"""AI tool / function introduction request ORM model.

Mirrors :class:`AnnouncementRequest` but with the multi-field form
the security-review template asks for:

    AI                 (text — name of function/product)
    How to use         (multi-select: API / Product / Module-Plugin)
    Data Level         (multi-select: L1 / L2 / L3 / L4)
    Purpose            (textarea)
    Data type          (textarea — what sensitive data is touched)
    Privacy Policy     (textarea — URLs of SOC2 / MSA / DPA / SLA)
    Attachment         (optional file count)

Once approved an entry is published company-wide on the Approved AI
Tools listing — same idea as the Feishu spreadsheet IT was
maintaining manually before. The status set keeps the Feishu
language ("已撤回" → cancelled, "已拒绝" → rejected, "已通过" →
approved) so historical xlsx rows backfill cleanly.

Legacy backfill columns let us import the pre-Helix Feishu records
(``legacy_request_no`` + ``source_id``) without rewriting the actor
linkage when the original applicant isn't yet a HRIS-synced
``IdentityPerson`` (e.g. on local dev seeded with only a couple of
users). New live submissions populate ``person_id`` and leave the
legacy columns ``NULL``.
"""
from __future__ import annotations

import uuid
from datetime import datetime

from sqlalchemy import DateTime, ForeignKey, String, Text, func
from sqlalchemy.orm import Mapped, mapped_column

from app.core.database import Base

AI_TOOL_STATUS_PENDING = "pending"
AI_TOOL_STATUS_APPROVED = "approved"
AI_TOOL_STATUS_REJECTED = "rejected"
AI_TOOL_STATUS_CANCELLED = "cancelled"
# Admin-initiated revoke (typically via offboarding force-revoke
# at T+0). Distinct from ``cancelled`` (applicant withdrew) so
# leaver-driven revocations stay auditable + filterable.
AI_TOOL_STATUS_REVOKED = "revoked"

AI_TOOL_HOW_TO_USE_OPTIONS = ("API", "Product", "Module/Plugin")
AI_TOOL_DATA_LEVEL_OPTIONS = ("L1", "L2", "L3", "L4")


class AiToolApplication(Base):
    __tablename__ = "ai_tool_applications"

    id: Mapped[str] = mapped_column(
        String(36), primary_key=True, default=lambda: str(uuid.uuid4()),
    )
    person_id: Mapped[str | None] = mapped_column(
        String(36),
        ForeignKey("identity_persons.id"),
        nullable=True,
    )
    ai_name: Mapped[str] = mapped_column(String(255), nullable=False)
    # Comma-separated multi-select values, stored verbatim from the
    # Feishu widget (e.g. "API, Product, Module/Plugin"). We don't
    # split into a join table because the option set is small and the
    # listing page just renders chips from the raw string.
    how_to_use: Mapped[str] = mapped_column(String(255), nullable=False)
    data_level: Mapped[str] = mapped_column(String(64), nullable=False)
    purpose: Mapped[str] = mapped_column(Text, nullable=False)
    data_type: Mapped[str] = mapped_column(Text, nullable=False)
    privacy_policy: Mapped[str] = mapped_column(Text, nullable=False)
    attachment_note: Mapped[str | None] = mapped_column(
        String(255), nullable=True,
    )

    status: Mapped[str] = mapped_column(
        String(32), nullable=False, default=AI_TOOL_STATUS_PENDING,
    )
    feishu_instance_code: Mapped[str | None] = mapped_column(
        String(64), nullable=True,
    )
    approval_request_id: Mapped[str | None] = mapped_column(
        String(36),
        ForeignKey("approval_requests.id", ondelete="SET NULL"),
        nullable=True,
    )
    granted_at: Mapped[datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True,
    )

    # Backfill / Feishu source-of-truth columns.
    legacy_request_no: Mapped[str | None] = mapped_column(
        String(32), nullable=True,
    )
    legacy_applicant_name: Mapped[str | None] = mapped_column(
        String(128), nullable=True,
    )
    legacy_applicant_department: Mapped[str | None] = mapped_column(
        String(128), nullable=True,
    )
    source_id: Mapped[str | None] = mapped_column(
        String(255), nullable=True, unique=True,
    )

    created_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), nullable=False,
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(),
        onupdate=func.now(), nullable=False,
    )