"""Gemeo orchestrator — the Gemeo class and high-level entry points.

This is the public face of the module. Wraps every capability behind one
clean async surface:

    twin = await build_gemeo(case_text="...", patient_info={...}, context={...})
    print(twin.cohort, twin.subgraph, twin.trajectory, twin.risk, ...)

    new = await evolve_gemeo(twin.id, new_phenotypes=[...])
    cf  = await what_if(twin.id, intervention={"type": "treatment", "drug": "Cerezyme"})
"""
from __future__ import annotations
import asyncio
import logging
from typing import Optional

from .types import GemeoTwin, VizData
from . import encoder as gencoder
from . import cohort as gcohort
from . import subgraph as gsub
from . import trajectory as gtraj
from . import risk as grisk
from . import repurpose as gdrugs
from . import ask as gask
from . import ground_sus as gsus
from . import viz as gviz
from . import whatif as gwhatif
from . import ddi as gddi
from . import pharmacogen as gpharm
from . import family as gfamily
from . import reverse_pheno as grpheno
from . import protocol_compliance as gpcdt
from . import consult as gconsult
from . import simulate as gsim

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


# in-memory twin registry — case_id → GemeoTwin
# (Persistent state still lives in PatientSpace + Neo4j; this is a fast cache)
_TWINS: dict = {}


# ─── helpers ───────────────────────────────────────────────────────────────

async def _ensure_space(case_text: str, patient_info: dict, context: dict, run_diagnosis: bool):
    """Create or load a PatientSpace via the existing digital_twin_workflow."""
    try:
        from digital_twin_workflow import create_digital_twin
    except ImportError as e:
        logger.error(f"digital_twin_workflow not importable: {e}")
        raise

    payload = await create_digital_twin(
        case_text=case_text,
        patient_info=patient_info or {},
        context=context or {},
        run_diagnosis=run_diagnosis,
        run_full_analysis=False,
    )
    return payload


async def _load_space(case_id: str):
    from digital_twin_workflow import _get_or_load_space
    return await _get_or_load_space(case_id)


def _extract_inputs(space):
    """Pull HPO/Gene lists + dx + sus_region from a PatientSpace."""
    hpo_ids, gene_symbols, diagnoses = [], [], []
    sus_region = None
    snap = space.get_current_snapshot() if hasattr(space, "get_current_snapshot") else None
    if snap is not None:
        for p in snap.phenotypes:
            if p.get("hpo_id"):
                hpo_ids.append(p["hpo_id"])
        for g in snap.genes:
            if g.get("symbol"):
                gene_symbols.append(str(g["symbol"]).upper())
        for d in snap.diagnoses:
            if d.get("orpha"):
                diagnoses.append({
                    "orpha": d["orpha"],
                    "name": d.get("disease") or d.get("name"),
                    "probability": d.get("probability", 0.5),
                    "status": d.get("status", "active"),
                })
        sus_region = (snap.context or {}).get("sus_region")
    # also pull active hypotheses
    for hyp in (getattr(space, "_hypotheses", {}) or {}).values():
        orpha = getattr(hyp, "orpha_code", None)
        if orpha and orpha not in {d["orpha"] for d in diagnoses}:
            diagnoses.append({
                "orpha": orpha,
                "name": getattr(hyp, "disease_name", "") or getattr(hyp, "name", ""),
                "probability": getattr(hyp, "probability", 0.5),
                "status": getattr(hyp, "status", "active"),
            })
    diagnoses.sort(key=lambda d: d.get("probability", 0), reverse=True)
    return hpo_ids, gene_symbols, diagnoses, sus_region


# ─── primary entry points ──────────────────────────────────────────────────

async def build_gemeo(
    *,
    case_text: str,
    patient_info: dict = None,
    context: dict = None,
    run_diagnosis: bool = True,
    cohort_k: int = 10,
    horizons_months: list[int] = None,
    fast: bool = False,
) -> GemeoTwin:
    """Build a complete digital twin from a clinical case.

    Args:
        case_text: free-text or structured case description (PT-BR or EN)
        patient_info: {age, sex, ethnicity, ...}
        context: {sus_region, access_level, ...}
        run_diagnosis: run engine_v2 to populate hypotheses
        cohort_k: patients-like-mine size
        horizons_months: trajectory horizons (default [6, 12, 24])
        fast: skip slow stages (drugs, trials) for snappy demos
    """
    horizons_months = horizons_months or [6, 12, 24]

    # 1) PatientSpace creation (existing pipeline)
    payload = await _ensure_space(case_text, patient_info, context, run_diagnosis)
    case_id = payload.get("case_id") or payload.get("id")
    space = await _load_space(case_id) if case_id else None
    if space is None:
        logger.warning(f"could not load PatientSpace for case {case_id}")

    # 2) extract working set
    hpo_ids, gene_symbols, diagnoses, sus_region = ([], [], [], None)
    if space is not None:
        hpo_ids, gene_symbols, diagnoses, sus_region = _extract_inputs(space)
    if context and context.get("sus_region"):
        sus_region = sus_region or context["sus_region"]

    # 3) embed
    embedding, enc_quality = (None, "skipped")
    if space is not None:
        try:
            embedding, enc_quality = gencoder.encode_patient_space(space)
        except Exception as e:
            logger.warning(f"encode failed: {e}")

    # 4) parallelize the read-only stages
    # Prefer an explicit override (passed via patient_info or context) so
    # callers can pre-seed the Orphanet code BEFORE the diagnosis pipeline
    # has had a chance to run. Falls back to the top diagnosis.
    target_orpha = None
    if context and context.get("target_orpha"):
        target_orpha = str(context["target_orpha"])
    elif patient_info and patient_info.get("target_orpha"):
        target_orpha = str(patient_info["target_orpha"])
    elif diagnoses:
        target_orpha = diagnoses[0].get("orpha")

    async def _safe(awaitable):
        try:
            return await awaitable
        except Exception as e:
            logger.warning(f"stage failed: {e}")
            return None

    cohort_task = _safe(gcohort.find_cohort(
        embedding=embedding,
        hpo_ids=hpo_ids,
        orpha_codes=[d["orpha"] for d in diagnoses if d.get("orpha")],
        k=cohort_k,
        include_literature=True,
    )) if (embedding is not None or hpo_ids or diagnoses) else asyncio.sleep(0, result=None)

    subgraph_task = _safe(gsub.extract(
        patient_id=case_id or "anon",
        hpo_ids=hpo_ids,
        gene_symbols=gene_symbols,
        target_orpha=target_orpha,
    )) if hpo_ids or gene_symbols or target_orpha else asyncio.sleep(0, result=None)

    # trajectory uses 3 LLM calls — heaviest stage by 5x. Skip in fast mode.
    trajectory_task = (
        _safe(gtraj.predict(space, horizons_months))
        if (space and not fast)
        else asyncio.sleep(0, result=None)
    )
    risk_task = _safe(grisk.assess(space, embedding=embedding)) if space else asyncio.sleep(0, result=None)
    ask_task = _safe(gask.recommend(space, top_n=5)) if space else asyncio.sleep(0, result=None)

    if not fast:
        drugs_task = _safe(gdrugs.find(space, embedding=embedding, sus_region=sus_region)) if space else asyncio.sleep(0, result=None)
    else:
        drugs_task = asyncio.sleep(0, result=None)

    # Case-driven additions: family / reverse-pheno / pcdt / ddi (medications)
    medications_in_snapshot = []
    if space is not None:
        snap_now = space.get_current_snapshot() if hasattr(space, "get_current_snapshot") else None
        if snap_now is not None:
            medications_in_snapshot = list(snap_now.medications or [])

    family_task = _safe(gfamily.assess(
        orpha=target_orpha,
        family_history=(snap_now.family_history if (space and hasattr(space, "get_current_snapshot") and space.get_current_snapshot()) else []),
        sex=(patient_info or {}).get("sex"),
    )) if target_orpha else asyncio.sleep(0, result=None)

    rpheno_task = _safe(grpheno.look_for(
        orpha=target_orpha,
        already_present=hpo_ids,
        top_n=10,
    )) if target_orpha else asyncio.sleep(0, result=None)

    async def _pcdt_async():
        return gpcdt.assess(
            orpha=target_orpha,
            current_treatments=medications_in_snapshot,
            current_labs=(snap_now.labs if snap_now else []),
            current_imaging=(snap_now.imaging if snap_now else []),
        )
    pcdt_task = _safe(_pcdt_async()) if target_orpha else asyncio.sleep(0, result=None)

    ddi_task = _safe(gddi.predict(
        medications=medications_in_snapshot,
    )) if (medications_in_snapshot and len(medications_in_snapshot) >= 2) else asyncio.sleep(0, result=None)

    cohort_v, subgraph_v, traj_v, risk_v, ask_v, drugs_v, family_v, rpheno_v, pcdt_v, ddi_v = await asyncio.gather(
        cohort_task, subgraph_task, trajectory_task, risk_task, ask_task, drugs_task,
        family_task, rpheno_task, pcdt_task, ddi_task,
    )

    # Pharmacogenomics — needs both genes and drug candidates
    pharm_v = None
    if space is not None and (snap_now.genes if snap_now else []) and drugs_v and drugs_v.candidates:
        try:
            pharm_v = await gpharm.assess(
                genes=snap_now.genes,
                drug_candidates=drugs_v.candidates[:8],
            )
        except Exception as e:
            logger.debug(f"pharmacogen failed: {e}")

    # 5) trials — wrap existing
    # trial_matcher.TrialMatchResult uses `trials: list[TrialMatch]`,
    # NOT `matches`. Each TrialMatch has trial_id (NCT number),
    # match_score (not "score"), conditions/interventions/url/etc.
    # The previous mapping looked for nct_id/score/eligibility_summary
    # which don't exist on the dataclass → every field serialized as
    # None and the count came out 0 even when ClinicalTrials.gov
    # returned real hits.
    trials_v = None
    if space and not fast:
        try:
            from .types import TrialSpec
            from trial_matcher import match_trials
            tm = await match_trials(space, max_trials=8)
            matches = []
            if tm is not None:
                raw = (
                    getattr(tm, "trials", None)
                    or getattr(tm, "matches", None)
                    or (tm.get("trials") if isinstance(tm, dict) else None)
                    or (tm.get("matches") if isinstance(tm, dict) else None)
                    or []
                )
                for m in raw[:8]:
                    if isinstance(m, dict):
                        matches.append(m)
                    else:
                        matches.append({
                            "nct_id": getattr(m, "trial_id", None) or getattr(m, "nct_id", None),
                            "title": getattr(m, "title", None),
                            "phase": getattr(m, "phase", None),
                            "status": getattr(m, "status", None),
                            "score": getattr(m, "match_score", None) or getattr(m, "score", None),
                            "conditions": getattr(m, "conditions", []) or [],
                            "interventions": getattr(m, "interventions", []) or [],
                            "explanation": getattr(m, "explanation", None),
                            "url": getattr(m, "url", None),
                            "has_brazil_site": getattr(m, "has_brazil_site", False),
                        })
            n_searched = int(getattr(tm, "total_found", 0) or 0) if tm is not None else 0
            trials_v = TrialSpec(matches=matches, model="trialgpt_bootstrap",
                                  n_searched=max(n_searched, len(matches)))
        except Exception as e:
            logger.debug(f"trial_matcher failed: {e}")

    # 6) SUS grounding
    sus_v = None
    if target_orpha:
        try:
            sus_v = gsus.check(
                orpha=target_orpha,
                disease_name=diagnoses[0].get("name") if diagnoses else None,
                uf=sus_region,
            )
        except Exception as e:
            logger.warning(f"SUS grounding failed: {e}")

    # 7) viz
    viz_v = None
    if subgraph_v is not None:
        try:
            viz_v = gviz.from_subgraph(subgraph_v, center_id=f"patient:{case_id}")
        except Exception as e:
            logger.warning(f"viz formatting failed: {e}")

    # 8) assemble
    snap = space.get_current_snapshot() if (space and hasattr(space, "get_current_snapshot")) else None
    twin = GemeoTwin(
        case_id=case_id,
        patient_id=(patient_info or {}).get("id"),
        embedding=(embedding.tolist() if embedding is not None and hasattr(embedding, "tolist") else None),
        embedding_dim=(int(embedding.shape[0]) if embedding is not None and hasattr(embedding, "shape") else 0),
        diagnoses=diagnoses,
        cohort=cohort_v,
        subgraph=subgraph_v,
        trajectory=traj_v,
        risk=risk_v,
        drugs=drugs_v,
        trials=trials_v,
        next_questions=ask_v or [],
        sus_check=sus_v,
        viz_data=viz_v,
        ddi=ddi_v,
        pharmacogen=pharm_v,
        family=family_v,
        reverse_pheno=rpheno_v,
        protocol_compliance=pcdt_v,
        snapshot_versions=[s.version for s in (space.get_trajectory() if space and hasattr(space, "get_trajectory") else [])],
        n_phenotypes=len(snap.phenotypes) if snap else 0,
        n_genes=len(snap.genes) if snap else 0,
        n_labs=len(snap.labs) if snap else 0,
        extra={
            "encoder_quality": enc_quality,
            "sus_region": sus_region,
            "target_orpha": target_orpha,
        },
    )
    if case_id:
        _TWINS[case_id] = twin
    return twin


async def evolve_gemeo(
    case_id: str,
    *,
    new_phenotypes: list = None,
    new_genes: list = None,
    new_labs: list = None,
    new_treatments: list = None,
    cohort_k: int = 10,
    horizons_months: list[int] = None,
) -> Optional[GemeoTwin]:
    """Add new clinical data to an existing twin and re-run all stages."""
    try:
        from digital_twin_workflow import evolve_digital_twin
    except ImportError as e:
        logger.error(f"evolve_digital_twin not importable: {e}")
        return None

    await evolve_digital_twin(
        case_id=case_id,
        new_phenotypes=new_phenotypes or [],
        new_genes=new_genes or [],
        new_labs=new_labs or [],
        new_treatments=new_treatments or [],
    )
    space = await _load_space(case_id)
    if space is None:
        return None

    hpo_ids, gene_symbols, diagnoses, sus_region = _extract_inputs(space)
    embedding, enc_quality = gencoder.encode_patient_space(space)
    target_orpha = diagnoses[0]["orpha"] if diagnoses else None

    cohort_v, subgraph_v, traj_v, risk_v, ask_v = await asyncio.gather(
        gcohort.find_cohort(embedding=embedding, hpo_ids=hpo_ids, k=cohort_k),
        gsub.extract(patient_id=case_id, hpo_ids=hpo_ids, gene_symbols=gene_symbols, target_orpha=target_orpha),
        gtraj.predict(space, horizons_months or [6, 12, 24]),
        grisk.assess(space, embedding=embedding),
        gask.recommend(space, top_n=5),
        return_exceptions=True,
    )

    # exceptions → None
    def _ok(v):
        return None if isinstance(v, Exception) else v

    twin = _TWINS.get(case_id) or GemeoTwin(case_id=case_id)
    twin.embedding = embedding.tolist() if hasattr(embedding, "tolist") else None
    twin.embedding_dim = int(embedding.shape[0]) if hasattr(embedding, "shape") else 0
    twin.diagnoses = diagnoses
    twin.cohort = _ok(cohort_v)
    twin.subgraph = _ok(subgraph_v)
    twin.trajectory = _ok(traj_v)
    twin.risk = _ok(risk_v)
    twin.next_questions = _ok(ask_v) or []
    if twin.subgraph:
        twin.viz_data = gviz.from_subgraph(twin.subgraph, center_id=f"patient:{case_id}")
    snap = space.get_current_snapshot()
    if snap:
        twin.n_phenotypes = len(snap.phenotypes)
        twin.n_genes = len(snap.genes)
        twin.n_labs = len(snap.labs)
    if target_orpha:
        twin.sus_check = gsus.check(orpha=target_orpha, disease_name=diagnoses[0].get("name") if diagnoses else None, uf=sus_region)

    from datetime import datetime, timezone
    twin.updated_at = datetime.now(timezone.utc).isoformat()
    _TWINS[case_id] = twin
    return twin


async def what_if(case_id: str, intervention: dict) -> Optional[dict]:
    """Run a counterfactual on the twin. Returns serialized WhatIfResult."""
    space = await _load_space(case_id)
    if space is None:
        return None
    twin = _TWINS.get(case_id)
    base_risk = twin.risk if twin else None
    base_traj = twin.trajectory if twin else None
    result = await gwhatif.simulate(
        space, intervention,
        baseline_risk=base_risk,
        baseline_trajectory=base_traj,
    )
    from dataclasses import asdict
    return asdict(result)


async def query_gemeo(case_id: str) -> Optional[GemeoTwin]:
    """Return the cached twin or rebuild lazily from PatientSpace."""
    twin = _TWINS.get(case_id)
    if twin is not None:
        return twin
    space = await _load_space(case_id)
    if space is None:
        return None
    return await build_gemeo(
        case_text="<reload>",
        patient_info=None,
        context=None,
        run_diagnosis=False,
        fast=True,
    )


def get_gemeo(case_id: str) -> Optional[GemeoTwin]:
    """Synchronous in-memory lookup (no Neo4j fallback)."""
    return _TWINS.get(case_id)


async def consult(case_id: str, panel: list[str] = None, question: str = None) -> Optional[dict]:
    """Run a multi-specialist consult on the twin."""
    twin = await query_gemeo(case_id)
    if twin is None:
        return None
    spec = await gconsult.consult(
        twin,
        panel=panel,
        question=question or "Synthesise your opinion on this case.",
    )
    twin.consult = spec
    if case_id:
        _TWINS[case_id] = twin
    from dataclasses import asdict
    return asdict(spec)


async def simulate(
    case_id: str,
    *,
    n_runs: int = 30,
    intervention: dict = None,
    horizons_months: list[int] = None,
) -> Optional[dict]:
    """Monte-Carlo simulation of trajectory under stochastic intervention."""
    space = await _load_space(case_id)
    if space is None:
        return None
    spec = await gsim.run(
        space,
        n_runs=n_runs,
        intervention=intervention,
        horizons_months=horizons_months,
    )
    from dataclasses import asdict
    return asdict(spec)