Update app.py

app.py

@@ -21,7 +21,7 @@ except ImportError:
 # --- Configuration ---
 # Model path is set to sojka
 MODEL_PATH = os.getenv("MODEL_PATH", "AndromedaPL/sojka")
-TOKENIZER_PATH = os.getenv("MODEL_PATH", "sdadas/mmlw-roberta-base") 
+TOKENIZER_PATH = os.getenv("TOKENIZER_PATH", "sdadas/mmlw-roberta-base") 
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 LABELS = ["self-harm", "hate", "vulgar", "sex", "crime"]
@@ -45,9 +45,10 @@ logger = logging.getLogger(__name__)
 
 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}")
+    logger.info(f"Loading tokenizer from {tokenizer_path}")
 
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
+    logger.info(f"Tokenizer loaded: {tokenizer.name_or_path}")
 
     if tokenizer.pad_token is None:
         if tokenizer.eos_token:
@@ -57,6 +58,8 @@ def load_model_and_tokenizer(model_path: str, tokenizer_path: str, device: str)
 
     tokenizer.truncation_side = "right"
 
+    logger.info(f"Loading model from {model_path}")
+
     model_load_kwargs = {
         "torch_dtype": torch.float16 if device == 'cuda' else torch.float32,
         "device_map": 'auto' if device == 'cuda' else None,
@@ -92,7 +95,7 @@ try:
     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}")
+    logger.error(f"FATAL: Failed to load the model from {MODEL_PATH} or tokenizer from {TOKENIZER_PATH}: {e}", e)
     model, tokenizer, model_loaded = None, None, False
 
 def predict(text: str) -> Dict[str, Any]: