"""Load the irrigation model trained in session 16 and serve predictions (session 17)."""
from datetime import datetime, timedelta

import config
import db
import weather

_model = {"bundle": None, "mtime": None}


def pct_to_vwc(pct):
    """Capacitive sensor % -> volumetric water content using the 2-point calibration in config."""
    return config.VWC_AT_0_PCT + (config.VWC_AT_100_PCT - config.VWC_AT_0_PCT) * (pct / 100.0)


def _load():
    if not config.MODEL_PATH.exists():
        return None
    mtime = config.MODEL_PATH.stat().st_mtime
    if _model["mtime"] != mtime:                 # hot-reload when you retrain the model
        import joblib
        try:
            _model["bundle"] = joblib.load(config.MODEL_PATH)
        except Exception as exc:                 # e.g. trained with another scikit-learn version -> retrain (s16)
            print("model load failed, using the rule fallback:", exc)
            _model["bundle"] = None
        _model["mtime"] = mtime
    return _model["bundle"]


def predict(node_id):
    latest = db.rows("SELECT * FROM readings WHERE node_id=? ORDER BY ts DESC LIMIT 1", (node_id,))
    if not latest or latest[0]["soil_moisture_pct"] is None:
        return {"ok": False, "error": "no readings for this node yet"}
    r = latest[0]
    since = (datetime.fromisoformat(r["ts"]) - timedelta(hours=24)).isoformat(timespec="seconds")
    old = db.rows("SELECT soil_moisture_pct FROM readings WHERE node_id=? AND ts<=? AND soil_moisture_pct IS NOT NULL "
                  "ORDER BY ts DESC LIMIT 1", (node_id, since))
    w = weather.current()
    vwc = pct_to_vwc(r["soil_moisture_pct"])
    features = {
        "soil_vwc": vwc,
        "vwc_drop_24h": (pct_to_vwc(old[0]["soil_moisture_pct"]) - vwc) if old else 0.0,
        "air_temp": r["air_temp_c"] if r["air_temp_c"] is not None else 25.0,
        "air_humidity": r["air_humidity_pct"] if r["air_humidity_pct"] is not None else 60.0,
        "soil_temp": w["soil_temp"], "et0_24h": w["et0_24h"], "rain_24h": w["rain_24h"],
        "radiation": w["radiation"], "wind": w["wind"], "hour": datetime.fromisoformat(r["ts"]).hour,
    }
    bundle = _load()
    if bundle is None:      # graceful fallback: agronomic rule
        prob = 1.0 if vwc < 0.30 else 0.0
        source = "rule (model file not found - run ai/s16_irrigation_classifier.py)"
    else:
        import pandas as pd
        X = pd.DataFrame([[features[f] for f in bundle["features"]]], columns=bundle["features"])
        prob = float(bundle["model"].predict_proba(X)[0][1])
        source = "ml"
    rec = "irrigate" if prob >= config.IRRIGATION_THRESHOLD else "wait"
    if w.get("rain_next_24h", 0) >= 5:
        rec, note = "wait", "rain expected in the next 24 h"
    else:
        note = ""
    # depth to refill the 40 cm root zone to field capacity, capped at one practical irrigation (40 mm)
    litres_m2 = round(min(40.0, max(0.0, (config.VWC_AT_100_PCT * 0.8 - vwc)) * 1000 * 0.40), 1) if rec == "irrigate" else 0
    return {"ok": True, "node_id": node_id, "probability": round(prob, 3), "threshold": config.IRRIGATION_THRESHOLD,
            "recommendation": rec,
            "note": note, "source": source, "vwc": round(vwc, 3), "water_mm": litres_m2,
            "weather": w, "features": {k: round(v, 3) if isinstance(v, float) else v for k, v in features.items()}}