"""ONNX weather inference and forecast comparison logic."""

from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any

import numpy as np
import pandas as pd

from weatherpred.config import (
    API_DISPLAY_COLUMNS,
    FORECAST_DAYS,
    LOOKBACK_DAYS,
    MODEL_PATH,
)


@dataclass
class WeatherBundle:
    history: pd.DataFrame
    api_forecast: pd.DataFrame
    model_forecast: pd.DataFrame
    comparison: pd.DataFrame
    summary: dict[str, Any]


def run_onnx_temperature_model(history: pd.DataFrame) -> np.ndarray:
    recent_highs = (
        history.sort_values("date")["high_c"].tail(LOOKBACK_DAYS).to_numpy(dtype=np.float32)
    )
    if len(recent_highs) < LOOKBACK_DAYS:
        raise ValueError(f"Need at least {LOOKBACK_DAYS} historical days for ONNX inference.")

    try:
        import onnxruntime as ort
    except ImportError as exc:
        raise RuntimeError(
            "onnxruntime is required to run models/mock_model/model.onnx. "
            "Install requirements.txt or deploy on HuggingFace Spaces."
        ) from exc

    session = ort.InferenceSession(str(MODEL_PATH), providers=["CPUExecutionProvider"])
    input_name = session.get_inputs()[0].name
    output_name = session.get_outputs()[0].name
    prediction = session.run([output_name], {input_name: recent_highs.reshape(1, -1)})[0]
    return np.asarray(prediction, dtype=float).reshape(-1)[:FORECAST_DAYS]


def seasonal_reference(history: pd.DataFrame, forecast_dates: list[pd.Timestamp]) -> pd.DataFrame:
    frame = history.copy()
    frame["day_of_year"] = frame["date"].dt.dayofyear
    rows = []
    for forecast_date in forecast_dates:
        day_of_year = forecast_date.dayofyear
        window = frame[
            (frame["day_of_year"] >= day_of_year - 14)
            & (frame["day_of_year"] <= day_of_year + 14)
        ]
        if window.empty:
            window = frame.tail(365)
        rows.append(
            {
                "date": forecast_date,
                "seasonal_high_c": float(window["high_c"].median()),
                "seasonal_low_gap": float((window["high_c"] - window["low_c"]).median()),
                "rain_mm": float(window["rain_mm"].median()),
                "wind_kmh": float(window["wind_kmh"].median()),
                "high_std": float(window["high_c"].std(ddof=0) or 3.0),
            }
        )
    return pd.DataFrame(rows)


def build_model_forecast(history: pd.DataFrame, api_forecast: pd.DataFrame) -> pd.DataFrame:
    forecast_dates = list(api_forecast["date"])
    seasonal = seasonal_reference(history, forecast_dates)
    predicted_highs = run_onnx_temperature_model(history)
    horizon = len(forecast_dates)
    if horizon <= len(predicted_highs):
        model_highs = predicted_highs[:horizon]
    else:
        seasonal_extension = seasonal["seasonal_high_c"].to_numpy(dtype=float)[len(predicted_highs) : horizon]
        model_highs = np.concatenate([predicted_highs, seasonal_extension])

    result = pd.DataFrame({"date": forecast_dates, "model_high_c": model_highs})
    result = result.merge(seasonal, on="date", how="left")
    result["model_low_c"] = result["model_high_c"] - result["seasonal_low_gap"]
    result["model_rain_mm"] = result["rain_mm"].clip(lower=0)
    result["model_wind_kmh"] = result["wind_kmh"].clip(lower=0)
    result["model_confidence"] = (1 - (result["high_std"] / 12)).clip(lower=0.45, upper=0.88)
    return result[
        [
            "date",
            "model_high_c",
            "model_low_c",
            "model_rain_mm",
            "model_wind_kmh",
            "model_confidence",
        ]
    ]


def build_weather_bundle(force_refresh_history: bool = False, horizon_days: int = FORECAST_DAYS) -> WeatherBundle:
    from weatherpred.data import fetch_api_forecast, load_or_fetch_history

    history = load_or_fetch_history(force_refresh=force_refresh_history)
    api_forecast = fetch_api_forecast(days=horizon_days)
    model_forecast = build_model_forecast(history, api_forecast)

    comparison = api_forecast.rename(columns=API_DISPLAY_COLUMNS).merge(
        model_forecast, on="date", how="inner"
    )
    comparison["date"] = comparison["date"].dt.strftime("%Y-%m-%d")
    comparison["high_delta_c"] = comparison["model_high_c"] - comparison["api_high_c"]
    comparison["rain_delta_mm"] = comparison["model_rain_mm"] - comparison["api_rain_mm"]

    summary = {
        "history_rows": int(len(history)),
        "history_start": history["date"].min().strftime("%Y-%m-%d"),
        "history_end": history["date"].max().strftime("%Y-%m-%d"),
        "fetched_at": datetime.now(timezone.utc).isoformat(),
        "avg_model_confidence": float(model_forecast["model_confidence"].mean()),
        "max_api_high_c": float(api_forecast["high_c"].max()),
        "max_model_high_c": float(model_forecast["model_high_c"].max()),
    }
    return WeatherBundle(history, api_forecast, model_forecast, comparison, summary)