feat: add export_site_data.py website fold-in exporter

scripts/export_site_data.py

@@ -0,0 +1,198 @@
+"""Export a spread's leaderboard.json into website-ready artifacts.
+
+Bridges the run-analysis pipeline to rle.appsprout.dev: reads
+results/<run>/leaderboard.json (written by analyze_spread.py) and emits
+
+  <out>/data.fragment.ts   META + MODELS blocks matching the ModelRow
+                           interface in AppSprout-Site client/src/rle/data.ts
+  <out>/site_data.json     the same payload as JSON (HF dataset / API ingestion)
+
+Editorial blocks (HERO_LOG, STORY, AGENTS, OG_*) stay hand-curated — this
+covers the mechanical half of a fold-in so leaderboard numbers are never
+hand-transcribed again.
+
+Usage:
+    python scripts/export_site_data.py --spread-dir results/spread --date 2026-06-11
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from decimal import ROUND_HALF_UP, Decimal
+from pathlib import Path
+
+# Display name / family / family swatch per model slug, mirroring the palette
+# in AppSprout-Site client/src/rle/data.ts. Unknown slugs fall back to a
+# derived family + neutral color with a warning, so a new model never blocks
+# an export.
+MODEL_REGISTRY: dict[str, tuple[str, str, str]] = {
+    "x-ai/grok-4.3": ("Grok 4.3", "xAI", "#C77DB8"),
+    "mistralai/mistral-medium-3-5": ("Mistral Medium 3.5", "Mistral", "#FF7000"),
+    "google/gemini-3.5-flash": ("Gemini 3.5 Flash", "Google", "#6B9BF0"),
+    "qwen/qwen3.7-max": ("Qwen3.7 Max", "Alibaba", "#A36BFF"),
+    "claude-fable-5": ("Claude Fable 5", "Anthropic", "#E8A06A"),
+    "nvidia/nemotron-3-super-120b-a12b": ("Nemotron 3 Super 120B", "NVIDIA", "#76B900"),
+    "claude-opus-4-8": ("Claude Opus 4.8", "Anthropic", "#D97757"),
+    "z-ai/glm-5.1": ("GLM-5.1", "Zhipu", "#00D4D4"),
+    "openai/gpt-5.5": ("GPT-5.5", "OpenAI", "#56B79A"),
+    "deepseek/deepseek-v4-pro": ("DeepSeek-V4 Pro", "DeepSeek", "#8A7CF0"),
+    "moonshotai/kimi-k2.6": ("Kimi K2.6", "Moonshot", "#9AA0A6"),
+    "nvidia/nemotron-3-nano-30b-a3b": ("Nemotron 3 Nano 30B", "NVIDIA", "#76B900"),
+    "unsloth/nvidia-nemotron-3-nano-4b": ("Nemotron 3 Nano 4B", "NVIDIA", "#76B900"),
+}
+FALLBACK_COLOR = "#B7AC8B"
+
+GITHUB_URL = "https://github.com/AppSprout-dev/RLE"
+GITHUB_REPO = "AppSprout-dev/RLE"
+DISCORD_URL = "https://discord.gg/vKuyjqNEea"
+
+
+def rnd(value: float, places: int) -> float:
+    """Round half away from zero (matches hand-rounded site data, unlike
+    Python's banker's rounding)."""
+    q = Decimal(1).scaleb(-places)
+    return float(Decimal(str(value)).quantize(q, rounding=ROUND_HALF_UP))
+
+
+def fmt(value: float, places: int) -> str:
+    """Fixed-decimal literal for the TS output (keeps trailing zeros: 0.710, 4.00)."""
+    return f"{rnd(value, places):.{places}f}"
+
+
+def registry_entry(slug: str) -> tuple[str, str, str]:
+    if slug in MODEL_REGISTRY:
+        return MODEL_REGISTRY[slug]
+    family = slug.split("/")[0] if "/" in slug else slug.split("-")[0]
+    print(f"WARNING: {slug} not in MODEL_REGISTRY — using derived family "
+          f"{family!r} + fallback color. Add it to the registry.", file=sys.stderr)
+    return slug, family, FALLBACK_COLOR
+
+
+def build_model(row: dict) -> dict:
+    slug = row["model"]
+    display, family, color = registry_entry(slug)
+    real = row.get("real_cost_usd")
+    cost = real if real is not None else row["est_cost_usd"]
+    return {
+        "slug": slug,
+        "key": row["name"],
+        "model": display,
+        "family": family,
+        "color": color,
+        "meanComposite": rnd(row["mean_composite"], 3),
+        "finalComposite": rnd(row["final_composite"], 3),
+        "vsBaselineDelta": rnd(row["vs_baseline_mean_delta"], 3),
+        "ticksAboveBaseline": row["ticks_above_baseline"],
+        "endDay": int(row["end_day"]),
+        "rawActionSuccess": rnd(row["raw_action_success"], 2),
+        "exArtifactSuccess": rnd(row["ex_artifact_success"], 2),
+        "avgLatencyS": rnd(row["avg_latency_s"], 1),
+        "wallMin": rnd(row["wall_min"], 1),
+        "costUsd": rnd(cost, 2),
+        "costEstimated": real is None,
+    }
+
+
+def build_meta(board: dict, rows: list[dict], args: argparse.Namespace) -> dict:
+    metered = [r["real_cost_usd"] for r in board["rows"] if r.get("real_cost_usd") is not None]
+    ticks = max((len(r.get("trajectory", [])) for r in board["rows"]), default=0)
+    return {
+        "scenario": args.scenario,
+        "seed": args.seed,
+        "ticks": args.ticks if args.ticks is not None else ticks,
+        "nRuns": args.n_runs,
+        "date": args.date,
+        "totalSpendUsd": rnd(sum(metered), 2),
+        "baselineMeanTteDays": int(board["baseline"]["mean_time_to_end_days"]),
+        "githubUrl": GITHUB_URL,
+        "githubRepo": GITHUB_REPO,
+        "discordUrl": DISCORD_URL,
+    }
+
+
+def emit_ts(meta: dict, models: list[dict], spread_dir: Path) -> str:
+    lines = [
+        "// =====================================================================",
+        "// GENERATED by scripts/export_site_data.py from",
+        f"// {spread_dir.as_posix()}/leaderboard.json",
+        "// Paste over the META + MODELS blocks in AppSprout-Site",
+        "// client/src/rle/data.ts. Editorial blocks (HERO_LOG, STORY, AGENTS,",
+        "// OG_*) stay hand-curated — update those from stories.md separately.",
+        "// =====================================================================",
+        "",
+        "export const META = {",
+        f'  scenario: "{meta["scenario"]}",',
+        f"  seed: {meta['seed']},",
+        f"  ticks: {meta['ticks']},",
+        f"  nRuns: {meta['nRuns']},",
+        f'  date: "{meta["date"]}",',
+        "  // OpenRouter spend for the metered models; subscription-billed models",
+        "  // (costEstimated: true) are not included.",
+        f"  totalSpendUsd: {fmt(meta['totalSpendUsd'], 2)},",
+        f"  baselineMeanTteDays: {meta['baselineMeanTteDays']},",
+        f'  githubUrl: "{meta["githubUrl"]}",',
+        f'  githubRepo: "{meta["githubRepo"]}",',
+        f'  discordUrl: "{meta["discordUrl"]}",',
+        "} as const;",
+        "",
+        "// Ranked by mean composite over the run (analyze_spread.py ordering).",
+        "export const MODELS: ModelRow[] = [",
+    ]
+    for m in models:
+        lines += [
+            "  {",
+            f'    slug: "{m["slug"]}", key: "{m["key"]}", model: "{m["model"]}",'
+            f' family: "{m["family"]}", color: "{m["color"]}",',
+            f"    meanComposite: {fmt(m['meanComposite'], 3)},"
+            f" finalComposite: {fmt(m['finalComposite'], 3)},"
+            f" vsBaselineDelta: {fmt(m['vsBaselineDelta'], 3)},"
+            f' ticksAboveBaseline: "{m["ticksAboveBaseline"]}",',
+            f"    endDay: {m['endDay']}, rawActionSuccess: {fmt(m['rawActionSuccess'], 2)},"
+            f" exArtifactSuccess: {fmt(m['exArtifactSuccess'], 2)},"
+            f" avgLatencyS: {fmt(m['avgLatencyS'], 1)}, wallMin: {fmt(m['wallMin'], 1)},",
+            f"    costUsd: {fmt(m['costUsd'], 2)},"
+            f" costEstimated: {'true' if m['costEstimated'] else 'false'},",
+            "  },",
+        ]
+    lines += ["];", ""]
+    return "\n".join(lines)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--spread-dir", type=Path, required=True,
+                        help="run dir containing leaderboard.json (e.g. results/spread)")
+    parser.add_argument("--date", required=True, help="run date, YYYY-MM-DD")
+    parser.add_argument("--out", type=Path, default=None,
+                        help="output dir (default: <spread-dir>/site)")
+    parser.add_argument("--scenario", default="Crashlanded")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--ticks", type=int, default=None,
+                        help="override tick count (default: longest trajectory)")
+    parser.add_argument("--n-runs", type=int, default=1)
+    args = parser.parse_args()
+
+    board = json.loads((args.spread_dir / "leaderboard.json").read_text(encoding="utf-8"))
+    models = [build_model(r) for r in board["rows"]]
+    meta = build_meta(board, models, args)
+
+    out = args.out or (args.spread_dir / "site")
+    out.mkdir(parents=True, exist_ok=True)
+
+    ts_path = out / "data.fragment.ts"
+    ts_path.write_text(emit_ts(meta, models, args.spread_dir), encoding="utf-8")
+
+    json_path = out / "site_data.json"
+    json_path.write_text(
+        json.dumps({"meta": meta, "models": models}, indent=2) + "\n",
+        encoding="utf-8")
+
+    print(f"Wrote {ts_path}")
+    print(f"Wrote {json_path}")
+    print(f"{len(models)} models | total metered spend ${meta['totalSpendUsd']:.2f} "
+          f"| date {meta['date']}")
+
+
+if __name__ == "__main__":
+    main()