# api/v1/models.py
import httpx
import json
from fastapi import APIRouter
from config import settings
from models.llm_model import LmModelMeta
from services import redis_service
from typing import List
from utils.logging import logger
from config import settings

router = APIRouter()

@router.get("/models", response_model=List[str])
async def list_models():
    try:
        async with httpx.AsyncClient(timeout=settings.REQUEST_TIMEOUT) as client:
            resp = await client.get(settings.LM_STUDIO_MODELS)
            resp.raise_for_status()
            data = resp.json()
            return [m["id"] for m in data.get("data", [])]
    except Exception:
        logger.exception("Error fetching models")
        raise HTTPException(status_code=500, detail="Failed to fetch models")

@router.get("/default-model", response_model=str)
async def get_default_model():
    return settings.MODEL_NAME

@router.get("/models-info", response_model=List[LmModelMeta])
async def get_model_info():
    raw = redis_service.r.get("modelInfoList")
    if not raw:
        # fallback: aggiorna subito
        return await update_model_info()
    try:
        data = json.loads(raw)
        return [LmModelMeta(**item) for item in data]
    except Exception:
        return []

@router.post("/models-info/update", response_model=List[LmModelMeta])
async def update_model_info():
    import httpx

    # 🔹 Invalida la cache prima di ricostruire
    try:
        redis_service.r.delete("modelInfoList")
    except Exception:
        logger.exception("Errore durante la cancellazione della cache Redis")

    try:
        async with httpx.AsyncClient(timeout=settings.REQUEST_TIMEOUT) as client:
            resp = await client.get(settings.LM_STUDIO_MODELS)
            resp.raise_for_status()
            model_list = [m["id"] for m in resp.json().get("data", [])]
    except Exception:
        raise HTTPException(status_code=500, detail="Failed to fetch models")

    enriched = []
    for name in model_list:
        enriched.append(LmModelMeta(
            name=name,
            description=f"Modello {name.split('/')[-1]}",
            year=2024,  # puoi migliorare con heuristics
            features=[
                "Quantizzazione INT4" if "int4" in name.lower() else "Precisione standard",
                f"{name.split('-')[0].upper()} origin",
                f"{name.split('-')[1]} variant" if "-" in name else "Generico"
            ]
        ))

    redis_service.r.set("modelInfoList", json.dumps([m.dict() for m in enriched]), ex=86400)
    return enriched