"""Akinator engine — adaptive question flow + grant scoring.

Implements docs/akinator-flow/QUESTION_TREE.md and SCORING_LOGIC.md:
each answer narrows the pool of 10 grants (hard elimination), questions that
can no longer discriminate are skipped, and the survivors are scored on
fit (50%) / timing (25%) / effort (25%) into a match percentage.

Session state is a plain dict so it round-trips through gr.State:
  {"answers": [(qid, key), ...], "profile": {...}}
"""
from __future__ import annotations

from grants import GRANTS, TODAY

# TRL bands shown to the user (numbers hidden, per QUESTION_TREE.md)
TRL_BANDS = {
    "idea": (1, 3),
    "prototype": (4, 5),
    "demo": (6, 8),
    "market": (9, 9),
}

QUESTIONS = [
    {
        "id": "entity",
        "ask": "Who seeks the gold?",
        "sub": "What best describes your situation?",
        "options": [
            ("company", "🏢", "I run a registered company (SME / startup)"),
            ("research", "🎓", "I'm at a university or research institute"),
            ("homeowner", "🏠", "I'm a private person (home, EV, energy savings)"),
            ("individual", "👤", "I'm an individual who wants to start a business"),
            ("exploring", "🧭", "Not sure yet — I'm exploring my options"),
        ],
    },
    {
        "id": "location",
        "ask": "Where does your story unfold?",
        "sub": "EU programmes mostly require EU or associated-country presence.",
        "options": [
            ("eu", "🇪🇺", "In an EU member state"),
            ("associated", "🤝", "In a Horizon-Europe associated country"),
            ("relocating", "✈️", "Outside the EU — but willing to relocate or register there"),
            ("outside", "🌍", "Outside the EU, and staying there"),
        ],
    },
    {
        "id": "trl",
        "ask": "How far has your magic travelled?",
        "sub": "Be honest — each stage opens different doors.",
        "options": [
            ("idea", "💡", "Just an idea or early research"),
            ("prototype", "🔧", "A working prototype, tested in the lab"),
            ("demo", "🚀", "A demo or pilot, validated in real conditions"),
            ("market", "📦", "Already on the market, selling to customers"),
        ],
    },
    {
        "id": "budget",
        "ask": "How much treasure do you need?",
        "sub": "Rough order of magnitude is enough.",
        "options": [
            ("small", "🪙", "Up to €60,000 — a small project or planning"),
            ("medium", "💰", "€60,000 – €500,000 — development and validation"),
            ("large", "👑", "More than €500,000 — scale-up or major R&D"),
            ("unsure", "🤷", "I honestly don't know yet"),
        ],
    },
    {
        "id": "sector",
        "ask": "What is the nature of your craft?",
        "sub": "The main focus of your innovation.",
        "options": [
            ("ai", "🤖", "AI / digital technology / software"),
            ("green", "🌱", "Green tech / clean energy / sustainability"),
            ("health", "🏥", "Healthcare / biotech"),
            ("manufacturing", "🏭", "Manufacturing / industrial"),
            ("creative", "🎨", "Creative / cultural industries"),
            ("other", "📦", "Something else entirely"),
        ],
    },
    {
        "id": "prior_eu",
        "ask": "Has EU gold touched your work before?",
        "sub": "Prior Horizon / ERC / Pathfinder funding opens one special door.",
        "options": [
            ("yes", "✅", "Yes — results from a prior EU-funded project"),
            ("no", "❌", "No, never"),
            ("unsure", "🤔", "I'm not sure"),
        ],
    },
    {
        "id": "consortium",
        "ask": "Would you join forces across borders?",
        "sub": "Some programmes require a partner in another country.",
        "options": [
            ("yes", "🤝", "Yes — I already have contacts abroad"),
            ("maybe", "🔍", "Maybe, with help finding partners"),
            ("no", "🚶", "No — I want to apply alone"),
        ],
    },
    {
        "id": "equity",
        "ask": "Would you trade shares for treasure?",
        "sub": "Some programmes blend grants with equity investment.",
        "options": [
            ("yes", "📈", "Yes — open to equity for larger funding"),
            ("no", "🛡️", "No — grants only, I keep my shares"),
            ("depends", "⚖️", "It depends on the terms"),
        ],
    },
    {
        "id": "academic_level",
        "ask": "What is your academic standing?",
        "sub": "Some research grants require a doctoral degree.",
        "options": [
            ("postdoc_early", "🎓", "I have a PhD (received within the last 7 years)"),
            ("postdoc_senior", "🏛️", "I have a PhD (more than 7 years ago)"),
            ("phd_student", "📚", "I'm currently doing a PhD"),
            ("no_phd", "🙅", "I don't have a PhD"),
        ],
    },
    {
        "id": "age_range",
        "ask": "How old are you?",
        "sub": "A few programmes are specifically for young people.",
        "options": [
            ("under_30", "🧑", "Under 30"),
            ("30_plus", "🧑‍💼", "30 or older"),
        ],
    },
    {
        "id": "employment_status",
        "ask": "What is your employment situation?",
        "sub": "Some programmes specifically support the unemployed.",
        "options": [
            ("unemployed", "📋", "Currently unemployed or seeking work"),
            ("employed", "💼", "Employed (full-time, part-time, or freelance)"),
            ("student", "🎒", "Student"),
        ],
    },
    {
        "id": "woman_led",
        "ask": "Is your company led by a woman?",
        "sub": "One programme specifically supports women-led deep-tech startups.",
        "options": [
            ("yes", "👩‍💼", "Yes — CEO, founder, or CTO is a woman"),
            ("no", "➡️", "No"),
        ],
    },
]
QUESTIONS_BY_ID = {q["id"]: q for q in QUESTIONS}

# Effort scores from SCORING_LOGIC.md live on the grant dicts ("effort").
FIT_WEIGHT, TIMING_WEIGHT, EFFORT_WEIGHT = 0.50, 0.25, 0.25


def new_session() -> dict:
    return {"answers": [], "profile": {}}


# ── elimination ──────────────────────────────────────────────────────────────

def grant_alive(grant: dict, profile: dict) -> bool:
    """Hard eligibility gate per the Grant Elimination Matrix."""
    entity = profile.get("entity")
    if entity and entity != "exploring" and entity not in grant["entities"]:
        return False

    loc = profile.get("location")
    if loc == "outside":
        return False  # no EU presence, not relocating — nothing fits
    if loc and loc not in grant["locations"]:
        return False

    trl = profile.get("trl")
    if trl:
        lo, hi = TRL_BANDS[trl]
        if hi < grant["trl_min"] or lo > grant["trl_max"]:
            return False

    budget = profile.get("budget")
    if budget == "small" and grant["min_amount"] > 60_000:
        return False
    if budget == "medium" and (grant["max_amount"] < 60_000 or grant["min_amount"] > 500_000):
        return False
    if budget == "large" and grant["max_amount"] < 500_000:
        return False

    sector = profile.get("sector")
    if sector and "any" not in grant["sectors"]:
        wanted = {sector, "digital"} if sector == "ai" else {sector}
        if not (wanted & grant["sectors"]):
            return False

    if profile.get("consortium") == "no" and grant["requires_consortium"]:
        return False

    if profile.get("prior_eu") == "no" and grant["requires_prior_eu"]:
        return False

    # Academic level filter
    academic = profile.get("academic_level")
    req_academic = grant.get("requires_academic")
    if academic and req_academic:
        # "phd" = must have completed a PhD (postdoc_early or postdoc_senior pass)
        if req_academic == "phd" and academic in ("no_phd", "phd_student"):
            return False
        # "postdoc_early" = PhD within last 7 years (only postdoc_early passes)
        if req_academic == "postdoc_early" and academic not in ("postdoc_early",):
            return False
        # "postdoc_senior" = PhD 7+ years ago (postdoc_early OR postdoc_senior pass)
        if req_academic == "postdoc_senior" and academic in ("no_phd", "phd_student"):
            return False

    # Age filter
    age = profile.get("age_range")
    if age == "30_plus" and grant.get("requires_under_30"):
        return False

    # Employment status filter
    employment = profile.get("employment_status")
    if employment and grant.get("requires_unemployed"):
        if employment not in ("unemployed",):
            return False

    # Woman-led filter
    if profile.get("woman_led") == "no" and grant.get("requires_woman_led"):
        return False

    return True


def remaining_grants(profile: dict) -> list[dict]:
    return [g for g in GRANTS if grant_alive(g, profile)]


# ── question selection ───────────────────────────────────────────────────────

CORE_QUESTIONS = {"entity", "location", "trl", "budget", "sector"}  # always asked


def _core_for_profile(profile: dict) -> set[str]:
    """Homeowners skip TRL and sector (all personal green finance)."""
    if profile.get("entity") == "homeowner":
        return CORE_QUESTIONS - {"trl", "sector"}
    return CORE_QUESTIONS


def _question_discriminates(q: dict, profile: dict, remaining: list[dict]) -> bool:
    """True if at least one answer to q would eliminate at least one grant."""
    for key, _icon, _label in q["options"]:
        trial = dict(profile, **{q["id"]: key})
        if len([g for g in remaining if grant_alive(g, trial)]) < len(remaining):
            return True
    return False


def next_question(session: dict) -> dict | None:
    profile = session["profile"]
    remaining = remaining_grants(profile)
    answered = {qid for qid, _ in session["answers"]}

    if not remaining:
        return None
    core = _core_for_profile(profile)
    core_done = core <= answered

    skip = CORE_QUESTIONS - core  # questions excluded for this entity type
    for q in QUESTIONS:
        if q["id"] in answered or q["id"] in skip:
            continue
        if q["id"] in core:
            return q
        # conditional questions: only if they still matter, and only while the
        # pool needs narrowing (Akinator stops once it is confident)
        if len(remaining) <= 2 and core_done:
            return None
        if _question_discriminates(q, profile, remaining):
            return q
        if q["id"] == "equity" and any(g["has_equity"] for g in remaining):
            return q  # doesn't eliminate, but changes the recommendation
    return None


def apply_answer(session: dict, qid: str, key: str) -> dict:
    session["answers"].append((qid, key))
    session["profile"][qid] = key
    return session


def is_finished(session: dict) -> bool:
    return next_question(session) is None


# ── scoring (SCORING_LOGIC.md) ───────────────────────────────────────────────

def _fit(grant: dict, p: dict) -> int:
    score = 0
    sector = p.get("sector")
    if sector and (sector in grant["bonus_sectors"] or
                   ("any" not in grant["sectors"] and sector in grant["sectors"])):
        score += 30
    elif "any" in grant["sectors"]:
        score += 15

    budget = p.get("budget")
    mids = {"small": 40_000, "medium": 250_000, "large": 1_200_000}
    need = mids.get(budget)
    if need is None:
        score += 15
    elif grant["min_amount"] <= need <= grant["max_amount"]:
        score += 25
    elif need <= grant["max_amount"]:
        score += 15

    trl = p.get("trl")
    if trl:
        lo, hi = TRL_BANDS[trl]
        user_mid = (lo + hi) / 2
        grant_mid = (grant["trl_min"] + grant["trl_max"]) / 2
        score += max(0, round(20 - abs(user_mid - grant_mid) * 5))
    else:
        score += 10

    consortium = p.get("consortium")
    if grant["requires_consortium"]:
        score += 15 if consortium == "yes" else (8 if consortium == "maybe" else 0)
    else:
        score += 15

    equity = p.get("equity")
    if grant["has_equity"]:
        score += 10 if equity == "yes" else (5 if equity == "depends" else 2)
    else:
        score += 10

    # Non-grant instruments (debt, vouchers, exchanges) are a different kind of
    # money — they should support, not outrank, the actual grants
    # (SCORING_LOGIC.md example: InvestEU fit 45 for a grant-seeking scale-up).
    if grant["instrument"] != "grant":
        score -= 30

    return max(0, min(score, 100))


def _days_score(days: int) -> int:
    if days < 0:
        return 0
    if days < 14:
        return 20
    if days < 30:
        return 50
    if days < 90:
        return 80
    if days < 180:
        return 95
    return 100


def _timing(grant: dict) -> int:
    if grant["rolling"]:
        return 90
    if not grant["deadlines"]:
        return 70
    best = max(_days_score((d - TODAY).days) for d in grant["deadlines"])
    return best if best > 0 else 5  # all cycles passed — heavily penalise


def near_miss_grants(profile: dict) -> tuple[list[dict], str | None]:
    """When elimination empties the pool, relax soft constraints one at a time
    and return (ranked near-misses, the constraint that was relaxed)."""
    for relax in ("budget", "trl", "sector", "consortium"):
        if relax in profile:
            trial = {k: v for k, v in profile.items() if k != relax}
            if remaining_grants(trial):
                return score_grants(trial), relax
    return [], None


def _is_expired(grant: dict) -> bool:
    """True if the grant has fixed deadlines and ALL have passed."""
    if grant["rolling"] or not grant.get("deadlines"):
        return False
    return all(d < TODAY for d in grant["deadlines"])


def score_grants(profile: dict) -> list[dict]:
    """Ranked survivors with match % and per-dimension breakdown."""
    out = []
    for g in remaining_grants(profile):
        fit, timing, effort = _fit(g, profile), _timing(g), g["effort"]
        total = round(fit * FIT_WEIGHT + timing * TIMING_WEIGHT + effort * EFFORT_WEIGHT, 1)
        out.append({"grant": g, "score": total,
                    "fit": fit, "timing": timing, "effort": effort})
    out.sort(key=lambda r: r["score"], reverse=True)
    # Hide expired grants if there are active alternatives
    active = [r for r in out if not _is_expired(r["grant"])]
    if active:
        return active
    return out


# ── human-readable profile (for the AI prompt and the dossier) ──────────────

ANSWER_PHRASES = {
    ("entity", "company"): "a registered company (SME/startup)",
    ("entity", "research"): "a university or research institute",
    ("entity", "homeowner"): "a private homeowner seeking personal green finance",
    ("entity", "individual"): "an individual planning to start a business",
    ("entity", "exploring"): "exploring options (not yet decided on entity type)",
    ("location", "eu"): "based in an EU member state",
    ("location", "associated"): "based in a Horizon-Europe associated country",
    ("location", "relocating"): "outside the EU but willing to relocate/register there",
    ("location", "outside"): "outside the EU with no plans to relocate",
    ("trl", "idea"): "at idea/early-research stage (TRL 1-3)",
    ("trl", "prototype"): "with a lab-tested prototype (TRL 4-5)",
    ("trl", "demo"): "with a validated demo/pilot (TRL 6-8)",
    ("trl", "market"): "with a product already on the market (TRL 9)",
    ("budget", "small"): "needing up to €60K",
    ("budget", "medium"): "needing €60K-€500K",
    ("budget", "large"): "needing more than €500K",
    ("budget", "unsure"): "with funding needs still unclear",
    ("sector", "ai"): "working in AI/digital technology",
    ("sector", "green"): "working in green/clean technology",
    ("sector", "health"): "working in healthcare/biotech",
    ("sector", "manufacturing"): "working in manufacturing/industry",
    ("sector", "creative"): "working in the creative/cultural sector",
    ("sector", "other"): "working in another sector",
    ("prior_eu", "yes"): "with results from prior EU-funded research",
    ("prior_eu", "no"): "without prior EU funding",
    ("prior_eu", "unsure"): "unsure about prior EU funding",
    ("consortium", "yes"): "able to partner across borders",
    ("consortium", "maybe"): "open to cross-border partners with help",
    ("consortium", "no"): "preferring to apply alone",
    ("equity", "yes"): "open to equity investment",
    ("equity", "no"): "wanting non-dilutive funding only",
    ("equity", "depends"): "equity-flexible depending on terms",
    ("academic_level", "postdoc_early"): "with a recent PhD (0-7 years)",
    ("academic_level", "postdoc_senior"): "with a PhD (7+ years ago)",
    ("academic_level", "phd_student"): "currently doing a PhD",
    ("academic_level", "no_phd"): "without a doctoral degree",
    ("age_range", "under_30"): "under 30 years old",
    ("age_range", "30_plus"): "30 or older",
    ("employment_status", "unemployed"): "currently unemployed",
    ("employment_status", "employed"): "currently employed",
    ("employment_status", "student"): "a student",
    ("woman_led", "yes"): "company led by a woman",
    ("woman_led", "no"): "not specifically woman-led",
}


def profile_sentence(session: dict) -> str:
    bits = [ANSWER_PHRASES.get((qid, key), f"{qid}={key}")
            for qid, key in session["answers"]]
    return "; ".join(bits)


def path_key(session: dict) -> str:
    """Deterministic cache key for this answer path (CACHING_STRATEGY.md)."""
    import hashlib
    path = "|".join(f"{q}:{a}" for q, a in sorted(session["answers"]))
    return hashlib.sha256(path.encode()).hexdigest()