"""
Hermes-Agent-Status (Control-Plane-Read). MC betreibt Hermes NICHT — es zeigt nur
Status + verlinkt das standalone hermes-webui. Voller Zugriff + Tools/MCP werden in
Hermes' eigener Config verdrahtet (siehe docs/HERMES_SETUP.md).
"""

import logging
import os
import re

import httpx
import psutil

from config import HERMES_TERMINAL_URL, HERMES_API_URL, HERMES_HOME, PC_EXECUTOR_URL

log = logging.getLogger(__name__)


def _hermes_version(name: str) -> float | None:
    """Versionszahl aus 'Hermes-4.3', 'Hermes-4', 'Nous-Hermes-2' → 4.3/4.0/2.0."""
    low = (name or "").lower()
    if "hermes" not in low:
        return None
    m = re.search(r"hermes[-_ ]?(\d+(?:\.\d+)?)", low)
    return float(m.group(1)) if m else None


def hermes_brain_info() -> dict:
    """Aktuelles Agent-Hirn (hermes-Rolle) + bestes verfügbares NousResearch-Hermes-Modell,
    das auf diese Hardware passt. Für den Modell-Manager: Brain sichtbar + updatebar,
    sobald NousResearch eine neuere Hermes-Generation veröffentlicht."""
    from services import discover, llamaswap
    from services.fit import evaluate_fit, extract_params_b

    models = llamaswap.list_models()
    cur = next((m for m in models if m.get("role") == "hermes"), None)
    cur_ver = _hermes_version(cur["name"]) if cur else None
    cur_params = (cur.get("capabilities") or {}).get("params_b") if cur else None
    current = None
    if cur:
        current = {"name": cur["name"], "filename": cur.get("filename"),
                   "params_b": cur_params, "quant": cur.get("quant"),
                   "size_bytes": cur.get("size_bytes"), "version": cur_ver,
                   "gguf_path": cur.get("gguf_path"), "incomplete": cur.get("incomplete")}

    ram = psutil.virtual_memory().total / (1024 ** 3)
    best = None
    try:
        cands = []
        for r in discover._fetch_author_models("NousResearch"):
            rid = r.get("id", "")
            if "hermes" not in rid.lower():
                continue
            pb = extract_params_b(rid)
            fit = evaluate_fit(pb, "Q4_K_M", 8192, ram, name=rid)
            if fit["level"] == "too_tight":
                continue
            cands.append({"repo": rid, "name": rid.split("/")[-1],
                          "version": _hermes_version(rid) or 0.0, "params_b": pb,
                          "downloads": int(r.get("downloads") or 0), "fit": fit})
        # neueste Hermes-Version zuerst, dann größer/fähiger, dann beliebter
        cands.sort(key=lambda c: (c["version"], c["params_b"], c["downloads"]), reverse=True)
        best = cands[0] if cands else None
    except Exception:
        log.debug("hermes_brain_info: HF-Abfrage fehlgeschlagen", exc_info=True)

    update = False
    if best is not None:
        if cur_ver is None:
            update = True
        elif best["version"] > cur_ver:
            update = True
        elif best["version"] == cur_ver and best["params_b"] > (cur_params or 0) * 1.05:
            update = True
        # gleiche Datei schon installiert? dann kein Update
        if current and best["repo"].split("/")[-1].lower() in (current["name"] or "").lower():
            update = False

    # Fit-Check: passt das EMPFOHLENE Brain als Always-On noch ins Budget, sodass das
    # größte on-demand-Modell daneben lädt? (Brain muss immer resident sein.)
    budget = None
    try:
        from services.budget import footprint_gb, gtt_budget_gb
        from services.fit import estimate_memory_gb
        groups = llamaswap.list_groups()
        persist = set()
        for g in groups.values():
            if isinstance(g, dict) and g.get("persist"):
                persist.update(g.get("members") or [])

        cur_name = cur["name"] if cur else None
        brain_ctx = int((cur.get("ctx") if cur else None) or 32768)
        if best:
            brain_gb = estimate_memory_gb(float(best["params_b"]), "Q4_K_M", brain_ctx)
        elif cur:
            brain_gb = footprint_gb(cur)
        else:
            brain_gb = 0.0
        # voller Always-Warm-Footprint (alle persist, Brain=Empfehlung) — nur Info
        warm = brain_gb + sum(footprint_gb(m) for m in models
                              if m["name"] in persist and m["name"] != cur_name)
        largest_od = max((footprint_gb(m) for m in models if m["name"] not in persist), default=0.0)
        gtt = gtt_budget_gb()
        # Brain muss immer resident sein → passt Brain + größtes on-demand zusammen?
        # (fast/vision dürfen beim Laden eines großen Modells verdrängt werden.)
        budget = {
            "gtt_gb": gtt,
            "brain_gb": round(brain_gb, 1),
            "warm_projected_gb": round(warm, 1),
            "largest_ondemand_gb": round(largest_od, 1),
            "fits": (brain_gb + largest_od) <= gtt,
            "free_after_gb": round(gtt - brain_gb - largest_od, 1),
        }
    except Exception:
        log.debug("hermes_brain_info: Budget-Berechnung fehlgeschlagen", exc_info=True)

    return {"current": current, "recommended": best, "update_available": update, "budget": budget}


def _reach(url: str, path: str = "") -> bool:
    try:
        with httpx.Client(timeout=3.0) as c:
            return c.get(f"{url}{path}").status_code < 500
    except httpx.HTTPError:
        return False


def _count_enabled_mcp_servers() -> int:
    config_path = HERMES_HOME / "config.yaml"
    if not config_path.exists():
        return 0
    try:
        from ruamel.yaml import YAML
        r_yaml = YAML()
        with config_path.open("r", encoding="utf-8") as f:
            cfg = r_yaml.load(f) or {}
        mcp_servers = cfg.get("mcp_servers", {}) if isinstance(cfg, dict) else {}
        if not isinstance(mcp_servers, dict):
            return 0
        return sum(1 for v in mcp_servers.values() if isinstance(v, dict) and v.get("enabled", True))
    except Exception:
        log.debug("_count_enabled_mcp_servers: Fehler", exc_info=True)
        return 0


def agent_status() -> dict:
    """Erreichbarkeit von Gateway (:8642) + WebUI (:8787) + lokale Hinweise."""
    home = HERMES_HOME
    brain_model = "auto"
    config_path = home / "config.yaml"
    if config_path.exists():
        try:
            from ruamel.yaml import YAML
            r_yaml = YAML()
            with config_path.open("r", encoding="utf-8") as f:
                cfg = r_yaml.load(f) or {}
                if isinstance(cfg, dict):
                    # Hermes nutzt model.default als aktives Modell (model.model = Provider-Param).
                    m = cfg.get("model", {}) or {}
                    brain_model = m.get("default") or m.get("model") or "auto"
        except Exception:
            log.debug("agent_status: Hermes-config.yaml nicht lesbar", exc_info=True)


    return {
        "gateway_url": HERMES_API_URL,
        # Interaktives Web-Terminal (ttyd → `hermes chat`), eingebettet in MC2.
        "terminal_url": HERMES_TERMINAL_URL,
        "gateway_reachable": _reach(HERMES_API_URL, "/health"),
        "terminal_reachable": _reach(HERMES_TERMINAL_URL, "/"),
        "home_exists": home.exists(),
        "brain_model": brain_model,
        # Best-effort: welche Verdrahtung lokal sichtbar ist (auf der Box aussagekräftig).
        "has_config": (home / "config.yaml").exists() or (home / "config.json").exists(),
        "has_skills": (home / "skills").exists(),
        "has_memories": (home / "memories").exists(),
        # Neue Felder: Telegram, MCP-Server-Anzahl, PC-Executor-Erreichbarkeit.
        "telegram_enabled": bool(os.environ.get("TELEGRAM_BOT_TOKEN", "")),
        "mcp_server_count": _count_enabled_mcp_servers(),
        "pc_executor_reachable": _reach(PC_EXECUTOR_URL, "/health"),
    }


def set_agent_brain(model_id: str) -> dict:
    """Setzt ein (bereits installiertes) Modell als Agent-Hirn — WARM-bewusst:
    1) vergibt den 'hermes'-Alias (das Agent-Hirn-Slot),
    2) tauscht es in die residente brains-Gruppe (altes Hirn raus, fast/vision bleiben),
    3) zeigt die Hermes-Config auf den 'hermes'-Alias + Gateway-Restart.
    So bleibt das neue Hirn warm und der Agent nutzt es sofort."""
    from services import llamaswap
    models = {m["name"]: m for m in llamaswap.list_models()}
    if model_id not in models:
        return {"ok": False, "reason": "Modell nicht installiert — erst über Modelle-finden laden."}
    old = next((m["name"] for m in models.values() if m.get("role") == "hermes"), None)
    if model_id == old:
        return {"ok": True, "old": old, "new": model_id, "note": "ist bereits das Agent-Hirn"}
    try:
        llamaswap.set_role(model_id, "hermes")                       # 1) Alias
        brains = (llamaswap.list_groups().get("brains") or {}).get("members") or []
        new_members = [x for x in brains if x not in (old, model_id)] + [model_id]
        llamaswap.set_group("brains", new_members, swap=False, persist=True)   # 2) warm
    except PermissionError as exc:
        return {"ok": False, "reason": str(exc)}
    update_brain_model("hermes")                                     # 3) Config + Restart
    return {"ok": True, "old": old, "new": model_id}


def update_brain_model(new_model: str) -> bool:
    from config import HERMES_HOME
    home = HERMES_HOME
    config_path = home / "config.yaml"
    
    # Ensure home directory exists
    home.mkdir(parents=True, exist_ok=True)
    
    cfg = {}
    if config_path.exists():
        try:
            from ruamel.yaml import YAML
            r_yaml = YAML()
            with config_path.open("r", encoding="utf-8") as f:
                cfg = r_yaml.load(f) or {}
        except Exception:
            log.debug("update_brain_model: bestehende config.yaml nicht lesbar", exc_info=True)
            cfg = {}
            
    if not isinstance(cfg, dict):
        cfg = {}
        
    if "model" not in cfg or not isinstance(cfg["model"], dict):
        cfg["model"] = {}
        
    # Hermes liest model.default als aktives Modell; model.model ist der Provider-Param.
    # Beide setzen, sonst greift die Umschaltung nicht (latenter Bug: nur model.model gesetzt).
    cfg["model"]["default"] = new_model
    cfg["model"]["model"] = new_model

    try:
        from ruamel.yaml import YAML
        r_yaml = YAML()
        with config_path.open("w", encoding="utf-8") as f:
            r_yaml.dump(cfg, f)
            
        # Restart the user-space service to apply changes
        try:
            import services.maintenance as maintenance
            maintenance.restart_service("hermes-gateway")
        except Exception:
            log.warning("update_brain_model: hermes-gateway-Restart fehlgeschlagen", exc_info=True)

        return True
    except Exception:
        log.warning("update_brain_model: Schreiben der config.yaml fehlgeschlagen", exc_info=True)
        return False