vector/backend/schemas.py

from __future__ import annotations

from datetime import datetime
from typing import Any

from pydantic import BaseModel, ConfigDict, Field


class CaseBase(BaseModel):
    model_config = ConfigDict(extra='allow')

    age: int | None = None
    age_years: int | None = None
    gender: str | None = None
    child_name: str | None = None
    height_build: str | None = None
    clothes_upper: str | None = None
    clothes_lower: str | None = None
    clothes_shoes: str | None = None
    shoes: str | None = None
    clothes_description: str | None = None
    special_marks: str | None = None
    phone_status: str | None = None
    health_flags: list[str] = Field(default_factory=list)
    has_diagnosis: bool | None = None
    diagnosis_type: list[str] = Field(default_factory=list)
    fitness_level: str | None = None
    has_transport: str | None = None
    cant_swim: bool | None = None
    psychotype: str | None = None
    psychotype_answers: dict[str, Any] | None = None
    elapsed_hours: float | None = None
    last_known_place: str | None = None
    direction: str | None = None
    reason: str | None = None
    loss_reason: str | None = None
    loss_time: datetime | None = None
    last_seen_direction: str | None = None
    last_seen_reliability: str | None = None
    last_seen_description: str | None = None
    behavior_description: str | None = None
    familiar_places: str | None = None
    lost_before: str | None = None
    terrain_primary: str | None = None
    terrain: list[str] = Field(default_factory=list)
    weather: str | None = None
    season: str | None = None
    temperature_c: float | None = None
    precipitation: str | None = None
    visibility: str | None = None
    wind: str | None = None
    gps: dict[str, float] | None = None
    gps_lat: float | None = None
    gps_lon: float | None = None
    tnp_lat: float | None = None
    tnp_lon: float | None = None
    tnp_address: str | None = None
    resources: list[str] = Field(default_factory=list)
    teams_count: int | None = None
    team_size: int | None = None
    has_dog: bool | None = None
    extra_resources: list[str] = Field(default_factory=list)
    notes: str | None = None
    note: str | None = None
    status: str | None = None
    found_alive: bool | None = None
    found_distance_km: float | None = None
    found_direction: str | None = None
    found_location_type: str | None = None
    found_lat: float | None = None
    found_lon: float | None = None
    search_duration_hours: float | None = None
    who_found: str | None = None
    raw_text: str | None = None
    analysis_log: dict[str, Any] | None = None
    mobile: bool = False


class CaseCreate(CaseBase):
    status: str | None = 'new'


class CaseUpdate(CaseBase):
    pass


class CaseResponse(CaseBase):
    id: str
    created_at: datetime
    updated_at: datetime
    status: str
    health_flags: list[str] = Field(default_factory=list)
    diagnosis_type: list[str] = Field(default_factory=list)
    terrain: list[str] = Field(default_factory=list)
    resources: list[str] = Field(default_factory=list)
    extra_resources: list[str] = Field(default_factory=list)
    result: dict[str, Any] | None = None


class CaseListResponse(BaseModel):
    items: list[CaseResponse]
    total: int
    page: int
    page_size: int


class DashboardResponse(BaseModel):
    total_cases: int
    found_alive_count: int
    found_deceased_count: int
    unknown_outcome_count: int
    median_found_distance_km: float | None
    recent_activity_count: int


class RecommendationRequest(BaseModel):
    age: int | None = None
    gender: str | None = None
    health_flags: list[str] = Field(default_factory=list)
    elapsed_hours: int | None = None
    terrain_primary: str | None = None
    weather: str | None = None
    resources: list[str] = Field(default_factory=list)


class AnalyzeResponse(BaseModel):
    urgency: str
    primary_zones: list[dict[str, Any]]
    immediate_actions: list[str]
    behavioral_prediction: str
    psychotype_recommendations: dict[str, Any]
    key_locations: list[str]
    case_id: str


class ParseDocResponse(BaseModel):
    filename: str
    parsed: bool
    preview: dict[str, Any]
    raw_text: str | None = None


# ================= ЗАВЕРШЁННЫЕ ПОИСКИ (ручной ввод, без ИИ) =================
# Оператор руками вносит данные ЗАВЕРШЁННОГО поиска: параметры субъекта/среды
# (вход матмодели) + реальный исход. Цель — датасет для калибровки модели
# (сравнение прогноза ВЕКТОРА с фактом) и архив 14 лет без LLM.

class ClosedCaseCreate(BaseModel):
    # --- Субъект (вход модели) ---
    age_years: int
    gender: str = 'м'                       # 'м' | 'ж'
    diagnosis_type: list[str] = Field(default_factory=list)   # РАС, эпилепсия, СДВГ, ЗПР...
    has_transport: str = 'none'             # none|bike|scooter|other
    cant_swim: bool = False
    fitness_level: str | None = None        # низкий|средний|высокий

    # --- Обстоятельства (вход матмодели) ---
    elapsed_hours: float
    loss_time: datetime | None = None       # время потери (для time_of_day)
    terrain: list[str] = Field(default_factory=list)          # лес, болото, поле...
    weather: str | None = None
    season: str | None = None
    temperature_c: float | None = None
    tnp_lat: float | None = None
    tnp_lon: float | None = None
    tnp_address: str | None = None

    # --- Психотип (опционально, вход скоринга) ---
    psychotype: str | None = None
    psychotype_answers: dict[str, Any] | None = None

    # --- Ресурсы ---
    teams_count: int | None = None
    team_size: int | None = None
    has_dog: bool = False

    # --- Исход (факт, для сравнения с прогнозом) ---
    found_alive: bool
    found_distance_km: float | None = None
    found_direction: str | None = None
    found_location_type: str | None = None
    found_lat: float | None = None
    found_lon: float | None = None
    search_duration_hours: float | None = None
    who_found: str | None = None

    # --- Мета ---
    notes: str | None = None
    source: str | None = None               # откуда: спецдонесение 2015, бумажный журнал...


class ClosedCaseResponse(ClosedCaseCreate):
    id: str
    created_at: datetime
    # Сохранённый прогноз модели на момент внесения (для калибровки)
    model_prediction: dict[str, Any] | None = None
    # Насколько прогноз совпал с фактом
    accuracy_note: str | None = None


class ClosedCaseListResponse(BaseModel):
    items: list[ClosedCaseResponse]
    total: int