"""
Call-Metrics fuer den Proxy-Client.

Pro Claude-Call wird ein Eintrag in /data/metrics.jsonl angehaengt:

  {"ts": <ms>, "model": "...", "in": <tokens_in_estimate>, "out": <tokens_out_estimate>}

Tokens-Schaetzung: characters / 4 (Anthropic-Default-Heuristik). Nicht exakt
aber gut genug fuer Quota-Monitoring. Wir summieren nicht in-memory weil
der Brain-Container neugestartet werden kann — alles auf Disk.

Auswertung via aggregate(window_seconds) — liefert {calls, tokens_in, tokens_out}
fuer die letzten N Sekunden. Lazy gelesen, keine grossen Datenmengen erwartet
(bei 1000 Calls/Tag ~70 KB pro Monat).

Auto-Rotate: bei > 50k Zeilen werden die aeltesten 25k weggeschnitten.
"""

from __future__ import annotations

import json
import logging
import os
import time
from pathlib import Path
from typing import List

logger = logging.getLogger(__name__)

METRICS_FILE = Path(os.environ.get("METRICS_FILE", "/data/metrics.jsonl"))
ROTATE_AT = 50_000
ROTATE_KEEP = 25_000


def _estimate_tokens(text: str) -> int:
    """Anthropic-Default: ~4 chars pro Token. Grob genug."""
    if not text:
        return 0
    return max(1, len(text) // 4)


def _messages_tokens(messages: list) -> int:
    total = 0
    for m in messages:
        # Pydantic-Model oder dict
        if hasattr(m, "content"):
            total += _estimate_tokens(m.content or "")
        elif isinstance(m, dict):
            c = m.get("content") or ""
            if isinstance(c, str):
                total += _estimate_tokens(c)
    return total


def log_call(model: str, messages_in: list, reply_text: str = "") -> None:
    """Eine Call-Metric anhaengen. Robust gegen Fehler (silent fail)."""
    try:
        tokens_in = _messages_tokens(messages_in)
        tokens_out = _estimate_tokens(reply_text)
        line = json.dumps({
            "ts": int(time.time() * 1000),
            "model": model,
            "in": tokens_in,
            "out": tokens_out,
        })
        METRICS_FILE.parent.mkdir(parents=True, exist_ok=True)
        with METRICS_FILE.open("a", encoding="utf-8") as f:
            f.write(line + "\n")
        # Sanftes Rotate ohne hohe IO-Kosten — nur alle 1000 Calls checken
        if (tokens_in + tokens_out) % 1000 < 4:
            _maybe_rotate()
    except Exception as exc:
        logger.warning("metrics.log_call: %s", exc)


def _maybe_rotate() -> None:
    try:
        if not METRICS_FILE.exists():
            return
        with METRICS_FILE.open("r", encoding="utf-8") as f:
            lines = f.readlines()
        if len(lines) > ROTATE_AT:
            keep = lines[-ROTATE_KEEP:]
            METRICS_FILE.write_text("".join(keep), encoding="utf-8")
            logger.info("metrics rotated: %d → %d Zeilen", len(lines), len(keep))
    except Exception as exc:
        logger.warning("metrics rotate: %s", exc)


def aggregate(window_seconds: int) -> dict:
    """Aggregiert die Calls der letzten N Sekunden."""
    now_ms = int(time.time() * 1000)
    cutoff_ms = now_ms - (window_seconds * 1000)
    calls = 0
    tokens_in = 0
    tokens_out = 0
    by_model: dict[str, int] = {}
    if METRICS_FILE.exists():
        try:
            for raw in METRICS_FILE.read_text(encoding="utf-8").splitlines():
                raw = raw.strip()
                if not raw:
                    continue
                try:
                    obj = json.loads(raw)
                except Exception:
                    continue
                if obj.get("ts", 0) < cutoff_ms:
                    continue
                calls += 1
                tokens_in += int(obj.get("in") or 0)
                tokens_out += int(obj.get("out") or 0)
                m = obj.get("model", "?")
                by_model[m] = by_model.get(m, 0) + 1
        except Exception as exc:
            logger.warning("metrics aggregate: %s", exc)
    return {
        "window_seconds": window_seconds,
        "calls": calls,
        "tokens_in": tokens_in,
        "tokens_out": tokens_out,
        "by_model": by_model,
    }


def stats() -> dict:
    """Komplett-Snapshot mit den drei wichtigsten Fenstern."""
    return {
        "h1": aggregate(3600),
        "h5": aggregate(5 * 3600),
        "h24": aggregate(24 * 3600),
        "d30": aggregate(30 * 24 * 3600),
    }