Update app.py

app.py

@@ -69,26 +69,29 @@ class TextClassifier:
         self.initialize_model()
 
     def initialize_model(self):
-        """Initialize the model and tokenizer."""
-        logger.info("Initializing model and tokenizer...")
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-        
-        # First initialize the base model
-        self.model = AutoModelForSequenceClassification.from_pretrained(
-            self.model_name,
-            num_labels=2
-        ).to(self.device)
-        
-        # Look for model file in the same directory as the code
-        model_path = "model.pt"  # Your model file should be uploaded as model.pt
-        if os.path.exists(model_path):
-            logger.info(f"Loading custom model from {model_path}")
-            checkpoint = torch.load(model_path, map_location=self.device)
-            self.model.load_state_dict(checkpoint['model_state_dict'])
-        else:
-            logger.warning("Custom model file not found. Using base model.")
+            """Initialize the model and tokenizer."""
+            logger.info("Initializing model and tokenizer...")
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.model_name,
+                use_fast=False  # Use the slow tokenizer instead of the fast one
+            )
             
-        self.model.eval()
+            # First initialize the base model
+            self.model = AutoModelForSequenceClassification.from_pretrained(
+                self.model_name,
+                num_labels=2
+            ).to(self.device)
+            
+            # Look for model file in the same directory as the code
+            model_path = "model.pt"  # Your model file should be uploaded as model.pt
+            if os.path.exists(model_path):
+                logger.info(f"Loading custom model from {model_path}")
+                checkpoint = torch.load(model_path, map_location=self.device)
+                self.model.load_state_dict(checkpoint['model_state_dict'])
+            else:
+                logger.warning("Custom model file not found. Using base model.")
+                
+            self.model.eval()
 
     def predict_with_sentence_scores(self, text: str) -> Dict:
         """Predict with sentence-level granularity using overlapping windows."""