Jacob Hinkle
5584022a23
- Merge opencode-serve into the web container via entrypoint script - Add /api/agent/* JSON endpoints for workouts, sets, checkins - Rewrite fitness-trainer.md to use API instead of markdown files - Pass recent workouts and check-ins as chat context to the coach - Show current training phase on dashboard - Clarify check-ins as morning check-ins (calories/steps = yesterday) - Add NixOS deployment section to README - Make all check-in fields explicitly optional in UI
53 lines
2.5 KiB
Markdown
53 lines
2.5 KiB
Markdown
# Fitness Web Agent Notes
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## Project Overview
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Multi-user fitness tracking web app with AI coaching.
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Replaces the fitness-agent markdown-based training repo.
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## Related Repo
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- `~/git/fitness-agent` — Training markdown logs, agent config, historical data, Juggernaut cycles
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- This repo (`fitness-web`) is the web app successor. When this is fully operational, the old repo can be archived.
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## Architecture
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- **FastAPI** — async Python web framework
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- **Jinja2 + Pico.css** — server-rendered templates with minimal CSS framework (CDN)
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- **SQLite + SQLAlchemy 2.0 async** — database with aiosqlite driver
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- **opencode serve** — AI coach service (Big Pickle model, free). Runs alongside uvicorn in the same container, managed by a shell entrypoint.
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## Key Files
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- `app/main.py` — App factory, route registration, lifespan
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- `app/config.py` — Environment-based configuration
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- `app/auth.py` — Password hashing, session management, `get_current_user` dependency
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- `app/models/` — SQLAlchemy ORM models
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- `app/routers/` — Route handlers (one per feature)
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- `app/routers/agent_api.py` — JSON API for the AI coach to create workouts/check-ins
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- `scripts/schema.py` — DB initialization
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- `scripts/seed.py` — Seed data (exercises, phases)
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- `opencode/fitness-trainer.md` — Agent config for AI coach (copied from fitness-agent)
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## Commands
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```bash
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uv sync # Install deps
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uv run python scripts/schema.py # Create DB tables
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uv run python scripts/seed.py # Seed initial data
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uv run uvicorn app.main:app --reload # Dev server on :8000
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```
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## Key Decisions
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- Session-based auth with DB-backed tokens (simple, no OAuth dependencies)
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- SQLAlchemy async with aiosqlite (works well with FastAPI async handlers)
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- SSE streaming for chat responses
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- Pico.css from CDN (no build step)
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- Chat messages stored in DB per session for history
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- Docker Compose for deployment (opencode serve runs as background process in same container)
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## Next Steps / TODOs
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1. Seed exercises and phases (done via `python scripts/seed.py`)
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2. Add exercise progress chart (matplotlib or chart.js)
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3. Enable opencode serve integration (Dockerfile + entrypoint done, single-container approach)
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4. Add agent API for DB writes (endpoints + agent config done)
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5. Migrate existing markdown logs from fitness-agent repo into DB
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6. Migrate Juggernaut training xlsx data into DB
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7. Add calendar view for training history
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8. Update `fitness-trainer.md` agent config to work with DB-backed context instead of markdown files (done)
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