"""
Cookbook Router: Verbindet die HuggingFace API mit der Odysseus-Hardware-Berechnung.
"""

import httpx
import re
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel
import psutil

from ruamel.yaml.scalarstring import LiteralScalarString

from auth import auth
from hw_math import evaluate_fit, max_ctx_for
from config import MODELS_DIR, CMD_TEMPLATE, DEFAULT_TTL
from llamaswap import read_config, write_config
from jobengine import start_job, JOBS
from recipes import RECIPES, UPGRADES

router = APIRouter(prefix="/api/cookbook", dependencies=[Depends(auth)])

_FIT_ORDER = {"perfect": 0, "marginal": 1, "too_tight": 2}

class AnalyzeRequest(BaseModel):
    repo_id: str
    ctx: int = 8192

class EvaluateRequest(BaseModel):
    params_b: float
    quant: str
    ctx: int

class InstallRecipeReq(BaseModel):
    recipe_id: str
    hf_token: str | None = None

class InstallModelReq(BaseModel):
    repo: str
    role: str
    params_b: float
    quant: str = "Q4_K_M"
    hf_token: str | None = None

def extract_params_b(repo_id: str) -> float:
    """Extrahiert die Parametergröße (in Milliarden) aus dem Repo-Namen."""
    # z.B. Qwen2.5-Coder-32B -> 32
    # 8x7B -> 56 (MoE)
    moe = re.search(r"(\d+)x(\d+(?:\.\d+)?)[bB]", repo_id)
    if moe:
        return float(moe.group(1)) * float(moe.group(2))
    m = re.search(r"(\d+(?:\.\d+)?)[bB](?![a-zA-Z])", repo_id)
    if m:
        return float(m.group(1))
    return 7.0  # Fallback

def extract_quant(filename: str) -> str:
    m = re.search(r"(Q\d_[A-Z0-9_]+|IQ\d_[A-Z0-9_]+|FP16|BF16)", filename, re.IGNORECASE)
    return m.group(1).upper() if m else "Q4_K_M"

@router.post("/analyze")
async def analyze_repo(req: AnalyzeRequest):
    """Holt die GGUF Dateien von HuggingFace und berechnet den Hardware-Fit."""
    url = f"https://huggingface.co/api/models/{req.repo_id}/tree/main"
    
    async with httpx.AsyncClient() as client:
        try:
            resp = await client.get(url, timeout=10.0)
            resp.raise_for_status()
            tree = resp.json()
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"HuggingFace Fehler: {str(e)}")
            
    gguf_files = [f["path"] for f in tree if f.get("path", "").endswith(".gguf")]
    
    if not gguf_files:
        return {"files": []}
        
    params_b = extract_params_b(req.repo_id)
    
    # Ermittle RAM des Systems (da APU = Shared Memory)
    ram_gb = psutil.virtual_memory().total / (1024**3)
    
    results = []
    for f in gguf_files:
        quant = extract_quant(f)
        fit = evaluate_fit(params_b, quant, req.ctx, ram_gb)
        
        # Priority-Score, um den besten Fit an oberste Stelle zu setzen.
        # "Q4_K_M" ist oft der Sweetspot.
        priority = 0
        if fit["level"] == "perfect":
            priority += 10
            if quant == "Q4_K_M": priority += 5
            elif quant.startswith("Q4"): priority += 4
            elif quant.startswith("Q5"): priority += 3
        
        results.append({
            "filename": f,
            "quant": quant,
            "fit": fit,
            "optimal_ctx": max_ctx_for(params_b, quant, ram_gb),
            "priority": priority
        })
        
    # Sortieren: Highest priority first, dann nach tps (schnellste zuerst)
    results.sort(key=lambda x: (x["priority"], x["fit"]["tps"]), reverse=True)
    
    return {
        "repo": req.repo_id,
        "params_b": params_b,
        "sys_ram_gb": round(ram_gb, 1),
        "files": results
    }

@router.post("/evaluate")
def evaluate_single(req: EvaluateRequest):
    ram_gb = psutil.virtual_memory().total / (1024**3)
    fit = evaluate_fit(req.params_b, req.quant, req.ctx, ram_gb)
    fit["optimal_ctx"] = max_ctx_for(req.params_b, req.quant, ram_gb)
    return fit


@router.get("/recipes")
def recipes():
    """Use-Case-Setups mit Hardware-Fit pro Modell + Stack-Gesamturteil. Da llama-swap nur EIN
    Modell gleichzeitig lädt, ist das Stack-Urteil der schlechteste (= größte) Einzel-Fit."""
    ram_gb = psutil.virtual_memory().total / (1024 ** 3)
    out = []
    for r in RECIPES:
        models, worst = [], "perfect"
        for m in r["models"]:
            fit = evaluate_fit(m["params_b"], m["quant"], 8192, ram_gb)
            models.append({**m, "fit": fit, "optimal_ctx": max_ctx_for(m["params_b"], m["quant"], ram_gb)})
            if _FIT_ORDER[fit["level"]] > _FIT_ORDER[worst]:
                worst = fit["level"]
        out.append({**r, "models": models, "fit_level": worst})
    return {"recipes": out, "sys_ram_gb": round(ram_gb, 1)}


@router.post("/install-recipe")
def install_recipe(req: InstallRecipeReq):
    """Komplettes Setup installieren: jedes Modell als Download-Job starten UND sofort mit
    optimalem (gedeckeltem) Kontext in die config.yaml eintragen. llama-swap (-watch-config)
    übernimmt es, sobald die Datei da ist."""
    recipe = next((r for r in RECIPES if r["id"] == req.recipe_id), None)
    if not recipe:
        raise HTTPException(404, "Setup nicht gefunden.")
    ram_gb = psutil.virtual_memory().total / (1024 ** 3)
    env = {"HF_XET_HIGH_PERFORMANCE": "1"}
    if req.hf_token:
        env["HF_TOKEN"] = req.hf_token
    cfg = read_config()
    job_ids = []
    for m in recipe["models"]:
        file = _pick_gguf(m["repo"], m.get("quant", "Q4_K_M"))
        if not file:
            continue  # kein GGUF im Repo gefunden -> Modell ueberspringen (Rest installiert trotzdem)
        target = MODELS_DIR / m["repo"].split("/")[-1]
        target.mkdir(parents=True, exist_ok=True)
        args = ["hf", "download", m["repo"], file, "--local-dir", str(target)]
        jid = start_job(args, f"download {m['name']}", env=env)
        JOBS[jid]["result_path"] = str(target / file)
        job_ids.append(jid)
        # Eintrag jetzt schon schreiben — optimaler Kontext, aber gedeckelt fuer schnellen Erststart.
        ctx = min(max_ctx_for(m["params_b"], m["quant"], ram_gb), 32768)
        path = str(target / file)
        cmd = CMD_TEMPLATE.replace("{model}", path).replace("{ctx}", str(ctx))
        cfg["models"][m["role"]] = {"cmd": LiteralScalarString(cmd + "\n"), "ttl": DEFAULT_TTL}
    write_config(cfg)
    return {"job_ids": job_ids, "count": len(job_ids)}


def _pick_gguf(repo: str, quant: str = "Q4_K_M") -> str | None:
    """Beste GGUF-Datei eines Repos auflösen: bevorzugt gewünschten Quant, keine Split-Teile."""
    try:
        with httpx.Client(timeout=10.0) as c:
            tree = c.get(f"https://huggingface.co/api/models/{repo}/tree/main").json()
    except Exception:  # noqa: BLE001
        return None
    ggufs = [f["path"] for f in tree if isinstance(f, dict) and str(f.get("path", "")).endswith(".gguf")]
    if not ggufs:
        return None
    pref = [g for g in ggufs if quant.lower() in g.lower() and "-of-" not in g]
    nosplit = [g for g in ggufs if "-of-" not in g]
    return (pref or nosplit or ggufs)[0]


@router.get("/upgrades")
def upgrades():
    """Schlägt für installierte 'alte' Modelle ein aktuelleres vor (UPGRADES-Map)."""
    ram_gb = psutil.virtual_memory().total / (1024 ** 3)
    installed = (read_config().get("models") or {})
    out, seen = [], set()
    for up in UPGRADES:
        new_base = up["repo"].split("/")[-1].lower()
        if any(new_base in str(s.get("cmd", "")).lower() for s in installed.values()):
            continue  # neueres Modell schon installiert
        old = None
        for alias, spec in installed.items():
            hay = (alias + " " + str(spec.get("cmd", ""))).lower()
            if any(k in hay for k in up["match"]):
                old = alias
                break
        if not old or up["repo"] in seen:
            continue
        seen.add(up["repo"])
        out.append({
            "name": up["name"], "repo": up["repo"], "params_b": up["params_b"], "quant": up["quant"],
            "why": up["why"], "old": old, "role": old,
            "fit": evaluate_fit(up["params_b"], up["quant"], 8192, ram_gb),
            "optimal_ctx": max_ctx_for(up["params_b"], up["quant"], ram_gb),
        })
    return {"upgrades": out}


@router.post("/install-model")
def install_model(req: InstallModelReq):
    """Ein einzelnes Modell installieren (Download + Einpflegen unter 'role', optimaler ctx)."""
    file = _pick_gguf(req.repo, req.quant)
    if not file:
        raise HTTPException(404, "Keine GGUF-Datei im Repo gefunden.")
    ram_gb = psutil.virtual_memory().total / (1024 ** 3)
    env = {"HF_XET_HIGH_PERFORMANCE": "1"}
    if req.hf_token:
        env["HF_TOKEN"] = req.hf_token
    target = MODELS_DIR / req.repo.split("/")[-1]
    target.mkdir(parents=True, exist_ok=True)
    jid = start_job(["hf", "download", req.repo, file, "--local-dir", str(target)],
                    f"download {req.repo.split('/')[-1]}", env=env)
    JOBS[jid]["result_path"] = str(target / file)
    cfg = read_config()
    ctx = min(max_ctx_for(req.params_b, req.quant, ram_gb), 32768)
    cmd = CMD_TEMPLATE.replace("{model}", str(target / file)).replace("{ctx}", str(ctx))
    cfg["models"][req.role] = {"cmd": LiteralScalarString(cmd + "\n"), "ttl": DEFAULT_TTL}
    write_config(cfg)
    return {"job_id": jid}