Bring up FastAPI leaderboard backend

Dockerfile

@@ -0,0 +1,18 @@
+# MyHeartCounts-Bench leaderboard — HF Docker Space
+# Listens on $PORT (HF Spaces default 7860). Serves /api/data with CORS for the
+# public site at myheartcounts.stanford.edu.
+
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py leaderboard.json ./
+
+# HF Spaces sets $PORT, default to 7860 for local runs
+ENV PORT=7860
+EXPOSE 7860
+
+CMD ["sh", "-c", "uvicorn app:app --host 0.0.0.0 --port ${PORT}"]

README.md

@@ -1,11 +1,65 @@
 ---
-title: MyHeartCounts
-emoji: 👀
+title: MyHeartCounts-Bench Leaderboard
+emoji: 🫀
 colorFrom: red
-colorTo: purple
+colorTo: gray
 sdk: docker
+app_port: 7860
 pinned: false
-license: openrail
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MyHeartCounts-Bench leaderboard Space
+
+This is the API backend behind the leaderboard on the public site at
+[myheartcounts.stanford.edu/benchmark](https://myheartcounts.stanford.edu/benchmark).
+
+## What this Space serves
+
+A single endpoint:
+
+- `GET /api/data` — returns the JSON shape documented in
+  [data/leaderboard/README.md of the website repo](https://github.com/NarayanSchuetz/myheartcounts.stanford.edu/blob/main/data/leaderboard/README.md).
+
+Plus health/info:
+
+- `GET /` — service info
+- `GET /health` — liveness check
+
+## Files
+
+| file | role |
+|---|---|
+| `Dockerfile` | Builds the container HF Spaces runs |
+| `app.py` | FastAPI app, ~50 LOC |
+| `requirements.txt` | `fastapi` + `uvicorn` |
+| `leaderboard.json` | The canonical leaderboard data |
+
+## Updating the leaderboard
+
+1. Edit `leaderboard.json`. Keep the schema (see linked README); JSON-validate.
+2. Commit + push to this Space (HF git remote).
+3. HF rebuilds the container; the new JSON is live on the next `/api/data` call.
+
+The website's 5-second fetch on page mount picks it up — no website redeploy.
+
+## Local dev
+
+```bash
+pip install -r requirements.txt
+uvicorn app:app --reload --port 7860
+curl http://localhost:7860/api/data | jq .
+```
+
+## Cross-origin
+
+`app.py` allows the public site origin and localhost dev. If the site moves to
+a different origin, add it to `ALLOWED_ORIGINS` in `app.py`.
+
+## Submission ingestion
+
+GitHub issues filed via the [submission template](https://github.com/kellywzhang/MHC-benchmark/blob/master/.github/ISSUE_TEMPLATE/submission.yml)
+contain `method_name`, `submitter_team`, metrics, etc. A future workflow
+(or a manual edit) appends them as new rows under `downstream` /
+`imputation` / `forecasting`. Until that workflow exists, submission
+ingestion is manual: edit `leaderboard.json`, commit, push.

app.py

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+"""FastAPI app for the MyHeartCounts-Bench leaderboard HF Space.
+
+Serves a single JSON blob at /api/data — the canonical leaderboard, matching
+the schema documented in data/leaderboard/README.md of the public website
+repo. Updates flow as `git push` to this Space (or programmatic commits).
+
+Cross-origin: the public website at https://myheartcounts.stanford.edu
+fetches from https://myheartcounts-myheartcounts-bench.hf.space/api/data.
+"""
+
+import json
+import os
+from pathlib import Path
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+
+DATA_PATH = Path(__file__).parent / "leaderboard.json"
+ALLOWED_ORIGINS = [
+    "https://myheartcounts.stanford.edu",
+    "https://www.myheartcounts.stanford.edu",
+    "http://localhost:3000",
+    "http://localhost:5173",
+]
+
+app = FastAPI(
+    title="MyHeartCounts-Bench leaderboard",
+    description="Read-only leaderboard JSON for the /benchmark page on the public site.",
+    version="1.0.0",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=ALLOWED_ORIGINS,
+    allow_methods=["GET", "OPTIONS"],
+    allow_headers=["*"],
+    max_age=3600,
+)
+
+
+@app.get("/")
+def root():
+    return {
+        "name": "MyHeartCounts-Bench leaderboard",
+        "endpoints": ["/api/data", "/health"],
+        "schema_doc": "https://github.com/NarayanSchuetz/myheartcounts.stanford.edu/blob/main/data/leaderboard/README.md",
+    }
+
+
+@app.get("/api/data")
+def get_data():
+    if not DATA_PATH.exists():
+        raise HTTPException(status_code=503, detail="leaderboard.json missing on disk")
+    try:
+        return json.loads(DATA_PATH.read_text())
+    except json.JSONDecodeError as e:
+        raise HTTPException(status_code=500, detail=f"leaderboard.json invalid: {e}") from e
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "data_present": DATA_PATH.exists()}

leaderboard.json

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+{
+  "schema_version": 1,
+  "generated_at": "2026-04-15",
+  "downstream": [
+    { "method": "LSM-2",          "type": "Self-Supervised", "skill": "+14.4", "fair_skill": "+7.6",  "rank": "2.76", "demographics": "1.87", "medical": "3.40", "body": "3.74", "mental": "2.86", "sleep": "1.95", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "XGBoost",        "type": "Tree Boosting",   "skill": "+12.7", "fair_skill": "+5.4",  "rank": "3.41", "demographics": "1.14", "medical": "4.38", "body": "2.59", "mental": "6.02", "sleep": "2.92", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Mamba-CON",      "type": "Self-Supervised", "skill": "+4.2",  "fair_skill": "-1.4",  "rank": "4.27", "demographics": "3.63", "medical": "4.65", "body": "4.02", "mental": "4.45", "sleep": "4.58", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "GRU-D",          "type": "Deep Learning",   "skill": "+2.2",  "fair_skill": "-4.7",  "rank": "4.89", "demographics": "4.69", "medical": "3.93", "body": "3.62", "mental": "6.22", "sleep": "5.98", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Linear (baseline)", "type": "Statistical",  "skill": "0.0",   "fair_skill": "-6.2",  "rank": "4.59", "demographics": "6.98", "medical": "3.99", "body": "4.28", "mental": "3.19", "sleep": "4.52", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "MultiRocket",    "type": "Deep Learning",   "skill": "-0.4",  "fair_skill": "-7.2",  "rank": "5.18", "demographics": "4.22", "medical": "4.83", "body": "5.90", "mental": "5.52", "sleep": "5.43", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Chronos-2",      "type": "Foundation Model","skill": "-3.8",  "fair_skill": "-9.9",  "rank": "5.13", "demographics": "6.50", "medical": "4.83", "body": "5.87", "mental": "2.70", "sleep": "5.77", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Toto 1.0",       "type": "Foundation Model","skill": "-4.8",  "fair_skill": "-12.7", "rank": "5.76", "demographics": "6.98", "medical": "5.99", "body": "5.98", "mental": "5.03", "sleep": "4.85", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" }
+  ],
+  "imputation": [
+    { "type": "h1", "text": "Daily (single-day) methods" },
+    { "type": "method", "method": "LSM-2 (daily)",     "mtype": "Self-Supervised", "skill": "+80.9", "fair_skill": "+79.0", "rank": "2.5",  "activity": "+40.4", "physiology": "+33.5", "sleep": "+91.9",  "workout": "+93.1", "semantic": "+47.6", "sectionRank": 1,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Linear",            "mtype": "Statistical",     "skill": "+29.7", "fair_skill": "+18.5", "rank": "7.8",  "activity": "+11.7", "physiology": "+14.0", "sleep": "+66.4",  "workout": "+36.7", "semantic": "+7.8",  "sectionRank": 2,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "DLinear",           "mtype": "Deep Learning",   "skill": "+18.5", "fair_skill": "+8.2",  "rank": "7.9",  "activity": "+20.6", "physiology": "+1.4",  "sleep": "-12.3",  "workout": "+21.6", "semantic": "+29.0", "sectionRank": 3,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Temporal mean",     "mtype": "Statistical",     "skill": "+0.2",  "fair_skill": "-6.4",  "rank": "8.6",  "activity": "+12.9", "physiology": "-1.5",  "sleep": "+2.4",   "workout": "-17.1", "semantic": "+21.7", "sectionRank": 4,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "LOCF (baseline)",   "mtype": "Statistical",     "skill": "0.0",   "fair_skill": "-9.1",  "rank": "10.4", "activity": "0.0",   "physiology": "0.0",   "sleep": "0.0",    "workout": "0.0",   "semantic": "0.0",   "sectionRank": 5,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "BRITS",             "mtype": "Deep Learning",   "skill": "-11.4", "fair_skill": "-15.4", "rank": "10.4", "activity": "-17.4", "physiology": "-32.0", "sleep": "+27.8",  "workout": "-13.8", "semantic": "-12.8", "sectionRank": 6,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "TimesNet",          "mtype": "Deep Learning",   "skill": "-17.5", "fair_skill": "-22.1", "rank": "11.0", "activity": "+19.9", "physiology": "-5.0",  "sleep": "-170.7", "workout": "-41.2", "semantic": "+19.1", "sectionRank": 7,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Temporal mode",     "mtype": "Statistical",     "skill": "-22.9", "fair_skill": "-28.1", "rank": "11.9", "activity": "+9.2",  "physiology": "-12.2", "sleep": "-71.0",  "workout": "-61.8", "semantic": "+23.0", "sectionRank": 8,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "FEDformer",         "mtype": "Deep Learning",   "skill": "-29.1", "fair_skill": "-34.6", "rank": "12.7", "activity": "+10.5", "physiology": "-9.2",  "sleep": "-217.9", "workout": "-61.0", "semantic": "+22.0", "sectionRank": 9,  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Mode",              "mtype": "Statistical",     "skill": "-32.6", "fair_skill": "-37.9", "rank": "12.1", "activity": "+9.2",  "physiology": "-11.2", "sleep": "-305.7", "workout": "-61.8", "semantic": "+22.6", "sectionRank": 10, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Mean",              "mtype": "Statistical",     "skill": "-34.1", "fair_skill": "-38.7", "rank": "11.7", "activity": "+11.9", "physiology": "-4.9",  "sleep": "-305.7", "workout": "-61.8", "semantic": "+10.7", "sectionRank": 11, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "h1", "text": "Personalized-context (extended-history) methods" },
+    { "type": "method", "method": "LSM-2-Sparse (7-day)", "mtype": "Self-Supervised", "skill": "+86.3", "fair_skill": "+85.1", "rank": "1.1",  "activity": "+42.2", "physiology": "+38.5", "sleep": "+94.2",  "workout": "+96.3", "semantic": "+49.7", "sectionRank": 1, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Pers. temp. mean",     "mtype": "Statistical",     "skill": "+68.9", "fair_skill": "+61.3", "rank": "4.6",  "activity": "+16.7", "physiology": "+13.1", "sleep": "+66.4",  "workout": "+88.7", "semantic": "+29.3", "sectionRank": 2, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Pers. mean",           "mtype": "Statistical",     "skill": "+31.0", "fair_skill": "+15.9", "rank": "7.5",  "activity": "+12.7", "physiology": "+2.1",  "sleep": "-175.6", "workout": "+60.3", "semantic": "+12.3", "sectionRank": 3, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "DLinear (7-day)",      "mtype": "Deep Learning",   "skill": "+5.3",  "fair_skill": "-2.6",  "rank": "8.8",  "activity": "+20.1", "physiology": "+0.3",  "sleep": "-39.2",  "workout": "-3.8",  "semantic": "+26.6", "sectionRank": 4, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "type": "method", "method": "Pers. mode",           "mtype": "Statistical",     "skill": "-32.1", "fair_skill": "-37.2", "rank": "11.6", "activity": "+9.2",  "physiology": "-10.0", "sleep": "-301.4", "workout": "-61.7", "semantic": "+23.5", "sectionRank": 5, "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" }
+  ],
+  "forecasting": [
+    { "method": "Chronos-2",                 "mtype": "Foundation Model", "skill": "+25.8", "fair_skill": "+21.1", "rank": "2.4", "step": "+25.2", "flights": "+34.4", "hr": "+32.2", "energy": "+24.3", "sleep": "+59.2", "workout": "+14.6", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "DLinear",                   "mtype": "Deep Learning",   "skill": "+23.9", "fair_skill": "+18.4", "rank": "3.2", "step": "+24.5", "flights": "+24.3", "hr": "+23.9", "energy": "+24.2", "sleep": "+61.7", "workout": "+11.9", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "MixLinear",                 "mtype": "Deep Learning",   "skill": "+21.0", "fair_skill": "+15.0", "rank": "4.1", "step": "+21.5", "flights": "+18.6", "hr": "+21.5", "energy": "+20.0", "sleep": "+55.8", "workout": "+11.0", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "SegRNN",                    "mtype": "Deep Learning",   "skill": "+20.7", "fair_skill": "+16.7", "rank": "3.8", "step": "+25.9", "flights": "+19.1", "hr": "+26.4", "energy": "+26.2", "sleep": "+59.2", "workout": "+6.7",  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Toto 1.0 (FT)",             "mtype": "Foundation Model", "skill": "+19.0", "fair_skill": "+14.8", "rank": "4.2", "step": "+23.6", "flights": "+34.6", "hr": "+31.4", "energy": "+25.3", "sleep": "+47.7", "workout": "+6.1",  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Toto 1.0",                  "mtype": "Foundation Model", "skill": "+17.2", "fair_skill": "+13.0", "rank": "4.7", "step": "+22.5", "flights": "+35.1", "hr": "-1.9",  "energy": "+20.3", "sleep": "+49.4", "workout": "+6.5",  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "Seasonal Naive (baseline)", "mtype": "Statistical",      "skill": "0.0",   "fair_skill": "-4.8",  "rank": "7.2", "step": "0.0",   "flights": "0.0",   "hr": "0.0",   "energy": "0.0",     "sleep": "0.0",  "workout": "0.0",   "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "AutoARIMA",                 "mtype": "Statistical",      "skill": "-4.2",  "fair_skill": "-9.2",  "rank": "8.2", "step": "0.0",   "flights": "-3.8",  "hr": "-2.4",  "energy": "-5.1",    "sleep": "+20.2", "workout": "-11.0", "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" },
+    { "method": "AutoETS",                   "mtype": "Statistical",      "skill": "-21.4", "fair_skill": "-33.4", "rank": "7.2", "step": "+1.4",  "flights": "-0.7",  "hr": "-11.3", "energy": "-3078.9", "sleep": "+38.9", "workout": "+1.1",  "submitter": "MHC-Bench team", "submitted_on": "2026-04", "paper_url": "", "code_url": "" }
+  ]
+}

requirements.txt

@@ -0,0 +1,2 @@
+fastapi==0.110.3
+uvicorn[standard]==0.29.0