Add pretty_name, quiet the version-warning wall in the example, and record the root widget copy's checksum

README.md

@@ -1,6 +1,7 @@
 ---
 license: mit
 library_name: sklearn
+pretty_name: "Cost-Aware Protocol Routing: Metadata-Only Protocol Router"
 # A real checkpoint ships here: router_metadata_only/model.joblib, with its
 # fitted feature builders, an explicit label map, and a runnable example.
 # `inference: false` because this is a local scikit-learn artifact -- there is
@@ -72,6 +73,9 @@ explicit label map, held-out predictions, and a runnable example.
 
 A byte-identical copy of `model.joblib` also sits at the repository root as
 `sklearn_model.joblib`, purely so Hugging Face's auto-generated snippet resolves.
+`router_metadata_only/model_metadata.json` records the SHA-256 of both, so the
+two cannot drift apart unnoticed: if they ever disagree, the one under
+`router_metadata_only/` is canonical.
 
 ```bash
 pip install scikit-learn pandas scipy joblib

router_metadata_only/model_metadata.json

@@ -88,5 +88,11 @@
       "bytes": 17241,
       "sha256": "5bcb554174f5cc34588d201938835473170c1c57154718dfede792341fcf3ee1"
     }
+  },
+  "root_widget_copy": {
+    "path": "sklearn_model.joblib",
+    "purpose": "byte-identical duplicate so Hugging Face's auto-generated sklearn snippet resolves",
+    "canonical": "router_metadata_only/model.joblib",
+    "sha256": "63b5358423b430b39f2a586913d48bf84f2b6774cda78bf1bc383c76de7f0c3b"
   }
 }

router_metadata_only/predict_example.py

@@ -15,6 +15,8 @@ from __future__ import annotations
 import json
 from pathlib import Path
 
+import warnings
+
 import joblib
 import pandas as pd
 from scipy import sparse
@@ -25,10 +27,41 @@ HERE = Path(__file__).resolve().parent
 # Decode with this file, not with sorted(labels): the order is the paper's fixed
 # cost order, and alphabetical decoding disagrees with the released predictions
 # on 71 of 423 test rows while looking perfectly plausible.
+#: The scikit-learn version this checkpoint was fitted under, recorded in
+#: model_metadata.json. Used only to phrase the version note accurately.
+FITTED_WITH = "1.8.0"
+
 MAPPING = json.loads((HERE / "label_mapping.json").read_text())
 INDEX_TO_LABEL = {int(k): v for k, v in MAPPING["index_to_label"].items()}
 
 
+def _quiet_version_warning():
+    """Silence the version-mismatch warning, after saying it out loud once.
+
+    The estimator was pickled under scikit-learn 1.8.0. Loading it under any
+    other version makes scikit-learn emit an InconsistentVersionWarning for
+    EVERY unpickled object -- here that is twelve lines of traceback-looking
+    text, including the reader's own filesystem paths, before a single line of
+    output. That reads like a broken artifact.
+
+    It is not suppressed silently: one plain sentence replaces the wall, so the
+    reader still learns the fact the warning was trying to convey.
+    """
+    try:
+        from sklearn.exceptions import InconsistentVersionWarning
+    except ImportError:          # very old scikit-learn: no such class
+        return
+    import sklearn
+    if sklearn.__version__ != FITTED_WITH:
+        print(
+            f"note: this checkpoint was fitted with scikit-learn {FITTED_WITH}; "
+            f"you are running {sklearn.__version__}.\n"
+            "      predict() is expected to work. If you need exact "
+            "probabilities, match the fitted version or retrain.\n"
+        )
+    warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
+
+
 def load():
     return (
         joblib.load(HERE / "model.joblib"),
@@ -62,6 +95,7 @@ def build_features(df: pd.DataFrame, builders: dict):
 
 
 def main() -> None:
+    _quiet_version_warning()
     model, builders = load()
 
     # Three made-up problems, in the schema the router expects.