from __future__ import annotations
import math, time
from sqlalchemy import select, func
from app.models.request_log import RequestLog
from app.models.model_catalog import ModelCatalog


def estimate_tokens(text: str) -> int:
    words = len(text.split())
    chars = len(text)
    return int(words * 1.35 + chars / 4)


async def estimate_cost(session, model: str, tokens_in: int, tokens_out: int) -> float:
    result = await session.execute(
        select(ModelCatalog).where(ModelCatalog.id == model)
    )
    row = result.scalar_one_or_none()
    if not row:
        return 0.0
    cost_in = (tokens_in / 1_000_000) * row.input_price
    cost_out = (tokens_out / 1_000_000) * row.output_price
    return round(cost_in + cost_out, 6)


async def get_latency_leaderboard(session, hours: int = 24) -> list:
    cutoff = time.time() - (hours * 3600)
    result = await session.execute(
        select(
            RequestLog.model,
            func.avg(RequestLog.latency_ms).label("avg_latency"),
            func.count(RequestLog.id).label("request_count"),
        )
        .where(
            RequestLog.latency_ms > 0,
            RequestLog.model.isnot(None),
        )
        .group_by(RequestLog.model)
        .order_by(func.avg(RequestLog.latency_ms).asc())
        .limit(20)
    )
    return [
        {
            "model": row[0],
            "avg_latency_ms": round(row[1], 1),
            "request_count": row[2],
        }
        for row in result.all() if row[0]
    ]