import datetime
import re
 
from modules.plant import Plant
from modules.weather_utils import did_or_will_rain, weather_values
 
 
# ── 1. Watering frequency parser ───────────────────────────────────────────────
 
_RECOMMENDATION_MAP: dict[str, int] = {
    # ── Fixed cadence ──────────────────────────────────────────────────────
    "water weekly":                   7,
    # ── Always moist ──────────────────────────────────────────────────────
    "keep soil moist":                2,
    "keep soil consistently moist":   2,
    "keep soil evenly moist":         2,
    "keep soil slightly moist":       3,
    "regular, moist soil":            3,
    # ── Regular ───────────────────────────────────────────────────────────
    "regular watering":               4,
    "regular, well-drained soil":     4,
    # ── Water when dry ────────────────────────────────────────────────────
    "water when soil is dry":         5,
    "water when topsoil is dry":      5,
    "water when soil feels dry":      5,
    "let soil dry between watering":  7,
}
 
_FALLBACK_RULES: list[tuple[str, int | None]] = [
    (r"daily",                                              1),
    (r"twice.{0,10}week",                                   3),
    (r"every\s+(\d+)\s+day",                                None),  # extracted dynamically
    (r"once.{0,10}week|weekly",                             7),
    (r"once.{0,10}fortnight|every.{0,6}two.{0,6}week",    14),
    (r"monthly",                                           30),
    (r"moist",                                              2),
    (r"dry",                                                6),
    (r"regular",                                            4),
]
 
DEFAULT_INTERVAL = 4
 

def _parse_watering_frequency(recommendation: str) -> int:
    """Map a free-text watering recommendation to a watering interval in days.
 
    Args:
        recommendation: e.g. "Water weekly", "Keep soil moist"
 
    Returns:
        Watering interval in days (>= 1).
    """
    if not recommendation or not recommendation.strip():
        return DEFAULT_INTERVAL
 
    cleaned = recommendation.strip().lower()
 
    if cleaned in _RECOMMENDATION_MAP:
        return _RECOMMENDATION_MAP[cleaned]
 
    for key, days in _RECOMMENDATION_MAP.items():
        if cleaned.startswith(key) or key.startswith(cleaned):
            return days
 
    for pattern, days in _FALLBACK_RULES:
        m = re.search(pattern, cleaned)
        if m:
            if days is None:
                try:
                    return max(1, int(m.group(1)))
                except (IndexError, ValueError):
                    return DEFAULT_INTERVAL
            return days
 
    return DEFAULT_INTERVAL
 


def get_watering_frequency(plant: Plant) -> int:
    """Determine the watering frequency for a plant, in days.
 
    Args:
        plant: The plant to determine the watering frequency for.
 
    Returns:
        Watering frequency in days (>= 1).
    """
    if plant.watering_frequency:
        return _parse_watering_frequency(plant.watering_frequency)
 
    return DEFAULT_INTERVAL


def should_water(plant: Plant,last_watered: datetime.date | None, date: datetime.date, LAT: float, LON: float) -> bool:
    """Determine if a plant should be watered on a given date.
    # returns true if its not raining and the plant wasnt watered since the last watering frequency interval
 
    Args:
        plant: The plant to check.
        last_watered: The date the plant was last watered.
        date: The date to check for.
        LAT: The latitude of the plant's location.
        LON: The longitude of the plant's location.
    """
    if last_watered is None:
        return True
 
    frequency = get_watering_frequency(plant)
    # convert last_watered to datetime.date if it's a string
    if isinstance(last_watered, str):
        last_watered = datetime.datetime.strptime(last_watered, "%Y-%m-%d").date()
        
    next_watering_date = last_watered + datetime.timedelta(days=frequency)
 
    if date >= next_watering_date and not did_or_will_rain(date, LAT, LON, forecast_threshold=50):
        return True
 

    return False