Add custom handler.py for sklearn inference

handler.py

@@ -0,0 +1,62 @@
+"""
+Custom inference handler for the MLForge test sklearn LinearRegression model.
+
+This handler lets the Hugging Face Inference API serve sklearn/joblib models
+that are not natively supported by the built-in pipeline loaders.
+
+HuggingFace loads this file automatically when the repo uses:
+  library_name: sklearn
+  pipeline_tag: tabular-regression
+
+Expected request format:
+    {"inputs": [[5.0, 3.0]]}       → single or multi-sample list of feature vectors
+"""
+
+from pathlib import Path
+from typing import Any
+
+import joblib
+import numpy as np
+
+
+class EndpointHandler:
+    """Custom HF Endpoint handler for sklearn regression models."""
+
+    def __init__(self, path: str = ""):
+        model_dir = Path(path)
+
+        # Try the canonical name first, then fall back to any .joblib file
+        candidate = model_dir / "sklearn_model.joblib"
+        if not candidate.exists():
+            matches = list(model_dir.glob("*.joblib"))
+            if not matches:
+                raise FileNotFoundError(
+                    f"No .joblib model file found in {model_dir}"
+                )
+            candidate = matches[0]
+
+        self.model = joblib.load(candidate)
+        self._model_file = candidate.name
+
+    def __call__(self, data: dict[str, Any]) -> list[float]:
+        """Run inference.
+
+        Args:
+            data: Dict with key ``"inputs"`` containing a list of feature vectors,
+                  e.g. ``{"inputs": [[5.0, 3.0], [1.0, 2.0]]}``
+
+        Returns:
+            List of predicted float values, one per input row.
+        """
+        raw = data.get("inputs", data.get("data"))
+        if raw is None:
+            raise ValueError(
+                "Request must contain 'inputs' key with a list of feature vectors."
+            )
+
+        X = np.array(raw, dtype=float)
+        if X.ndim == 1:
+            X = X.reshape(1, -1)
+
+        predictions = self.model.predict(X)
+        return predictions.tolist()