The certificate fix shipped and failed on the machine it was written for, with
the exact traceback it was meant to prevent. The retry was written
except ssl.SSLCertVerificationError:
and urllib never raises that from urlopen. It catches it and re-raises
urllib.error.URLError(err), carrying the original on .reason. So the except
matched nothing, ever, and the fallback could not fire.
The unit test passed throughout, because the stub it used raised the bare SSL
error - a shape real urllib never produces. That is the lesson: a fake that
agrees with the author is worse than no test, because it converts an untested
path into a tested-looking one. This file already says that about
UPDATE ... RETURNING and about the in-memory API fake, and it got written
again anyway.
_is_cert_failure checks the exception and its .reason, and the tests now raise
URLError(SSLCertVerificationError(...)) - what the traceback actually shows. A
plain URLError is re-raised untouched, and a test asserts no second attempt is
made for one.
Beside the stubs there is now a real reproduction, opt-in behind
BEHAVISION_NETWORK_TESTS=1. Python's default context honours SSL_CERT_FILE, so
an empty file gives a context that trusts nobody - the python.org condition
exactly - while certifi is loaded by path and is unaffected. It skips rather
than passes where it cannot reproduce that, and the difference is measured:
macOS Command Line Tools LibreSSL 2.8.3 128 CAs with an empty CA file
python.org / pyenv build OpenSSL 3.5.8 0 CAs -> reproduces it
Checked for teeth by putting the shipped except back: both the corrected stub
test and the live one fail, and pass again when it is restored.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KGcjxF1cNLcuwc3DAPcnfj
With the version ceiling and the widened numpy pin in place, setup succeeded
on the Mac that found them - Python 3.14 chosen and accepted, numpy 2.5.3,
onnxruntime 1.30, faiss 1.15.1, the engine itself - and died on the last step,
fetching the YuNet model:
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED]
certificate verify failed: unable to get local issuer certificate
A python.org macOS build ships its own OpenSSL with NO trust store, and
populates one only when somebody double-clicks Install Certificates.command in
the Python folder. Nobody installing face-recognition software has a reason to
know that exists, and the failure is forty lines of traceback about _ssl.c at
the end of a ten-minute install.
_urlopen tries the default context first and retries with certifi's bundle on
a verification failure. The order is the design:
- Default first, because on Windows and on a system or Homebrew Python the
default context reads the machine's own certificate store, which is what
makes a corporate proxy with its own root CA work. Replacing it
unconditionally would break every site that has one to fix a different
platform.
- certifi second, because it is already installed: requests is a hard
dependency and brings it.
- URLError is re-raised untouched. "No route to host" and "no trust store" are
different problems, and retrying the first with a different CA list only
delays the real message.
urlretrieve had to go, since it offers no way to pass a context - exactly the
kind of rewrite that silently drops something. The `download: <label> <n>%`
lines are a contract: supervisor.go's progressRe parses them to put first-run
progress in the tray, because the API is not up yet and a shop PC showing a
stopped engine for five minutes looks broken. A test asserts them, and the
rewritten fetch was checked against the real URL: 232,589 bytes, sha256
identical to the model already on disk.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KGcjxF1cNLcuwc3DAPcnfj
The first launch was a code box with a link under it, then an empty
Live screen with 'No cameras' in amber in a far corner, then a form
asking for an IP address, and for the first few minutes of all of it
the engine silently downloading 275 MB with nothing on screen but a
stopped-looking status. Walked in a browser with the new mock; nobody
who was not an installer would have got through it.
Now: a welcome that asks the one question a shop owner can answer -
managed from a head office, or on this PC only - with each path in a
sentence; a Getting Started checklist on Live that reads its three steps
from the engine and ticks them itself (recognition ready, camera added
and connected, camera proven by a walk-past), with the one button for
the next step, and that disappears the moment somebody is recognised;
and the model download reported as a percentage in the tray, the
sidebar and the checklist, parsed by the supervisor from the engine's
own progress lines.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KGcjxF1cNLcuwc3DAPcnfj
Five components that ship as one product:
- behavision/ the recognition engine. RTSP ingest, YuNet detection, IoU
tracking, ArcFace embeddings, a FAISS/SQLite gallery, and a
FastAPI dashboard. Identity is decided once per TRACK from an
average of at least three embeddings, never per frame.
- agent/ the Go edge agent: supervises the engine, holds a durable
spool, and drains it to MQTT. Nothing is acked before the
broker confirms.
- desktop/ the shop PC application (Wails + React + tray).
- server/ the cloud API, MQTT consumer, reports and assistant.
- web/ platform.loyaly.ai, the head-office app, embedded in the
server binary.
The gallery stores 512-float embeddings and timestamps - no images unless
`app.store_faces` is switched on. Those embeddings are biometric personal
data under GDPR and India's DPDP: template inversion reconstructs a
recognisable face from an ArcFace vector, so data/behavision.db is treated
as a biometric database and DELETE /api/visitors/{id} is a real erasure.
CLAUDE.md carries the reasoning behind every non-obvious decision here,
including the ones that were measured and the ones that were wrong first.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HViLj9gYNRtSr7YVZmW5sn
