feat(web): Nightjar web app - Plaud-style record/transcribe/summarise UI

A runnable, self-hosted web interface so the whole workflow can be tested end to
end: record or upload audio in the browser, watch it transcribe and summarise,
browse a library, play back, and export.

What changed:
- server/app/store.py: SQLite + on-disk audio storage for recordings (stdlib only).
- server/app/pipeline.py: background task audio -> transcribe -> summarise via the
  existing provider layer, updating status (queued/transcribing/summarising/done/error).
- server/app/main.py: web API - POST upload, list, detail, audio (Range), delete, and
  export (txt/md/srt/vtt/json) - and serves the SPA at /.
- server/app/web/: Plaud-style single-page UI (index.html, styles.css, app.js). Sidebar
  library, in-browser recording (MediaRecorder) + file upload, live status polling,
  audio player, summary (overview/key points/actions), timestamped transcript, exports.
- server/Dockerfile + README: two-minute run instructions (default provider: Groq free
  tier for both Whisper + LLM), and a Docker option.
- config: env prefix switched OPENSCRIBE_ -> NIGHTJAR_ to match the brand and the site
  tutorials; .env.example rewritten with a ready Groq quick-start.
- state/TODO: web app recorded as done.

Why:
- User asked for a Plaud-like web interface to test how it all works. Nothing testable
  existed before (marketing site is a brochure; pipeline was unwired). This delivers a
  real, runnable product demo and effectively lands M5/M6 for the HTTP providers.

Notes:
- Slim by design: AI is offloaded to the configured provider, so no local ML deps needed
  for the demo. Byte-compiles clean; JS passes node --check. Local faster-whisper still
  needs its model (M5) for the fully-offline path.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

server/Dockerfile

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: GPL-3.0-only
+# Nightjar web app - runs the Plaud-style UI + API. AI is done by your configured
+# providers (default: Groq), so this image stays slim (no local ML deps).
+FROM python:3.11-slim
+WORKDIR /app
+RUN pip install --no-cache-dir \
+    "fastapi>=0.111" "uvicorn[standard]>=0.30" "pydantic>=2.7" \
+    "pydantic-settings>=2.3" "python-multipart>=0.0.9" "httpx>=0.27" "anthropic>=0.40"
+COPY app ./app
+EXPOSE 8000
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]