# syntax=docker/dockerfile:1.2
# ---- deps stage: needs git + a credentialed clone of the private ARF repos
#      (agentic_reliability_framework, ARF-Bayesian-Pricing-Calculator).
#      This stage is discarded after build -- the credential never reaches
#      the final image's layers, env, or git config. ----
#
# GH_PAT is read via a BuildKit secret mount, not `ARG` -- an ARG's value is
# printed in plaintext as part of the logged RUN command that uses it (this
# is exactly how a real, live token ended up visible in a Render deploy log
# this session). A secret mount's value is never written to a log line or
# an image layer. REQUIRES a matching setup step in Render's dashboard
# before this will build: Render's Docker service settings -> Secret Files
# -> add a file named exactly `gh_pat` containing the token value (nothing
# else in the file). The old `GH_PAT` environment variable is no longer
# read by this Dockerfile and can be removed once this is confirmed working.
FROM python:3.12-slim AS deps
RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
RUN --mount=type=secret,id=gh_pat,dst=/etc/secrets/gh_pat \
    git config --global url."https://$(cat /etc/secrets/gh_pat)@github.com/".insteadOf "https://github.com/"
RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
WORKDIR /app
COPY requirements.txt .
# torch has no explicit pin anywhere in this dependency tree -- it's pulled in
# transitively by sentence-transformers (for agentic_reliability_framework's
# RAG/semantic-memory features) and, left to the default PyPI index, resolves
# to the CUDA-enabled build (nvidia-cusparselt, cuda-toolkit, nvidia-nccl, ...)
# even though this service runs on CPU-only Render instances. That variant's
# extra weight is a real contributor to out-of-memory deploy failures.
#
# A separate `pip install torch==... --index-url .../cpu` RUN before this one
# does NOT work: it's a distinct resolve that only knows about the CPU wheel;
# the very next `pip install -r requirements.txt`, seeing no --index-url, only
# has the default PyPI index in view and re-resolves torch from there,
# silently replacing the CPU build with the CUDA one at the same version
# number (confirmed happening in a real deploy -- final `pip install` log
# showed plain `torch-2.13.0` plus the full nvidia/cuda-toolkit/triton stack,
# not `torch-2.13.0+cpu`). Putting torch and -r requirements.txt in one
# `pip install` call, with the CPU wheelhouse as the primary --index-url and
# PyPI as --extra-index-url, makes it a single resolve: torch is satisfied
# from the CPU index and nothing later re-derives a different build for it.
# Version pinned to 2.13.0 to match exactly what pip's resolver already chose
# for this dependency tree (confirmed available on the CPU index for
# cp312/manylinux before pinning it here, not assumed).
RUN pip install --no-cache-dir \
    --index-url https://download.pytorch.org/whl/cpu \
    --extra-index-url https://pypi.org/simple \
    torch==2.13.0 \
    -r requirements.txt

# ---- final stage: just the built venv + app code, no git, no credential ----
FROM python:3.12-slim
COPY --from=deps /opt/venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
WORKDIR /app
COPY . .
# Shell form (not exec/JSON-array form) deliberately -- ${PORT:-7860} only
# expands with a real shell interpreting the command; exec form passes
# arguments literally with no variable substitution at all. Render injects
# PORT and expects the app to bind to it (its deploy log explicitly failed
# port-scanning for it: "Bind your service to at least one port"); the
# Hugging Face Space mirror sets no such variable and expects the
# conventional default, 7860. One image, correct on both targets.
CMD uvicorn app.main:app --host 0.0.0.0 --port ${PORT:-7860}