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/app/pipeline.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: GPL-3.0-only
+"""The processing pipeline: audio -> transcript -> summary, using the provider layer.
+
+Run as a background task after upload. Updates the recording's status as it goes so the
+web UI can show progress (queued -> transcribing -> summarising -> done).
+"""
+from __future__ import annotations
+
+from . import store
+from .providers import build_summariser, build_transcriber
+
+
+def process(rec_id: str) -> None:
+    path = store.audio_path(rec_id)
+    try:
+        store.set_status(rec_id, "transcribing")
+        transcript = build_transcriber().transcribe(path)
+
+        store.set_status(rec_id, "summarising")
+        summary = build_summariser().summarise(rec_id, transcript.text)
+
+        store.set_result(
+            rec_id,
+            language=transcript.language,
+            transcript={
+                "text": transcript.text,
+                "segments": [s.model_dump() for s in transcript.segments],
+            },
+            summary={
+                "overview": summary.overview,
+                "key_points": summary.key_points,
+                "action_items": summary.action_items,
+            },
+        )
+    except Exception as exc:  # surface any provider/network error to the UI
+        store.set_status(rec_id, "error", f"{type(exc).__name__}: {exc}")