02e91892f4
The vision training APIs now automatically set up the Python environment when first called: 1. Detects if venv exists with tensorflow installed 2. If not, finds best Python (ARM on Apple Silicon for Metal GPU) 3. Creates venv and installs tensorflow (with Metal on M-series Macs) 4. Caches result so subsequent calls are instant This eliminates the need to manually set up the venv before deployment. The setup happens lazily on first hardware detection or training request. Also includes various other working tree changes that were in progress. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
65 lines
753 B
Plaintext
65 lines
753 B
Plaintext
# Dependencies
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node_modules/
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.pnpm-store/
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# Build outputs
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.next/
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dist/
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*.tsbuildinfo
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# Generated CSS (Panda CSS / styled-system)
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**/styled-system/
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# Generated build info
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**/generated/build-info.json
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# Environment
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.env*
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# Logs
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*.log
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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pnpm-debug.log*
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lerna-debug.log*
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# Cache
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.turbo/
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.cache/
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*.cache
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# Turbo build logs
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**/.turbo/turbo-build.log
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# IDE
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.vscode/
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.idea/
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# OS
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.DS_Store
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Thumbs.db
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# Test coverage
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coverage/
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# Test reports
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playwright-report/
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# Database files
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*.db
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*.db-shm
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*.db-wal
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# Temporary files
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tmp/
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temp/
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# Claude settings (keep local only)
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.claude/settings.local.json
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*storybook.log
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storybook-static
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apps/web/data/sqlite.db.backup.*
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# Vision training data
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data/vision-training/
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