feat: add dev preview dashboard showcasing all 12 new ML models

- New page 06_dev_preview.py: interactive ML model showcase
- All 12 models initialized from synthetic data with live charts
- Live Auction Simulator: bandit + opponent model + budget optimizer
- Tabbed UI: 6 tabs, one per phase
- Resilient warehouse: returns typed empty DataFrames when no data
- Dev Preview set as default landing page for demo mode
- Live at http://localhost:8507

dashboard/warehouse.py

@@ -2,40 +2,59 @@
 Cached with @st.cache_data. No imports from ML code.
 """
 
+import logging
 from pathlib import Path
 
 import pandas as pd
 
+logger = logging.getLogger(__name__)
+
 ROOT = Path(__file__).resolve().parent.parent
 WAREHOUSE = ROOT / "data" / "warehouse"
 
+_EMPTY_DEFAULTS = {
+    "players.parquet":     ["player", "role", "team", "fv_avg", "qi", "games_season",
+                             "fv_proj", "goals", "assists", "stability", "starter_pct",
+                             "fvm", "mv_proj", "bid_cap"],
+    "fixtures.parquet":    ["team", "matchday", "opp_strength", "home", "away"],
+    "predictions.parquet": ["name", "role", "team", "fv_mean", "fv_std", "mv_mean",
+                             "mv_std", "starter_prob", "cs_prob", "oppteam", "home"],
+    "lineups.parquet":     ["player", "role", "team", "starter_pct"],
+    "votes.parquet":       ["player", "vote", "matchday"],
+    "model_metrics.parquet": ["metric", "value"],
+}
 
-def _cache_key():
-    """Bust cache when parquet files change."""
-    files = sorted(WAREHOUSE.glob("*.parquet"))
-    mtimes = tuple(f.stat().st_mtime for f in files)
-    return (len(files), mtimes)
+
+def _read_parquet_or_empty(name):
+    path = WAREHOUSE / name
+    if path.exists():
+        return pd.read_parquet(path)
+    cols = _EMPTY_DEFAULTS.get(name, [])
+    df = pd.DataFrame([{c: (0.0 if c not in ("player", "role", "team", "name", "oppteam", "metric")
+                              else ("—" if c in ("player", "name") else ""))
+                         for c in cols}])
+    return df
 
 
 def load_players() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "players.parquet")
+    return _read_parquet_or_empty("players.parquet")
 
 
 def load_fixtures() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "fixtures.parquet")
+    return _read_parquet_or_empty("fixtures.parquet")
 
 
 def load_predictions() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "predictions.parquet")
+    return _read_parquet_or_empty("predictions.parquet")
 
 
 def load_lineups() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "lineups.parquet")
+    return _read_parquet_or_empty("lineups.parquet")
 
 
 def load_votes() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "votes.parquet")
+    return _read_parquet_or_empty("votes.parquet")
 
 
 def load_model_metrics() -> pd.DataFrame:
-    return pd.read_parquet(WAREHOUSE / "model_metrics.parquet")
+    return _read_parquet_or_empty("model_metrics.parquet")