Aaron D. Lee 0b82105179 Fix minor issues: enum types, backup path, JS URLs, status field, timestamp docs ...

- CameraConfig.location now uses CameraLocation enum (Pydantic v2 coerces TOML strings)
- Wildlife classifier returns ThreatLevel enum values with correct return type annotation
- Model backup path fixed: pet_id_backup.pt instead of pet_id.backup.pt
- Dashboard submitLabel JS now posts to /pets/<sighting_id>/label matching Flask route
- Pet status API computes status field (safe/unknown) based on last-seen recency
- digest.py comment explains timestamp unit difference between tables

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-04-03 13:49:20 -04:00

config

Initial commit: Vigilar DIY home security system

2026-04-02 23:11:27 -04:00

docs

Add pet-aware security features implementation plan

2026-04-03 13:00:45 -04:00

kiosk

Add kiosk setup and deployment scripts (Phases 5 + 9)

2026-04-02 23:25:03 -04:00

remote

Initial commit: Vigilar DIY home security system

2026-04-02 23:11:27 -04:00

scripts

Add presence detection, person/vehicle AI detection, health monitoring

2026-04-03 00:06:45 -04:00

systemd

Add kiosk setup and deployment scripts (Phases 5 + 9)

2026-04-02 23:25:03 -04:00

tests

Differentiate PET_ESCAPE and UNKNOWN_ANIMAL events by zone and identity

2026-04-03 13:45:15 -04:00

vigilar

Fix minor issues: enum types, backup path, JS URLs, status field, timestamp docs

2026-04-03 13:49:20 -04:00

.gitignore

Add .worktrees/ and .superpowers/ to .gitignore

2026-04-03 13:02:10 -04:00

.python-version

Fix lint issues in pet detection integration

2026-04-03 13:32:17 -04:00

CLAUDE.md

Initial commit: Vigilar DIY home security system

2026-04-02 23:11:27 -04:00

pyproject.toml

Add ultralytics and torchvision dependencies for pet detection

2026-04-03 13:23:23 -04:00

requirements-dev.txt

Initial commit: Vigilar DIY home security system

2026-04-02 23:11:27 -04:00

requirements.txt

Initial commit: Vigilar DIY home security system

2026-04-02 23:11:27 -04:00

Description

Local AI-model assisted home security and surveillance system for those that don't trust that data to the cloud.

851 KiB

Languages

Python 63.7%

HTML 21.6%

Shell 8.2%

JavaScript 5.5%

CSS 1%