Adds an opt-in High Quality mode to the Object Eraser, backed by a new inpaint-hq feature bundle (Stable Diffusion 1.5 inpainting via diffusers). The default fast LaMa path is unchanged. Both arch archives are published to deepsafe/feature-bundles and the manifest carries their real sha256/sizes.
Verified end to end: a fresh container pulls the bundle from HuggingFace, checksum-verifies it, extracts torch/diffusers plus the fp16 model, and the HQ sidecar erases a large object with a plausible fill.
Refs #141
Editing a file from the library used to silently supersede it: the worker auto-saved every result as a new version and the leaf-only listing hid the original, which read as a destructive overwrite. Tool pages now show a per-edit choice for library-sourced files. The default saves the result as an independent new file and keeps the original; picking overwrite keeps the old superseding-version behavior.
The client sends a saveMode multipart field next to fileId, validated with a 400 on unknown values, and autoSaveToLibrary branches on it. Every hand-written route that honors fileId parses the field the same way as the factory. The review panel shows where an auto-saved result went instead of offering a second, duplicate save. Tools whose route or submitter ignores fileId keep the selector hidden via a shared unsupported-tools set, and the choice resets to the non-destructive default whenever a new file is staged.
Closes#495
The Convert Audio tool promised configurable bitrate, sample rate, and channel count, but only format and bitrate were exposed. Adds an optional sampleRate setting (8000 to 96000 Hz, omitted = preserve source) wired through the Zod schema, the FFmpeg -ar flag, the standalone settings panel, and the pipeline builder controls.
Impossible combinations fail loudly instead of degrading silently: MP3 + 96000 Hz is rejected (libmp3lame caps at 48 kHz), and MP3 bitrates above the encoder ceiling at low rates (64 kbps at 8 kHz, 160 kbps at 16/22.05 kHz) are rejected rather than clamped. The UI offers only legal combinations and sanitizes stored pipeline settings on load.
Docs updated in English plus all 20 localized pages with refreshed i18n_source_hash stamps; two new UI strings added to all 21 locales.
Fixes#558
Comprehensive telemetry quality improvements across Sentry and PostHog, grounded in an audit of the live data plus current best-practice research.
Sentry: job_id/instance_id tags, operational fingerprinting, PII-safe settings context on bug events, web tag population + extension-noise filtering, an early-crash buffer, http status/method kept on breadcrumbs, and a gated-off-by-default performance-tracing re-enable (tracesSampler that zeroes db/redis/queue-poll root spans + drops the Redis integration) with worker job spans and canonical-host cron monitors.
PostHog: history_change SPA pageviews, instance_id super property for fleet rollups, enriched tool_used (formats, byte sizes, is_batch, execution_hint, real error_kind taxonomy), the previously-dead result_saved/batch_processed/ai_bundle_prompted events fired, search click-through, editor + Automate authoring + auth instrumentation, a before_send PII boundary, and minimal opt-in landing-site pageviews.
A genuine pdf.js load failure (corrupt or password-protected file) was swallowed by the same catch that silences teardown rejections, leaving a blank canvas that looks like it is still loading. The two cases are now distinguished by the load effect's cancelled flag, and a real failure renders a clear message pointing at Unlock PDF for encrypted files. New loadFailed string in all 21 locales.
Item 4 of #478.
Both PyPI onnxruntime flavors unpack into the same site-packages directory, so a bundle carrying the CPU build (transcription, via faster-whisper) overwrote the GPU build's native libraries during install while the stale onnxruntime_gpu dist-info kept claiming otherwise. Every ONNX-backed tool then silently ran on CPU.
The installer now reconciles the flavor before the venv merge and the GPU build always wins, in both install orders; reinstalling any GPU bundle repairs a previously clobbered venv. gpu.py's warning now says exactly that. Build-side, build-bundle.sh gains the same reconcile and verify-bundle-compatibility.sh layers bundles through the real installer merge and asserts a single flavor.
Verified live on an RTX 4070 against the published bundles: reproduced the clobber with the stock installer, then confirmed both the prevention and repair paths with the patched one.
Fixes#490
removeBackground failures wrap in a SafeError so the specific reason survives the Sentry scrubber; the OOM lighter-model fallback and bridge SafeError passthrough are preserved.
Postgres auth (28xxx), permission (42501), resource (class 53), and operator-intervention (class 57) failures now classify as operational via a cause-chain walk, not bug. pg query bugs (e.g. 42601) stay bugs.
Route both Python exit paths through pythonExitError so the reason survives the scrubber; OOM/segfault stay operational and keep "out of memory" for the lighter-model fallback.
Route every doc_* helper JSON.parse through a guarded helper; non-JSON stdout now yields a diagnosable SafeError with the raw output in the cause instead of a bare SyntaxError.
Fixes#529 (opened investigating #515).
Setting MFA policy to "required"/"admins only" saved regardless of whether the mfa enterprise feature was licensed, and there was no enrollment UI at all, so any instance that flipped the toggle locked every unenrolled user out with no way back in. The login page and Settings save also both collapsed the resulting error into a generic message, hiding the real reason.
- Reject saving mfaPolicy to admins_only/required server-side unless mfa is licensed
- Surface the specific server error on login and on a failed settings save instead of a generic fallback
- Add a self-service two-factor authentication enrollment flow (QR code, manual entry, recovery codes, verify, disable) so a licensed admin can actually satisfy the policy before it's enforced
- Fix a pending-enrollment dead end, silent error swallowing in verify/disable, and a silent clipboard-copy failure on the recovery codes screen
- Add the integration test that actually proves the fix: a real login attempt returns 403 MFA_ENROLLMENT_REQUIRED
Add a proportion chip row (Free, Original, 1:1, 4:3, 3:2, 16:9, 3:4, 9:16) to the Resize tool's Custom tab. Picking a ratio locks width and height so editing one recomputes the other, and prefills the largest box of that ratio that fits the source so it never upscales. Free stays the default, preserving existing behavior. Replaces the previously non-functional lock-aspect button. Frontend only, no backend or schema change; adds strings to all 21 locales.
Target-size compression had only a coarse DPI lever, so it undershot badly (a 350KB target could land at 216KB) and silently missed unreachable targets. Adds JPEG quality as a second lever (forced re-encode so it bites on JPEG scans), folds both into one monotonic quality axis that target-size binary-searches, reports targetMet honestly in the panel across 21 locales, and flips the tool to async for the extra passes. Quality-mode output sizes shift intentionally (slider now drives JPEG quality at full resolution in its top half).
Renames 18 ambiguous or hard-to-search tool names so image tools self-qualify like the other modalities ("Compress" becomes "Compress Image"), and cleans up a few awkward names. Propagated across search (constants.ts), display (en.ts + 20 locales), the OpenAPI base spec + 20 locale specs, and the docs tool-page headings in 21 languages. Removes the duplicate "Normalize Audio" summary shared by the video and audio endpoints. Tool ids and routes are unchanged, so no API paths or bookmarks break.
Add a once-per-boot instance_started event (arch, os, deploy_mode,
gpu_present) so the fleet architecture mix is measurable. It reuses the
existing per-instance instance_id and is exempt from the volume sample
rate, since a census that fires once per boot must not be thinned.
Restore useful capture depth now that the sponsored plan removes the
quota pressure behind the earlier hardening:
- PostHog sample rate 0.1 to 1.0 (full analytics when enabled); the
property allowlist still blocks file data.
- Sentry per-instance ceiling 20 to 500/hr, breadcrumb trail restored
(sanitized: urls/paths redacted, data payloads dropped), full stack
paths kept; local vars, request bodies, and PII still dropped. Both
api and web.
Honor ANALYTICS_ENABLED=false as an opt-out alias: it was documented on
the Docker Hub README but never wired in 2.x, so anyone who set it was
still tracked.
All capture stays behind the analytics opt-out gate.
Adds a Brush | Lasso toggle to the object eraser. Lasso lets the user drag a freeform loop that auto-closes and fills into the mask, so they select around a subject instead of painting every pixel. Frontend-only; the mask contract is unchanged. Also un-skips the erase-object e2e suite via a shared mockAiFeaturesInstalled helper (7 tests now run; 2 multi-file tests fixme'd for a pre-existing tool-page remount bug). Closes#492.
Dilate the mask, crop a padded box around it, run LaMa on the crop at 512, and composite back cleanly. Fixes the ghost remnants (#491) and sharpens small/medium-object fills in high-res images (#141 core). Same model, still offline, no new bundle. Closes#491.
Adds a dedicated remove-gif-background AI tool: removes the background from an animated GIF, WebP, or APNG frame by frame and reassembles a transparent (or composited) animation in WebP, APNG, or GIF, with full per-frame effects. Reuses the background-removal bundle. Verified end-to-end with the real rembg model.
Closes#496.
Follow-ups to the v2.1.0 Sentry telemetry overhaul, found by reviewing live release:2.1.0 events:
- error_code tag was empty because reportError read only the top-level err.code; add extractErrorCode() to walk the cause chain (pg SQLSTATE, node E-code, else first short code).
- InputValidationError from a tool's processV2 in the worker was logged as error_class=bug; classify it as expected for any source. Worker-side ZodError stays a bug (schema drift).
- AI dispatcher timeouts rejected with a bare Error, which the sanitizer scrubbed to a message-less "Error: Error"; reject with an operational SafeError (code "timeout") at both timeout sites.
Each fix written failing-test-first; affected and adjacent unit suites green plus full CI (integration + e2e).
Regenerate the social/OG card (200+ tools, Private file processing, self-hosted infrastructure) and sync to landing/web/docs; update banner, press kit, package + OpenAPI + Docker Hub descriptions, a leaked docs count, and the English About string.
Removes Sentry tracing entirely (BullMQ idle polling burned 4.8M transactions in 2 days at the baked 0.1 rate), decouples PostHog sampling, and replaces the type-only error scrub with a vetted-field sanitizer plus SafeError/ToolInputError contracts. One classified capture path with per-signature throttles and a per-process ceiling makes storms impossible (NODE-1E was 4,541 events from one 30s loop). Browser errors move to a dedicated web Sentry project with their own source maps. Adds the SNAPOTTER_TELEMETRY runtime kill switch and silences test fleets.
Crash fixes: remote 204/304 SSRF process kill (NODE-20), conversion-preset boot crash loop (NODE-21), Redis version preflight + unhandled subscribe rejection (NODE-1T), Sign PDF on plain-http origins (NODE-1K/1M), wavesurfer/pdf.js teardown rejections (NODE-1P/1N), bundle-import ZlibError to 400 (NODE-1Z), chart-maker input errors declassified (NODE-1H/1J), asset requests skip the session DB lookup (NODE-1D).
Make on-demand AI feature-bundle installs reliable and self-healing, closing
the failure modes behind most "some tool doesn't work" reports.
Multi-bundle installs: tools needing more than one bundle (Passport Photo,
Enhance Faces) install every required bundle from one action and stay
not-installed until all are present. Verified across all 19 AI tools.
Downloads: self-heal the accelerated Hugging Face (Xet) client so an upgraded
venv no longer silently falls back to slow urllib; restart instead of
corrupting a resumed partial when a proxy ignores Range and returns 200;
verify the completed size; fail fast on disk-full and HTTP 4xx; retry
transient errors five times; add hf_transfer fallback and document Xet egress.
Install integrity: crash-atomic venv writes so a killed or out-of-space
install can no longer tear the shared venv and break other tools; a boot
breadcrumb reseeds a torn venv to a clean state automatically; a post-install
smoke import test refuses to record a bundle whose libraries cannot load; an
install watchdog stops a wedged installer that would otherwise hold the venv
writer lock forever.
Adds unit and end-to-end tests for every failure mode above.
Uninstalling a bundle only deletes its downloaded model weights, never the
shared venv's site-packages, so self-hosters who already hit an AI bundle
conflict (e.g. the scipy ABI strand) have no clean self-service path via
uninstall+reinstall: reinstalling just overlays corrected files on top of
stale ones. Adds POST /api/v1/admin/features/reset, which wipes
/data/ai/{venv,models,pip-cache}, resets installed.json, and reseeds a real
working venv from the image's baked /opt/venv (extracted docker/reseed-ai-venv.sh,
now shared with entrypoint.sh's existing base-venv-upgrade bootstrap instead
of duplicating that logic) -- leaving an empty venv directory here would
make the very next install fail with "spawn .../python3 ENOENT", caught by
testing this live rather than assuming it. Ships with a matching Settings UI
section (inline confirm, same pattern as per-bundle uninstall) and strings
across all 21 locales.
Verified against a real snapotter/snapotter:1.17.2 image migrated to 2.0.0,
with real multi-GB bundles installed (background-removal + OCR): confirmed
the migrated instance's inherited python3.11 venv (2.0.0 itself uses 3.12)
still imports the fixed scipy/numpy/paddleocr correctly, then reset + real
reinstall + actual tool execution (remove-background, verified output image)
all worked end-to-end.
* fix(api): prevent a crash when an over-limit upload stream has no consumer yet
busboy's "limit" handler destroyed the file stream with an error but never
attached its own error listener, relying entirely on whatever consumes
part.file downstream to do so. On a fast enough connection (or a fully
buffered body, e.g. Fastify inject()), busboy can process enough bytes to
hit the size limit before the route handler's receiveUpload() call has
attached its own stream listener, leaving the resulting "error" event with
zero listeners -- which crashes the whole process by default in Node.
Surfaced by tonight's FULL_MATRIX+FUZZ integration run (880 uncaught
exceptions, all the same root cause). Reproduces deterministically in
isolation; unrelated to this release's actual code delta (file untouched
since PR #413, well before the baseline QA pass).
Fix: attach a baseline no-op error listener the moment the stream is
created, guaranteeing at least one listener always exists. EventEmitter
delivers "error" to every registered listener, so the real consumer's own
error handling is unaffected.
* fix(ai-bundles): rebuild upscale-enhance and photo-restoration to reconcile scipy ABI
upscale-enhance and photo-restoration both depend on codeformer-pip, whose
transitive closure (basicsr -> realesrgan -> gfpgan) pulls in an unpinned
scipy. Both bundles were last built ~June 18-19, before PR #437 added the
manifest's `constraints` array (numpy==1.26.4, scipy==1.12.0, etc.) to pin
exactly this kind of dependency during bundle builds. Only the ocr bundle
was rebuilt after that fix landed.
install_feature.py has no pip install step -- it's a raw tarfile extraction
with no cross-bundle conflict resolution, so installing OCR alongside either
stale bundle left three incompatible scipy versions' files mixed in the same
site-packages directory (a compiled _rotation.*.so from one release next to
Python files expecting a different release's API), breaking the `upscale`
tool and OCR's higher-quality tiers with an ImportError.
Rebuilt both bundles for amd64-gpu and arm64-cpu from the current manifest,
verified scipy/scikit-learn/scikit-image/pandas all resolve to the pinned
versions in the tarballs themselves, then verified end-to-end on real
hardware (Mac arm64 CPU and ubuntu_gpu .248 RTX 4070): installing all
affected bundles together now yields exactly one version of each constrained
package, `upscale` produces correct output, and OCR's balanced/best tiers
correctly use PaddleOCR-GPU instead of erroring out.
Published the rebuilt tarballs to the public deepsafe/feature-bundles
HuggingFace repo and updated this manifest's sha256/sizes to match.
Also adds verify-bundle-compatibility.sh: verify-bundle.sh checks each
bundle in isolation (a fresh venv per bundle), which is exactly why this
shipped twice -- nothing ever checked that bundles built at different times
agree once layered into the one shared venv real installs use. The new
script installs every bundle for an arch into one venv and asserts each
constrained package has exactly one, correct version.
Known follow-up (not fixed here, needs separate discussion): uninstalling a
bundle only removes its downloaded model weights, never the site-packages
it added, so existing installations that already hit this bug have no clean
self-service fix via uninstall+reinstall -- they need a full AI-venv wipe.
* fix(docker): bake a real rate limit default for the all-in-one one-liner
The documented single-container `docker run` install had RATE_LIMIT_PER_MIN=0
(effectively unlimited, ~50k/min) baked in, since only docker-compose.yml
carried a hardened override. A self-hoster following the one-liner path got
no meaningful throttling anywhere, including auth-adjacent routes with no
dedicated per-route limit. Bakes a generous-but-real 1000/min default into
the Dockerfile, raises both compose files' fallback to match so the two
documented install paths converge on the same posture, and updates the Zod
schema default plus docs that quoted the old value.
* fix(api): boot log undercounted tool routes by the conversion-preset total
The "Tool routes: N active" line logged before registerConversionPresets(app)
ran, so it only ever reported the base 158 tools, 83 short of the real
241-tool total. Presets have to register after the base loop (they delegate
to each base tool's own processV2), so the fix moves the log line to after
that call and has registerConversionPresets return its count instead of
reordering the dependency.
* fix(ai): forward {info}/{warning} stderr JSON instead of dropping it
The dispatcher stderr parser only recognized {ready} and {progress,stage}
shaped JSON lines; anything else that parsed as valid JSON (like ocr.py's
GPU-to-tesseract downgrade notice, an {"info": ...} line) matched neither
branch and fell through silently, never reaching docker logs. Adds explicit
{info}/{warning} handling that forwards to console.log/console.warn, same as
the existing [prefix]-tagged non-JSON path.
* fix(api): fall back to a lower OCR tier when PaddleOCR itself is unusable
ocr.ts already retries lower quality tiers on a crashed dispatcher, but the
condition only matched crash-style messages (segfault, exited unexpectedly).
ocr.py's own ImportError/exception handlers already produce messages telling
the caller to use a lower tier (e.g. on the scipy ABI conflict class of bug),
but nothing ever acted on them, so a broken PaddleOCR hard-failed with 422
instead of degrading to Tesseract like ocr-pdf effectively does. Broadens the
retry condition to also catch PaddleOCR-engine-unusable messages.
Note: ocr-pdf's tesseract-only behavior turned out to be an unrelated,
pre-existing, deliberate design choice (PaddleOCR segfaults on rasterized PDF
pages on arm64), not a graceful-fallback mechanism to copy -- the two tools
weren't actually solving the same problem, so this fixes ocr.ts's own gap
rather than trying to mirror ocr-pdf.
* docs: add 1.x-to-2.0 migration guide and upgrade notice
Adds MIGRATING.md with backup and upgrade steps, plus a short
"coming from 1.x?" callout in README and the docs upgrade guide
pointing existing users at it.
* fix: replace stale image-only and pre-rename data copy across product
SnapOtter grew from an image-only tool into a 5-modality suite
(Image, Video, Audio, PDF, Files), but copy in several places never
caught up. Fixes:
- dropzone.defaultFormats (i18n): every non-English locale still had
the pure pre-2.0 image-only format list; English omitted Files
entirely. Corrected across all 21 locales.
- settings.about.appDescription (i18n): "document, and data" workflow
copy updated to "PDF, and file" across all 21 locales.
- constants.ts: Files category's raw name was still "Data Files".
- Landing hero subtitle, JSON-LD schema, llms.txt, and 7 spots in the
competitor-comparison pages.
- Docs: VitePress config, supported-formats, deployment, and an
architecture.md modality-naming nit.
- OpenAPI description, root package.json description/keyword, and a
GitHub issue template dropdown option.
DOCKERHUB.md's separate "v1.x, image tools only" pre-release notice
is left untouched since 2.0 hasn't published to Docker Hub yet.
* test: update dropzone format-hint assertion to match corrected copy
The expected string still had the stale image-only/duplicated
PDF-Documents text from before the dropzone.defaultFormats fix.
An audit comparing every locale's leaf values against en.ts found
structural key-parity was already perfect (0 missing/extra keys), but
~2,356 leaf values across the 20 non-English locales were still
byte-identical to the English source, meaning they'd shipped untranslated.
The largest single cause: the feedback widget (PR #428) shipped with
English placeholder text in every locale except Italian, which had a
separate community translation (#425/#426).
Translates the 1,289 strings that were genuinely missing translations,
locale by locale, matching each file's own established register,
terminology, and loanword conventions (verified against already-translated
sibling strings rather than assumed). Leaves the remaining ~1,067 flagged
values untouched where they're legitimately identical to English: brand
names, format/protocol acronyms, literal URLs, hex colors, and terms this
project already treats as loanwords in that language.
Verified against current main: pnpm typecheck 0 errors (9/9 workspaces),
i18n-parity/i18n-locale/tool-i18n/template-i18n 45/45 passing (23/23
cross-locale parity), biome check clean.
Lands albanobattistella's Italian translation update from #438, with
one duplicate key corrected so it passes CI.
Their PR retranslated several terms in it.ts, but a stray edit left
watermark-video with two `submit` keys (the second, "Applica
spaziatura interna", was unrelated leftover text). Removed the
duplicate so `submit` stays "Applica filigrana", consistent with the
other watermark strings in the same diff.
Original translation by albanobattistella in #438; duplicate-key fix
by the maintainer.
Claude-Session: https://claude.ai/code/session_018tNg52r7b3RMEeybv5LHCX
Co-authored-by: albanobattistella <34811668+albanobattistella@users.noreply.github.com>
gpu_available() answers "can ANY framework use a GPU" (torch, then ONNX, then
paddle). But torch tools consumed that shared boolean directly as
device = torch.device("cuda" if gpu_available() else "cpu"). On a GPU host where
gpu_available() is True via paddle or ONNX while torch is a CPU-only build, those
tools would route to a CUDA torch cannot use and crash. Transcription had the
mirror problem: it runs on CTranslate2 (not torch), so on a transcription-only
GPU box gpu_available() returned False and Whisper ran on CPU despite a GPU.
Add per-framework helpers to gpu.py:
- torch_gpu_available(): torch.cuda.is_available(), honoring SNAPOTTER_GPU.
- ctranslate2_gpu_available(): ctranslate2.get_cuda_device_count() > 0.
Point each tool at the helper for its own framework: upscale, noise_removal,
enhance_faces and restore use torch_gpu_available(); transcribe uses
ctranslate2_gpu_available(). ocr.py keeps gpu_available() (paddle-aware) and the
dispatcher keeps it for its startup GPU-status line. The SNAPOTTER_GPU override
check is factored into a shared _override_disables_gpu() helper.
TDD: 7 new tests in tests/test_gpu_detection.py cover both helpers (override,
CPU-only, absent framework), including the crux that torch_gpu_available() stays
False on a CPU-only torch build even when a GPU exists for another framework.
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
gpu_available() probed torch, then ONNX Runtime, then nvidia-smi, but never
paddle. The OCR bundle ships paddlepaddle-gpu with no torch or ONNX, so on an
OCR-only GPU host every probe missed the GPU: nvidia-smi saw it but returned
False by design, and OCR silently fell back to Tesseract (CPU, lower quality)
with no signal why.
Add a paddle probe as the last resort in gpu_available(). It runs only after
nvidia-smi confirms a GPU is physically present, and in an isolated subprocess,
because importing paddlepaddle-gpu on a GPU-less host segfaults and would wedge
the shared AI dispatcher. It returns True only when paddle reports both a CUDA
build and a visible device, signalling the result through the exit code so
paddle's own import chatter on stdout cannot corrupt the reading.
CPU-only and torch/ONNX GPU hosts are unaffected: the probe never runs on the
former (nvidia-smi finds nothing) and is never reached on the latter (the torch
step already returns True first).
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
* fix: ship RealESRGAN_x2plus.pth in the upscale-enhance bundle for offline CodeFormer
codeformer-pip 0.0.4 downloads RealESRGAN_x2plus.pth at import of
codeformer.app, unconditionally, even though enhance_faces calls
inference_app with background_enhance=False and never uses the background
upsampler. The weight was not bundled, so explicit CodeFormer face-enhance
(enhance-faces model=codeformer) failed in strict offline mode
(SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0) on a host that had never cached it -- the
guard raised before the import could complete.
Add RealESRGAN_x2plus.pth to the upscale-enhance bundle manifest (only that
bundle uses codeformer-pip; photo-restoration uses the CodeFormer ONNX path)
and link it in prepare_codeformer_weights alongside the other three weights,
replacing the download-or-error guard. Once the bundle ships it, the import
resolves offline and strict mode works.
Archive SHA256s updated in a follow-up once the bundle is rebuilt.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix: require face-detection bundle for enhance-faces + point manifest at the x2plus archives
enhance-faces runs MediaPipe face detection (blaze_face_short_range.tflite)
before CodeFormer/GFPGAN. That model ships in the face-detection bundle, not
the tool's primary upscale-enhance bundle, so a standalone upscale-enhance
install failed face detection (offline: hard error; online: a surprise
download) before reaching the codeformer path. Declare the dependency in
TOOL_EXTRA_BUNDLES like passport-photo does.
Update the upscale-enhance archive SHA256/sizes to the rebuilt bundles that
include RealESRGAN_x2plus.pth (amd64-gpu + arm64-cpu), verified to install and
run enhance-faces model=codeformer in strict offline mode with zero downloads.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* feat(api): parse DATA_DIR from env for 1.x import auto-detection
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* test(migrator): build 1.17.2 fixtures by replaying legacy migrations
Discovered the legacy migrations seed a Default team (0005) and builtin roles
(0007), so the replayed fixture carries them. Seed uses a distinct custom team.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* fix(migrator): self-adjusting column copy, jobs.status map, drop sessions, advisory lock
The importer now inserts only the intersection of source and live target columns,
so the three analytics_* columns 2.x dropped no longer break the first users INSERT
(and future dropped columns are handled generically). jobs.status is mapped onto the
2.x enum (error->failed). Sessions are no longer migrated. A pg_advisory_xact_lock
serializes concurrent replicas. Includes login-after-migrate and library assertions.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* test(migrator): CI drift guard fails when a required column is unfillable from 1.17.2
Introspects every NOT-NULL-no-default column of each migrated table in the current
schema and asserts the engine can fill it from a real 1.17.2 source. Turns a future
breaking schema change into a PR-time failure instead of a production import break.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* feat(migrator): orchestrator with detection, boot states, marker, blob count
sqlite-import.ts owns source resolution (explicit path, 'off' sentinel, DATA_DIR
probe), the four boot states (import/leftover/locked/none), the persisted
sqlite_import marker, and a read-only library-blob count. runBootImport wires them
together and catches TargetNonEmptyError as a benign multi-replica skip.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* feat(api): route boot through the 1.x import orchestrator; hide marker from non-admins
index.ts now calls runBootImport (which owns detection + the four boot states)
instead of the inline SQLITE_MIGRATE_PATH block. The sqlite_import marker is added
to SENSITIVE_KEYS (but not REDACTED_KEYS) so admins see the counts for the banner
while non-admins don't see the key at all.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* feat(migrator): add analyzeSqlite + dry-run/verify CLI
analyzeSqlite is a read-only pre-flight (no live Postgres): per-table row counts,
library-blob presence, and out-of-enum job statuses. The migrate:sqlite CLI now
lives in the orchestrator and supports --dry-run/--verify (prints the analysis and
exits without writing) alongside the existing import and --force.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* docs: add 1.x to 2.0 upgrade guide; fix volume-name casing
New apps/docs upgrade guide covering auto-detect, the SQLITE_MIGRATE_PATH override +
off opt-out, the dry-run, what carries over, locked-state recovery, and non-destructive
rollback. Leads with 'back up the WHOLE /data volume, not just snapotter.db' because
1.x WAL mode leaves data in snapotter.db-wal (surfaced by the real-image upgrade test).
Standardizes README/DOCKERHUB compose volume names on the canonical SnapOtter-data
casing so they match the repo compose and don't orphan an upgrader's volume.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* feat(web): admin 1.x migration banner + 21-locale strings
A one-time admin banner reads the sqlite_import marker from /v1/settings and shows
the import result (user + saved-file counts) on success, or a warning when a 1.x
database was found but not imported. Dismissal persists to a sqlite_import.dismissedAt
settings key. shouldShowMigrationBanner/parseMigrationMarker sit in feedback.ts with
the other shouldShow helpers; strings added to en.ts and all 20 other locales.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
* style(landing): biome-format Hero.astro trustBadges array
Pre-existing formatting drift on main (its Lint check was skipped on the merge that
introduced it); this PR's full Lint run surfaced it. Formatting-only, applied via
the repo's own biome formatter to unblock the required Lint check.
Claude-Session: https://claude.ai/code/session_01721WHAUGxnVk22qEeTub7w
Keep the top-nav feedback button always visible (icon plus label on desktop, icon-only on mobile) instead of hiding it when an instance opts out of analytics. When analytics is off, the dialog keeps the typed message and hands off to a prefilled GitHub issue plus a contact@snapotter.com email, with no fake Thanks. Adds a feedback.yml issue template, URL builders, and feedback strings across all 21 locales.
Claude-Session: https://claude.ai/code/session_01XVrHKXwzZDWBWgkGQdPZ3A
Adds a prominent Keep it free sponsor button to the top nav, linking to https://github.com/sponsors/snapotter-hq. Solid orange pill on desktop (left of the avatar), orange heart icon on mobile. Opens in a new tab with rel=noopener noreferrer, so no referrer or user data leaks, and it adds no passive network activity (offline-mode compatible). Fires an opt-in, property-less sponsor_clicked analytics event. Adds sidebar.sponsor and a11y.sponsorLink across all 21 locales.
Claude-Session: https://claude.ai/code/session_01DnYLLA5z4Uf1GDeEPENVgr
Lands albanobattistella's Italian translation of the feedback strings from #425, with two mistyped keys corrected (great and adminCardDescription). Verified against main: typecheck 0 errors, i18n parity 23/23, Biome clean.
Co-authored-by: albanobattistella <34811668+albanobattistella@users.noreply.github.com>
* fix: remove all automatic third-party egress (OSM tiles, Scalar fonts, editor Google Fonts, AI model download fallbacks)
Phone-home audit follow-up. The product no longer makes any automatic
third-party request; user-initiated click-outs stay, and production now
fails closed on missing AI models.
1. GPS leak via OSM tiles: the strip-metadata panel auto-loaded
tile.openstreetmap.org tiles encoding the photo's GPS position. The
Leaflet mini-map is gone; coordinates render as text plus an explicit
View on map link (openstreetmap.org, opens on click only). Removed
tile.openstreetmap.org from the CSP img-src, dropped the leaflet
dependency, added the viewOnMap i18n key to all 21 locales.
2. Scalar docs fonts: /api/docs loaded Inter and JetBrains Mono from
fonts.scalar.com. Scalar now renders with withDefaultFonts: false and
both --scalar-font and --scalar-font-code pinned to system stacks;
fonts.scalar.com removed from the docs CSP font-src. Verified by
injecting GET /api/docs/: config carries withDefaultFonts false and
the served page has no fonts.scalar.com reference.
3. Editor Google Fonts: the editor font picker built
fonts.googleapis.com stylesheet URLs for 25 web fonts the served CSP
already blocked. The remote loading path is deleted; the picker now
offers system fonts only, with a SELF_HOSTED_FONTS seam (FontFace API,
same origin) for bundling fonts later. Unknown families saved in old
documents fall back to the browser default.
4. Python sidecar fails closed on model downloads: new
packages/ai/python/offline_guard.py gates every runtime download
fallback (inpaint, outpaint, restore, noise_removal, detect_faces,
enhance_faces, face_landmarks, red_eye_removal, remove_bg, ocr,
transcribe, upscale) behind SNAPOTTER_ALLOW_MODEL_DOWNLOAD=1 with an
actionable error. Bundled models keep working untouched.
5. OCR and transcription library-internal downloads: unbundled PaddleOCR
language and detection fallbacks now raise the guard error naming the
language instead of resolving models over the network; faster-whisper
gets local_files_only when downloads are off.
6. GFPGAN and CodeFormer cwd-relative weights: facexlib and
codeformer-pip resolve helper weights relative to the process cwd and
fetch them from GitHub when absent. They are now symlinked from the
installed bundle files under MODELS_PATH/gfpgan/facelib before the
libraries load, failing closed when unresolvable.
Defense in depth: HF_HUB_OFFLINE=1 and TRANSFORMERS_OFFLINE=1 are set in
the runtime image and in the sidecar spawn env; install_feature.py lifts
them for user-initiated bundle installs and restores them afterwards
(it can run in-process inside the dispatcher). SNAPOTTER_ALLOW_MODEL_DOWNLOAD
is documented in .env.example, default off.
Validation: typecheck 9/9 workspaces, Biome clean on touched files,
5178 unit tests pass, py_compile on all touched scripts, guard behavior
exercised in both dispatcher exec and per-request import modes, zero
remaining runtime references to the three hosts. Docker build and live
AI inference need post-merge verification on the GPU host.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix: allow AI model downloads by default, make strict offline mode opt-in
Product call: ease of use first. The download gating from the previous
commit inverts its default: runtime model fetches (public model weights
only, never user data) are allowed out of the box so AI tools self-heal,
and SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0 becomes the explicit strict offline
mode for airgapped deployments, where every fallback raises the
actionable error instead of fetching.
Changes: offline_guard blocks only on an explicit 0/false; the
unconditional HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE image ENV is removed
and bridge.ts sets those flags for the sidecar only in strict mode;
.env.example documents the new default; install_feature's lift/restore
stays. All bundled-path preferences, pre-existence checks, and symlink
pre-placement remain, so installed bundles never trigger a download.
The OSM, Scalar font, and editor font fixes are unchanged.
Validation rerun: typecheck 9/9, Biome clean on touched files, 5178
unit tests pass, py_compile on touched scripts, guard behavior verified
for unset/1 (allowed) and 0/false (blocked with the new message).
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Conversion engines wrote their own names into PDF metadata: LibreOffice,
Ghostscript, pdfcpu, WeasyPrint, and PDFKit all stamped Producer/Creator
on generated files. A new doc_scrub_meta docs-profile script (PyMuPDF)
rewrites both fields to SnapOtter and drops the stale XMP copy; the
worker applies it to the 25 PDF-generating tools before outputs reach
object storage. Best effort by design: any failure keeps the original
bytes and only logs a warning.
Deliberately untouched: tools that edit the user's own PDF and preserve
its metadata (qpdf edits, sign, flatten), encrypted outputs (copied
through), and pdfa-convert, where a metadata rewrite risks PDF/A
conformance.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Fixes 15 defects found by a max-effort multi-agent review of the last 6
merged PRs (#388, #390, #391, #392, #393, #394), all adversarially
verified before fixing.
Install queue + dispatcher (the serious cluster):
- features.ts: finalize the installer child exactly once. A failed spawn
fires both "error" and "close", and the second event released the file
lock and active slot that pump() had just handed to the next queued
bundle, letting two pip processes write the same venv concurrently.
Outcome recording now happens before pump() so the next bundle's first
progress frame cannot race the previous install's bookkeeping.
- feature-status.ts: keep failed-install errors in a per-bundle map
instead of the single progress slot. With the queue auto-starting the
next install, the slot was overwritten within seconds and a failed
install vanished without ever surfacing to GET /features.
- bridge.ts: scope child lifecycle per process (stopped-children set +
request generation tags) instead of an instance-wide shuttingDown flag
that the next spawn reset. A stale SIGTERMed child's late close event
could record a phantom crash (5 of which permanently disable the
dispatcher), null out the freshly spawned child, and reject the new
child's pending requests. The request-timeout kill path still counts
as a real crash.
- install_feature.py: the pre-write disk re-check measured ai_dir's
filesystem even when budgeting the cross-filesystem copy that lands on
the venv's disk; now each budget is checked against the filesystem the
bytes actually land on, so ENOSPC cannot strike mid-write and leave
site-packages half overwritten.
Behavior regressions:
- embed-subtitles: preserve pre-existing subtitle tracks (0:s?) and MKV
attachments (0:t?) that the -map 0:v:0/0:a? rewrite silently dropped;
data streams stay unmapped on purpose (the actual MPEG remux fix). The
new subtitle maps first so the language tag hits the right stream.
- usage-survey-overlay: fail closed when the settings fetch fails; the
fail-open path rendered the blocking survey against an unhealthy API
and soft-locked admins, the lock-out class #392 fixed.
- features-store: queued bundles poll instead of each holding an SSE
connection (Install All could pin 7 EventSources and exhaust the
browser's 6-per-origin HTTP/1.1 limit, hanging the whole app);
listenToProgress closes any prior stream and stops any poll before
subscribing; installAll skips bundles already installing or queued.
Contracts, tests, i18n:
- openapi.yaml: add "queued" to the features status enum and document
downloadBytes/installedBytes (Schemathesis conformance).
- feature-lifecycle e2e: queue transcription (~0.5 GB) instead of ocr
(~6 GB) and give the test a budget that covers both install drains
(the stacked waits exceeded the old 900s timeout).
- docker-compose.qa.yml: parameterize the host port (QA_APP_PORT) so
QA_PROJECT_NAME concurrent stacks can actually bind.
- compare + watermark-image: restore per-input error attribution
("Invalid first/second image", "Invalid watermark image") lost in the
shared-handler migration.
- ai-features-section: the "{size} on disk" suffix now goes through
i18n; key added to all 21 locales.
- watermark-image + content-aware-resize: migrate to the shared
inputHandlerFor("image") chain like compare/vectorize/compose, fixing
drift in the inline copies (no SVG sanitize, no RAW extension hint,
no AVIF probe).
Verified: typecheck across 9 workspaces, Biome clean on all changed
files, 584 targeted unit tests and 249 integration tests green
(including real-ffmpeg embed-subtitles runs). One unit test updated to
the new poll-while-queued contract with a single-EventSource assertion.
Claude-Session: https://claude.ai/code/session_017mR1HiHaf3a1BmUtrHX4j3
* fix(api): correct format/filename/container handling across tool routes
Found during a comprehensive QA sweep exercising every tool against its
full accepted-format matrix:
- watermark-image, compose: preserve the requested output format and a
matching download filename/extension instead of always emitting the
source format
- compose: crop oversized overlays to the visible base area instead of
crashing Sharp's composite, and reject only overlays fully outside the
base image instead of any oversized one
- compare, vectorize: switch to the shared image input handler so
filenames and formats like .svgz/.tga/RAW survive validation instead
of being rejected pre-processing
- tool-factory, images-to-video: normalize frames through Sharp before
handing them to FFmpeg, fixing GIF/AVIF/RAW image-to-video jobs that
previously failed or hung
- media-tool, replace-audio, embed-subtitles: fix legacy container
MIME/codec handling for MPEG sources and subtitle remux cases
- files: expand download MIME mapping for text/data/document/video/audio
outputs that were falling back to a generic content type
- convert-document/presentation/spreadsheet: same-format conversions now
return the original validated file instead of erroring or producing
corrupt tiny output
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): dropzone a11y, stale localStorage getter, dead code
- dropzone: stop making the whole drop-zone section clickable/focusable.
A section acting as an interactive element around a real upload button
is a nested-interactive-element anti-pattern that confuses screen
readers; drag-and-drop doesn't need focus semantics, only the button
fallback does. Keeps that button semantic and keyboard-reachable.
Updates the two e2e call sites that clicked the section directly.
- api, use-auth: read through window.localStorage via the existing API
storage helper instead of the bare global, which resolves to Node's
experimental localStorage getter under Vitest and threw
- find-duplicates-settings, info-settings, login-page: remove dead code
(unused zip-download handler, a stale mount-only effect dependency
that left cached info stuck at reused indices, an unused response
variable)
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(i18n): pt-BR, zh-CN, zh-TW were silently falling back to English
The locale loader looked up dynamic-import exports by the raw locale
code (mod["pt-BR"], mod["zh-CN"], mod["zh-TW"]), but those three modules
export camelCased bindings (ptBR, zhCN, zhTW) since identifiers can't
contain hyphens. The lookup returned undefined and every consumer
silently fell back to English for these three locales. Replaces the
generic lookup with explicit per-locale loaders so the mapping can't
drift out of sync again.
Also updates the dropzone helper copy across all 21 locales to match
the drag-only dropzone wording from the previous commit.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(docs): clear build warnings in the VitePress site
- config.mts: add an onwarn handler for the @vueuse INVALID_ANNOTATION
warnings emitted during the docs build
- deployment.md: the caddyfile code fence language isn't a shiki grammar
VitePress ships with, so it warned on every build; use txt instead
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* test(qa): update QA harness for the drag-only dropzone and regen metadata
- api-sweep, qa-helpers, verify-ai: add JSON-body tools, multi-input
secondary fixtures, async polling for slow valid jobs, 501
FEATURE_NOT_INSTALLED skip handling, and safer per-tool settings
- input-preview, pipeline-ui specs: update upload flow for the
drag-only dropzone surface
- add tests/fixtures/data/valid/chart.json, a valid chart fixture the
updated helpers route to
- regenerate tools-meta.json against current TOOLS[]
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(security): close a login timing side-channel, harden zip-slip tests
Found during a black-box security sweep of the real auth-enabled
production container: a nonexistent username returned 401 in ~3-10ms,
while a wrong password for a real user took ~35-42ms, because scrypt
verification only ran when a user row existed. That timing gap lets an
attacker enumerate valid usernames without ever guessing a password.
Now runs verification against a cached dummy hash on the unknown-user
path too, so both cases cost the same regardless of outcome.
extract-zip already had a relative-traversal regression test
(../evil.txt), but its absolute-path rejection branches
(name.startsWith("/") / startsWith("\\")) had none. Added the three
missing cases: deep relative traversal, absolute Unix path, and
Windows-style absolute path.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* test(qa): add UI-driven AI bundle install scripts
QA_PROMPT.md's Phase 2 requires installing AI models the way a user
does -- through the UI, on demand from HuggingFace -- and treats the
curl-based admin install endpoint as fallback-only. Nothing in the
harness actually drove that flow; tests/qa/seed-ai-models.sh installs
via docker exec + pip, which is further from a real user than even the
API fallback.
install-ai-bundles-ui.mts logs in, opens Settings > AI Features,
screenshots the pre-install state, clicks Install All, and screenshots
progress -- then exits, since installs continue server-side once
triggered. verify-ai-install-complete.mts polls bundle status,
screenshots the completed state, and runs one real tool per installed
bundle to prove the freshly-downloaded model actually executes.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(qa): correct the apiToolPath import in the AI verify script
Dynamic import of the package name failed under tsx's module resolution
from apps/api's node_modules context; use the same relative-path import
api-sweep.mts already uses successfully.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): correct AI bundle size estimates shown before install
Measured real downloads during GPU-node QA verification: photo-restoration
pulls ~4.4GB (was advertised as 800MB-1GB, off by 4-5x) and ocr pulls
~5.5GB (was advertised as 3-4GB). Both estimates only accounted for model
weights, not the pip dependencies (torch/paddle) that come down with them.
Updated to reflect actual total download size, since that's what a user
deciding whether they have the disk/bandwidth actually needs to know.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): make desktop Settings reachable when auth is disabled
AvatarDropdown (the only desktop entry point to Settings) was gated
behind `!isMobile && authEnabled`. With AUTH_ENABLED=false the synthetic
anonymous admin user should have full Settings access per how auth.ts
documents this mode -- and the mobile bottom nav already worked this way,
showing Settings unconditionally. Desktop just had a stray extra gate the
component doesn't need: AvatarDropdown already resolves its own username
internally (falling back to "admin") and reads authEnabled itself where
it actually matters (hiding the Logout button). Removed the outer gate;
verified end-to-end against a fresh AUTH_ENABLED=false instance -- avatar
now renders, Settings opens, shows the anonymous/Admin identity correctly.
Also documents (not changes) a related finding in install_feature.py:
detect_arch() always resolves amd64 hosts to the GPU-bundled archive
variant regardless of actual GPU presence, since no CPU-only amd64
archive is published to the bundle repo yet. Left as a code comment
rather than a behavior change, since requesting an unpublished archive
key would hard-fail installs entirely -- worse than the current
oversized-but-working download. Full detail in the QA report.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(ai): stop logging expected dispatcher reloads as crashes
After each AI bundle install the Python dispatcher reloads because the
venv changed, and after every app shutdown it's SIGTERMed. Both took the
close handler's `code !== 0` branch (SIGTERM makes the exit code null),
so they were counted as crashes -- producing an alarming "crash" line in
the logs and a pointless ~1s recovery backoff after each of 7 installs.
A `stopping` flag set in shutdown() lets the close handler tell an
intentional stop apart from a real crash. The request-timeout kill path
deliberately does not set it, so a genuinely hung script still records a
crash and the 5-in-60s permanent-disable threshold is untouched.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(api): return a clean message when content-aware resize times out
Carving a very high-resolution image down to a tiny target could exceed
the caire subprocess timeout, and the raw error forwarded to the user was
caire's terminal output -- ANSI color codes and progress-spinner control
characters -- instead of anything actionable. Now: the timeout path
throws a clear "timed out; try a smaller image or larger target" message
(keeping the raw stderr as `cause` for server logs); friendlyError()
strips ANSI/control chars centrally so any subprocess dump surfaced
through the shared sanitizer is plain text; and the content-aware-resize
route (a custom route that bypassed the sanitizer) now routes its error
paths through friendlyError like every other tool.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(ai): stop bundle installs from exhausting host disk
Installing an AI bundle on a tight-disk host could push the root
filesystem to zero bytes free after the preflight check had already
passed. Two root causes:
- move_tree used copytree+rmtree, so during the move the extracted
payload existed in both staging and the venv at once -- a full
transient doubling on disk. Rewrote it to rename entries (a cheap
metadata op on the same filesystem, no copy), falling back to a copy
only across filesystems.
- the preflight budget used the manifest's extractedSize verbatim, which
is 0 for several archives, collapsing the estimate to just the
compressed size. Added a conservative fallback (3x compressed) so a
missing value can't under-reserve.
Also added a real-on-disk re-check immediately before the first
destructive venv write (measuring the actual extracted payload and
whether the move needs extra space for a cross-filesystem copy), which
also now covers the offline-import path that previously skipped the disk
check entirely; wrapped the moves so an out-of-space failure returns a
clean actionable error instead of a traceback; and made the disk check
resolve the nearest existing ancestor so it never throws on a
not-yet-created venv path.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* feat(web): show the real per-arch AI bundle download size
The bundle cards and install prompt showed a hardcoded, architecture-blind
estimatedSize string. That's misleading: amd64 hosts always pull the
CUDA-inclusive archive (there's no CPU-only amd64 variant published), so a
bundle labelled "1-2 GB" can actually download several times that, while
arm64 pulls a much smaller archive for the same label. The manifest
already carries the real per-arch compressedSize (and extractedSize where
measured), so surface those: a new optional downloadBytes/installedBytes
on FeatureBundleState, populated in getFeatureStates() for this host's
arch (resolver mirrors install_feature.py detect_arch), shown by the UI
when present with estimatedSize kept as the fallback label. Also nudged
upscale-enhance's fallback string (4-5 -> 5-6 GB) to match its real
compressed size, consistent with the earlier photo-restoration/ocr fixes.
Fields are optional so demo/mock and existing tests stay compiling; the
manifest's extractedSize is 0 for a few archives, which now surfaces as
null rather than a bogus 0.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): move the AI install queue to the server so it survives tab close
Installing multiple bundles could silently lose all but the first. The
server rejected a concurrent install with 409, so the client worked
around it by queueing the rest in browser-local state and only POSTing
each once it saw the previous finish. A single POSTed install is durable
(the installer child is detached from the request), but a queued one had
zero server footprint -- close the tab mid-queue and those installs
vanished with no error, while the UI still showed them "Queued". The
client "mutex" didn't even serialize: the queued bundles' local waits all
resolved at once and raced into concurrent POSTs that 409'd each other.
Now the queue lives on the server (a small in-memory FIFO leaf module).
The install endpoint enqueues instead of 409-ing and returns
202 {jobId, queued}; a pump starts the next bundle when the current one's
child exits (and after an offline import releases the lock), all behind
the existing venv + file locks, which are unchanged. The client just
POSTs every bundle immediately and reflects the server-reported
queued/installing status; Install All fires all POSTs and lets the server
serialize them, keeping the one-shot retry-on-failure. Adds "queued" to
FeatureStatus (the bundle card already rendered that state) and surfaces
it from getFeatureStates. In-memory is deliberate: it matches the
existing contract (survives a tab close, not a server restart, which
already clears the lock on boot).
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(qa): don't log env-derived credentials in the AI-install script
CodeQL flagged clear-text logging of sensitive information: the login
status line interpolated the QA base URL and username (both read from
the process environment) into a console.log. Replaced with a static
message. QA helper only, but it's a real hygiene issue and cleared the
high-severity code-scanning alert on the PR.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
