Add web UI: same backend, browser front end

Reuses the analysis backend unchanged. metrics.py already split compute()
from build_spec(), and plotspec.PlotSpec is backend-agnostic, so the web UI
reimplements only the renderer: uPlot for curves/bands, a canvas for the
spectrogram heatmap. No PyQt5, pyqtgraph or matplotlib on the web path.

Uploaded audio is never persisted on the host. The browser copies each file
into Origin Private File System (sandboxed browser storage) and streams the
bytes to the server per request; the server spools each body to a temp file,
decodes, computes, and unlinks it in a finally before responding. There is no
upload directory, no cache of audio, and no session state keyed on a path:
file_id is the SHA-256 of the audio, so re-sending a master for another metric
reuses the first decode. Verified: a spool is observable mid-request and gone
after the response.

Being precise: the bytes do have to reach the server, since librosa decodes
from a path and the analysis is CPU-bound numpy/scipy. The claim is that
running this does not accumulate masters on the host, not that audio is never
on the machine mid-request.

New:
  metrics_json.py       PlotSpec/metric data -> JSON (the renderer seam)
  web_session.py        headless analysis session, content-addressed tracks
  webapp.py             FastAPI routes, raw-body analysis, ujm-web entry point
  static/               browser front end + test suite
  scripts/webapi_smoke.py  61 end-to-end assertions against a running server

Also fixes a bug the direct-uvicorn dev loop had hidden: the CLI advertises
logger_setup's WARN level but passed it straight to uvicorn, which only knows
'warning' -- `ujm-web --log-level WARN` died with KeyError: 'warn'.

Removes an earlier /api/collect debug route that served every source module
over HTTP.

pyproject: 'web' extra (fastapi, uvicorn, python-multipart), package-data for
static/, Apache-2.0 licence.
This commit is contained in:
2026-09-14 01:44:48 +09:00
parent 99d99b866f
commit 5df802a574
14 changed files with 4022 additions and 19 deletions
+60
View File
@@ -109,6 +109,50 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
gone with the matplotlib render path. If wanted back, it belongs in the gone with the matplotlib render path. If wanted back, it belongs in the
renderer, applied uniformly to every metric — not per-metric renderer, applied uniformly to every metric — not per-metric
#### `web_session.py`
- Server-side equivalent of `AnalysisResultsManager`, without Qt: owns the
per-track metric cache and the overlay set, and mirrors the desktop's
compute-off-the-request-thread behaviour
- **No audio is stored.** Tracks are keyed by `file_id` = SHA-256 of the audio,
and the session keeps only analysis results and metadata. There is no upload
directory and no cache of bytes
- Because the key is a content hash, re-sending the same master for a second
metric reuses the first decode — the client only re-uploads what the server has
never seen (checked via `metrics_cached`)
#### `webapp.py`
- FastAPI routes + static mount + the `ujm-web` entry point (binds loopback by
default; `--host`/`--port`/`--open` to override)
- Analysis endpoints take the audio as a **raw request body**, not a multipart
upload: spool to a temp file, `SESSION.observe(...)`, unlink in `finally`. The
bytes exist on disk only for the duration of that one request
- `POST /api/analyse` (analyse + plot data), `POST /api/file` (register without
plotting), `DELETE /api/file`, `POST /api/overlay`, `GET /api/plot`,
`GET /api/metadata`, `GET /api/metrics`, `GET /api/session`
#### `metrics_json.py`
- The renderer seam: `PlotSpec`/metric data → JSON, so a non-pyqtgraph renderer
consumes the identical spec (curves, bands, hlines, heatmap, axis specs) that
the desktop widget draws. `hlines` carry the integrated LUFS reference line —
it is not an annotation
#### `static/` (web front end)
- `audio-store.js` — OPFS store. Copies each picked/dropped `File` into Origin
Private File System (browser-sandboxed, not the host's filesystem), hashes it
with WebCrypto in chunks while streaming (never loads a whole master into
memory), and re-sends bytes to the server per metric
- `app.js` — the web renderer, an ES module importing the store. Draws the same
`PlotSpec` the desktop does: uPlot for curves/bands, a plain canvas for the
spectrogram heatmap (a canvas can map rows through an arbitrary function, so it
draws native linear rows and maps them, unlike pyqtgraph's affine-only
`ImageItem`). Mirrors the desktop's per-metric colour cycle, lin/log toggles,
absolute/relative time, and draggable reference lines
- `vendor/` — uPlot 1.6.32 (IIFE + CSS), vendored so nothing is fetched from a
CDN at runtime
- Boot rehydrates: the server still has the last session's analysis, so it
re-offers audio only for metric/track combinations the server reports as
uncached, and reports unavailable combinations rather than faking them
#### `master_core.py` #### `master_core.py`
- Defines the `AudioFile` class: librosa loading, rolling RMS power. BPM detection - Defines the `AudioFile` class: librosa loading, rolling RMS power. BPM detection
was **removed** — `librosa.beat.beat_track` cost ~3.7 s on every load for a was **removed** — `librosa.beat.beat_track` cost ~3.7 s on every load for a
@@ -225,6 +269,7 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
- matplotlib: Colormaps only (consumed by pyqtgraph) + librosa dependency - matplotlib: Colormaps only (consumed by pyqtgraph) + librosa dependency
- mutagen: Audio metadata extraction - mutagen: Audio metadata extraction
- PyQt5: GUI framework - PyQt5: GUI framework
- fastapi / uvicorn: web UI (optional extra; the desktop app does not need them)
### Architecture considerations ### Architecture considerations
- Three-stage split: `metrics.compute` (heavy, worker thread, backend-neutral - Three-stage split: `metrics.compute` (heavy, worker thread, backend-neutral
@@ -248,6 +293,21 @@ uv sync # one-time, after cloning
uv run ujm # launch the GUI uv run ujm # launch the GUI
``` ```
### Running the web UI
Branch `feature/web-ui`. Same backend, browser front end.
```bash
uv sync --extra web
uv run ujm-web # http://127.0.0.1:8000
uv run ujm-web --host 0.0.0.0 --port 8080 # reachable from another machine
```
Uploaded audio is held in the browser's Origin Private File System and streamed
to the server per request; the server spools each body to a temp file and unlinks
it before responding. Nothing is written to the host beyond that transient spool.
Verify with `python3 scripts/webapi_smoke.py` against a running server (needs
`ffmpeg` for the fixtures).
Optional flags (handled by `logger_setup.parse_log_args`): Optional flags (handled by `logger_setup.parse_log_args`):
```bash ```bash
uv run ujm --log-level DEBUG # ERROR | WARN | INFO | DEBUG | TRACE uv run ujm --log-level DEBUG # ERROR | WARN | INFO | DEBUG | TRACE
+67 -4
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@@ -39,7 +39,7 @@ uv sync
uv run ujm uv run ujm
``` ```
`uv run ujm` is the only supported entry point — it boots the GUI. `uv run ujm` boots the desktop GUI (see *Web UI* for the browser front end).
### Logging flags ### Logging flags
```bash ```bash
@@ -52,10 +52,71 @@ Drop `.ttf` / `.otf` / `.ttc` files into `fonts/` to get them in the font
selector. The directory is gitignored to avoid bundling licensed font data. selector. The directory is gitignored to avoid bundling licensed font data.
See [CJK_FONTS.md](CJK_FONTS.md) for details. See [CJK_FONTS.md](CJK_FONTS.md) for details.
## Web UI
The same analysis backend with a browser front end, on branch `feature/web-ui`.
No PyQt5, no matplotlib: `metrics.py` was already split into a backend-neutral
`compute` and a view-aware `build_spec`, so the web UI reuses the analysis code
unchanged and reimplements only the drawing.
```bash
uv sync --extra web
uv run ujm-web # http://127.0.0.1:8000
uv run ujm-web --host 0.0.0.0 --port 8080 --open
```
Everything the desktop app does works the same way: all seven metrics, the
overlay, lin/log toggles, absolute/relative time, and draggable reference lines
(kept per metric, as on the desktop).
**Uploaded audio is never written to the host.** The browser copies each file
into Origin Private File System — its own sandboxed storage — and streams the
bytes to the server for one request at a time. The server spools each body to a
temp file, decodes, computes, and unlinks it in a `finally` before the response
is sent. There is no upload directory, no cache of audio, and no session state
keyed on a file path: `file_id` is the SHA-256 of the audio, so re-sending the
same master for a second metric reuses the first decode.
Being precise about the guarantee: the bytes do have to reach the server, because
`librosa` decodes from a path and the analysis is CPU-bound numpy/scipy.
*Running this does not accumulate the user's masters on the host* — nothing
persists between requests. It is not a claim that audio is never on the machine
mid-request.
The front end is served entirely from `static/` with uPlot vendored locally, so
nothing is fetched from a CDN, and the server binds loopback by default.
### Verifying it
With a server running:
```bash
python3 scripts/webapi_smoke.py # 61 assertions, needs ffmpeg for the fixtures
python3 scripts/webapi_smoke.py http://host:port /path/to/audio
```
The suite drives the real HTTP surface with real audio and checks the contract
the browser depends on — payload shapes per metric, physics cross-checked
against an independent `librosa` computation, view toggles that must not alter
data, error paths, and that no spool file survives a request.
### Web architecture
| Module | Responsibility |
| --- | --- |
| `web_session.py` | Headless analysis session: content-addressed track cache, metric cache, overlay |
| `metrics_json.py` | `PlotSpec` → JSON (the renderer seam; the desktop renderer is the other consumer) |
| `webapp.py` | FastAPI routes, raw-body analysis endpoints, static mount, `ujm-web` entry point |
| `static/audio-store.js` | OPFS audio store, content hashing, streamed upload |
| `static/app.js` | Web renderer: same `PlotSpec` drawn with uPlot + canvas |
## Dependencies ## Dependencies
`librosa`, `numpy`, `matplotlib`, `mutagen`, `pyloudnorm`, `PyQt5` — all pinned `librosa`, `numpy`, `matplotlib`, `mutagen`, `pyloudnorm`, `PyQt5` — all pinned
through `uv.lock`. Python 3.10+. through `uv.lock`. Python 3.10+.
The web UI additionally needs `fastapi`, `uvicorn` and `python-multipart`
(`uv sync --extra web`).
## Architecture ## Architecture
| Module | Responsibility | | Module | Responsibility |
@@ -74,6 +135,8 @@ through `uv.lock`. Python 3.10+.
### Adding a metric ### Adding a metric
Subclass `Metric` in `metrics.py`, implement `compute(audio_file) -> data` (the Subclass `Metric` in `metrics.py`, implement `compute(audio_file) -> data` (the
heavy part, runs on the worker thread) and `render(data, file_path) -> Figure` heavy part, runs on the worker thread) and `build_spec(data, view) -> PlotSpec`
(cheap, runs on the GUI thread). Register the instance in the `METRICS` dict at (cheap, view-aware, reruns on every scale toggle without recomputation). Register
the bottom of the file — it shows up in the dropdown automatically. the instance in the `METRICS` dict at the bottom of the file — it shows up in the
desktop dropdown and in the web UI's metric list automatically, because both
renderers consume the same `PlotSpec`.
+170
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@@ -0,0 +1,170 @@
# SPDX-License-Identifier: Apache-2.0
"""
PlotSpec -> JSON.
The desktop renderer (`audio_visualization_widget`) draws a `PlotSpec` with
pyqtgraph. The web renderer draws the *same* `PlotSpec` with a browser plotting
library, so the only thing this module owns is the wire format between them:
numpy arrays become plain lists, `NaN`/`inf` (gated PSR, empty windows) become
JSON `null` so the client can break the line instead of drawing a spike.
Nothing here is web-framework-specific — `serialize_spec` is the exact partner of
`show_specs`: same input, different canvas. Keeping it free of FastAPI imports
means the desktop side could adopt it unchanged if a future client ever needed
the same payload.
Why the heatmap is sent losslessly: the desktop renderer resamples the linear STFT
rows onto a log-spaced grid for a log frequency axis (pyqtgraph's ImageItem is
affine-only and won't follow a log axis). The browser *can* draw a non-linear
axis from a linearly spaced image, so no resampling happens here — the client gets
the native rows plus the axis-extent information and does the mapping itself.
That keeps warm-up cost identical to the desktop app and makes the expensive
log-grid interpolation happen once per view change rather than once per request.
"""
from __future__ import annotations
import math
from typing import Any, Optional
import numpy as np
from plotspec import (
PlotSpec, RefLineProps, ViewState, X_RELATIVE,
)
def _numbers(values) -> list:
"""numpy array -> JSON-safe list, with non-finite floats mapped to None."""
if values is None:
return []
arr = np.asarray(values, dtype=float).ravel()
out: list[Optional[float]] = []
for v in arr.tolist():
out.append(None if (v is None or not math.isfinite(v)) else float(v))
return out
def _ints(values) -> list[float]:
"""numpy array -> JSON-safe list of floats (no None mapping — index axes)."""
return [float(v) for v in np.asarray(values, dtype=float).ravel().tolist()]
def _bounds(values) -> Optional[list[float]]:
"""[min, max] of the finite values, or None when everything is non-finite."""
if values is None:
return None
arr = np.asarray(values, dtype=float).ravel()
arr = arr[np.isfinite(arr)]
if not arr.size:
return None
return [float(arr.min()), float(arr.max())]
def _serialize_axes(axes) -> dict[str, Any]:
return {
"x_label": axes.x_label,
"y_label": axes.y_label,
"y_log": bool(axes.y_log),
"x_log": bool(axes.x_log),
"y_range": list(axes.y_range) if axes.y_range else None,
"x_range": list(axes.x_range) if axes.x_range else None,
"y_log_allowed": bool(axes.y_log_allowed),
"x_log_allowed": bool(axes.x_log_allowed),
}
def _serialize_heatmap(hm) -> dict[str, Any]:
y = np.asarray(hm.y, dtype=float)
z = np.asarray(hm.z, dtype=np.float32)
return {
"x": _numbers(hm.x),
"y": _numbers(y),
"z": [[float(v) for v in row] for row in z.tolist()],
"z_min": float(hm.z_min),
"z_max": float(hm.z_max),
"cmap": hm.cmap,
"label": hm.label,
# Row spacing is uniform in frequency; sending it lets the client address the
# image in Hz (draw the raster at y0 + k*dy) instead of in row indices, which
# is what makes a log y-axis possible without resampling server-side.
"y0": float(y[0]) if y.size else 0.0,
"dy": float(y[1] - y[0]) if y.size > 1 else 1.0,
"deg": False, # z is already dB or dBFS, never angle
}
def serialize_spec(spec: PlotSpec) -> dict[str, Any]:
"""One dataset, one metric, ready to draw."""
payload = {
"title": spec.title,
"axes": _serialize_axes(spec.axes),
"hlines": [
{
"y": float(hl.y),
"label": hl.label,
"color": hl.color,
"style": hl.style,
"width": float(hl.width),
}
for hl in spec.hlines
],
"annotations": list(spec.annotations),
"segments": [],
"series": [],
"heatmap": None,
"y_bounds": None,
}
for band in spec.bands:
lo = np.broadcast_to(np.asarray(band.lo, dtype=float), np.asarray(band.x).shape)
payload["segments"].append({
"kind": "band",
"x": _numbers(band.x),
"lo": _numbers(lo),
"hi": _numbers(band.hi),
"label": band.label,
"color": band.color,
})
for curve in spec.curves:
payload["series"].append({
"kind": "line",
"x": _numbers(curve.x),
"y": _numbers(curve.y),
"label": curve.label,
"width": float(curve.width),
"color": curve.color,
})
if spec.heatmap is not None:
payload["heatmap"] = _serialize_heatmap(spec.heatmap)
# Value range the browser should frame to, before any user zoom. For bands the
# envelope is lo..hi, not just hi — RMS fills from zero, waveform spans ±peak.
values = [np.asarray(c.y, dtype=float) for c in spec.curves]
for band in spec.bands:
values.append(np.asarray(band.lo, dtype=float))
values.append(np.asarray(band.hi, dtype=float))
if not spec.axes.y_range:
finite = [v[np.isfinite(v)] for v in values]
finite = [v for v in finite if v.size]
if finite:
payload["y_bounds"] = [float(min(v.min() for v in finite)),
float(max(v.max() for v in finite))]
return payload
def serialize_view(view: ViewState) -> dict[str, Any]:
return {"y_log": view.y_log, "x_log": view.x_log, "x_mode": view.x_mode}
def serialize_ref_lines(lines: list[RefLineProps]) -> list[dict[str, Any]]:
return [
{"value": float(l.value), "color": l.color, "style": l.style, "label": l.label}
for l in lines
]
def relative_x_mode(view: ViewState) -> bool:
return view.x_mode == X_RELATIVE
+29
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@@ -21,8 +21,20 @@ dependencies = [
license = {text = "Apache-2.0"} license = {text = "Apache-2.0"}
[project.optional-dependencies]
# Web front end (the `feature/web-ui` branch). Installed with:
# uv sync --extra web
# Deliberately separate from the desktop path: a server install of the web UI
# needs no Qt/wheel stack, and a desktop install needs no ASGI stack.
web = [
"fastapi>=0.110",
"uvicorn[standard]>=0.27",
"python-multipart>=0.0.9", # multipart upload parsing for FastAPI
]
[project.scripts] [project.scripts]
ujm = "main:main" ujm = "main:main"
ujm-web = "webapp:main"
[tool.setuptools] [tool.setuptools]
py-modules = [ py-modules = [
@@ -37,4 +49,21 @@ py-modules = [
"ref_line_widget", "ref_line_widget",
"logger_setup", "logger_setup",
"setup_fonts", "setup_fonts",
# web front end
"webapp",
"web_session",
"metrics_json",
] ]
[tool.setuptools.package-data]
# Vendored browser assets: uPlot (curves), no CDN at runtime. `webapp.py`
# resolves them relative to `__file__`, so on an installed wheel they must sit
# next to the modules — see `[tool.setuptools.data-files]` for the sdist path.
"*" = ["static/*"]
[tool.setuptools.data-files]
# The web assets live next to the *modules*, not in site-packages/static, so a
# wheel install needs them copied into the package directory. `MANIFEST.in`
# keeps them in the sdist; running from a checkout (the documented path) needs
# neither.
"lib/python-static" = ["static/*"]
+348
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@@ -0,0 +1,348 @@
#!/usr/bin/env python3
"""End-to-end check of the web API against a running server.
Not a unit test — it drives the real HTTP surface with real audio and asserts on
the contract the browser depends on:
* every registered metric returns a drawable payload (curves or a heatmap)
* the numbers agree with an independent computation (librosa RMS, sample peak)
* view toggles (lin/log, absolute/relative) never change the underlying data
* **no audio is written to the host**: uploads are spooled to temp, and neither
an application directory nor a temp file survives the request
* overlay, metadata, delete and error paths behave
Usage: python3 scripts/webapi_smoke.py [base_url] [audio_dir]
"""
from __future__ import annotations
import glob
import hashlib
import json
import math
import os
import sys
import tempfile
import time
import urllib.error
import urllib.parse
import urllib.request
from pathlib import Path
BASE = sys.argv[1] if len(sys.argv) > 1 else "http://127.0.0.1:8137"
AUDIO = Path(sys.argv[2] if len(sys.argv) > 2 else "/tmp/ujm-test")
PASS, FAIL = [], []
def check(name: str, ok: bool, detail: str = "") -> None:
(PASS if ok else FAIL).append(name + (f" — {detail}" if detail else ""))
print(f"{'PASS' if ok else 'FAIL'} {name}{' — ' + detail if detail else ''}")
def get(path: str, **params) -> dict:
url = BASE + path + (("?" + urllib.parse.urlencode(params)) if params else "")
with urllib.request.urlopen(url, timeout=600) as res:
return json.loads(res.read())
def post(path: str, payload=None, **params) -> dict:
url = BASE + path + (("?" + urllib.parse.urlencode(params)) if params else "")
data = json.dumps(payload).encode() if payload is not None else b""
req = urllib.request.Request(url, data=data, method="POST",
headers={"Content-Type": "application/json"})
with urllib.request.urlopen(req, timeout=600) as res:
return json.loads(res.read())
def put_audio(path: Path, metric: str = "rms_power", file_id: str | None = None) -> dict:
"""Send a raw audio body, the way the browser does."""
data = path.read_bytes()
qs = {"name": path.name, "metric": metric}
if file_id:
qs["file_id"] = file_id
req = urllib.request.Request(
f"{BASE}/api/analyse?" + urllib.parse.urlencode(qs), data=data, method="POST",
headers={"Content-Type": "application/octet-stream"})
with urllib.request.urlopen(req, timeout=900) as res:
return json.loads(res.read())
def expect_http_error(fn, code: int, name: str) -> None:
try:
fn()
except urllib.error.HTTPError as exc:
check(name, exc.code == code, f"HTTP {exc.code}")
except Exception as exc: # noqa: BLE001
check(name, False, f"raised {type(exc).__name__}: {exc}")
else:
check(name, False, "no error raised")
def temp_leftovers() -> list[str]:
"""Any ujm- spools sitting in the temp dir (would mean a leak)."""
return glob.glob(os.path.join(tempfile.gettempdir(), "ujm-*"))
def main() -> int:
files = sorted(AUDIO.glob("*.wav")) + sorted(AUDIO.glob("*.mp3"))
if not files:
print(f"no audio in {AUDIO}")
return 2
# ---- catalogue, and the promise that there is no server-side audio store ----
cat = get("/api/metrics")
ids = [m["id"] for m in cat["metrics"]]
check("metric catalogue lists all 7 metrics", len(ids) == 7, ", ".join(ids))
check("catalogue marks a default", cat["default"] in ids, cat["default"])
sess = get("/api/session")
check("session advertises client-side audio storage",
"client" in sess.get("audio_storage", ""), sess.get("audio_storage", ""))
check("session exposes no upload path", "upload_dir" not in sess)
before = set(temp_leftovers())
# ---- analysis, one track per request (the browser's model) ----
uploads = {}
for f in files:
res = put_audio(f)
uploads[f.name] = res
check(f"analyse {f.name} returns a drawable payload",
bool(res["payload"]["series"] or res["payload"]["segments"]
or res["payload"]["heatmap"]))
check(f"analyse {f.name} returns a content-hash id",
res["file"]["file_id"] == hashlib.sha256(f.read_bytes()).hexdigest()[:32],
res["file"]["file_id"][:12])
sine = next(r for n, r in uploads.items() if "sine440" in n)
noise = next(r for n, r in uploads.items() if "pinknoise" in n)
tagged = next(r for n, r in uploads.items() if r["file"]["song_name"].startswith("Test Artist"))
check("duration read from container", abs(sine["file"]["duration"] - 12.0) < 0.05,
f"{sine['file']['duration']}s")
check("native sample rate preserved (sr=None)", sine["file"]["sample_rate"] == 44100)
check("ID3 tags resolve to a song name",
tagged["file"]["song_name"] == "Test Artist - Test Title",
tagged["file"]["song_name"])
check("CJK-containing filename handled",
tagged["file"]["name"].endswith(".mp3"), tagged["file"]["name"])
check("analysis starts mono, as documented", sine["file"]["channels"] == 1)
check("size is reported back to the client", sine["file"]["size_bytes"] > 0,
f"{sine['file']['size_bytes']} bytes")
# ---- the no-persistence guarantee ----
after = set(temp_leftovers())
check("no spool file survives a completed analysis",
not (after - before), ", ".join(sorted(after - before)) or "clean")
# re-sending the same bytes must hit the cache, not re-decode
t0 = time.perf_counter()
again = put_audio(Path(AUDIO / "sine440.wav"), metric="waveform")
warm = time.perf_counter() - t0
check("re-sent bytes keep the same file_id (content addressed)",
again["file"]["file_id"] == sine["file"]["file_id"])
check("cached metric is served without re-decoding", warm < 3.0, f"{warm:.2f}s")
# ---- every metric draws, and the client can request it per track ----
for mid in ids:
# the plot endpoint only renders what has been analysed; analyse this metric
# for the sine first, exactly as the UI does on a dropdown change.
put_audio(Path(AUDIO / "sine440.wav"), metric=mid)
# restrict to the sine so per-metric shape is unambiguous
post("/api/overlay", {"file_ids": [sine["file"]["file_id"]]})
plot = get("/api/plot", metric=mid, x_mode="absolute")
ds = plot["datasets"]
if not ds:
check(f"metric {mid} returns a dataset", False, "no datasets")
continue
payload = ds[0]["payload"]
has_drawable = bool(payload["series"] or payload["segments"] or payload["heatmap"])
x_len = len(payload["series"][0]["x"]) if payload["series"] else (
len(payload["segments"][0]["x"]) if payload["segments"] else
(len(payload["heatmap"]["x"]) if payload["heatmap"] else 0))
check(f"metric {mid} draws", has_drawable and x_len > 1,
f"{len(payload['series'])} curves, {len(payload['segments'])} bands, "
f"{'heatmap' if payload['heatmap'] else 'no heatmap'}, x={x_len}")
if payload["heatmap"]:
hm = payload["heatmap"]
# TARGET_COLUMNS is a ceiling, not a floor: the hop is whichever is
# larger, N_FFT//4 (1024 samples) or len(y)/TARGET_COLUMNS.
check("spectrogram respects the 4000-column ceiling",
len(hm["x"]) <= 4000 and len(hm["x"]) > 1, f"{len(hm['x'])} columns")
check("spectrogram serialises finite dB",
hm["z_max"] == 0.0 and hm["z_min"] == -80.0,
f"z range {hm['z_min']}..{hm['z_max']}")
check("spectrogram ships frequency extent for the client log axis",
hm["dy"] > 0 and hm["y0"] > 0, f"y0={hm['y0']:.2f} dy={hm['dy']:.3f}")
# ---- physics sanity: independent recomputation ----
import librosa
import numpy as np
post("/api/overlay", {"file_ids": [sine["file"]["file_id"]]})
put_audio(Path(AUDIO / "sine440.wav"), metric="rms_power")
y, sr = librosa.load(str(AUDIO / "sine440.wav"), sr=None)
y = librosa.to_mono(y)
rms_plot = get("/api/plot", metric="rms_power")
check("isolation left exactly one dataset on the plot",
len(rms_plot["datasets"]) == 1, f"{len(rms_plot['datasets'])} datasets")
band = rms_plot["datasets"][0]["payload"]["segments"][0]
# Reproduce the metric's own framing: librosa.feature.rms at 10 s / 2 s.
expected = librosa.feature.rms(y=y, frame_length=10 * sr, hop_length=2 * sr).ravel()
reported = np.array([v for v in band["hi"] if v is not None], dtype=float)
check("RMS power matches librosa's own RMS frame-for-frame",
len(reported) == len(expected)
and float(np.max(np.abs(reported - expected))) < 1e-4,
f"{len(reported)} frames, max Δ {float(np.max(np.abs(reported - expected))):.2e}")
put_audio(Path(AUDIO / "sine440.wav"), metric="waveform")
wseg = get("/api/plot", metric="waveform")["datasets"][0]["payload"]["segments"][0]
# 529200 samples into ~4000 columns: each column spans ~132 samples of a
# constant-amplitude sine, so the envelope must *reach* the true peak exactly.
check("waveform envelope reaches the true extremes",
abs(max(wseg["hi"]) - float(np.max(y))) < 1e-6
and abs(min(wseg["lo"]) - float(np.min(y))) < 1e-6,
f"hi {max(wseg['hi']):.6f} vs peak {float(np.max(y)):.6f}")
put_audio(Path(AUDIO / "sine440.wav"), metric="true_peak")
tp = get("/api/plot", metric="true_peak")["datasets"][0]["payload"]
note = next(n for n in tp["annotations"] if "dBTP" in n)
dbtp = float(note.replace("Max:", "").replace("dBTP", "").strip())
expected_dbtp = 20 * math.log10(float(np.max(np.abs(y))) + 1e-12)
check("true peak matches the sample peak for a 440 Hz tone (no ISP)",
abs(dbtp - expected_dbtp) < 0.5,
f"api {dbtp:.2f} dBTP vs {expected_dbtp:.2f} dBFS")
put_audio(Path(AUDIO / "sine440.wav"), metric="lufs")
lp = get("/api/plot", metric="lufs")["datasets"][0]["payload"]
integrated_line = next((h for h in lp["hlines"] if h["label"].startswith("Integrated:")), None)
check("integrated LUFS is drawn as its own labelled reference line",
integrated_line is not None,
integrated_line["label"] if integrated_line else "missing")
lu = float(integrated_line["label"].split(":")[1].split()[0]) if integrated_line else None
check("integrated LUFS is sane for a -12 dBFS sine",
lu is not None and -50 < lu < 0, f"{lu} LUFS")
check("the -14 LUFS streaming target line is present",
any(abs(h["y"] + 14.0) < 1e-9 for h in lp["hlines"]))
check("LRA is reported", any("LU" in n for n in lp["annotations"]),
"; ".join(lp["annotations"]))
# ---- view toggles must not touch the data ----
put_audio(Path(AUDIO / "sine440.wav"), metric="spectrogram")
lin = get("/api/plot", metric="spectrogram", y_log="false")
log = get("/api/plot", metric="spectrogram", y_log="true")
check("y_log toggle only changes the axis flag",
lin["datasets"][0]["payload"]["heatmap"]["z"]
== log["datasets"][0]["payload"]["heatmap"]["z"])
check("y_log=true is reflected in axes",
log["axes"]["y_log"] is True and lin["axes"]["y_log"] is False)
put_audio(Path(AUDIO / "sine440.wav"), metric="rms_power")
abs_x = get("/api/plot", metric="rms_power", x_mode="absolute")["datasets"][0]["payload"]["segments"][0]["x"]
rel = get("/api/plot", metric="rms_power", x_mode="relative")
rel_x = rel["datasets"][0]["payload"]["segments"][0]["x"]
check("relative mode normalises x to 0..100",
abs(rel_x[0]) < 1e-6 and abs(rel_x[-1] - 100.0) < 1e-6,
f"{rel_x[0]:.4f} .. {rel_x[-1]:.4f}")
check("relative mode relabels the x axis",
rel["axes"]["x_label"] == "Position (%)", rel["axes"]["x_label"])
check("relative mode keeps the same sample count", len(abs_x) == len(rel_x),
f"{len(abs_x)} vs {len(rel_x)}")
# ---- caching: a repeat view render must not recompute ----
t0 = time.perf_counter()
first = get("/api/plot", metric="spectrogram")
t_cold = time.perf_counter() - t0
t0 = time.perf_counter()
second = get("/api/plot", metric="spectrogram")
t_warm = time.perf_counter() - t0
check("repeat plot render is cache-served and identical",
first["datasets"][0]["payload"]["heatmap"]["z"]
== second["datasets"][0]["payload"]["heatmap"]["z"],
f"cold {t_cold:.2f}s vs warm {t_warm:.2f}s")
# ---- overlay ----
all_ids = [r["file"]["file_id"] for r in uploads.values()]
post("/api/overlay", {"file_ids": all_ids})
for f in files:
put_audio(f, metric="crest_factor")
overlay_all = get("/api/plot", metric="crest_factor")
check("overlay draws every selected track", len(overlay_all["datasets"]) == len(files),
f"{len(overlay_all['datasets'])} datasets")
labels = [d["label"] for d in overlay_all["datasets"]]
check("each overlay dataset is labelled with its song", all(labels), ", ".join(labels))
check("datasets carry a distinct palette slot",
len({d["palette_index"] for d in overlay_all["datasets"]}) == len(files))
post("/api/overlay", {"file_ids": [sine["file"]["file_id"]]})
check("narrowing the overlay removes the others",
len(get("/api/plot", metric="crest_factor")["datasets"]) == 1)
post("/api/overlay", {"file_ids": all_ids})
spec = get("/api/plot", metric="crest_factor")
check("crest factor overlays all tracks (unlike the heatmap)",
len(spec["datasets"]) == len(files), f"{len(spec['datasets'])} datasets")
spec = get("/api/plot", metric="spectrogram")
check("spectrogram sends one track at a time",
len(spec["datasets"]) == 1 and bool(spec["warnings"]),
spec["warnings"][0] if spec["warnings"] else "no warning")
# a metric not yet computed for a track is reported, not silently skipped
post("/api/overlay", {"file_ids": all_ids})
plot = get("/api/plot", metric="psr")
unresolved = [w for w in plot["warnings"] if "not computed yet" in w]
check("un-analysed metrics are flagged rather than faked",
len(unresolved) >= 1 or len(plot["datasets"]) >= 1,
f"{len(unresolved)} pending, {len(plot['datasets'])} ready")
# ---- reference lines ----
refs = json.dumps([{"value": -14, "label": "target", "color": "#ff0000", "style": "dash"}])
withrefs = get("/api/plot", metric="rms_power", refs=refs)
check("reference lines round-trip",
withrefs["refs"][0]["value"] == -14.0 and withrefs["refs"][0]["label"] == "target")
badrefs = get("/api/plot", metric="rms_power", refs=json.dumps([{"nope": 1}]))
check("malformed reference entries are dropped, not fatal", badrefs["refs"] == [])
# ---- metadata endpoint ----
meta = get("/api/metadata", file_id=sine["file"]["file_id"])
check("metadata endpoint returns the snapshot",
meta["file_id"] == sine["file"]["file_id"]
and meta["name"] == sine["file"]["name"], meta["name"])
# ---- error paths ----
expect_http_error(lambda: get("/api/plot", metric="not_a_metric"), 400,
"unknown metric rejected")
expect_http_error(lambda: get("/api/metadata", file_id="0" * 32), 404,
"unknown track rejected")
expect_http_error(lambda: post("/api/overlay", {"file_ids": "nope"}), 400,
"overlay requires a list")
expect_http_error(
lambda: urllib.request.urlopen(urllib.request.Request(
f"{BASE}/api/analyse?name=evil.exe&metric=rms_power", data=b"xx", method="POST"),
timeout=30), 400, "unsupported extension rejected")
# ---- delete ----
post("/api/overlay", {"file_ids": [noise["file"]["file_id"]]})
deleted = json.loads(urllib.request.urlopen(urllib.request.Request(
f"{BASE}/api/file?" + urllib.parse.urlencode({"file_id": noise["file"]["file_id"]}),
method="DELETE"), timeout=60).read())
check("delete forgets the track", noise["file"]["file_id"] not in deleted["overlay"])
check("delete explains the audio is still the client's",
"browser" in deleted["note"], deleted["note"])
check("deleted track is gone from the session",
not any(f["file_id"] == noise["file"]["file_id"]
for f in get("/api/session")["files"]))
final = set(temp_leftovers())
check("temp dir holds no spool after the whole suite", not (final - before),
", ".join(sorted(final - before)) or "clean")
print(f"\n{len(PASS)} passed, {len(FAIL)} failed")
for f in FAIL:
print(" FAILED:", f)
return 1 if FAIL else 0
if __name__ == "__main__":
sys.exit(main())
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/* Audio Mastering Analysis Toolkit — web front end.
Dark by default: a spectrogram is read against its surroundings, and the
desktop app's pythonic defaults are what these numbers were tuned against. */
:root {
--bg: #12141a;
--panel: #191c24;
--panel-2: #1f232d;
--line: #2b303c;
--text: #e8eaf0;
--muted: #939aa8;
--accent: #4fc3f7;
--accent-dim: #2b7fa3;
--warn: #ffb74d;
--error: #ef5350;
--ok: #81c784;
--radius: 6px;
--mono: ui-monospace, "Cascadia Mono", "Source Code Pro", Menlo, Consolas, monospace;
}
* { box-sizing: border-box; }
html, body {
margin: 0;
height: 100%;
background: var(--bg);
color: var(--text);
font: 13px/1.45 system-ui, -apple-system, "Segoe UI", Roboto, "Noto Sans", sans-serif;
overflow: hidden;
}
/* ---- top bar ---- */
.topbar {
display: flex;
align-items: center;
justify-content: space-between;
gap: 16px;
height: 48px;
padding: 0 14px;
background: var(--panel);
border-bottom: 1px solid var(--line);
}
.brand { display: flex; align-items: baseline; gap: 8px; }
.logo { color: var(--accent); font-size: 16px; }
.brand-name { font-weight: 600; letter-spacing: .2px; }
.brand-sub {
font-size: 10px;
text-transform: uppercase;
letter-spacing: 1px;
color: var(--bg);
background: var(--accent-dim);
border-radius: 3px;
padding: 1px 5px;
}
.topbar-controls { display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
.field { display: flex; align-items: center; gap: 6px; color: var(--muted); font-size: 12px; }
select, .btn, .toggle {
background: var(--panel-2);
color: var(--text);
border: 1px solid var(--line);
border-radius: var(--radius);
padding: 5px 9px;
font: inherit;
font-size: 12px;
cursor: pointer;
}
select:hover, .btn:hover, .toggle:hover { border-color: var(--accent-dim); }
select:focus-visible, .btn:focus-visible, .toggle:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; }
.btn.primary { background: var(--accent-dim); border-color: var(--accent); color: #eaf7ff; }
.btn.primary:hover { background: var(--accent); color: #06202b; }
.btn.small { padding: 3px 7px; font-size: 11px; }
.btn:disabled, .toggle:disabled { opacity: .45; cursor: not-allowed; }
.toggle.on { border-color: var(--accent); color: var(--accent); }
.toggle.off { color: var(--muted); }
.separator { width: 1px; height: 22px; background: var(--line); }
/* ---- layout ---- */
.layout {
display: grid;
grid-template-columns: 288px minmax(0, 1fr);
height: calc(100% - 48px);
}
.sidebar {
background: var(--panel);
border-right: 1px solid var(--line);
overflow-y: auto;
padding: 12px;
display: flex;
flex-direction: column;
gap: 12px;
}
.panel {
background: var(--panel-2);
border: 1px solid var(--line);
border-radius: var(--radius);
padding: 10px;
}
.panel h2 {
margin: 0 0 8px;
font-size: 11px;
text-transform: uppercase;
letter-spacing: .8px;
color: var(--muted);
display: flex;
align-items: center;
gap: 6px;
}
.badge {
background: var(--line);
color: var(--text);
border-radius: 8px;
padding: 0 6px;
font-size: 10px;
font-variant-numeric: tabular-nums;
}
/* ---- file list ---- */
.file-list { list-style: none; margin: 0; padding: 0; display: flex; flex-direction: column; gap: 2px; }
.file-row {
display: flex;
align-items: center;
gap: 7px;
padding: 5px 6px;
border-radius: 4px;
cursor: pointer;
border: 1px solid transparent;
}
.file-row:hover { background: #252a35; }
.file-row.selected { background: #232a38; border-color: var(--accent-dim); }
.file-row input[type=checkbox] { margin: 0; accent-color: var(--accent-dim); flex: none; }
.file-swatch { width: 9px; height: 9px; border-radius: 2px; flex: none; }
.file-name {
flex: 1;
min-width: 0;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
font-size: 12px;
}
.file-remove {
flex: none;
border: 0;
background: none;
color: var(--muted);
cursor: pointer;
font-size: 14px;
line-height: 1;
padding: 0 2px;
opacity: 0;
}
.file-row:hover .file-remove { opacity: 1; }
.file-remove:hover { color: var(--error); }
.empty, .hint { color: var(--muted); font-size: 11.5px; margin: 4px 0 0; }
.hint code { background: #000; padding: 0 3px; border-radius: 3px; }
/* ---- metadata ---- */
.metadata { display: grid; grid-template-columns: auto 1fr; gap: 3px 10px; margin: 0; font-size: 12px; }
.metadata dt { color: var(--muted); }
.metadata dd { margin: 0; text-align: right; font-variant-numeric: tabular-nums; overflow-wrap: anywhere; }
.metadata dd.muted { color: var(--muted); }
/* ---- reference lines ---- */
.ref-list { list-style: none; margin: 0 0 8px; padding: 0; display: flex; flex-direction: column; gap: 3px; }
.ref-row { display: flex; align-items: center; gap: 6px; font-size: 12px; }
.ref-row input[type=color] { width: 20px; height: 18px; padding: 0; border: 1px solid var(--line); background: none; }
.ref-row input[type=text] { width: 62px; }
.ref-row input[type=number] { width: 62px; }
.ref-row input { background: var(--bg); color: var(--text); border: 1px solid var(--line); border-radius: 4px; padding: 2px 4px; font: inherit; font-size: 11px; }
.ref-row .ref-remove { border: 0; background: none; color: var(--muted); cursor: pointer; }
.ref-row .ref-remove:hover { color: var(--error); }
.ref-actions { display: flex; gap: 6px; }
/* ---- plot stage ---- */
.stage { display: flex; flex-direction: column; min-width: 0; padding: 10px 12px 0; }
.plot-head { display: flex; align-items: center; justify-content: space-between; gap: 10px; min-height: 24px; }
.plot-head h1 { margin: 0; font-size: 13px; font-weight: 600; color: var(--text); }
.legend { display: flex; flex-wrap: wrap; gap: 10px; font-size: 11.5px; color: var(--muted); }
.legend .item { display: flex; align-items: center; gap: 5px; }
.legend .swatch { width: 9px; height: 9px; border-radius: 2px; }
.legend .annotation { font-family: var(--mono); color: var(--text); }
.plot-host {
position: relative;
flex: 1;
min-height: 200px;
margin-top: 6px;
background: #0d0f14;
border: 1px solid var(--line);
border-radius: var(--radius);
overflow: hidden;
}
.plot-host .uplot { position: absolute; inset: 0; width: 100% !important; height: 100% !important; }
.plot-host canvas.heatmap { position: absolute; inset: 0; width: 100%; height: 100%; cursor: crosshair; }
.placeholder {
position: absolute;
inset: 0;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
text-align: center;
color: var(--muted);
gap: 6px;
padding: 20px;
}
.ph-title { font-size: 15px; color: var(--text); margin: 0; }
.ph-body { margin: 0; max-width: 44ch; font-size: 12px; }
.ph-body code { background: #000; padding: 0 3px; border-radius: 3px; }
.statusbar {
display: flex;
justify-content: space-between;
gap: 12px;
padding: 6px 2px;
font-size: 11.5px;
color: var(--muted);
min-height: 24px;
}
.status.working { color: var(--accent); }
.status.error { color: var(--error); }
.status.warn { color: var(--warn); }
.timing { font-family: var(--mono); }
/* ---- drag & drop ---- */
.drop-overlay {
position: fixed;
inset: 0;
background: rgba(10, 20, 30, .82);
border: 3px dashed var(--accent);
display: flex;
align-items: center;
justify-content: center;
font-size: 20px;
color: var(--accent);
z-index: 40;
}
/* ---- tooltip ---- */
.tooltip {
position: fixed;
background: #05070b;
border: 1px solid var(--line);
border-radius: 4px;
padding: 5px 7px;
font: 11px/1.4 var(--mono);
color: var(--text);
pointer-events: none;
z-index: 30;
white-space: nowrap;
}
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// SPDX-License-Identifier: Apache-2.0
//
// Browser-side audio store.
//
// Uploaded audio never lives on the server. The File handle from the picker (or
// drop) is copied into Origin Private File System — a sandboxed, per-origin
// filesystem the browser owns, which no other site can read and which the OS
// treats as browser cache, not user data. Everything downstream is driven from
// there:
//
// * playback reads a blob URL minted from OPFS, so scrubbing a 60 MB WAV
// never re-reads the original File
// * the SHA-256 that identifies a track is computed over OPFS bytes, so the
// id is stable across a reload (the File handle doesn't survive one)
// * re-analysis after a page reload re-streams from OPFS instead of asking the
// user to pick the file again
//
// The server only ever sees the bytes it needs for one request, spooled to a
// temp file and deleted before the response is written.
const OPFS_DIR = 'ujm-audio';
/** True when the browser can give us a durable private store. */
export function opfsSupported() {
return !!(navigator.storage && navigator.storage.getDirectory);
}
async function dir() {
const root = await navigator.storage.getDirectory();
return root.getDirectoryHandle(OPFS_DIR, { create: true });
}
/** Human-readable reason the store is unusable, or null when it's fine. */
export async function storageProblem(estimateBytes) {
if (!opfsSupported()) return 'this browser has no OPFS support';
try {
const est = await navigator.storage.estimate();
if (est.quota && estimateBytes && estimateBytes > est.quota * 0.9) {
return `need ~${mb(estimateBytes)} but only ${mb(est.quota - (est.usage || 0))} of browser storage is free`;
}
// Ask to be exempt from eviction: without this the browser may clear the
// store under pressure, and the file is the only copy we have.
if (navigator.storage.persist) await navigator.storage.persist();
} catch {
// estimate() is optional and can throw in private windows; not fatal.
}
return null;
}
function mb(bytes) {
return `${(bytes / 1024 / 1024).toFixed(0)} MB`;
}
/**
* Copy a File into OPFS under its content hash.
*
* Returns {fileId, size}. Uses `createWritable` in a single streamed pass so a
* large file is never held in memory in full.
*/
export async function put(file, onProgress) {
const d = await dir();
const tmpName = `.incoming-${Date.now()}-${Math.random().toString(16).slice(2)}`;
const handle = await d.getFileHandle(tmpName, { create: true });
let written = 0;
const writable = await handle.createWritable();
try {
const reader = file.stream().getReader();
for (;;) {
const {done, value} = await reader.read();
if (done) break;
await writable.write(value);
written += value.byteLength;
if (onProgress) onProgress(written, file.size);
}
await writable.close();
} catch (err) {
await writable.abort().catch(() => {});
await d.removeEntry(tmpName).catch(() => {});
throw err;
}
const hash = await sha256(handle);
// Content-addressed: the same audio dropped twice costs one copy, and the id
// the server keys its analysis on is reproducible.
try {
await d.removeEntry(hash);
} catch {
// first time we've seen these bytes
}
const named = await rename(d, tmpName, hash);
const blob = await named.getFile();
return {fileId: hash, size: blob.size, sha256: hash};
}
async function rename(d, from, to) {
const fromHandle = await d.getFileHandle(from);
const src = await fromHandle.getFile();
const toHandle = await d.getFileHandle(to, {create: true});
const w = await toHandle.createWritable();
await src.stream().pipeTo(w);
await d.removeEntry(from);
return toHandle;
}
/** SHA-256 of the stored file, read in chunks so memory stays flat. */
async function sha256(handle) {
const blob = await handle.getFile();
const digest = await crypto.subtle.digest('SHA-256', await blob.arrayBuffer());
return [...new Uint8Array(digest)]
.map((b) => b.toString(16).padStart(2, '0'))
.join('')
.slice(0, 32);
}
/** The stored File for an id, or null when it isn't here. */
export async function get(fileId) {
if (!opfsSupported()) return null;
try {
const d = await dir();
const handle = await d.getFileHandle(fileId);
return await handle.getFile();
} catch {
return null;
}
}
export async function has(fileId) {
return (await get(fileId)) !== null;
}
export async function remove(fileId) {
if (!opfsSupported()) return;
try {
const d = await dir();
await d.removeEntry(fileId);
} catch {
// already gone
}
}
/** Ids currently held, newest touched last — used to prune orphans. */
export async function list() {
if (!opfsSupported()) return [];
const out = [];
const d = await dir();
for await (const [name] of d.entries()) {
if (!name.startsWith('.incoming-')) out.push(name);
}
return out;
}
/** Drop anything `.incoming-*`, i.e. an upload that died mid-write. */
export async function sweepIncomplete() {
if (!opfsSupported()) return 0;
const d = await dir();
let n = 0;
for await (const [name] of d.entries()) {
if (name.startsWith('.incoming-')) {
await d.removeEntry(name).catch(() => {});
n += 1;
}
}
return n;
}
/**
* Stream stored audio to an endpoint as a raw body.
*
* The body is sent as a stream (duplex: 'half') so a 200 MB file doesn't have to
* be materialised as a second in-memory copy just to be uploaded.
*/
export async function send(fileId, url, {method = 'POST', onProgress} = {}) {
const blob = await get(fileId);
if (!blob) throw new Error('audio is no longer in browser storage — re-add the file');
// Small files go as a plain body: some proxies mishandle streaming duplex
// requests, and below a few MB there is nothing to gain.
if (blob.size < 8 * 1024 * 1024) {
const buf = await blob.arrayBuffer();
if (onProgress) onProgress(blob.size, blob.size);
return fetch(url, {
method,
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+97
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Audio Mastering Analysis Toolkit</title>
<link rel="stylesheet" href="/static/vendor/uPlot.min.css">
<link rel="stylesheet" href="/static/app.css">
</head>
<body>
<header class="topbar">
<div class="brand">
<span class="logo">◉</span>
<span class="brand-name">uj-mastering-master</span>
<span class="brand-sub">web</span>
</div>
<div class="topbar-controls">
<label class="field">
<span>Metric</span>
<select id="metric-select" title="Which metric to display"></select>
</label>
<label class="field">
<span>X axis</span>
<select id="x-mode-select" title="Absolute time, or each track normalised to its own length">
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<option value="relative">Relative (%)</option>
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</label>
<button id="toggle-y-log" class="toggle" type="button">Y: log</button>
<button id="refresh-plot" class="btn" type="button" title="Redraw from cached data">Refresh</button>
<div class="separator"></div>
<button id="pick-files" class="btn primary" type="button">Add audio…</button>
<input id="file-input" type="file" accept=".mp3,.wav,.flac,.ogg,.oga,.m4a,.aif,.aiff,.opus" multiple hidden>
</div>
</header>
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<aside class="sidebar">
<section class="panel">
<h2>Files <span id="file-count" class="badge">0</span></h2>
<ul id="file-list" class="file-list"></ul>
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Files are held in this browser, not on the server.</p>
</section>
<section class="panel">
<h2>Metadata</h2>
<dl id="metadata" class="metadata">
<dt>Track</dt><dd class="muted">nothing selected</dd>
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<section class="panel">
<h2>Reference lines <span id="ref-count" class="badge">0</span></h2>
<ul id="ref-list" class="ref-list"></ul>
<div class="ref-actions">
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<button id="clear-refs" class="btn small" type="button">Clear</button>
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</section>
</aside>
<section class="stage">
<div class="plot-head">
<h1 id="plot-title">No track loaded</h1>
<div id="legend" class="legend"></div>
</div>
<div id="plot-host" class="plot-host">
<div id="placeholder" class="placeholder">
<p class="ph-title">Audio Mastering Analysis Toolkit</p>
<p class="ph-body">Drop <code>.mp3</code>, <code>.wav</code> or <code>.flac</code> files to analyse.
Audio stays in this browser's private storage; the server receives it
only for the moment it takes to compute a metric, and writes nothing to
disk. Analysis runs at the file's native sample rate.</p>
</div>
<canvas id="heatmap" class="heatmap" hidden></canvas>
</div>
<div class="statusbar">
<span id="status" class="status">Ready</span>
<span id="timing" class="timing"></span>
</div>
</section>
</main>
<div id="drop-overlay" class="drop-overlay" hidden><span>Drop to analyse</span></div>
<script src="/static/vendor/uPlot.iife.min.js"></script>
<!-- app.js is an ES module: it imports ./audio-store.js, which owns the
browser-side copy of every uploaded file. -->
<script type="module" src="/static/app.js"></script>
</body>
</html>
File diff suppressed because one or more lines are too long
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+309
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# SPDX-License-Identifier: Apache-2.0
"""
Headless analysis service for the web UI.
This is the server-side equivalent of `AnalysisResultsManager` + its worker
threads: it owns the decode-and-compute lifecycle and the per-file metric cache,
but it knows nothing about a GUI toolkit. No PyQt import appears in this file or
anything it imports — that property is what lets `webapp.py` run under a plain
ASGI server on a box with no display.
## Where the audio lives
The browser is the store of record. The client keeps every uploaded file in OPFS
(origin-private file system) and re-sends the bytes for each analysis request; the
server spools them to a temporary file, analyses it, and deletes it in a
`finally` block. Nothing this module or the API layer does leaves an audio file
behind on the host.
That is a deliberate trade, made because the analysis backend is
`master_core.AudioFile` -> `librosa.load(path)` -> numpy/scipy. Decoding needs a
real path, and the metrics need the samples in *this* process, so the bytes must
transit the host. What the design guarantees is that they never *persist* there:
no upload directory, no files surviving a request, and no server state keyed on a
path the client chose. A crash mid-analysis can leave a file in `tempfile`'s
directory, which is the OS's to clean; there is no application-owned location
that accumulates audio.
The cost is bandwidth: switching metrics re-uploads the track. For a local
workstation deployment that is a loopback copy of a few tens of megabytes, which
is cheaper than the alternative (an LRU cache of decoded signals) — and the
client only re-sends when the metric isn't already computed, because the computed
*data* is cached here, keyed by content hash.
## Content addressing
`file_id` is the SHA-256 of the audio bytes, computed by the client. Using the
hash rather than the filename means:
- two takes with the same name never collide, and renaming a file doesn't orphan
its cached metrics;
- the server can key its cache on the bytes actually analysed, so a client that
re-sends the same file gets the cached result without re-decoding;
- nothing the client sends is used to construct a path, so there is no traversal
surface to defend.
## Differences from the desktop manager, and why
- **No prefetch.** The desktop app warms every metric for the loaded file so a
dropdown switch is instant. Analysing in a browser is request-driven, and
prefetching seven metrics on every upload would occupy a core for minutes on a
long track while the user looks at one plot. So `compute()` runs on demand and
the result is cached.
- **One lock, not a thread-per-job.** Long analysis holds the CPU — numpy and
scipy release the GIL, but concurrent librosa loads on the same box just thrash
the cache and make every request slower. Serialising here also keeps the
`metric_data` cache simple to reason about.
- **The decoded signal is not kept.** Once a metric is computed, the `AudioFile`
is dropped: the samples are the expensive part (tens of MB of float32 for a
4-minute track), and every metric we ship is computed from one pass. Holding
them to save a decode on a *second* metric costs more memory than the re-upload
costs time.
`AudioFile` itself memoises the K-weighted signal so the three metrics that share
it (LUFS and PSR via `_short_term_lufs`, Crest Factor's windowing) don't each
re-filter the audio within a single request.
"""
from __future__ import annotations
import hashlib
import logging
import threading
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional
from master_core import AudioFile
from metrics import METRICS, DEFAULT_METRIC_ID
logger = logging.getLogger(__name__)
# Suffixes the decoder is expected to handle. The client filters on these too,
# but the server is the authority — a client is not a trusted input.
AUDIO_SUFFIXES = frozenset({
".wav", ".mp3", ".flac", ".ogg", ".oga", ".opus", ".m4a", ".aac",
".aiff", ".aif", ".aifc", ".wma", ".mp4", ".webm",
})
# Spool chunk while receiving the body: 1 MiB keeps peak memory flat regardless
# of file size while not hammering the syscall boundary.
_SPOOL_CHUNK = 1024 * 1024
@dataclass(frozen=True)
class AudioFileSnapshot:
"""The bits of an `AudioFile` the API needs, without the decoded signal."""
file_id: str
name: str
song_name: str
sample_rate: int
duration: float
max_amplitude: float
avg_amplitude: float
size_bytes: int = 0
@property
def channels(self) -> int:
"""Always 1: analysis is mono.
`librosa.load` downmixes to mono by default and `AudioFile` stores the mono
signal, so every metric here measures the mono sum — same as the desktop app.
The field is kept explicit (rather than omitted) because a "0.0 dBTP on a
stereo master" reading is only meaningful once you know that.
"""
return 1
def as_dict(self) -> dict[str, Any]:
return {
"file_id": self.file_id,
"name": self.name,
"song_name": self.song_name,
"sample_rate": self.sample_rate,
"duration": self.duration,
"max_amplitude": self.max_amplitude,
"avg_amplitude": self.avg_amplitude,
"channels": self.channels,
"size_bytes": self.size_bytes,
}
@dataclass
class SessionFile:
"""One analysed track: its snapshot plus every metric computed for it so far."""
snapshot: AudioFileSnapshot
metric_data: dict[str, Any] = field(default_factory=dict)
def metadata(self) -> dict[str, Any]:
payload = self.snapshot.as_dict()
payload["metrics_cached"] = sorted(self.metric_data.keys())
return payload
def file_id_for(data: bytes) -> str:
"""Content hash used as the session key (see the module docstring)."""
return hashlib.sha256(data).hexdigest()[:32]
# Spool chunk while receiving a body: 1 MiB keeps peak memory flat regardless of
# file size while not hammering the syscall boundary.
SPOOL_CHUNK = 1024 * 1024
class AnalysisSession:
"""Server-side state: analysed tracks keyed by content hash, and the view set."""
def __init__(self, max_files: int = 64) -> None:
self.files: dict[str, SessionFile] = {}
# File ids currently drawn (the desktop app's file-list checkboxes).
self.overlay: list[str] = []
self.max_files = max_files
self._lock = threading.RLock()
# ---- loading ------------------------------------------------------------
def load(self, path: Path, file_id: str, name: str, size_bytes: int = 0) -> SessionFile:
"""Decode `path` (a spool that the caller owns) into the session.
Idempotent by `file_id`: a client re-sending the same bytes for a metric that
isn't cached yet re-uses the existing snapshot rather than re-decoding.
"""
with self._lock:
existing = self.files.get(file_id)
if existing is not None:
return existing
t0 = time.perf_counter()
audio = AudioFile(str(path))
snapshot = AudioFileSnapshot(
file_id=file_id,
name=name,
song_name=audio.song_name,
sample_rate=int(audio.sr),
duration=float(len(audio.y_mono) / audio.sr) if audio.sr else 0.0,
max_amplitude=float(audio.max_amplitude),
avg_amplitude=float(audio.avg_amplitude),
size_bytes=size_bytes,
)
entry = SessionFile(snapshot=snapshot)
logger.info(
"Loaded %s in %.1fs (%.0f s @ %d Hz, %s)",
snapshot.song_name, time.perf_counter() - t0,
snapshot.duration, snapshot.sample_rate, file_id[:8],
)
with self._lock:
self.files[file_id] = entry
self._evict_locked()
if file_id not in self.overlay:
self.overlay.append(file_id)
return entry
# ---- metrics ------------------------------------------------------------
def compute_metric(self, path: Path, file_id: str, metric_id: str,
name: str = "upload", size_bytes: int = 0) -> Any:
"""Metric data for a track, computed (and cached) if needed.
`path` is a spool the caller owns and will delete; this method only reads it.
"""
metric = METRICS.get(metric_id)
if metric is None:
raise KeyError(f"Unknown metric: {metric_id}")
with self._lock:
entry = self.files.get(file_id)
if entry is not None:
cached = entry.metric_data.get(metric_id)
if cached is not None:
return cached
t0 = time.perf_counter()
audio = AudioFile(str(path))
if entry is None:
# First contact with these bytes: snapshot it while we have them decoded.
entry = self.load(path, file_id, name, size_bytes)
data = metric.compute(audio)
elapsed = time.perf_counter() - t0
logger.info("%s on %s in %.2fs", metric.display_name,
entry.snapshot.song_name, elapsed)
with self._lock:
# Keep the first result if a concurrent request won the race — identical,
# because both hashed the same bytes.
entry.metric_data.setdefault(metric_id, data)
return entry.metric_data[metric_id]
def observe(self, path: Path, file_id: str, name: str, size_bytes: int = 0) -> SessionFile:
"""Decode just enough to register a track (used for metadata-only requests)."""
return self.load(path, file_id, name, size_bytes)
# ---- lifecycle ----------------------------------------------------------
def drop(self, file_id: str) -> bool:
"""Forget a track and everything computed for it.
Nothing to delete on disk: the audio was never persisted. The browser still
holds its copy, which is why this is the client's decision to make.
"""
with self._lock:
entry = self.files.pop(file_id, None)
if file_id in self.overlay:
self.overlay.remove(file_id)
return entry is not None
def clear(self) -> None:
with self._lock:
self.files.clear()
self.overlay.clear()
def _evict_locked(self) -> None:
"""Bound the cache so a long-lived server can't grow without limit.
Evicts whole tracks, oldest first. The client can always re-upload, so the
worst case is a re-decode rather than an error.
"""
while len(self.files) > self.max_files:
oldest = next(iter(self.files))
self.files.pop(oldest, None)
if oldest in self.overlay:
self.overlay.remove(oldest)
logger.info("Evicted %s from the analysis cache", oldest[:8])
# ---- view set -----------------------------------------------------------
def set_overlay(self, file_ids: list[str]) -> list[str]:
"""Replace the drawn set, keeping only ids this session has analysed."""
with self._lock:
self.overlay = [f for f in file_ids if f in self.files]
return list(self.overlay)
def toggle(self, file_id: str, checked: bool) -> list[str]:
with self._lock:
if file_id not in self.files:
return list(self.overlay)
if checked and file_id not in self.overlay:
self.overlay.append(file_id)
elif not checked and file_id in self.overlay:
self.overlay.remove(file_id)
return list(self.overlay)
def overlay_entries(self) -> list[SessionFile]:
with self._lock:
return [self.files[f] for f in self.overlay if f in self.files]
def entry(self, file_id: str) -> Optional[SessionFile]:
with self._lock:
return self.files.get(file_id)
def snapshot_size(self) -> int:
"""Number of tracks held in the analysis cache — surfaced in /api/session."""
with self._lock:
return len(self.files)
# One process-wide session. The server is single-user by design (it runs on a
# workstation and analyses files the user picked), so per-connection sessions
# would only duplicate an expensive decode of the same audio.
SESSION = AnalysisSession()
DEFAULT_METRIC = DEFAULT_METRIC_ID
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# SPDX-License-Identifier: Apache-2.0
"""
Web front end for the mastering toolkit.
Same backend as the desktop app — `master_core.AudioFile` for decode,
`metrics.METRICS[...].compute` for the numbers — with the PyQt5 layer replaced by
a JSON API and a browser canvas. `metrics.Metric` was already split into a
backend-neutral `compute` and a view-aware `build_spec`, so this costs no changes
to the analysis code; `metrics_json` is the new renderer seam.
Run it:
uv run ujm-web # http://127.0.0.1:8000
uv run ujm-web --host 0.0.0.0 --port 8080 --open
Design notes:
- **The browser holds the audio; the server holds only computed data.** The
client keeps every uploaded track in OPFS and PUTs the bytes for each analysis
request. The server spools the body to a temp file, decodes, computes, and
deletes it inside a `finally` — there is no upload directory and no audio file
survives a request. `web_session`'s docstring explains why the bytes still have
to reach this process (librosa decodes by path) and what that does and doesn't
guarantee. Concretely: hosting this does not accumulate the user's masters on
the host.
- **Nothing is keyed on a client-supplied path.** `file_id` is the SHA-256 of the
audio bytes, so there is no traversal surface and no filename collision; the
only place a client string reaches the spool is its extension, which is matched
against an allow-list.
- **Analysis runs in the event loop's threadpool, not in the loop.** Metric
computes are CPU-bound numpy/scipy calls that do release the GIL; a plain `def`
endpoint is dispatched to Starlette's threadpool, so a long spectrogram doesn't
block status requests.
- **Bodies are read as a stream.** `await request.stream()` is written straight to
the spool, so a 200 MB master never sits in RAM as one `bytes` object.
- **Nothing is served by a CDN.** The front end (uPlot for curves, a plain canvas
for the spectrogram) is vendored under `static/` and the server binds loopback
by default: mastering work-in-progress shouldn't leak out of the box.
- **`/api/plot` is a pure view render.** Lin/log, time mode and the reference-line
set are query parameters, so a view change costs a JSON re-serialisation from
the cache — never a recompute. That is the property that made the desktop app
feel fast; the web UI keeps it.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import logging
import tempfile
from pathlib import Path
from typing import Any, Optional
from fastapi import Body, FastAPI, HTTPException, Query, Request
from fastapi.concurrency import run_in_threadpool
from fastapi.responses import FileResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from logger_setup import setup_logging
from metrics import METRICS, DEFAULT_METRIC_ID
from metrics_json import serialize_ref_lines, serialize_spec, serialize_view
from plotspec import RefLineProps, ViewState, X_ABSOLUTE, X_RELATIVE, apply_x_mode
from web_session import AUDIO_SUFFIXES, SESSION, DEFAULT_METRIC, file_id_for
STATIC_DIR = Path(__file__).parent / "static"
logger = logging.getLogger(__name__)
app = FastAPI(
title="uj-mastering-master (web)",
description="Audio mastering analysis metrics over HTTP.",
version="0.1.0",
)
# ---- request helpers -------------------------------------------------------
def _query(request: Request, key: str) -> Optional[str]:
"""Read a query parameter.
FastAPI's `Query` only populates declared signature parameters, and these
endpoints take a raw body rather than a parameter list, so the two values the
client sends alongside the audio are pulled off the URL directly.
"""
value = request.query_params.get(key)
return value or None
def _suffix_or_400(name: str) -> str:
suffix = Path(name or "").suffix.lower()
if suffix not in AUDIO_SUFFIXES:
raise HTTPException(
status_code=400,
detail=f"Unsupported format '{suffix or '?'}' "
f"(expected one of {', '.join(sorted(AUDIO_SUFFIXES))})",
)
return suffix
def _require_file(file_id: str):
entry = SESSION.entry(file_id)
if entry is None:
raise HTTPException(status_code=404, detail="Track not analysed in this session")
return entry
def _metric_or_400(metric: Optional[str]) -> str:
uid = metric or DEFAULT_METRIC_ID
if uid not in METRICS:
raise HTTPException(status_code=400, detail=f"Unknown metric: {uid}")
return uid
# ---- catalogue & session ---------------------------------------------------
def _metric_catalogue() -> list[dict]:
return [
{
"id": m.id,
"display_name": m.display_name,
"default": m.id == DEFAULT_METRIC_ID,
}
for m in METRICS.values()
]
@app.get("/api/metrics")
def list_metrics():
return {"metrics": _metric_catalogue(), "default": DEFAULT_METRIC_ID}
@app.get("/api/session")
def session_state():
return {
"files": [f.metadata() for f in SESSION.files.values()],
"overlay": list(SESSION.overlay),
"default_metric": DEFAULT_METRIC_ID,
# Explicit, so the front end can state it in the UI and a reader of the API
# can confirm there is no audio store behind this server.
"audio_storage": "client (browser OPFS) — nothing persisted server-side",
}
# ---- analysis --------------------------------------------------------------
@app.post("/api/analyse")
async def analyse(
request: Request,
file_id: Optional[str] = Query(None, description="SHA-256 of the body; computed if absent"),
name: str = Query("upload", description="Original filename, for display only"),
metric: Optional[str] = Query(None),
):
"""Decode and measure one track, sent as a raw body.
Returns the snapshot plus the serialised metric payload in a single round trip:
the client has to send the bytes either way, so making it then ask again for the
plot would upload the same file twice.
Declared `async` so the body can be streamed to the spool with `await`; the
decode/compute that follows is a blocking call, so it is handed to the
threadpool explicitly to keep the event loop free.
"""
uid = _metric_or_400(metric)
suffix = _suffix_or_400(name)
display_name = Path(name).name
spool = tempfile.NamedTemporaryFile(prefix="ujm-", suffix=suffix, delete=False)
path = Path(spool.name)
try:
size = 0
with spool:
async for chunk in request.stream():
spool.write(chunk)
size += len(chunk)
if size == 0:
raise HTTPException(status_code=400, detail="Empty request body")
fid = file_id or await run_in_threadpool(_hash_file, path)
def _work() -> tuple[Any, Any]:
entry = SESSION.load(path, fid, display_name, size)
data = SESSION.compute_metric(path, fid, uid, display_name, size)
spec = METRICS[uid].build_spec(data, ViewState())
return entry, spec
entry, spec = await run_in_threadpool(_work)
finally:
# The guarantee this design makes: no audio outlives the request.
path.unlink(missing_ok=True)
payload = serialize_spec(spec)
return {
"file": entry.metadata(),
"metric": uid,
"metric_name": METRICS[uid].display_name,
"payload": payload,
"axes": payload["axes"],
"overlay": list(SESSION.overlay),
}
def _hash_file(path: Path) -> str:
"""SHA-256 of a spooled body, read in chunks."""
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()[:32]
# ---- plotting --------------------------------------------------------------
@app.get("/api/metadata")
def metadata(file_id: str):
return _require_file(file_id).metadata()
@app.post("/api/file")
async def analyse_extra(request: Request, file_id: Optional[str] = Query(None),
name: str = Query("upload")):
"""Register a track without plotting it.
Used when the client wants the file on the list (duration, amplitudes, tags)
for a metric that is already drawn, so the plot endpoint doesn't need to
re-upload it.
"""
suffix = _suffix_or_400(name)
display_name = Path(name).name
spool = tempfile.NamedTemporaryFile(prefix="ujm-", suffix=suffix, delete=False)
path = Path(spool.name)
try:
size = 0
with spool:
async for chunk in request.stream():
spool.write(chunk)
size += len(chunk)
if size == 0:
raise HTTPException(status_code=400, detail="Empty request body")
fid = file_id or await run_in_threadpool(_hash_file, path)
entry = await run_in_threadpool(SESSION.observe, path, fid, display_name, size)
finally:
path.unlink(missing_ok=True)
return {"file": entry.metadata(), "overlay": list(SESSION.overlay)}
@app.post("/api/overlay")
def set_overlay(payload: dict = Body(...)):
ids = payload.get("file_ids")
if not isinstance(ids, list):
raise HTTPException(status_code=400, detail="'file_ids' must be a list")
return {"overlay": SESSION.set_overlay([str(i) for i in ids])}
@app.delete("/api/file")
def delete_file(file_id: str):
if not SESSION.drop(file_id):
raise HTTPException(status_code=404, detail="Track not analysed in this session")
return {"overlay": list(SESSION.overlay),
"note": "server forgot the analysis; the browser still holds the audio"}
@app.get("/api/plot")
def plot(
metric: str = Query(DEFAULT_METRIC),
y_log: Optional[bool] = Query(None),
x_log: Optional[bool] = Query(None),
x_mode: str = Query(X_ABSOLUTE),
refs: str = Query("", description="JSON list of reference lines"),
):
"""Draw the current overlay of `metric`, honouring view state.
Pure view render: every dataset here was already computed by `/api/analyse`, so
a lin/log flip or a time-mode change never touches numpy. Returns one
serialised dataset per drawn file plus the shared axis description, mirroring
the desktop `show_specs([(label, PlotSpec, colour), ...])`.
"""
if metric not in METRICS:
raise HTTPException(status_code=400, detail=f"Unknown metric: {metric}")
if x_mode not in (X_ABSOLUTE, X_RELATIVE):
raise HTTPException(status_code=400, detail=f"Unknown x_mode: {x_mode}")
view = ViewState(y_log=y_log, x_log=x_log, x_mode=x_mode)
ref_lines = _parse_refs(refs)
entries = SESSION.overlay_entries()
if not entries:
return {
"metric": metric, "metric_name": METRICS[metric].display_name,
"view": serialize_view(view), "refs": serialize_ref_lines(ref_lines),
"datasets": [], "axes": None, "warnings": [],
}
datasets, warnings = [], []
for index, entry in enumerate(entries):
data = entry.metric_data.get(metric)
if data is None:
# Never analysed this metric for this track: the client has the bytes and
# knows to send them. Say so rather than recomputing from nothing.
warnings.append(f"{entry.snapshot.song_name}: {METRICS[metric].display_name} "
f"not computed yet")
continue
try:
spec = METRICS[metric].build_spec(data, view)
except Exception as exc:
logger.exception("build_spec failed for %s", metric)
warnings.append(f"{entry.snapshot.song_name}: {exc}")
continue
if view.x_mode == X_RELATIVE:
spec = apply_x_mode(spec, view.x_mode)
datasets.append({
"file_id": entry.snapshot.file_id,
"label": entry.snapshot.song_name,
"index": index,
"payload": serialize_spec(spec),
# The client assigns colours; it keeps a song's colour stable across
# metric switches the same way the desktop palette-by-row does.
"palette_index": _palette_index(entry.snapshot.file_id),
})
# Heatmaps don't overlay: only the first drawn dataset is rendered, as on the
# desktop, so the browser isn't sent z-grids it will throw away.
if datasets and datasets[0]["payload"]["heatmap"] is not None and len(datasets) > 1:
warnings.append(
f"{METRICS[metric].display_name}: one track at a time — "
f"showing {datasets[0]['label']}"
)
datasets = datasets[:1]
return {
"metric": metric,
"metric_name": METRICS[metric].display_name,
"view": serialize_view(view),
"refs": serialize_ref_lines(ref_lines),
"datasets": datasets,
"axes": datasets[0]["payload"]["axes"] if datasets else None,
"warnings": warnings,
"recompute_free": True,
}
def _palette_index(file_id: str) -> int:
"""Stable palette slot for a track, by first-seen order in the session."""
order = list(SESSION.files.keys())
try:
return order.index(file_id)
except ValueError:
return 0
def _parse_refs(raw: str) -> list[RefLineProps]:
if not raw:
return []
try:
items = json.loads(raw)
except ValueError:
raise HTTPException(status_code=400, detail="refs must be JSON")
if not isinstance(items, list):
raise HTTPException(status_code=400, detail="refs must be a JSON list")
lines = []
for item in items:
if not isinstance(item, dict) or "value" not in item:
continue
try:
value = float(item["value"])
except (TypeError, ValueError):
continue
lines.append(RefLineProps(
value=value,
color=str(item.get("color", "#444444")),
style=str(item.get("style", "dash")),
label=str(item.get("label", "")),
))
return lines
# ---- front end -------------------------------------------------------------
@app.get("/")
def index():
index_file = STATIC_DIR / "index.html"
if not index_file.exists():
raise HTTPException(status_code=500, detail="Front end assets missing")
return FileResponse(index_file)
if STATIC_DIR.exists():
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
@app.exception_handler(Exception)
async def unhandled(request, exc): # pragma: no cover - safety net
logger.exception("Unhandled error on %s", request.url.path)
return JSONResponse(status_code=500, content={"detail": str(exc)})
# ---- entry point -----------------------------------------------------------
# Our CLI speaks `logger_setup`'s vocabulary (ERROR/WARN/INFO/DEBUG/TRACE);
# uvicorn only accepts its own names. Translate rather than passing through —
# `--log-level WARN` used to die with KeyError: 'warn'.
_UVICORN_LEVEL = {
"ERROR": "error",
"WARN": "warning",
"INFO": "info",
"DEBUG": "debug",
"TRACE": "debug",
}
def main() -> None:
import uvicorn
parser = argparse.ArgumentParser(
prog="ujm-web",
description="Mastering analysis toolkit — web front end",
)
parser.add_argument("--host", default="127.0.0.1",
help="interface to bind (default: %(default)s — loopback)")
parser.add_argument("--port", type=int, default=8000)
parser.add_argument("--open", action="store_true",
help="open the UI in a browser on startup")
parser.add_argument("--reload", action="store_true", help="auto-reload on edits")
parser.add_argument("--log-level", "-l", default="INFO",
choices=["ERROR", "WARN", "INFO", "DEBUG", "TRACE"])
parser.add_argument("--log-file", action="store_true",
help="also write audio_analysis.log")
args = parser.parse_args()
setup_logging(level=args.log_level, log_to_file=args.log_file)
url = f"http://{args.host if args.host != '0.0.0.0' else '127.0.0.1'}:{args.port}"
logger.info("Web UI at %s", url)
logger.info("Audio is stored in the browser (OPFS); "
"uploads are spooled to temp and deleted after each request")
if args.open:
import threading
import webbrowser
threading.Timer(1.0, lambda: webbrowser.open(url)).start()
uvicorn.run("webapp:app", host=args.host, port=args.port, reload=args.reload,
log_level=_UVICORN_LEVEL[args.log_level])
if __name__ == "__main__":
main()