"""Config loading for the Boardroom Map orchestrator.

Defaults mirror startos/file-models/config.ts. The StartOS actions only persist
the fields the user actually touched, and Python (unlike the zod schema) does not
auto-fill defaults — so we apply them here. Keep in sync with the zod schema.
"""
from __future__ import annotations

import os

import spark_client as sc

DATA_DIR = os.environ.get("BM_DATA_DIR", "/data")
HF_TOKEN_PATH = os.path.join(DATA_DIR, "secrets", "hf_token")
BDEF_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "bdef.md")

# Scoring weights (composite 0-100 = quant 60 + qual 40 - penalties).
# Every knob the deterministic scorer uses lives here so the user can retune
# without a rebuild. Keep flat: StartOS action inputs are flat number fields.
WEIGHTS_DEFAULTS = {
    "profitabilityKpi": 30,       # profitability KPI attainment bucket
    "otherKpi": 20,               # non-profitability measurable KPI bucket
    "forecastIntegrity": 10,      # deck N actuals vs deck N-1 stated targets
    "qualCategoryMax": 5,         # each BDEF category A-H maxes at this (8x5=40)
    "redFlagCap": 15,             # max total penalty
    "kpiCreditFloor": 0.5,        # actual/target ratio below which credit = 0
    "droppedKpiPenalty": 2,       # severity of a KPI that silently disappeared
    "droppedKpiMax": 3,           # count at most this many dropped-KPI flags
    "evidenceFullCredit": 400,    # quote chars for full qualitative weight
    "singleSourceFlagFactor": 0.5,  # damping for flags raised by one source only
}

CONFIG_DEFAULTS = {
    # Spark connection
    "primarySparkHost": "",
    "primarySparkUser": "nvidia",
    "sshPort": 22,
    "secondarySparkHost": None,
    "useBothSparks": False,
    "headInternalHost": "127.0.0.1",
    "remoteWorkDir": "/home/nvidia/boardroom-map",
    # Images
    "servingImage": "boardroom-vllm:latest",
    "graderImage": "boardroom-grader:latest",
    # Serving
    "gpuMemoryUtilization": "0.85",
    "maxModelLen": 32768,
    "toolCallParser": "hermes",
    "proxyPort": 4000,
    "maxConcurrentModels": 1,
    "models": [
        {"alias": "grader-a", "hfModel": "Qwen/Qwen3-32B-FP8", "spark": "primary", "port": 8001},
    ],
    # Grading panel
    "graders": [
        {"name": "munger-lens", "model": "grader-a", "persona": "", "temperature": None},
    ],
    # Which catalog model runs the stage-1 structured extractor ("" = first model)
    "extractorModel": "",
    # Grading job settings
    "bdefOverride": "",           # non-empty replaces the baked-in bdef.md rubric
    "weights": dict(WEIGHTS_DEFAULTS),
    "networkMode": "airgapped",
    "searxngUrl": "",
    "adjudicatorEnabled": True,
    "adjudicatorModel": "",
    "adjudicatorPersona": "",
    "wipeRemoteDocs": True,
    "autoRunOnDrop": False,
    "networkName": "boardroom-net",
    # Portfolio companies (authoritative source of pinned targets / aliases).
    # pinnedTargets: [{kpi, target, unit, direction: gte|lte, profitability}]
    # kpiAliases: newline-separated "canonical=alias1;alias2" lines.
    "companies": [],
    # Flags
    "hfTokenSet": False,
}


def load() -> dict:
    """Return the merged config (defaults <- saved), or just defaults if unset."""
    merged = dict(CONFIG_DEFAULTS)
    try:
        saved = sc.load_config()
    except FileNotFoundError:
        return merged
    merged.update({k: v for k, v in saved.items() if v is not None})
    # weights merge key-by-key so a partially-saved weights object keeps defaults
    w = dict(WEIGHTS_DEFAULTS)
    w.update({k: v for k, v in (merged.get("weights") or {}).items() if v is not None})
    merged["weights"] = w
    return merged


def bdef_text(cfg: dict) -> str:
    """The grading rubric: config override if set, else the baked-in spec."""
    override = (cfg.get("bdefOverride") or "").strip()
    if override:
        return override
    with open(BDEF_PATH, encoding="utf-8") as f:
        return f.read()


def hf_token() -> str | None:
    if os.path.exists(HF_TOKEN_PATH):
        t = open(HF_TOKEN_PATH).read().strip()
        return t or None
    return None