Update script.py

script.py

@@ -1,45 +1,8 @@
 mport torch
 import numpy as np
-from sklearn.metrics import accuracy_score  # Example metric
-
-# Load your hidden test set (adjust path and format to your data)
-TEST_DATA_PATH = "test_data.pt"  # Replace with the actual path
-TEST_LABELS_PATH = "test_labels.pt"
-
-test_data = torch.load(TEST_DATA_PATH)
-test_labels = torch.load(TEST_LABELS_PATH)
-
-# Evaluation script entry point
-def evaluate_submission(model_checkpoint_path: str):
-    """
-    Evaluates the submitted model on the hidden test set.
-    Args:
-        model_checkpoint_path (str): Path to the submitted model checkpoint.
-    
-    Returns:
-        dict: A dictionary containing the evaluation metrics.
-    """
-    # Load the participant's model
-    model = torch.load(model_checkpoint_path)
-    model.eval()
-
-    # Move model and data to the appropriate device (e.g., GPU if available)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(device)
-    test_data_tensor = test_data.to(device)
-
-    # Perform inference
-    with torch.no_grad():
-        predictions = model(test_data_tensor)
-        predictions = torch.argmax(predictions, axis=1).cpu().numpy()
-
-    # Calculate evaluation metric (e.g., accuracy)
-    accuracy = accuracy_score(test_labels, predictions)
-
-    return {"accuracy": accuracy}  # Replace with other metrics as needed
+from sklearn.metrics import accuracy_score  
 
 if __name__ == "__main__":
     # For local testing, you can pass a sample model path here
-    sample_model_path = "sample_submission.pt"  # Replace with a test checkpoint
-    result = evaluate_submission(sample_model_path)
-    print(result)
+    
+    print("inside script.py")