KoalaTrapLord
a5fe8cb3fa
Complete frontend rewrite from vanilla HTML/CSS/JS to React 19 + TypeScript + Tailwind CSS v4. Frontend (frontend/commander-ai-lab-ui/): - Vite + React 19 + TypeScript + Tailwind CSS v4 + React Router - 7 pages: BatchSim, Collection, Decks, AutoGen, Simulator, Coach, Training - Reusable components: Spinner, StatusBadge, CardImage, ManaSymbols, EmptyState - Full typed API client layer (8 modules matching all backend endpoints) - Recharts for mana curves, win rate charts, progress bars - Lucide icons throughout - Custom dark theme with MTG mana color tokens - Code splitting (recharts + react in separate chunks) Backend (lab_api.py): - SPA-aware static file serving with catch-all for client-side routing - Fallback to legacy ui/ folder if React dist not built - Updated startup log with SPA route list Build system: - .gitignore added (was missing - removes pyc, db, node_modules, etc.) - build-full-installer-v2.py: v23 with Node.js check + React build step - Vite build config with manual chunks for optimal loading Also includes: V3 deck generator fixes, coach improvements, DeepSeek sim modules
55 lines
2.8 KiB
Batchfile
55 lines
2.8 KiB
Batchfile
@echo off
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REM ═══════════════════════════════════════════════════════════
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REM Commander AI Lab — Overnight AFK Runner
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REM ═══════════════════════════════════════════════════════════
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REM
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REM Runs batch simulations for ~11.5 hours, then auto-trains
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REM the ML policy network. Designed to run while you sleep.
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REM
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REM PREREQUISITES:
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REM 1. Lab server must already be running (start-lab.bat)
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REM 2. LM Studio must be running with DeepSeek loaded
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REM 3. At least 1 deck available in the lab
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REM
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REM USAGE:
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REM Double-click this file, or run from command prompt:
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REM overnight-run.bat
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REM
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REM To customize, edit the settings below or pass arguments:
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REM overnight-run.bat --games 50 --hours 8 --epochs 200
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REM ═══════════════════════════════════════════════════════════
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echo.
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echo ╔══════════════════════════════════════════════════╗
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echo ║ Commander AI Lab — Overnight AFK Runner ║
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echo ╠══════════════════════════════════════════════════╣
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echo ║ ║
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echo ║ This will run batch simulations for ~11.5h ║
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echo ║ then auto-train the ML policy network. ║
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echo ║ ║
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echo ║ Make sure: ║
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echo ║ - Lab server is running (start-lab.bat) ║
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echo ║ - LM Studio has DeepSeek loaded ║
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echo ║ - Power settings: prevent sleep ║
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echo ║ ║
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echo ║ Press Ctrl+C at any time to stop early. ║
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echo ║ ║
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echo ╚══════════════════════════════════════════════════╝
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echo.
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REM ── Pause to let user read the message ─────────────────
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timeout /t 5 /nobreak >nul
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REM ── Run the Python overnight script ────────────────────
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python "%~dp0overnight-run.py" %*
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if %ERRORLEVEL% neq 0 (
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echo.
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echo Overnight run ended with errors. Check the output above.
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echo.
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)
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echo.
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echo Overnight run complete. Press any key to close.
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pause >nul
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