"""
Statistical search-priority recommendation.

Single home for the recommendation scoring rules (B3). Previously duplicated
between `backend/routers/stats.py` (inline, exact-match on terrain/weather,
plus a health_flags rule) and `backend/services/stats_service.py` (substring
match on tolerant key aliases, no health_flags rule).

The unified rules below are the union of the two: identical weights and
threshold, substring matching (a superset of the old exact match), tolerant
input keys, and the health_flags rule kept. This is NOT the 7-factor zonal
scoring of `scoring_service` — it only labels a case high/normal priority.

Deliberately free of any database import so both the router and the service
layer can use it without side effects.
"""
from __future__ import annotations

from typing import Any

# Scoring weights and threshold — unchanged from both previous implementations.
WEIGHT_YOUNG_CHILD = 20
WEIGHT_LONG_ELAPSED = 20
WEIGHT_RISKY_TERRAIN = 15
WEIGHT_ADVERSE_WEATHER = 15
WEIGHT_MULTIPLE_HEALTH_FLAGS = 15

HIGH_PRIORITY_THRESHOLD = 40

YOUNG_CHILD_AGE = 12
LONG_ELAPSED_HOURS = 12
MULTIPLE_HEALTH_FLAGS = 2

RISKY_TERRAIN_TOKENS = ('лес', 'болото', 'вода')
ADVERSE_WEATHER_TOKENS = ('дождь', 'туман', 'снег', 'ночь')

HIGH_PRIORITY_TEXT = 'Высокий приоритет на прочёс и дрон'
NORMAL_PRIORITY_TEXT = 'Стандартный приоритет поиска'


def score_recommendation(
    age: int | None = None,
    elapsed_hours: int | None = None,
    terrain: str | None = None,
    weather: str | None = None,
    health_flags: list[str] | None = None,
) -> dict[str, Any]:
    """Score a case and return {'score', 'priority', 'recommendation'}."""
    score = 0

    if age is not None and age < YOUNG_CHILD_AGE:
        score += WEIGHT_YOUNG_CHILD
    if elapsed_hours is not None and elapsed_hours >= LONG_ELAPSED_HOURS:
        score += WEIGHT_LONG_ELAPSED
    if any(token in str(terrain or '').lower() for token in RISKY_TERRAIN_TOKENS):
        score += WEIGHT_RISKY_TERRAIN
    if any(token in str(weather or '').lower() for token in ADVERSE_WEATHER_TOKENS):
        score += WEIGHT_ADVERSE_WEATHER
    if len(health_flags or []) >= MULTIPLE_HEALTH_FLAGS:
        score += WEIGHT_MULTIPLE_HEALTH_FLAGS

    is_high = score >= HIGH_PRIORITY_THRESHOLD

    return {
        'score': score,
        'priority': 'high' if is_high else 'normal',
        'recommendation': HIGH_PRIORITY_TEXT if is_high else NORMAL_PRIORITY_TEXT,
    }


def get_statistical_recommendation(case_data: dict[str, Any]) -> dict[str, Any]:
    """Dict-based entry point, tolerant of the field aliases used across
    the desktop / mobile / admin payloads."""
    return score_recommendation(
        age=case_data.get('age') if case_data.get('age') is not None else case_data.get('age_years'),
        elapsed_hours=case_data.get('elapsed_hours'),
        terrain=case_data.get('terrain') or case_data.get('terrain_primary'),
        weather=case_data.get('weather') or case_data.get('precipitation'),
        health_flags=case_data.get('health_flags'),
    )