"""API schemas (frozen contract, #34). Dashboard (lane C) is built against these; lane B implements them (#12).

Change only via a separate small contract PR. Colors follow ADR-006: red = acute, yellow = high/moderate,
green = nothing open, grey = info only (e.g. silence).
"""

from datetime import date, datetime
from typing import Literal

from pydantic import BaseModel, Field

from app.dialog.contracts import Event, Reply
from app.integrations.base import Prescription

Color = Literal["red", "yellow", "green", "grey"]
LevelName = Literal["info", "moderate", "high", "acute"]
Module = Literal["veteran", "depression", "family"]
Metric = Literal["phq9", "pcl5", "gad7", "auditc", "dar5", "mood", "sleep_hours", "alcohol"]


class DiscontinuationRisk(BaseModel):
    """Rule-based index (not ML) of the risk to drop therapy: missed doses, side effects, quit intent, low mood."""

    score: int = Field(ge=0, le=100)
    level: Literal["low", "medium", "high"]
    factors: list[str] = Field(default_factory=list)  # short Russian labels for the doctor


class PatientListItem(BaseModel):
    token: str
    display_name: str  # "Андрей С." — minimal identification for the treating doctor
    age: int
    modules: list[Module]
    drug: str | None = None  # "сертралин 50 мг"
    therapy_day: int
    next_visit: date | None = None
    status_color: Color
    top_alert_level: LevelName | None = None
    open_alerts: int = 0
    phq9_last: int | None = None
    pcl5_last: int | None = None
    trend: Literal["up", "down", "flat", "unknown"] = "unknown"  # direction of symptom scores (up = worse)
    adherence_7d: float | None = Field(default=None, ge=0, le=1)
    discontinuation_risk: DiscontinuationRisk
    last_event: str = ""


class SeriesPoint(BaseModel):
    day: date
    metric: Metric
    value: float
    scheduled: bool = True  # False for unscheduled scale sessions


class DoseOut(BaseModel):
    planned_at: datetime
    drug: str
    status: Literal["taken", "missed", "skipped", "pending"]


class AlertOut(BaseModel):
    id: int
    token: str
    patient_name: str
    ts: datetime
    level: LevelName
    color: Color
    code: str
    reason: str
    acked_by: str | None = None
    acked_at: datetime | None = None


class PatientCard(BaseModel):
    patient: PatientListItem
    prescriptions: list[Prescription]
    series: list[SeriesPoint]
    doses: list[DoseOut]
    alerts: list[AlertOut]
    safety_plan: list[str] | None = None  # Stanley–Brown steps, if composed with the doctor
    summary: str = ""  # LLM summary built from structured data only (#19)


class AckIn(BaseModel):
    doctor_id: str


class ConnectPatientIn(BaseModel):
    doctor_id: str
    display_name: str
    birth_year: int
    sex: Literal["m", "f"]
    snils: str  # synthetic only
    drug: str
    dose: str
    times: list[str] = Field(default_factory=lambda: ["08:00"])
    instructions: str = ""
    next_visit: date | None = None
    modules: list[Module] = Field(default_factory=lambda: ["veteran"])


class ConnectPatientOut(BaseModel):
    token: str
    code: str  # one-time connect code (esia_mock)
    deep_link: str  # t.me/<bot>?start=<code> or web-console link; dashboard renders it as QR


class DialogIn(Event):
    """Same as dialog Event; kept as a separate name for OpenAPI clarity."""


class DialogOut(BaseModel):
    replies: list[Reply]


class MessageOut(BaseModel):
    ts: datetime
    direction: Literal["in", "out"]
    text: str  # scrubbed for "in", as sent for "out"
    kind: str = "normal"


class ClockOut(BaseModel):
    now: datetime
    virtual: bool
    offset_days: float


class ClockAdvanceIn(BaseModel):
    days: float = 0
    hours: float = 0


class DemoPersona(BaseModel):
    token: str
    display_name: str
    therapy_day: int
    scenario: str | None = None  # path of data/synthetic/<persona>.jsonl if scripted