from __future__ import annotations
from dataclasses import dataclass
from core.runner import run_skill_script


@dataclass(frozen=True)
class SelectResult:
    selected_ids: tuple[str, ...]   # ordered — the determinism artifact carried verbatim to Phase E
    indicator_defs: dict[str, dict]      # get_indicators "indicators" map, keyed by id
    count_by_sector: dict[str, int]


def select_step(sectors: list[str], *, subpillars: list[str] | None = None) -> SelectResult:
    """Deterministic Step-2 selection. No LLM. Calls two vendored scripts via run_skill_script.

    select_indicators.py gives the ordered candidate id list (the determinism contract);
    get_indicators.py gives the sliced field defs the bind loop (Phase E) consumes.
    """
    if subpillars is None:
        subpillars = []
    args = ["--sectors", *sectors]
    if subpillars:
        args += ["--subpillars", *subpillars]
    selection = run_skill_script("select_indicators.py", args)
    selected_ids = tuple(item["id"] for item in selection["selected"])
    count_by_sector = selection.get("count_by_sector", {})

    if selected_ids:
        fetched = run_skill_script("get_indicators.py", ["--ids", *selected_ids])
        indicator_defs = fetched["indicators"]
    else:
        indicator_defs = {}

    return SelectResult(
        selected_ids=selected_ids,
        indicator_defs=indicator_defs,
        count_by_sector=count_by_sector,
    )