"""Build a compact index of all ADC products for the web demo.

The demo cannot load the 800 MB structured dataset, so this script extracts
the fields needed to recognise a product in a question (name, active
substances, form, prescription status, ...) and links every product to the
LightRAG document that represents its active substance set (`rep`). Products
without a representative have no clinical data in the knowledge graph.

    python scripts/demo/build_product_index.py
"""

from __future__ import annotations

import argparse
import json
import sys
from collections import Counter
from pathlib import Path

ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "scripts" / "lightrag_ingest"))
from ingest_adc_structured import DEFAULT_INPUT, dedupe_key, load_json, write_json  # noqa: E402

DEFAULT_SECTION_DOCS = Path("outputs/lightrag_sections/adc_section_docs.json")
DEFAULT_OUTPUT = Path("outputs/demo/product_index.json")

if hasattr(sys.stdout, "reconfigure"):
    sys.stdout.reconfigure(encoding="utf-8")


def as_list(value) -> list[str]:
    if not value:
        return []
    if isinstance(value, str):
        return [value]
    return [str(item) for item in value if str(item).strip()]


def compact(record: dict, representative: str | None) -> dict:
    product = record.get("product") or {}
    hints = record.get("graph_hints") or {}
    urls = record.get("urls") or {}
    fields = product.get("detail_fields") or {}
    classes = [c.get("name") for c in hints.get("classification_codes") or [] if isinstance(c, dict)]
    return {
        "name": product.get("name") or hints.get("drug") or "",
        "url": urls.get("detail") or "",
        "pil": urls.get("pil") or "",
        "subs": as_list(hints.get("active_substances")),
        "form": hints.get("dosage_form") or "",
        "rx": fields.get("vydaj") or "",
        "type": fields.get("typ_produktu") or "",
        "group": classes[-1] if classes else "",
        "sukl": hints.get("sukl_code") or "",
        "ind": str(hints.get("indications_text") or "")[:500],
        "rep": representative,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
    parser.add_argument("--input", type=Path, default=DEFAULT_INPUT)
    parser.add_argument("--section-docs", type=Path, default=DEFAULT_SECTION_DOCS)
    parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
    args = parser.parse_args()

    # Same grouping as build_section_docs.py: group key -> URL of the uploaded document.
    representatives = {
        dedupe_key(doc, "substance"): doc["urls"]["detail"] for doc in load_json(args.section_docs)
    }

    print(f"Loading {args.input} ...")
    records = load_json(args.input)
    index = []
    stats: Counter = Counter()
    for record in records:
        rep = representatives.get(dedupe_key(record, "substance"))
        stats["in_graph" if rep else "not_in_graph"] += 1
        index.append(compact(record, rep))

    write_json(args.output, index)
    print(f"Products: {len(index)} | {dict(stats)}")
    print(f"Saved: {args.output}")


if __name__ == "__main__":
    main()