"""
Read-only tools exposed to the MAF ChatAgent. Each tool is a thin wrapper
around existing SQLModel queries / service helpers — they do NOT duplicate
business logic. The active DB session is resolved via a ContextVar so tool
signatures stay clean for the LLM.

POLICY: every tool here must stay READ-ONLY. Transaction descriptions reach
the model from external emails (indirect prompt-injection surface), so a
write-capable tool would let crafted text mutate financial data. Any future
write tool requires an explicit user-confirmation step in the UI.
"""

from __future__ import annotations

import contextvars
from datetime import datetime
from typing import Annotated, Optional

from pydantic import Field
from sqlalchemy import case
from sqlmodel import Session, col, func, select

from app.models.models import (
    Account,
    BalanceOverride,
    Category,
    MunicipalReceipt,
    PensionSnapshot,
    RecurringItem,
    Transaction,
    TransactionSource,
    TransactionType,
    WaterMeterReading,
)
from app.services.budget_projection import (
    MAX_YEAR,
    MIN_YEAR,
    compute_monthly_projection,
    compute_yearly_projection_with_cumulative,
    get_cycle_range,
)
from app.services import exchange_rate as fx
from app.services.exchange_rate import (
    get_converted_amount_expr,
    get_current_rate,
)

_session_ctx: contextvars.ContextVar[Session] = contextvars.ContextVar("agent_session")


def set_session(session: Session) -> contextvars.Token:
    return _session_ctx.set(session)


def reset_session(token: contextvars.Token) -> None:
    _session_ctx.reset(token)


def _s() -> Session:
    try:
        return _session_ctx.get()
    except LookupError as exc:
        raise RuntimeError(
            "DB session not bound to agent context — tool called outside an "
            "HTTP request (the AG-UI middleware binds it per request)"
        ) from exc


# ─── Tools ──────────────────────────────────────────────────────────────────


def get_accounts() -> list[dict]:
    """List every account with current balance, currency, bank and type
    (BANK, PENSION, CRYPTO, SAVINGS, LIABILITY). Use this for net-worth and
    balance questions."""
    rows = _s().exec(select(Account).order_by(Account.account_type, Account.label)).all()
    return [
        {
            "id": a.id,
            "bank": a.bank.value,
            "label": a.label,
            "currency": a.currency.value,
            "balance": float(a.balance),
            "account_type": a.account_type.value,
            "next_payment": float(a.next_payment) if a.next_payment is not None else None,
        }
        for a in rows
    ]


def get_net_worth() -> dict:
    """Return total assets, liabilities and net worth in CRC (primary currency).
    USD/EUR balances are converted at the latest exchange rate."""
    session = _s()
    accounts = session.exec(select(Account)).all()
    multipliers = fx.get_crc_multipliers(session)
    assets_crc = 0.0
    liabilities_crc = 0.0
    excluded: list[str] = []
    for a in accounts:
        mult = multipliers.get(a.currency.value)
        if mult is None:
            # No live or last-known rate: say so instead of inventing a number
            excluded.append(f"{a.label} ({a.currency.value})")
            continue
        amt = float(a.balance) * float(mult)
        if a.account_type.value == "LIABILITY":
            liabilities_crc += amt
        else:
            assets_crc += amt
    result = {
        "assets_crc": round(assets_crc, 2),
        "liabilities_crc": round(liabilities_crc, 2),
        "net_crc": round(assets_crc - liabilities_crc, 2),
    }
    if excluded:
        result["excluded_accounts_no_rate"] = excluded
    return result


def get_recent_transactions(
    limit: Annotated[int, Field(ge=1, le=100, description="How many rows to return")] = 20,
    source: Annotated[
        Optional[str],
        Field(description="Filter by source: CREDIT_CARD, CASH, or TRANSFER"),
    ] = None,
    category_id: Annotated[Optional[int], Field(description="Filter by category id")] = None,
    search: Annotated[
        Optional[str], Field(description="Substring match against merchant name")
    ] = None,
    start_date: Annotated[
        Optional[str], Field(description="ISO date lower bound, inclusive")
    ] = None,
    end_date: Annotated[
        Optional[str], Field(description="ISO date upper bound, exclusive")
    ] = None,
) -> list[dict]:
    """Recent transactions, newest first. Use filters to narrow down. For
    billing-cycle scoped totals prefer get_cycle_summary."""
    q = select(Transaction).where(
        col(Transaction.transaction_type).notin_(
            [TransactionType.SALARY, TransactionType.DEPOSITO]
        )
    )
    if source:
        q = q.where(Transaction.source == TransactionSource(source))
    if category_id is not None:
        q = q.where(Transaction.category_id == category_id)
    if search:
        q = q.where(col(Transaction.merchant).ilike(f"%{search}%"))
    if start_date:
        q = q.where(Transaction.date >= datetime.fromisoformat(start_date))
    if end_date:
        q = q.where(Transaction.date < datetime.fromisoformat(end_date))
    q = q.order_by(col(Transaction.date).desc()).limit(limit)
    return [
        {
            "id": t.id,
            "date": t.date.isoformat(),
            "merchant": t.merchant,
            "amount": float(t.amount),
            "currency": t.currency.value,
            "source": t.source.value,
            "transaction_type": t.transaction_type.value,
            "bank": t.bank.value,
            "category_id": t.category_id,
        }
        for t in _s().exec(q).all()
    ]


def get_cycle_summary(
    cycle_year: Annotated[int, Field(description="Billing cycle year, e.g. 2026")],
    cycle_month: Annotated[
        int,
        Field(ge=1, le=12, description="Billing cycle month (cycle runs 18th→18th)"),
    ],
) -> dict:
    """Totals for a credit-card billing cycle (18th of month → 18th of next).
    Returns spend by source, count, and spend by category."""
    session = _s()
    amount_crc = get_converted_amount_expr(session)
    start, end = get_cycle_range(cycle_year, cycle_month)

    totals = session.exec(
        select(
            Transaction.source,
            func.count(),
            func.coalesce(func.sum(amount_crc), 0),
        )
        .where(
            Transaction.transaction_type == TransactionType.COMPRA,
            Transaction.date >= start,
            Transaction.date < end,
        )
        .group_by(Transaction.source)
    ).all()

    by_category = session.exec(
        select(
            Category.name,
            func.coalesce(func.sum(amount_crc), 0),
            func.count(),
        )
        .join(Category, Category.id == Transaction.category_id, isouter=True)
        .where(
            Transaction.transaction_type == TransactionType.COMPRA,
            Transaction.date >= start,
            Transaction.date < end,
        )
        .group_by(Category.name)
        .order_by(func.sum(amount_crc).desc())
    ).all()

    return {
        "cycle_year": cycle_year,
        "cycle_month": cycle_month,
        "range": [start.isoformat(), end.isoformat()],
        "by_source": [
            {"source": s.value, "count": c, "total_crc": float(t)}
            for s, c, t in totals
        ],
        "by_category": [
            {"category": n or "Uncategorized", "total_crc": float(t), "count": c}
            for n, t, c in by_category
        ],
    }


def get_budget_projection(
    year: Annotated[int, Field(description="Year to project")],
    month: Annotated[
        Optional[int],
        Field(ge=1, le=12, description="If given, return only that month's detail"),
    ] = None,
) -> dict:
    """Budget projection. If month is omitted, returns the yearly rollup; if
    given, returns the monthly detail with income items, expense items and
    actuals by source."""
    if not MIN_YEAR <= year <= MAX_YEAR:
        return {"error": f"year must be between {MIN_YEAR} and {MAX_YEAR}"}
    session = _s()
    if month is None:
        months_data = compute_yearly_projection_with_cumulative(session, year)
        return {
            "year": year,
            "months": months_data,
            "annual_income": sum(m["projected_income"] for m in months_data),
            "annual_expenses": sum(m["gran_total_egresos"] for m in months_data),
            "annual_net": sum(m["net_balance"] for m in months_data),
        }
    return compute_monthly_projection(session, year, month)


def list_recurring_items() -> list[dict]:
    """All recurring items (income and expense, SAVINGS excluded) used by the
    budget projection. Useful to explain what's driving a month's projection."""
    rows = _s().exec(
        select(RecurringItem)
        .where(RecurringItem.is_active == True)  # noqa: E712
        .order_by(RecurringItem.item_type, RecurringItem.name)
    ).all()
    return [
        {
            "id": r.id,
            "name": r.name,
            "amount": float(r.amount),
            "currency": r.currency.value,
            "item_type": r.item_type.value,
            "frequency": r.frequency.value,
            "day_of_month": r.day_of_month,
            "category_id": r.category_id,
        }
        for r in rows
    ]


def get_pension_snapshots(
    fund: Annotated[
        Optional[str],
        Field(description="Filter by fund bank code (FCL, ROP, VOL, etc.)"),
    ] = None,
    latest_only: Annotated[
        bool,
        Field(description="If true, return only the latest snapshot per fund"),
    ] = True,
) -> list[dict]:
    """Pension fund snapshots. Each snapshot covers a period with balances,
    contributions, returns, fees and the ending balance (saldo_final)."""
    if latest_only:
        # Latest snapshot per fund resolved in SQL instead of scanning all rows
        latest = (
            select(
                PensionSnapshot.fund.label("fund"),
                func.max(PensionSnapshot.period_end).label("period_end"),
            )
            .group_by(PensionSnapshot.fund)
            .subquery()
        )
        q = select(PensionSnapshot).join(
            latest,
            (PensionSnapshot.fund == latest.c.fund)
            & (PensionSnapshot.period_end == latest.c.period_end),
        )
    else:
        q = select(PensionSnapshot)
    q = q.order_by(col(PensionSnapshot.period_end).desc())
    if fund:
        q = q.where(PensionSnapshot.fund == fund)
    rows = _s().exec(q).all()
    return [
        {
            "fund": r.fund.value,
            "period_start": r.period_start.isoformat(),
            "period_end": r.period_end.isoformat(),
            "saldo_anterior": float(r.saldo_anterior),
            "aportes": float(r.aportes),
            "rendimientos": float(r.rendimientos),
            "retiros": float(r.retiros),
            "comision": float(r.comision),
            "saldo_final": float(r.saldo_final),
        }
        for r in rows
    ]


def get_salary_summary() -> dict:
    """Summary of salary deposits (count, total in CRC, latest date)."""
    session = _s()
    amount_crc = get_converted_amount_expr(session)
    row = session.exec(
        select(
            func.count(),
            func.coalesce(func.sum(amount_crc), 0),
            func.max(Transaction.date),
        ).where(Transaction.transaction_type == TransactionType.SALARY)
    ).first()
    count = row[0] if row else 0
    total = float(row[1]) if row else 0.0
    latest = row[2].isoformat() if row and row[2] else None
    return {"count": count, "total_crc": total, "latest_date": latest}


def get_municipal_receipts(
    limit: Annotated[int, Field(ge=1, le=50)] = 12,
    offset: Annotated[int, Field(ge=0, description="Skip the N most recent")] = 0,
    account: Annotated[
        Optional[str], Field(description="Municipal account/contract id")
    ] = None,
) -> list[dict]:
    """Recent municipal receipts (water + related services) with totals and
    water consumption in m³."""
    q = select(MunicipalReceipt).order_by(col(MunicipalReceipt.receipt_date).desc())
    if account:
        q = q.where(MunicipalReceipt.account == account)
    q = q.offset(offset).limit(limit)
    rows = _s().exec(q).all()
    # One grouped query for all receipts instead of one per receipt
    consumption: dict[int, float] = {}
    ids = [r.id for r in rows if r.id is not None]
    if ids:
        grouped = _s().exec(
            select(
                WaterMeterReading.receipt_id,
                func.sum(WaterMeterReading.consumption_m3),
            )
            .where(col(WaterMeterReading.receipt_id).in_(ids))
            .group_by(WaterMeterReading.receipt_id)
        ).all()
        consumption = {rid: float(total) for rid, total in grouped}
    out: list[dict] = []
    for r in rows:
        out.append(
            {
                "id": r.id,
                "receipt_date": r.receipt_date.isoformat(),
                "period": r.period,
                "account": r.account,
                "finca": r.finca,
                "subtotal": float(r.subtotal),
                "interests": float(r.interests),
                "iva": float(r.iva),
                "total": float(r.total),
                "water_consumption_m3": consumption.get(r.id, 0.0),
            }
        )
    return out


def get_analytics_by_category(
    cycle_year: Annotated[Optional[int], Field(description="Scope to a billing cycle")] = None,
    cycle_month: Annotated[Optional[int], Field(ge=1, le=12)] = None,
) -> list[dict]:
    """Spending breakdown by category in CRC (optionally scoped to a billing
    cycle). Percentages sum to 100."""
    session = _s()
    amount_crc = get_converted_amount_expr(session)
    q = (
        select(
            Transaction.category_id,
            func.sum(amount_crc).label("total"),
            func.count().label("count"),
        )
        .where(Transaction.transaction_type == TransactionType.COMPRA)
        .group_by(Transaction.category_id)
    )
    if cycle_year and cycle_month:
        start, end = get_cycle_range(cycle_year, cycle_month)
        q = q.where(Transaction.date >= start, Transaction.date < end)
    rows = session.exec(q).all()
    grand = sum(float(r[1]) for r in rows) or 1.0
    names = {c.id: c.name for c in session.exec(select(Category)).all()}
    out = []
    for cat_id, total, count in rows:
        name = names.get(cat_id, "Uncategorized") if cat_id else "Uncategorized"
        out.append(
            {
                "category_id": cat_id,
                "category": name,
                "total_crc": float(total),
                "count": count,
                "percentage": round(float(total) / grand * 100, 1),
            }
        )
    out.sort(key=lambda x: x["total_crc"], reverse=True)
    return out


def get_monthly_trend(
    months: Annotated[int, Field(ge=1, le=24, description="How many months back")] = 6,
) -> list[dict]:
    """Spending trend by billing cycle for the last N months."""
    session = _s()
    amount_crc = get_converted_amount_expr(session)
    now = datetime.now()
    results: list[dict] = []
    y, m = now.year, now.month
    for _ in range(months):
        start, end = get_cycle_range(y, m)
        row = session.exec(
            select(
                func.count(),
                func.coalesce(func.sum(amount_crc), 0),
                func.coalesce(
                    func.sum(
                        case((Transaction.currency == "USD", Transaction.amount), else_=0)
                    ),
                    0,
                ),
            ).where(
                Transaction.transaction_type == TransactionType.COMPRA,
                Transaction.date >= start,
                Transaction.date < end,
            )
        ).first()
        results.append(
            {
                "year": y,
                "month": m,
                "total_crc": float(row[1]) if row else 0.0,
                "total_usd_raw": float(row[2]) if row else 0.0,
                "count": row[0] if row else 0,
            }
        )
        if m == 1:
            y, m = y - 1, 12
        else:
            m -= 1
    return list(reversed(results))


def get_exchange_rate() -> dict:
    """Latest USD/CRC exchange rate (buy and sell). All multi-currency data
    in the app is normalized to CRC using these rates."""
    rate = get_current_rate(_s())
    if not rate:
        return {"buy_rate": None, "sell_rate": None, "date": None}
    return {
        "buy_rate": float(rate.buy_rate),
        "sell_rate": float(rate.sell_rate),
        "date": rate.date.isoformat(),
        "fetched_at": rate.fetched_at.isoformat() if rate.fetched_at else None,
    }


def list_categories() -> list[dict]:
    """All transaction categories (id, name, icon). Use when the user asks
    about a category and you need the id to filter by."""
    rows = _s().exec(select(Category).order_by(Category.name)).all()
    return [{"id": c.id, "name": c.name, "icon": c.icon} for c in rows]


# Registered with the agent in agent.py
TOOLS = [
    get_accounts,
    get_net_worth,
    get_recent_transactions,
    get_cycle_summary,
    get_budget_projection,
    list_recurring_items,
    get_pension_snapshots,
    get_salary_summary,
    get_municipal_receipts,
    get_analytics_by_category,
    get_monthly_trend,
    get_exchange_rate,
    list_categories,
]