Update app.py

app.py

@@ -30,12 +30,11 @@ import xgboost as xgb
 import lightgbm as lgb
 from catboost import CatBoostRegressor
 import tensorflow as tf
-# ---- Robust sklearn tag shim for 3rd-party estimators ----
+# ---------- universal sklearn_tags patcher ----------
 def _safe_sklearn_tags(self):
     """
     Return sklearn tags without relying on super().sklearn_tags().
-    Uses get_tags() when available (works on sklearn < 1.6) and
-    falls back to an empty dict otherwise.
+    Uses get_tags() when available and falls back to {} otherwise.
     """
     try:
         if hasattr(self, "get_tags"):
@@ -44,19 +43,33 @@ def _safe_sklearn_tags(self):
         pass
     return {}
 
-# Always override on these classes to avoid super() lookups that may fail
-try:
-    xgb.XGBRegressor.sklearn_tags = _safe_sklearn_tags
-except Exception:
-    pass
-try:
-    lgb.LGBMRegressor.sklearn_tags = _safe_sklearn_tags
-except Exception:
-    pass
-try:
-    CatBoostRegressor.sklearn_tags = _safe_sklearn_tags
-except Exception:
-    pass
+def ensure_sklearn_tags_on_mro(est):
+    """Attach a safe sklearn_tags() to every class in the estimator's MRO
+    that lacks it (or whose implementation fails)."""
+    try:
+        mro = getattr(est.__class__, "mro", lambda: [])()
+    except Exception:
+        mro = []
+    for cls in mro:
+        if cls is object:
+            continue
+        needs_patch = not hasattr(cls, "sklearn_tags") or not callable(getattr(cls, "sklearn_tags"))
+        if not needs_patch:
+            try:
+                # dry-run; if it errors, we’ll patch
+                getattr(est, "sklearn_tags")()
+                continue
+            except Exception:
+                needs_patch = True
+        if needs_patch:
+            try:
+                setattr(cls, "sklearn_tags", _safe_sklearn_tags)
+            except Exception:
+                try:
+                    # fallback: instance-level bind
+                    setattr(est, "sklearn_tags", _safe_sklearn_tags.__get__(est, est.__class__))
+                except Exception:
+                    pass
 
 
 # -----------------------------
@@ -627,21 +640,31 @@ def _load_ensemble(target: str) -> EnsembleBundle:
     cyc_cols   = cfg["num_cols_cycle_first"]
     num_plain  = cfg["num_cols_plain"]
 
+    # XGB
     xgb_model = xgb.XGBRegressor()
     xgb_model.load_model(str(base / "xgb.json"))
+    ensure_sklearn_tags_on_mro(xgb_model)   # <-- add this
 
+    # LGBM
     lgb_booster, lgb_model = None, None
     if (base / "lgb.txt").exists():
         lgb_booster = lgb.Booster(model_file=str(base / "lgb.txt"))
+        # Booster is not a sklearn estimator; no patch needed.
     elif (base / "lgb.joblib").exists():
         lgb_model = joblib.load(base / "lgb.joblib")
+        ensure_sklearn_tags_on_mro(lgb_model)  # <-- add this
     else:
         raise FileNotFoundError("Neither lgb.txt nor lgb.joblib found for LGBM.")
 
+    # CAT
     cat_model = CatBoostRegressor()
     cat_model.load_model(str(base / "cat.cbm"))
+    ensure_sklearn_tags_on_mro(cat_model)   # <-- add this
 
+    # MLP
     mlp_model = tf.keras.models.load_model(base / "mlp.keras")
+    # (tf models don’t use sklearn tags)
+
     meta = joblib.load(base / "meta.joblib")
 
     bundle = EnsembleBundle(