Merge pull request #2 from flamehaven01/codex/fix-linting-issues-with-black-k1h3bc

Fix ruff lint violations in ml module

Author: flamehaven01

src/slop_detector/ml/classifier.py

@@ -273,21 +273,21 @@ def predict(self, features: Dict[str, float]) -> Tuple[float, float]:
             raise RuntimeError("Model not trained. Call train() first.")
 
         # Convert features to vector
-        X = np.array([[features[feat] for feat in self.FEATURE_NAMES]])
+        x = np.array([[features[feat] for feat in self.FEATURE_NAMES]])
 
         if self.model_type == "random_forest":
-            proba = self.rf_model.predict_proba(X)[0]
+            proba = self.rf_model.predict_proba(x)[0]
             return proba[1], max(proba)
 
         elif self.model_type == "xgboost":
             if not self.xgb_model:
                 raise RuntimeError("XGBoost model not available")
-            proba = self.xgb_model.predict_proba(X)[0]
+            proba = self.xgb_model.predict_proba(x)[0]
             return proba[1], max(proba)
 
         elif self.model_type == "ensemble":
-            rf_proba = self.rf_model.predict_proba(X)[0]
-            xgb_proba = self.xgb_model.predict_proba(X)[0] if self.xgb_model else rf_proba
+            rf_proba = self.rf_model.predict_proba(x)[0]
+            xgb_proba = self.xgb_model.predict_proba(x)[0] if self.xgb_model else rf_proba
 
             # Weighted average
             ensemble_proba = 0.5 * rf_proba + 0.5 * xgb_proba

src/slop_detector/ml/training_data.py

@@ -186,8 +186,8 @@ def extract_features(self, file_path: Path, detector) -> Optional[TrainingExampl
                     avg_function_length = total_func_lines / len(functions) if functions else 0.0
                 else:
                     avg_function_length = 0.0
-            except:
-                avg_function_length = 0.0
+            except Exception:
+                avg_function_length = 0.0
 
             # Count cross-language and hallucination patterns
             cross_language_count = sum(