# OpenScribe server

Self-hosted FastAPI server: ingests recordings, transcribes them (faster-whisper),
summarises them (Ollama), and serves the open API with exports. Everything runs on
hardware you own.

> Status: M0 scaffold. The API shape is live and browsable at `/docs` with in-memory
> stubs. Transcription lands in M5, summaries in M6, real storage/DB alongside.

## Run (dev)

```bash
cd server
python -m venv .venv
. .venv/Scripts/activate     # Windows;  or:  . .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env         # edit as needed
uvicorn app.main:app --reload
```

- API docs (Swagger UI): http://localhost:8000/docs
- Health: http://localhost:8000/health

## Self-hosted dependencies

For the AI features (M5/M6) you run, on your own kit:

- **MinIO** (or WebDAV / NAS) for object storage - `OPENSCRIBE_STORAGE_BACKEND=s3`.
- **Ollama** for summaries - `ollama serve` and `ollama pull llama3.1`.
- **faster-whisper** downloads its model on first use; CPU works, CUDA is faster.

None of these are required for plain recording and transfer; they add transcription and
summaries.

## Layout

```
app/main.py     FastAPI app + routes (mirrors ../api/openapi.yaml)
app/config.py   Settings from env / .env
app/models.py   Pydantic models (kept in sync with the OpenAPI schemas)
requirements.txt
.env.example
```

## API

The contract is `../api/openapi.yaml`. The device implements the LAN "device" paths; this
server implements ingest, transcript, summary and export.