bambuddy/backend/app/models/archive.py

from datetime import datetime

from sqlalchemy import JSON, Boolean, DateTime, Float, ForeignKey, Integer, String, Text, func
from sqlalchemy.orm import Mapped, mapped_column, relationship

from backend.app.core.database import Base


class PrintArchive(Base):
    __tablename__ = "print_archives"

    id: Mapped[int] = mapped_column(primary_key=True)
    printer_id: Mapped[int | None] = mapped_column(ForeignKey("printers.id"), nullable=True)
    project_id: Mapped[int | None] = mapped_column(ForeignKey("projects.id", ondelete="SET NULL"), nullable=True)
    # Which library file this run was dispatched from (#1897). Set by the queue
    # scheduler when it archives a library-file print; older rows are matched by
    # content_hash/filename instead. SET NULL so deleting a file keeps history.
    library_file_id: Mapped[int | None] = mapped_column(
        ForeignKey("library_files.id", ondelete="SET NULL"), nullable=True
    )
    cost_center_id: Mapped[int | None] = mapped_column(
        ForeignKey("cost_centers.id", ondelete="SET NULL"), nullable=True
    )

    # File info
    filename: Mapped[str] = mapped_column(String(255))
    file_path: Mapped[str] = mapped_column(String(500))
    file_size: Mapped[int] = mapped_column(Integer)
    content_hash: Mapped[str | None] = mapped_column(String(64))  # SHA256 hash for duplicate detection
    thumbnail_path: Mapped[str | None] = mapped_column(String(500))
    timelapse_path: Mapped[str | None] = mapped_column(String(500))
    # The plate whose run produced ``timelapse_path`` (#3275). Every plate of a
    # Send All shares this archive, so a print start may delete the previous
    # video only when it is the same plate printing again; another plate's
    # video is the only copy of that plate's run. NULL when unknown -- a
    # manually attached video, or one attached before this column existed.
    timelapse_plate_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
    # True when Bambuddy forced timelapse recording on for this print so the
    # finish-photo extractor (#1397) could pull the post-park-pre-drop frame.
    # The cleanup path uses this to know the timelapse should be deleted
    # both locally and on the printer's SD after extraction — the user
    # didn't opt in to a timelapse recording.
    bambuddy_forced_timelapse: Mapped[bool] = mapped_column(Boolean, default=False, server_default="0")
    # Video filenames present in the printer's /timelapse directory when this
    # print started (#2704). The printer writes its video only at print end, so
    # anything not in this list belongs to this print — a comparison that needs
    # no clock, which matters because a LAN-only printer can't reach Bambu's NTP
    # server and its filename timestamps are arbitrarily wrong. Persisted (not
    # just held in memory) so the diff survives a restart and so the manual
    # "Scan for Timelapse" button can use it instead of guessing from
    # timestamps. NULL for archives predating this, and for baselines taken at
    # completion time, which are useless by construction.
    timelapse_baseline: Mapped[list | None] = mapped_column(JSON, nullable=True)
    source_3mf_path: Mapped[str | None] = mapped_column(String(500))  # Original project 3MF from slicer
    f3d_path: Mapped[str | None] = mapped_column(String(500))  # Fusion 360 design file

    # Print details from 3MF / printer
    print_name: Mapped[str | None] = mapped_column(String(255))
    print_time_seconds: Mapped[int | None] = mapped_column(Integer)
    filament_used_grams: Mapped[float | None] = mapped_column(Float)
    filament_type: Mapped[str | None] = mapped_column(String(50))
    filament_color: Mapped[str | None] = mapped_column(String(200))
    layer_height: Mapped[float | None] = mapped_column(Float)
    total_layers: Mapped[int | None] = mapped_column(Integer)
    nozzle_diameter: Mapped[float | None] = mapped_column(Float)
    bed_temperature: Mapped[int | None] = mapped_column(Integer)
    bed_type: Mapped[str | None] = mapped_column(String(64))  # e.g. "Cool Plate", "Textured PEI Plate"
    nozzle_temperature: Mapped[int | None] = mapped_column(Integer)

    # Printer model this file was sliced for (extracted from 3MF metadata)
    sliced_for_model: Mapped[str | None] = mapped_column(String(50), nullable=True)

    # Print result
    status: Mapped[str] = mapped_column(String(20), default="completed")
    started_at: Mapped[datetime | None] = mapped_column(DateTime)
    completed_at: Mapped[datetime | None] = mapped_column(DateTime)

    # Printer-assigned subtask identifier from MQTT. Used to resume the same
    # archive row across a backend restart during a long-running print (#972):
    # if the same subtask_id reappears after restart, we know it's the same
    # print and keep the original row instead of cancel-then-create.
    subtask_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
    # Durable Bambuddy UUID for billing idempotency. Unlike subtask_id, this is
    # not constrained by printer firmware and is replaced for every reprint.
    billing_run_id: Mapped[str | None] = mapped_column(String(36), nullable=True)

    # Which plate of a multi-plate 3MF this print was for (1-based), copied from
    # the queue item at dispatch (#2603). A whole multi-plate 3MF is uploaded
    # under one filename with no plate suffix, so the parser can't recover the
    # selected plate and extra_data holds all-plates aggregate metadata; without
    # this the history UI can't tell which plate was printed and falls back to
    # Plate 1. NULL for archives with no specific selected plate.
    plate_id: Mapped[int | None] = mapped_column(Integer, nullable=True)

    # Extended metadata (JSON blob for flexibility)
    extra_data: Mapped[dict | None] = mapped_column(JSON)

    # MakerWorld info (auto-extracted from 3MF)
    makerworld_url: Mapped[str | None] = mapped_column(String(500))
    designer: Mapped[str | None] = mapped_column(String(255))

    # User-defined external link (Printables, Thingiverse, etc.)
    external_url: Mapped[str | None] = mapped_column(String(500))

    # User additions
    is_favorite: Mapped[bool] = mapped_column(Boolean, default=False)
    wallet_charge_skipped: Mapped[bool] = mapped_column(Boolean, default=False)
    tags: Mapped[str | None] = mapped_column(Text)
    notes: Mapped[str | None] = mapped_column(Text)
    cost: Mapped[float | None] = mapped_column(Float)
    photos: Mapped[list | None] = mapped_column(JSON)  # List of photo filenames
    failure_reason: Mapped[str | None] = mapped_column(String(100))  # For failed prints
    quantity: Mapped[int] = mapped_column(Integer, default=1)  # Number of items printed

    # Post-print outcome confirmation (#1898). user_verdict is the USER's
    # quality judgement ('good' / 'reject'), deliberately orthogonal to the
    # machine-reported `status`: completed + reject means "printer finished
    # it, part is scrap". confirm_requested is copied from the queue item's
    # opt-in flag at dispatch (like plate_id) and drives the prompt + the
    # "unconfirmed" badge; confirm_token is a per-archive capability for the
    # one-tap verdict links in push notifications, minted when the prompt
    # fires. A landed verdict RETIRES the token by stamping
    # confirm_token_used_at rather than clearing the value: the link stays
    # resolvable so a second tap can say "already answered, here is what was
    # recorded" instead of the bare "invalid or already used" 404.
    # user_verdict_source records how the verdict arrived ('dialog', 'link',
    # 'plate_clear', 'printer_card', 'api', 'reaction') so the UI can explain
    # a verdict nobody remembers giving.
    user_verdict: Mapped[str | None] = mapped_column(String(10), nullable=True)
    user_verdict_source: Mapped[str | None] = mapped_column(String(16), nullable=True)
    # When the verdict on file was recorded (#1898). Written with every verdict,
    # unlike `confirm_token_used_at`, which marks the one moment the one-tap
    # capability was spent — a verdict changed later in the app must not be
    # dated by that older event.
    user_verdict_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
    # Nullable to match the ALTER that adds it to an existing install: a fresh
    # database would otherwise get NOT NULL while an upgraded one gets a
    # nullable column, and the two would disagree about the same table. The
    # default still means every row written by Bambuddy is True or False; only
    # `is_(True)` and truthiness read it, both of which treat NULL as off.
    confirm_requested: Mapped[bool | None] = mapped_column(Boolean, nullable=True, default=False, server_default="0")
    # Indexed and unique: the one-tap route is reachable with no credential
    # at all, so an unindexed lookup would let anyone turn a stream of
    # garbage tokens into a stream of full scans of this table. Uniqueness
    # costs nothing (the only writer is secrets.token_urlsafe(32)) and keeps
    # scalar_one_or_none from ever raising MultipleResultsFound.
    confirm_token: Mapped[str | None] = mapped_column(String(64), nullable=True, unique=True, index=True)
    confirm_token_used_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True, default=None)

    # Energy tracking
    energy_kwh: Mapped[float | None] = mapped_column(Float)  # Energy consumed in kWh
    energy_cost: Mapped[float | None] = mapped_column(Float)  # Cost of energy consumed
    # Printer wear for the first run, at the printer's hourly wear cost (#694).
    wear_cost: Mapped[float | None] = mapped_column(Float)
    # Plug lifetime counter captured at print start; delta at print end becomes energy_kwh.
    # Persisted so per-print tracking survives backend restarts mid-print (#941).
    energy_start_kwh: Mapped[float | None] = mapped_column(Float)
    # When and from which plug that counter was read, and the electricity price
    # then. With the plug's hourly snapshots in between, these let the print's
    # cost follow a price that changes while it runs (#1251).
    energy_start_at: Mapped[datetime | None] = mapped_column(DateTime)
    energy_start_plug_id: Mapped[int | None] = mapped_column(Integer)
    energy_start_price: Mapped[float | None] = mapped_column(Float)

    # Timestamps
    created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())
    # Soft-delete sentinel (#1343). When non-null, the UI hides this archive
    # from listings (its files have already been removed from disk) but the
    # stats endpoint keeps counting it — deleting nine of ten Benchies no
    # longer wipes their filament / time / cost contribution from Quick Stats.
    # The opt-in "Also remove from statistics" checkbox in the delete dialog
    # bypasses the soft-delete path and hard-deletes the row.
    deleted_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True, default=None, index=True)

    # User tracking (who uploaded/created this archive)
    created_by_id: Mapped[int | None] = mapped_column(ForeignKey("users.id", ondelete="SET NULL"), nullable=True)

    # Relationships
    printer: Mapped["Printer | None"] = relationship(back_populates="archives")
    project: Mapped["Project | None"] = relationship(back_populates="archives")
    cost_center: Mapped["CostCenter | None"] = relationship()
    created_by: Mapped["User | None"] = relationship()


from backend.app.models.finance import CostCenter  # noqa: E402, F811
from backend.app.models.printer import Printer  # noqa: E402, F811
from backend.app.models.project import Project  # noqa: E402, F811
from backend.app.models.user import User  # noqa: E402, F811