Fix inference feature mismatch with training metadata

services/ml/app/main.py

@@ -124,6 +124,7 @@ async def lifespan(app: FastAPI):
                     else:
                         logger.error(f"Unknown model_source: {inference_config.model_source}")
 
+                    state.feature_columns = state.model_info.get("feature_columns")
                     logger.info("Model loaded successfully")
                     logger.info(f"Model info: {state.model_info['model_name']} "
                                f"v{state.model_info['model_version']} "
@@ -181,6 +182,22 @@ class AppState:
 
 state = AppState()
 
+def _get_model_n_features(model: Any) -> Optional[int]:
+    """
+    Extract expected feature count from a model (supports wrappers).
+    """
+    if hasattr(model, "n_features_in_"):
+        try:
+            return int(model.n_features_in_)
+        except Exception:
+            return None
+    if hasattr(model, "model") and hasattr(model.model, "n_features_in_"):
+        try:
+            return int(model.model.n_features_in_)
+        except Exception:
+            return None
+    return None
+
 
 # --- Pydantic Models ---
 
@@ -467,7 +484,8 @@ def load_model_from_local(model_path: str) -> tuple[Any, Dict[str, Any]]:
             "dataset_version": metadata.get("dataset_version", None),
             "feature_engineering_enabled": metadata.get("feature_engineering_enabled", True),
             "labels": labels,
-            "per_class_metrics": metadata.get("per_class_metrics", [])
+            "per_class_metrics": metadata.get("per_class_metrics", []),
+            "feature_columns": metadata.get("feature_columns", None),
         }
         
         logger.info(f"Successfully loaded local model: {model_path.name}")
@@ -707,7 +725,25 @@ async def predict(request: PredictRequest):
         candles_data = [candle.model_dump() for candle in candles_sorted]
 
         # Preprocess candles (feature engineering + windowing)
-        X, window_times = preprocess_candles(candles_data, state.pipeline_config)
+        training_feature_columns = (
+            current_model_info.get("feature_columns") if current_model_info else None
+        )
+        X, window_times = preprocess_candles(
+            candles_data,
+            state.pipeline_config,
+            training_feature_columns=training_feature_columns
+        )
+
+        expected_n_features = _get_model_n_features(current_model)
+        if expected_n_features is not None and X.shape[1] != expected_n_features:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(
+                    f"Feature mismatch: model expects {expected_n_features} features, "
+                    f"but preprocessing produced {X.shape[1]}. "
+                    "Ensure the loaded model matches the inference preprocessing config."
+                ),
+            )
 
         # Get predictions and probabilities (using local reference, outside lock)
         if hasattr(current_model, 'predict_proba'):
@@ -765,7 +801,7 @@ async def predict(request: PredictRequest):
         logger.error(f"Prediction validation error: {e}")
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Internal server error"
+            detail=str(e)
         )
     except Exception as e:
         logger.error(f"Prediction failed: {e}", exc_info=True)
@@ -883,7 +919,25 @@ async def predict_batch(request: BatchPredictRequest):
             batch_candles = batch_df.to_dict('records')
             
             # Preprocess (feature engineering + windowing)
-            X, window_times = preprocess_candles(batch_candles, state.pipeline_config)
+            training_feature_columns = (
+                current_model_info.get("feature_columns") if current_model_info else None
+            )
+            X, window_times = preprocess_candles(
+                batch_candles,
+                state.pipeline_config,
+                training_feature_columns=training_feature_columns
+            )
+
+            expected_n_features = _get_model_n_features(current_model)
+            if expected_n_features is not None and X.shape[1] != expected_n_features:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail=(
+                        f"Feature mismatch: model expects {expected_n_features} features, "
+                        f"but preprocessing produced {X.shape[1]}. "
+                        "Ensure the loaded model matches the inference preprocessing config."
+                    ),
+                )
             
             # Predict (using local reference, outside lock)
             if hasattr(current_model, 'predict_proba'):
@@ -1755,6 +1809,7 @@ async def model_load(request: ModelLoadRequest):
     with _model_swap_lock:
         state.model = new_model
         state.model_info = new_model_info
+        state.feature_columns = new_model_info.get("feature_columns")
 
     logger.info(f"Model hot-swapped: run_id={request.run_id}, type={row.model_type}")