 # Thresholds are now hardcoded
 THRESHOLDS = {
     "self-harm": 0.5,
@@ -38,11 +43,11 @@ THRESHOLDS = {
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-def load_model_and_tokenizer(model_path: str, device: str) -> Tuple[AutoModelForSequenceClassification, AutoTokenizer]:
+def load_model_and_tokenizer(model_path: str, tokenizer_path: str, device: str) -> Tuple[AutoModelForSequenceClassification, AutoTokenizer]:
     """Load the trained model and tokenizer"""
     logger.info(f"Loading model from {model_path}")
 
-    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
 
     if tokenizer.pad_token is None:
         if tokenizer.eos_token:
@@ -84,7 +89,7 @@ def load_model_and_tokenizer(model_path: str, device: str) -> Tuple[AutoModelFor
 
 # --- Load model globally ---
 try:
-    model, tokenizer = load_model_and_tokenizer(MODEL_PATH, DEVICE)
+    model, tokenizer = load_model_and_tokenizer(MODEL_PATH, TOKENIZER_PATH, DEVICE)
     model_loaded = True
 except Exception as e:
     logger.error(f"FATAL: Failed to load the model from {MODEL_PATH}: {e}")