"""FYN-LLM quality assurance toolkit.

Subcommands:
  fitness  — Run LLM fitness tests across four categories
  score    — Score a Stage 5 technique page across 5 quality dimensions

Run with: python -m pipeline.quality <command>
"""
from __future__ import annotations

import argparse
import json
import sys

from config import get_settings
from pipeline.llm_client import LLMClient

from .fitness import FitnessRunner
from .scorer import ScoreRunner


def main() -> int:
    parser = argparse.ArgumentParser(
        prog="pipeline.quality",
        description="FYN-LLM quality assurance toolkit",
    )
    sub = parser.add_subparsers(dest="command")

    # -- fitness subcommand --
    sub.add_parser("fitness", help="Run LLM fitness tests across four categories")

    # -- score subcommand --
    score_parser = sub.add_parser(
        "score",
        help="Score a Stage 5 technique page across 5 quality dimensions",
    )
    source_group = score_parser.add_mutually_exclusive_group(required=True)
    source_group.add_argument(
        "--file",
        type=str,
        help="Path to a moments JSON file (creator_name, moments array)",
    )
    source_group.add_argument(
        "--slug",
        type=str,
        help="Technique slug to load from the database",
    )
    score_parser.add_argument(
        "--voice-level",
        type=float,
        default=None,
        help="Voice preservation dial (0.0=clinical, 1.0=maximum voice). Triggers re-synthesis before scoring.",
    )

    args = parser.parse_args()

    if args.command is None:
        parser.print_help()
        return 1

    if args.command == "fitness":
        settings = get_settings()
        client = LLMClient(settings)
        runner = FitnessRunner(client)
        return runner.run_all()

    if args.command == "score":
        return _run_score(args)

    return 0


def _run_score(args: argparse.Namespace) -> int:
    """Execute the score subcommand."""
    # -- Load source data --
    if args.slug:
        print("DB loading not yet implemented", file=sys.stderr)
        return 1

    try:
        with open(args.file) as f:
            data = json.load(f)
    except FileNotFoundError:
        print(f"File not found: {args.file}", file=sys.stderr)
        return 1
    except json.JSONDecodeError as exc:
        print(f"Invalid JSON in {args.file}: {exc}", file=sys.stderr)
        return 1

    moments = data.get("moments", [])
    creator_name = data.get("creator_name", "Unknown")

    if not moments:
        print("No moments found in input file", file=sys.stderr)
        return 1

    # -- Build page stub from moments for scoring --
    # When --voice-level is set, T02 will re-synthesize. For now, build a
    # minimal page representation from the moments so the scorer has
    # something to evaluate.
    page_json = {
        "title": f"{creator_name} — Technique Page",
        "creator_name": creator_name,
        "summary": f"Technique page synthesized from {len(moments)} key moments.",
        "body_sections": [
            {
                "heading": m.get("topic_tags", ["Technique"])[0] if m.get("topic_tags") else "Technique",
                "content": m.get("summary", "") + "\n\n" + m.get("transcript_excerpt", ""),
            }
            for m in moments
        ],
    }

    settings = get_settings()
    client = LLMClient(settings)
    runner = ScoreRunner(client)

    print(f"\nScoring page for '{creator_name}' ({len(moments)} moments)...")

    result = runner.score_page(page_json, moments)

    if result.error:
        runner.print_report(result)
        return 1

    runner.print_report(result)
    return 0


if __name__ == "__main__":
    sys.exit(main())