Added detect_bug using CodeBERT

bug_detector.py

@@ -5,13 +5,17 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 model = AutoModelForSequenceClassification.from_pretrained("microsoft/codebert-base", num_labels=2)
 tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
 
-def classify_code(code):
+def detect_bug(code):
+    """
+    Detects whether the provided code is buggy or correct using CodeBERT.
+    Returns "buggy" or "correct".
+    """
     inputs = tokenizer(code, return_tensors="pt", truncation=True, padding=True)
     outputs = model(**inputs)
     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
     return "buggy" if probabilities[0][1] > probabilities[0][0] else "correct"
 
-# Test example
+# Optional: test this locally
 if __name__ == "__main__":
-    test_code = "def add(a, b): return a * b"  # Buggy function
-    print(classify_code(test_code))
+    test_code = "def add(a, b): return a * b"  # Example of buggy code
+    print(detect_bug(test_code))