"""Ephemeral portfolio analysis — generates AI commentary from a pie that
exists only in the request's memory.

Phase G data-minimisation guarantee: this module **never writes the pie
to the database, to logs, to Redis, or to disk**. The positions list
enters as a function argument, is used to construct a prompt, the LLM
returns text, and the positions are dropped on function return. The
`ai_calls` ledger row written for the call contains model + token counts
+ cost — no holdings.

Inputs come from the browser's localStorage. The server's role is to:
1. Validate shape + sanitise free-text fields (prompt-injection defence).
2. Compute summary stats (concentration, top-N, currency mix) — these
   reduce the LLM payload and let us cap the prompt size.
3. Call OpenRouter via the existing `call_openrouter` helper.
4. Write the cost ledger row (no holdings).
5. Return the commentary text + token / cost metadata.
"""
from __future__ import annotations

import json
import math
import re
from dataclasses import dataclass
from datetime import datetime, timezone

import httpx
from sqlalchemy.ext.asyncio import AsyncSession

from app.config import get_settings
from app.db import utcnow
from app.logging import get_logger
from app.models import AICall
from app.services.i18n import LANGUAGES, respond_in_clause
from app.services.openrouter import (
    LogResult,
    active_model,
    build_system_prompt,
    call_llm,
)


log = get_logger("portfolio_analysis")


PROMPT_VERSION = 1
# Hard caps on prompt construction to keep token spend bounded regardless
# of pie size. A pie with 200 positions is real — we summarise the tail.
MAX_POSITIONS_INLINED = 25
MAX_NAME_LENGTH = 64
MAX_PROMPT_BYTES = 40_000


# ---------------------------------------------------------------------------
# Input shape
# ---------------------------------------------------------------------------


@dataclass
class Position:
    """One holding as supplied by the browser. Field names match the
    /api/portfolio/parse response shape."""
    yahoo_ticker: str
    name: str
    qty: float
    avg_cost: float
    currency: str | None = None


@dataclass
class AnalysisRequest:
    positions: list[Position]
    prices: dict[str, dict]   # {ticker: {p, c, d:{1d,1m,1y}, ...}}
    base_currency: str = "GBP"
    anchor: str | None = None
    tone: str = "INTERMEDIATE"     # NOVICE | INTERMEDIATE | PRO
    analysis: str = "SPECULATIVE"  # DRY | SPECULATIVE
    lang: str = "en"


@dataclass
class AnalysisResult:
    content: str
    model: str
    prompt_tokens: int | None
    completion_tokens: int | None
    cost_usd: float | None
    generated_at: datetime


# ---------------------------------------------------------------------------
# Input validation + sanitisation
# ---------------------------------------------------------------------------


_CONTROL_CHARS = re.compile(r"[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]")
# Prompt-injection markers commonly used to break out of context. Stripped
# *and* their presence flagged — caller can choose to reject.
_INJECTION_TOKENS = (
    "ignore previous", "ignore above", "system:", "assistant:",
    "you are now", "</system>", "<|im_start|>", "<|im_end|>",
)


def _sanitise_text(value: str, max_len: int) -> str:
    """Strip control chars, collapse whitespace, truncate. Used on
    user-supplied name fields before they reach the LLM."""
    if not isinstance(value, str):
        return ""
    cleaned = _CONTROL_CHARS.sub(" ", value).strip()
    cleaned = re.sub(r"\s+", " ", cleaned)
    return cleaned[:max_len]


def _looks_injected(value: str) -> bool:
    lower = value.lower()
    return any(token in lower for token in _INJECTION_TOKENS)


def parse_request(payload: dict) -> AnalysisRequest:
    """Validate + sanitise the JSON the browser sent. Raises ValueError on
    malformed input. The browser is trusted *minimally* — strings are
    sanitised, numbers coerced, oversized inputs truncated."""
    raw_positions = payload.get("positions") or []
    if not isinstance(raw_positions, list) or not raw_positions:
        raise ValueError("positions must be a non-empty list")

    positions: list[Position] = []
    for p in raw_positions[:200]:    # hard cap on input length
        if not isinstance(p, dict):
            continue
        ticker = _sanitise_text(p.get("yahoo_ticker", ""), 32).upper()
        if not ticker:
            continue
        name = _sanitise_text(p.get("name", ""), MAX_NAME_LENGTH)
        if _looks_injected(name):
            # Drop the name rather than the whole position — preserves
            # the ticker (which has structure that constrains injection).
            name = ticker
        try:
            qty = float(p.get("qty") or 0)
            avg_cost = float(p.get("avg_cost") or 0)
        except (TypeError, ValueError):
            continue
        # Reject NaN / inf — float() accepts these and they'd poison the
        # prompt with garbage if they reached the LLM.
        if not (math.isfinite(qty) and math.isfinite(avg_cost)):
            continue
        if qty <= 0:
            continue
        currency = _sanitise_text(p.get("currency", "") or "", 8) or None
        positions.append(Position(
            yahoo_ticker=ticker, name=name, qty=qty,
            avg_cost=avg_cost, currency=currency,
        ))

    if not positions:
        raise ValueError("no valid positions after sanitisation")

    prices = payload.get("prices") or {}
    if not isinstance(prices, dict):
        prices = {}

    base_currency = _sanitise_text(payload.get("base_currency", "GBP"), 8) or "GBP"
    anchor = _sanitise_text(payload.get("anchor") or "", 32) or None
    tone = _sanitise_text(payload.get("tone", "INTERMEDIATE"), 16) or "INTERMEDIATE"
    analysis = _sanitise_text(payload.get("analysis", "SPECULATIVE"), 16) or "SPECULATIVE"
    lang = (payload.get("lang") or "en").strip().lower()
    if lang not in LANGUAGES:
        lang = "en"

    return AnalysisRequest(
        positions=positions, prices=prices, base_currency=base_currency,
        anchor=anchor, tone=tone, analysis=analysis, lang=lang,
    )


# ---------------------------------------------------------------------------
# Pre-LLM summarisation: keep prompt size bounded
# ---------------------------------------------------------------------------


def _enrich(req: AnalysisRequest) -> list[dict]:
    """Join positions with their current prices; compute per-position
    value, P/L. Returns a list sorted by current value descending."""
    out = []
    for p in req.positions:
        pq = req.prices.get(p.yahoo_ticker) or {}
        price = pq.get("p")
        currency = p.currency or pq.get("c")
        value = (price * p.qty) if isinstance(price, (int, float)) else None
        invested = p.avg_cost * p.qty
        ppl = (value - invested) if value is not None else None
        ppl_pct = ((value / invested - 1) * 100) if (value is not None and invested) else None
        out.append({
            "ticker": p.yahoo_ticker,
            "name": p.name,
            "qty": round(p.qty, 6),
            "avg_cost": round(p.avg_cost, 4),
            "current_price": price,
            "currency": currency,
            "value": round(value, 2) if value is not None else None,
            "invested": round(invested, 2),
            "ppl": round(ppl, 2) if ppl is not None else None,
            "ppl_pct": round(ppl_pct, 2) if ppl_pct is not None else None,
            "change_1d_pct": pq.get("d", {}).get("1d") if isinstance(pq.get("d"), dict) else None,
        })
    out.sort(key=lambda r: r["value"] if r["value"] is not None else -1, reverse=True)
    return out


def _summarise(enriched: list[dict]) -> dict:
    """Aggregate stats for the model — concentration, currency mix,
    P/L overall. Saves tokens by not making the LLM compute these."""
    total_value = sum((r["value"] or 0) for r in enriched)
    total_invested = sum(r["invested"] for r in enriched)
    by_ccy: dict[str, float] = {}
    for r in enriched:
        if r["currency"] and r["value"] is not None:
            by_ccy[r["currency"]] = by_ccy.get(r["currency"], 0) + r["value"]
    top_n = enriched[:5]
    top_share = (sum(r["value"] or 0 for r in top_n) / total_value * 100) if total_value else None
    return {
        "n_positions": len(enriched),
        "total_value": round(total_value, 2),
        "total_invested": round(total_invested, 2),
        "total_ppl": round(total_value - total_invested, 2) if total_value else None,
        "total_ppl_pct": round((total_value / total_invested - 1) * 100, 2)
                         if (total_value and total_invested) else None,
        "top5_share_pct": round(top_share, 1) if top_share is not None else None,
        "currency_split_pct": {
            k: round(v / total_value * 100, 1)
            for k, v in by_ccy.items()
        } if total_value else {},
    }


# ---------------------------------------------------------------------------
# Prompt construction
# ---------------------------------------------------------------------------


_SYSTEM_OVERRIDES = """\
# Mode: portfolio commentary
You are writing a short read of ONE investor's portfolio. Be specific to
the holdings shown. Frame each observation as analysis ("this allocation
implies X under scenario Y"), not advice ("buy X" / "sell Y" are forbidden).

# Output
- Open with one TL;DR sentence on the portfolio's *posture* (defensive,
  cyclical, concentrated, etc.).
- Then 3-5 short paragraphs covering, in order of relevance to this pie:
  concentration / single-name risk; sector or geography tilt;
  currency exposure if multi-currency; notable winners or laggards;
  what would invalidate the current posture.
- ~350 words. No bullet lists. No buy/sell recommendations.
- Do not repeat the input data verbatim — interpret it.

# Rational vs irrational lens (mandatory)
Carry the base prompt's rational-vs-irrational framing through to every
paragraph of the portfolio read. For each section above, contrast:
- The RATIONAL read: what the underlying factors (fundamentals,
  macro/policy regime, valuation, currency dynamics) justify for this
  exposure;
- The IRRATIONAL read: what positioning, narrative momentum, sentiment
  or flows are doing to that same exposure right now.
Then name the GAP — does the holder's posture line up with the rational
read, or is it riding the irrational one? A paragraph that names only
the pie's numbers or only the macro backdrop, without placing the
holding on this rational-vs-irrational axis, is incomplete.
"""


def build_prompt(req: AnalysisRequest) -> tuple[str, str]:
    """Returns (system_message, user_message). Pure function — pie data
    flows in, prompt strings flow out, nothing is stored."""
    enriched = _enrich(req)
    summary = _summarise(enriched)

    # Truncate the per-position table to keep the prompt bounded.
    head = enriched[:MAX_POSITIONS_INLINED]
    tail_count = max(0, len(enriched) - MAX_POSITIONS_INLINED)

    system = build_system_prompt(req.tone, req.analysis) + "\n\n" + _SYSTEM_OVERRIDES + respond_in_clause(req.lang)

    user_parts = [
        f"# Portfolio commentary request — {utcnow().strftime('%Y-%m-%d')}",
        f"Base currency: {req.base_currency}",
    ]
    if req.anchor:
        user_parts.append(f"Anchor reference date: {req.anchor}")
    user_parts.append("\n## Portfolio summary")
    user_parts.append("```json\n" + json.dumps(summary, indent=2) + "\n```")
    user_parts.append(f"\n## Top {len(head)} positions by value"
                      + (f" ({tail_count} smaller positions omitted)" if tail_count else ""))
    user_parts.append("```json\n" + json.dumps(head, indent=2, default=str) + "\n```")
    user_parts.append(
        "\n## Task\nWrite the portfolio read per the system prompt. ~350 words. "
        "No preamble, no headers other than the TL;DR opener."
    )
    user = "\n".join(user_parts)

    # Cap on prompt size (token-cost protection).
    if len(user) > MAX_PROMPT_BYTES:
        user = user[:MAX_PROMPT_BYTES] + "\n[truncated]"

    return system, user


# ---------------------------------------------------------------------------
# Orchestration
# ---------------------------------------------------------------------------


async def analyse(
    session: AsyncSession,
    req: AnalysisRequest,
) -> AnalysisResult:
    """The whole pipeline: prompt → LLM → ledger row → result. The `req`
    object is a function-local — when this function returns, the pie is
    garbage-collected. No DB writes mention positions."""
    s = get_settings()
    system, user = build_prompt(req)

    async with httpx.AsyncClient() as client:
        try:
            llm: LogResult = await call_llm(
                client,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": user},
                ],
                max_tokens=2000,
            )
            status = "ok"
            error_msg = None
        except Exception as e:
            status = "failed"
            error_msg = str(e)[:500]
            llm = None
            log.error("portfolio_analysis.failed", error=error_msg)

    # Ledger row — NO portfolio data, just metadata. Same row whether the
    # call succeeded or failed, so cost-cap and rate-limit logic can
    # observe the attempt.
    session.add(AICall(
        called_at=utcnow(),
        model=llm.model if llm else active_model(),
        prompt_tokens=llm.prompt_tokens if llm else None,
        completion_tokens=llm.completion_tokens if llm else None,
        cost_usd=llm.cost_usd if llm else None,
        status=status,
        error=error_msg,
    ))
    await session.commit()

    if llm is None:
        raise RuntimeError(error_msg or "portfolio analysis failed")

    log.info(
        "portfolio_analysis.ok",
        n_positions=len(req.positions),
        prompt_tokens=llm.prompt_tokens,
        completion_tokens=llm.completion_tokens,
        cost_usd=llm.cost_usd,
    )
    return AnalysisResult(
        content=llm.content,
        model=llm.model,
        prompt_tokens=llm.prompt_tokens,
        completion_tokens=llm.completion_tokens,
        cost_usd=llm.cost_usd,
        generated_at=datetime.now(timezone.utc),
    )