src/severity.py - master - Chaizee/ZenithCode_Incident-LLM-analytics

src/severity.py
120 строк4 KB
Chaizee
fix: fix speed
12 июн 2026, 16:28
12 июн 2026, 16:28b2745cc
from __future__ import annotations

SEVERITY_LEVELS: dict[int, dict[str, str]] = {
    1: {
        "label": "МИКРО",
        "title": "Микро-проблемы",
        "description": "Мелкий дискомфорт, инфраструктура работает",
    },
    2: {
        "label": "ЛОКАЛ",
        "title": "Локальный сбой",
        "description": "Яма, лифт, переполненный бак ТБО, уборка снега",
    },
    3: {
        "label": "СИСТЕМ",
        "title": "Системный сбой",
        "description": "Нет воды, тепла, массовые неисправности",
    },
    4: {
        "label": "ВЫС",
        "title": "Высокая тяжесть",
        "description": "Затопление, прорыв трубы, срочное реагирование",
    },
    5: {
        "label": "ЧП",
        "title": "Региональное ЧП",
        "description": "Химвыбросы, обрушение моста, замерзание посёлка",
    },
}

CRITICAL_SEVERITY = 5
MODEL_SEVERITY_MIN = 1
MODEL_SEVERITY_MAX = 5

_SEVERITY_CAPS: tuple[tuple[int, tuple[str, ...]], ...] = (
    (2, (
        "уборк", "снег", "снега", "снегу", "снегом", "расчист", "снегоубор",
        "не убран", "не очищ", "налед", "гололед",
    )),
    (2, ("мусор", "контейнер", "тко", "свалк", "вывоз отход")),
    (2, ("освещен", "фонар", "не горит свет", "лампа")),
    (2, ("шум", "громко", "сосед", "благодар", "спасиб")),
    (3, ("яма", "выбоин", "тротуар", "асфальт", "люк")),
)

_SEVERITY_FLOORS: tuple[tuple[int, tuple[str, ...]], ...] = (
    (5, (
        "эвакуа", "химвыброс", "химическ", "обрушени", "чрезвыч", "ч.п",
        "взрыв", "погиб", "жертв", "замерз", "замёрз", "массов", "угроза жизн",
    )),
    (5, (
        "затопил", "затоплен", "затопило", "затопление", "затопл",
        "под топ", "залило", "залит", "залило",
    )),
    (4, ("прорвал", "прорыв", "прорвало", "прорв")),
    (4, ("нет вод", "отключил воду", "без вод", "нет горяч", "нет холодн")),
    (4, ("нет тепл", "нет отоплен", "холодные батар", "не топит", "не греет")),
    (4, ("обруш", "рухнул", "обвал")),
)

def clamp_severity(value: object, *, default: int = 1) -> int:
    try:
        n = int(value)
    except (TypeError, ValueError):
        return default
    return max(MODEL_SEVERITY_MIN, min(MODEL_SEVERITY_MAX, n))

def severity_label(severity: int) -> str:
    meta = SEVERITY_LEVELS.get(clamp_severity(severity), {})
    return str(meta.get("label", str(severity)))

def severity_title(severity: int) -> str:
    meta = SEVERITY_LEVELS.get(clamp_severity(severity), {})
    return str(meta.get("title", ""))

def _match_blob(text: str, theme: str = "") -> str:
    return f"{theme or ''} {text or ''}".lower()

def rule_severity_bounds(text: str, theme: str = "") -> tuple[int | None, int | None]:
    blob = _match_blob(text, theme)
    floor: int | None = None
    cap: int | None = None

    for min_sev, keywords in _SEVERITY_FLOORS:
        if any(k in blob for k in keywords):
            floor = max(floor or min_sev, min_sev)

    for max_sev, keywords in _SEVERITY_CAPS:
        if any(k in blob for k in keywords):
            cap = min(cap or max_sev, max_sev)

    return floor, cap

def refine_severity(
    ml_severity: int,
    text: str,
    *,
    theme: str = "",
    is_problem: bool = True,
) -> int:
    if not is_problem:
        return MODEL_SEVERITY_MIN

    sev = clamp_severity(ml_severity)
    floor, cap = rule_severity_bounds(text, theme)

    if floor is not None:
        sev = max(sev, floor)
    if cap is not None:
        sev = min(sev, cap)
    if floor is not None and cap is not None and floor > cap:
        sev = floor

    return clamp_severity(sev)

def apply_problem_flags(is_problem: bool, severity: int) -> tuple[bool, int]:
    sev = clamp_severity(severity)
    if not is_problem:
        return False, MODEL_SEVERITY_MIN
    return True, sev