"""
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 auth import auth
from hw_math import evaluate_fit

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

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

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

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,
            "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)
    return fit