"""Drug repurposing — TxGNN-style link prediction Disease ↔ Drug.

Bootstrap: wraps `drug_repurposer.find_repurposing_candidates` (KG walks
Disease → Gene → Drug via Cypher).

Phase 2: TxGNN fine-tune on PrimeKG + our enriched KG (gemeo/train/txgnn.py).
When checkpoint exists, overrides the bootstrap path.

We also enrich each candidate with SUS dispensation status — whether
the drug is in any PCDT and whether it's available in the patient's UF.
"""
from __future__ import annotations
import logging
import os
from typing import Optional

from .types import DrugSpec

logger = logging.getLogger("gemeo.repurpose")

TXGNN_CKPT = os.environ.get(
    "GEMEO_TXGNN_CKPT",
    os.path.join(os.path.dirname(__file__), "artifacts", "txgnn.pt"),
)


async def _try_txgnn(space, embedding):
    if not os.path.exists(TXGNN_CKPT):
        return None
    try:
        from .train import txgnn as tx_mod
        return await tx_mod.predict(space, embedding, TXGNN_CKPT)
    except Exception as e:
        logger.warning(f"TxGNN predict failed: {e}")
        return None


def _enrich_with_sus(candidates: list, sus_region: Optional[str]) -> list:
    """Mark each candidate with PCDT availability + UF dispensation."""
    try:
        from brazilian_context import get_pcdt
    except ImportError:
        return candidates

    out = []
    for c in candidates:
        item = dict(c) if isinstance(c, dict) else {
            "name": getattr(c, "name", None),
            "rxcui": getattr(c, "rxcui", None),
            "score": getattr(c, "score", None),
            "mechanism": getattr(c, "mechanism", None),
            "status": getattr(c, "status", None),
            "for_disease": getattr(c, "for_disease", None),
            "for_orpha": getattr(c, "for_orpha", None),
        }
        sus_avail = False
        in_pcdt = False
        if item.get("for_orpha"):
            try:
                pcdt = get_pcdt(item["for_orpha"])
            except Exception:
                pcdt = None
            if pcdt:
                in_pcdt = True
                # heuristic: drug name appears in PCDT therapy list
                therapies = (pcdt.get("therapies") or []) + (pcdt.get("medicamentos") or [])
                drug_name_lower = (item.get("name") or "").lower()
                if drug_name_lower and any(drug_name_lower in (str(t) or "").lower() for t in therapies):
                    sus_avail = True
        item["sus_in_pcdt"] = in_pcdt
        item["sus_dispensed"] = sus_avail  # TODO: cross-ref APAC by UF when available
        item["sus_uf"] = sus_region
        out.append(item)
    return out


async def find(space, embedding=None, sus_region: Optional[str] = None) -> DrugSpec:
    """Find drug candidates for the digital twin."""

    # try TxGNN first
    spec = await _try_txgnn(space, embedding)
    if spec is not None:
        spec.candidates = _enrich_with_sus(spec.candidates, sus_region)
        return spec

    candidates = []
    n_eval = 0
    try:
        from drug_repurposer import find_repurposing_candidates
        result = await find_repurposing_candidates(space)
        if result is None:
            pass
        elif isinstance(result, dict):
            candidates = result.get("candidates", []) or []
            n_eval = int(result.get("n_evaluated", len(candidates)))
        else:
            candidates = getattr(result, "candidates", []) or []
            n_eval = int(getattr(result, "n_evaluated", len(candidates)) or len(candidates))
    except Exception as e:
        logger.debug(f"drug_repurposer failed: {e}")

    # serialize candidate objects
    # The upstream `drug_repurposer.DrugCandidate` dataclass uses field
    # names `drug_name`/`drug_id`/`evidence_level`, so we map them into
    # the gemeo.types.DrugCandidate shape (`name`/`rxcui`/`status`).
    # Previously only `name`/`rxcui`/etc were probed → every candidate
    # serialized as {"name": None, "rxcui": None, ...}.
    serialized = []
    for c in candidates[:20]:
        if isinstance(c, dict):
            serialized.append(c)
        else:
            serialized.append({
                "name": getattr(c, "name", None) or getattr(c, "drug_name", None),
                "rxcui": getattr(c, "rxcui", None) or getattr(c, "drug_id", None),
                "score": getattr(c, "score", None),
                "mechanism": getattr(c, "mechanism", None),
                "status": getattr(c, "status", None) or getattr(c, "evidence_level", None),
                "for_disease": getattr(c, "for_disease", None) or getattr(c, "disease_name", None),
                "for_orpha": getattr(c, "for_orpha", None) or getattr(c, "orpha_code", None),
                "evidence": getattr(c, "evidence", None),
            })

    serialized = _enrich_with_sus(serialized, sus_region)

    return DrugSpec(
        candidates=serialized,
        model="kg_walks",
        n_evaluated=n_eval or len(serialized),
    )