FabledCurator/Dockerfile

# syntax=docker/dockerfile:1.25

FROM node:24-alpine AS frontend-builder
WORKDIR /build
COPY frontend/package.json frontend/package-lock.json* ./
# No package-lock.json is tracked yet (we don't run npm locally per
# feedback-no-local-runs), so `npm install` instead of `npm ci`. Flip to
# `npm ci` once a lockfile is committed.
RUN npm install --no-audit --no-fund
COPY frontend/ ./
RUN npm run build

FROM python:3.14-slim AS runtime
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1

# System deps: ffmpeg (transcode + thumbnails, FC-2), unar (archives, FC-2),
# libpq for psycopg, postgresql-client + zstd for FC-5 backup/restore
# (pg_dump + tar --zstd), image libs, megatools (mega.nz public-link downloads
# for off-platform file-host links, #830 — `megatools dl`; Debian-native, no
# external MEGA apt repo needed).
RUN apt-get update && apt-get install -y --no-install-recommends \
        ffmpeg \
        unar \
        libpq5 \
        postgresql-client \
        zstd \
        megatools \
        libjpeg62-turbo \
        libwebp7 \
        libpng16-16 \
        ca-certificates \
    # opencv-python-headless (via requirements-ml.txt) links these even in its
    # headless build. Came from Dockerfile.ml when the images merged
    # (milestone 422 step 6).
        libgl1 \
        libglib2.0-0 \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY requirements.txt requirements-ml.txt ./
RUN pip install -r requirements.txt

# --- ML, merged from Dockerfile.ml (milestone 422 step 6) --------------------
#
# ONE image now serves every lane. It was two because the ML lane ran in its
# own container; with the single-container layout (step 5) running every lane
# in one process tree, a second image would mean the `ml` lane could never be
# enabled from the UI — there would be no worker in this container to enable.
#
# THE COST, MEASURED from run 7273 rather than guessed — and it is far
# smaller than the estimate this comment first carried, which said "everyone
# pulls ~4GB":
#
#   torch 2.12.1+cpu wheel     192.3 MB
#   torchvision 0.27.1+cpu       1.8 MB
#   transformers / onnxruntime / opencv / sklearn and friends
#                               62.0, 35.3, 23.6, 16.7, 12.3, 9.2, 6.9 MB
#   largest newly-pushed layer 222.07 MB
#
# So the ML code adds a few hundred MB to the pull, not gigabytes. The CPU
# index is what makes that true: the default PyPI torch wheel bundles the
# NVIDIA CUDA runtime and is ~2GB on its own.
#
# The GIGABYTES are in the MODEL — ~3.5GB of SigLIP weights — and those are
# NOT in this image. They arrive only when the operator enables the lane,
# which is what lets rule 164 permit a runtime fetch at all ("optional and
# clearly off"). That also settles the trade this step was asked to weigh:
# baking the weights in would add ~3.5GB to every pull for a feature many
# adopters never enable, against ~350MB for the code that makes the switch
# available. Off-by-default wins by an order of magnitude, which was NOT
# obvious before measuring — the estimate had the two costs within 15% of
# each other.
#
# `--index-url`, not `--extra-index-url`: the latter would let pip resolve a
# +cu wheel anyway, and the whole saving above depends on it not doing that.
#
# CPU-only torch from the PyTorch CPU index. Nothing here uses a GPU — the
# GPU agent is a separate service with its own image.
RUN pip install --index-url https://download.pytorch.org/whl/cpu \
        "torch>=2.12,<3.0" "torchvision>=0.27,<0.28"
RUN pip install -r requirements-ml.txt

# Where the model lands. Deliberately NOT a VOLUME instruction: that mints an
# anonymous volume when nobody mounts one, which survives `docker rm` and
# accumulates 3.5GB copies nobody can find. The compose files mount it
# explicitly instead, so an unmounted run simply re-downloads — visible, and
# recoverable.
ENV HF_HOME=/models/.huggingface \
    TRANSFORMERS_CACHE=/models/.huggingface \
    ML_MODEL_DIR=/models

COPY backend/ ./backend/
COPY alembic/ ./alembic/
COPY alembic.ini ./
COPY entrypoint.sh ./
RUN chmod +x entrypoint.sh

COPY --from=frontend-builder /build/dist ./frontend/dist

# Which channel this image belongs to — `dev` or `main` (milestone 271 step 7).
# build.yml passes it; /api/extension/manifest reports it beside the version so
# an operator can tell which channel an install came from without the channel
# ever touching the version string.
#
# Empty by default, deliberately: a locally-built image then reports NO channel
# rather than claiming to be one, and the manifest omits the field entirely —
# indistinguishable from an image built before the field existed, which is
# exactly the shape every reader already has to handle.
#
# Declared LAST on purpose. An ARG/ENV invalidates every layer below it, and
# these are the values that differ between builds of otherwise identical
# source — put them any earlier and the two channels could never share a
# cached pip install.
#
# FC_VERSION is what the instance reports about itself in the UI. Since
# milestone 318 stopped publishing version image tags, that self-report is
# the only answer to "which build is this?" — nothing else names it.
ARG FC_CHANNEL=""
ENV FC_CHANNEL=${FC_CHANNEL}
ARG FC_VERSION=""
ENV FC_VERSION=${FC_VERSION}

EXPOSE 8080

ENTRYPOINT ["./entrypoint.sh"]
# The DEFAULT is the whole application, not one lane of it.
#
# `docker run fabledcurator` with no command starts hypercorn plus every
# worker lane under supervisord — the shape an adopter wants and the shape the
# consolidated stack runs. It was `web`, which meant the single-container
# layout only worked if you knew to ask for it by name, and a compose file
# that forgot `command:` got a web server with nothing processing its queues:
# a gallery that loads, accepts an import, and never finishes one.
#
# The multi-service stack is unaffected — every service there names its role
# explicitly, which is exactly what makes it the multi-service stack.
CMD ["all"]