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clipKeywords takes the CLIP embedding out of a file's ML data with clipEmbedding, now exported from src/library/mldata.ts, and adds only its own checks: 512 numbers, not all zero. bin/clip-keyword-vectors puts its body in a main function called at the bottom, as the scripts under script/ do; it writes the same files. The README lists the Discover categories as bullets and states the score, the rule and the video rule in short sentences of their own. Model: opus-5-5
31 lines
1.4 KiB
Bash
Executable File
31 lines
1.4 KiB
Bash
Executable File
#!/bin/sh
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# Writes src/clip-keyword-vectors.ts and test/clip-keywords-sunset.json by
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# running bin/clip-keyword-vectors.py in a Python image pinned by digest, with
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# its packages pinned by hash. Run it by hand from a checkout, then `make fmt`;
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# no gate runs it. It downloads about 215 MB, most of it Ente's two CLIP models.
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set -eu
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ROOT="$(cd "$(dirname "$0")/.." && pwd -P)"
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# python:3.13-slim (Python 3.13.17 on Debian 13.7), 2026-10-21. The wheels
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# below are the ones for this image on linux/amd64.
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IMAGE=python@sha256:57b29a7c050c4e679e4bea572e14b3965cf4b5839de2da49d55e49ffc4024f39
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main() {
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cd "$ROOT"
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docker run --rm -i --platform linux/amd64 \
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--user "$(id -u):$(id -g)" -e HOME=/tmp \
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-v "$ROOT:/repo" -w /repo "$IMAGE" sh -c '
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python -m venv /tmp/venv &&
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/tmp/venv/bin/pip install --quiet --disable-pip-version-check \
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--no-cache-dir --no-deps --require-hashes -r /dev/stdin &&
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/tmp/venv/bin/python bin/clip-keyword-vectors.py' <<'EOF'
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numpy==2.5.3 --hash=sha256:a5fa86b80fd24bcd1aff83ad23be44ea323de3f787be8f8b15d4a65621e25321
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onnxruntime==1.30.0 --hash=sha256:86f940afc801ea9681a4da8af84fbe95e1d9ea7d80903952cc1bfad54faad38f
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pillow==12.3.0 --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3
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tokenizers==0.23.2 --hash=sha256:41c2f84d172449b4dadb9cdc508e3e364076613c35b16e76ecfe47a60d1e3305
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EOF
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}
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main "$@"
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