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Runs pytest from backend/ (pyproject sets testpaths relative to it and the suite imports app as a top-level package), and oxlint plus the production build for the frontend -- npm run build is 'tsc -b && vite build', so it covers type errors and the bundle the image ships. Python and Node versions match the two stages of the Dockerfile. torch is installed from PyTorch's CPU index for the same reason the image does it: default PyPI resolves to the CUDA build and drags ~2.5 GB of NVIDIA wheels onto a runner that cannot use them. The only deploy example was a Caddyfile; this adds nginx for hosts already running it. It carries proxy_read_timeout 300s -- nginx defaults to 60s, which would cut off a first-time model download that the app allows 300s to finish and report it as a confusing 504. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Runs pytest from backend/ (pyproject sets testpaths relative to it and the suite imports app as a top-level package), and oxlint plus the production build for the frontend -- npm run build is 'tsc -b && vite build', so it covers type errors and the bundle the image ships. Python and Node versions match the two stages of the Dockerfile.
torch is installed from PyTorch's CPU index for the same reason the image does it: default PyPI resolves to the CUDA build and drags ~2.5 GB of NVIDIA wheels onto a runner that cannot use them.
The only deploy example was a Caddyfile; this adds nginx for hosts already running it. It carries proxy_read_timeout 300s -- nginx defaults to 60s, which would cut off a first-time model download that the app allows 300s to finish and report it as a confusing 504.