Upload app.py

app.py

@@ -15,24 +15,32 @@ tokenizer = None
 label_mapping = {0: "✅ Correct", 1: "🤔 Conceptually Flawed", 2: "🔢 Computationally Flawed"}
 
 def load_model():
-    """Load your trained model here"""
+    """Load your trained LoRA adapter with base model"""
     global model, tokenizer
     
     try:
-        # Replace these with your actual model path/name
-        # Option 1: Load from local files
-        # model = AutoModelForSequenceClassification.from_pretrained("./your_model_directory")
-        # tokenizer = AutoTokenizer.from_pretrained("./your_model_directory")
+        from peft import AutoPeftModelForSequenceClassification
         
-        # Option 2: Load from Hugging Face Hub (if you upload your model there)
-        # model = AutoModelForSequenceClassification.from_pretrained("your-username/your-model-name")
-        # tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
+        # Load the LoRA adapter model
+        # The adapter files should be in a folder (e.g., "./lora_adapter")
+        model = AutoPeftModelForSequenceClassification.from_pretrained(
+            "./lora_adapter",  # Path to your adapter files
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        
+        # Load tokenizer from the same directory
+        tokenizer = AutoTokenizer.from_pretrained("./lora_adapter")
+        
+        logger.info("LoRA model loaded successfully")
+        return "LoRA model loaded successfully!"
         
-        # For now, we'll use a placeholder - replace this with your actual model loading
+    except Exception as e:
+        logger.error(f"Error loading LoRA model: {e}")
+        # Fallback to placeholder for testing
         logger.warning("Using placeholder model loading - replace with your actual model!")
         
-        # Placeholder model loading (replace this!)
-        model_name = "distilbert-base-uncased"  # Replace with your model
+        model_name = "microsoft/DialoGPT-medium"  # Closer to Phi-4 architecture
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForSequenceClassification.from_pretrained(
             model_name, 
@@ -40,12 +48,7 @@ def load_model():
             ignore_mismatched_sizes=True
         )
         
-        logger.info("Model loaded successfully")
-        return "Model loaded successfully!"
-        
-    except Exception as e:
-        logger.error(f"Error loading model: {e}")
-        return f"Error loading model: {e}"
+        return f"Fallback model loaded. LoRA error: {e}"
 
 def classify_solution(question: str, solution: str):
     """