Function get_dashboard_data

dashboard.py:27–232

(db_path=DB_PATH)

def get_dashboard_data(db_path=DB_PATH):
    if not db_path.exists():
        return {"error": "Database not found. Run: python cli.py scan"}

    conn = sqlite3.connect(db_path)
    # The dashboard reads while a background scan may be committing (cmd_dashboard
    # serves first, scans in a background thread; /api/rescan scans in-process too).
    # Wait briefly for write locks instead of raising "database is locked".
    conn.execute("PRAGMA busy_timeout = 5000")
    conn.row_factory = sqlite3.Row
    # Ensure the schema is current before querying. cmd_dashboard binds and serves
    # *before* its background scan runs init_db, so on the first load after an
    # upgrade a pre-existing DB may still be on the old schema — the subagent
    # queries below reference the `agents` table and the `is_subagent`/`agent_id`
    # columns and would raise "no such table: agents" until the scan caught up.
    # init_db is idempotent (CREATE ... IF NOT EXISTS + additive column checks),
    # so this is a cheap no-op once migrated.
    init_db(conn)

    # ── All models (for filter UI) ────────────────────────────────────────────
    # GROUP BY uses the normalised expression too so NULL and '' don't end up
    # as two separate "unknown" rows.
    model_rows = conn.execute("""
        SELECT COALESCE(NULLIF(model, ''), 'unknown') as model
        FROM turns
        GROUP BY COALESCE(NULLIF(model, ''), 'unknown')
        ORDER BY SUM(input_tokens + output_tokens) DESC
    """).fetchall()
    all_models = [r["model"] for r in model_rows]

    # ── Daily per-model, ALL history (client filters by range) ────────────────
    daily_rows = conn.execute("""
        SELECT
            substr(timestamp, 1, 10)   as day,
            COALESCE(NULLIF(model, ''), 'unknown') as model,
            SUM(input_tokens)          as input,
            SUM(output_tokens)         as output,
            SUM(cache_read_tokens)     as cache_read,
            SUM(cache_creation_tokens) as cache_creation,
            COUNT(*)                   as turns
        FROM turns
        GROUP BY day, COALESCE(NULLIF(model, ''), 'unknown')
        ORDER BY day, model
    """).fetchall()

    daily_by_model = [{
        "day":            r["day"],
        "model":          r["model"],
        "input":          r["input"] or 0,
        "output":         r["output"] or 0,
        "cache_read":     r["cache_read"] or 0,
        "cache_creation": r["cache_creation"] or 0,
        "turns":          r["turns"] or 0,
    } for r in daily_rows]

    # ── Hourly per-day per-model (client filters by range + TZ-shifts) ────────
    # Timestamps are ISO8601 UTC (e.g. "2026-04-08T09:30:00Z"); chars 12-13 = hour.
    hourly_rows = conn.execute("""