# Deploying the POC

The app needs PyTorch + a ~360 MB model, so pick a host with **≥ 1–2 GB RAM**
(the smallest free tiers like Render's 512 MB will OOM). A `Dockerfile` is
included and works on any container host.

Set your Sarvam key as a **secret env var** on the host — never commit `.env`:

```
SARVAM_API_KEY=your_key
```

---

## Option A — Hugging Face Spaces (recommended, free, persistent)

Free CPU Spaces give 16 GB RAM and a stable public URL like
`https://<user>-voxsplit.hf.space` — ideal for an ML demo.

1. Create a Space at https://huggingface.co/new-space → **SDK: Docker** → Blank.
2. Push this folder to the Space's git repo (or upload files in the UI):
   ```bash
   git init && git add . && git commit -m "voxsplit poc"
   git remote add space https://huggingface.co/spaces/<user>/voxsplit
   git push space main
   ```
3. Space → **Settings → Variables and secrets** → add secret `SARVAM_API_KEY`.
4. It builds the Dockerfile and serves on port 7860 automatically. Share the URL.

> The Dockerfile pre-downloads the gender model during build, so the first
> request is fast.

## Option B — Render (Docker web service)

1. Push this repo to GitHub.
2. Render → **New → Web Service** → connect repo → it detects the `Dockerfile`.
3. Instance type: pick one with **≥ 2 GB RAM** (Starter/Standard, not Free).
4. Add env var `SARVAM_API_KEY`. Render injects `PORT`; the container already
   honors it. Deploy and share the `*.onrender.com` URL.

## Option C — Fly.io (Docker, good for long requests)

```bash
fly launch --no-deploy          # generates fly.toml from the Dockerfile
fly secrets set SARVAM_API_KEY=your_key
fly scale memory 2048           # give it 2 GB
fly deploy
```

## Option D — Instant link, zero deploy (temporary)

Fastest way to show a client *right now*, while your server runs locally:

```bash
# terminal 1: your app is already running on :8000
# terminal 2:
brew install cloudflared
cloudflared tunnel --url http://localhost:8000
```

This prints a public `https://*.trycloudflare.com` link. Downsides: the link is
temporary and your machine must stay on. (`ngrok http 8000` works the same way.)

---

## Heads-up: long transcription jobs

The `/api/transcribe` request blocks until Sarvam's **batch** job finishes, which
can take a while for long audio. Some platform proxies cut idle HTTP requests at
~60–100s. For a smooth client demo, **use short clips** (≤ ~1–2 min). If you need
long files in production, the next step is to make transcription async (return a
job id + poll for status) — ask and I'll wire that up.