47 lines
3.1 KiB
Markdown
47 lines
3.1 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Commands
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```bash
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uv run uvicorn app:app --reload # start dev server at http://localhost:8000
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uv run --env-file .env uvicorn app:app --reload # load env vars from .env file
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uv sync # install/sync dependencies
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uv add <package> # add a new dependency
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```
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Set `LLAMACPP_BASE_URL=http://host:port/v1` to point at a local LLM (inline, exported, or via `.env`).
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## Architecture
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Two-file app: a FastAPI backend (`app.py`) and a single-page frontend (`static/index.html`).
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**`app.py`** parses all GPX files in `garmin_gpx_exports/` at startup into an in-memory dict (`_rides`), keyed by filename stem. Stats (haversine distance, elevation gain/loss, HR zones) are computed once at load time. API routes:
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- `GET /api/rides` — summary list (no track points)
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- `GET /api/rides/{id}` — full ride metadata + cached insight
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- `GET /api/rides/{id}/points` — full lat/lon/ele/hr array
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- `POST /api/rides/{id}/insights` — streams AI coaching response
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- `POST /api/insights/generate-all` — SSE stream, generates insights for all rides in chronological order
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- `POST /api/fetch-rides` — SSE stream, fetches new rides from Garmin Connect (supports MFA)
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- `POST /api/mfa` — submits MFA code for an in-progress Garmin session
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**AI integration** uses the OpenAI Python SDK pointed at a local LLM endpoint (`LLAMACPP_BASE_URL` env var, defaults to `http://localhost:8080/v1`). The model name is auto-discovered from `GET /v1/models` at startup (falls back to `"local-model"`). If the endpoint is unreachable at startup a warning is logged and the app continues — only insight generation is affected. The chunk reader handles both `content` and `reasoning_content` delta fields for reasoning models.
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**Insights cache** is persisted to `insights.json` (gitignored) and loaded at startup. The prompt includes the previous ride's cached insight so the coach can reference its own prior advice.
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**`static/index.html`** is a self-contained SPA (no build step). Leaflet.js draws the route as per-segment polylines colored by HR intensity zone. Chart.js renders elevation and HR profiles against cumulative distance. AI insights stream via `fetch` + `ReadableStream`. Units (km/mi) toggle and resting HR are persisted in `localStorage`.
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**`main.py`** is a standalone CLI script for bulk-downloading Garmin Connect GPX files without running the web server.
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## GPX data notes
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- Garmin extension namespace for HR: `http://www.garmin.com/xmlschemas/TrackPointExtension/v1` (`ns3:hr`)
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- Activities with an empty `<trkseg/>` (e.g. indoor rides) get `has_gps: false` — frontend shows no map or charts
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- HR zone boundaries (fixed, used for API summary): Z1 <120, Z2 120–140, Z3 140–160, Z4 160–180, Z5 >180 bpm
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- Karvonen zones (used in the UI and AI prompt) are computed from resting HR input + lifetime max HR observed across all rides
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## Logging
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httpx, httpcore, and openai loggers are set to WARNING to suppress connection-level debug noise. The `bikeslop` logger runs at DEBUG.
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