* fix(ai-bundles): lock the numpy-1.x ABI closure so the OCR bundle can't strand scipy
The OCR bundle installs paddleocr[doc-parser] 3.4, whose dependency closure drags
numpy 1.26.4 up to 2.5.1 and pulls scipy/scikit-learn/pandas wheels built against
the numpy 2.x ABI. build-bundle.sh re-pinned only numpy (basePackages), so those
numpy-2.x wheels stayed behind; the by-dir-name site-packages diff then shipped
them, and once merged onto the numpy==1.26.4 base they raise "numpy.dtype size
changed" on import.
Because the dispatcher pre-imports every ML library at startup and disables all AI
after 5 crashes in 60s, one stranded scipy takes down every AI tool, not just OCR
(observed on a CPU host: remove-background worked before the OCR bundle and broke
after). All-7 installs escaped it through last-writer-wins ordering; a subset
install did not, which is why it surfaced only intermittently.
Fix: add a manifest "constraints" list (numpy, scipy, scikit-learn, scikit-image,
pandas pinned to numpy-1.x-ABI versions) and apply it via PIP_CONSTRAINT to every
bundle pip install, so no bundle can pull a numpy-2.x wheel. paddleocr 3.4.1 still
resolves cleanly under the lock and the pinned stack imports without ABI error on
numpy 1.26.4 (validated on py3.12). Also import scipy/sklearn in the OCR path of
verify-bundle.sh so CI catches this class in isolation, and add a manifest
regression test.
Note: the published bundles must be rebuilt and republished (ai-bundles.yml) for
this to reach already-installed bases.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* chore(ai-bundles): sync OCR manifest sha256 to the rebuilt numpy-1.x bundles
Rebuilt the OCR bundle for both arches with the numpy-1.x-ABI constraints from
this PR and republished the tars to deepsafe/feature-bundles/v2.0.0, then updated
the baked manifest sha256 and sizes so installs verify against the fixed archives:
amd64-gpu 5.93 GB sha 2a00a3184f6a635f1fa9ae2a6517ad740a11f9e5ff58c098d2fd369a2bb1e16b
arm64-cpu 1.98 GB sha 6868c264069dcb74c6675c0b1f58dc1c9f60d9aa4459725e3dbde07a99a6a09a
Both tars ship scipy 1.12.0 / scikit-learn 1.4.2 / pandas 2.2.2 (numpy-1.x-ABI)
and zero numpy-2.x wheels, verified by listing the archive contents.
Stopgap note: these tars were built against the ghcr.io latest base (the 2.0.0
image is not published to GHCR), so they are not byte-identical to what the CI
build will produce. When ai-bundles.yml rebuilds at the 2.0.0 release, it will
mint fresh sha256 values and this manifest must be re-synced to them.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* 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
* fix(passport-photo): require the face-detection bundle, not just background-removal
Passport Photo runs face-landmark detection (face_landmarks.py, gated to the
face-detection bundle) before background removal (background-removal bundle),
but it was only declared under and guarded against background-removal. A user
who installed only Background Removal passed every JS-side check, then hit a
late "feature_not_installed" from the Python dispatcher gate when the analyze
step ran face landmarks, and the UI never told them Face Detection was needed.
- shared: add TOOL_EXTRA_BUNDLES + getRequiredBundlesForTool so a tool can
declare more than one required bundle (passport-photo needs background-removal
and face-detection). enablesTools is untouched, so the one-tool-per-bundle
invariant still holds.
- api: isToolInstalled() now checks every required bundle; add
getFirstMissingBundleForTool() so the analyze and base routes, pipeline (both
guards) and batch report the bundle the user actually still needs.
- web: the proactive install prompt (tool-page) and features-store treat a tool
as installed only when all required bundles are present, and point the prompt
at the first missing one (sequential install, no new UI).
Refs #327
* test(passport-photo): deterministic integration coverage for the two-bundle guard
Boots the real API with an isolated DATA_DIR and controls installed.json to
prove the HTTP route behavior end-to-end:
- nothing installed -> 501 naming background-removal
- only background-removal installed -> 501 naming face-detection (issue #327)
- both installed -> guard passes (not 501)
- base route reports face-detection too
Refs #327
Three production crashes from the snapotter/node Sentry project.
feature-status (NODE-12): a valid-JSON-but-wrong-shape installed.json
crashed boot via Object.keys(data.bundles). readInstalled() now
normalizes any unusable shape to { bundles: {} }, and the boot recovery
call is wrapped so cleanup can never fatal startup.
image-viewer (NODE-15/17/18): drag-to-pan read .x off an undefined
use-gesture memo on pointerUp or a pinch-into-pan. A guarded pure helper
(resolvePanStart) now falls back to the live pan offset.
Fastify (NODE-14): raised pluginTimeout to 60s so slow self-hosted boots
do not fatal at @fastify/static.
Fix the Unit Tests CI job: bundleRepo now asserts deepsafe/feature-bundles (intentional, temporary); extractedSize relaxed to >= 0 (best-effort field, build script does not measure uncompressed size). sha256 + compressedSize remain strict. Full unit suite: 4546 passed.
Make the full pnpm test:docker suite pass the env-dependent tests (~85 failures):
- Dockerfile.test: ENV LD_LIBRARY_PATH=/usr/local/lib so the built libheif 1.21 is not shadowed by the base image's older system libheif (heif-dec failed with an undefined-symbol error -> 'No HEIF decoder found' on 72 HEIF tests); add libjxl-tools (JXL) and ghostscript + the ImageMagick policy.xml EPS allow-edit.
- docker-compose.test.yml: SYNC_WAIT_MS=30000 so sync-wait image tools do not fall back to 202 under single-container contention (10 tests).
- install_feature.py: guard tarfile.extractall(filter='data') behind Python>=3.12 (bookworm ships 3.11); the manual entry guards already protect.
- feature-status.test.ts / docker-file-secrets.test.ts: skip the two cases that cannot hold inside the container (/.dockerenv always present; root bypasses chmod). Verified on host: all still pass.
- Use /opt/venv directly when --entrypoint bash bypasses entrypoint.sh
- Use sys.executable for all pip calls (not bare pip)
- Override entrypoint in CI workflow to avoid startup banner
- Fix Biome formatting (template literals, try/catch blocks)
Closes the "e2e never runs in CI" hole. Adds per-PR e2e smoke gate,
nightly full-suite workflows, parallel vitest forks (per-fork DBs),
Playwright parallel/serial/visual projects against production builds,
metadata-generated test suites (drift guards, hostile inputs, format
matrix, pairwise settings, property-based fuzz), Stryker mutation
testing, Schemathesis API fuzz, coverage ratchet, and fixes for three
session-poisoning bugs that caused 200+ serial-bucket failures.
Bug fix included: favicon/split/bulk-rename could hang clients forever
when ZIP streaming failed after reply.hijack().
The enhance-faces tool requires MediaPipe for face detection, but the
upscale-enhance feature bundle did not include mediapipe in its pip
packages. Users who installed only the upscale-enhance bundle got
"Face detection requires MediaPipe" errors. Added mediapipe to both
amd64 and arm64 package lists, matching the pattern used by the
face-detection and photo-restoration bundles.
Also added feature-manifest.test.ts with 25 tests validating bundle
dependency completeness to prevent similar missing-dependency bugs.
Closes#129
- Add 55 unit tests for feature-status.ts (installed.json CRUD, cache
behavior, install lock, model verification, crash recovery, composite
state) using real temp directories
- Add 36 integration tests for full install/uninstall lifecycle against
Docker containers (face-detection bundle, SSE progress, tool gates,
shared model protection, concurrent install prevention, auth guards,
container restart recovery)
- Fix noise-removal CPU timeout by adding megapixel-based timeout
calculation (120s/MP, min 5 minutes)
- Fix Playwright auth storage state race condition (mkdirSync before
saving analytics-user.json)
- Fix 2 skipped tests in fixes-verification.spec.ts by replacing
external ~/Downloads/sample dependency with existing test fixtures
- Enable skipped analytics-consent settings toggle test
- Restructure features.spec.ts to manage bundle state (uninstall/
reinstall OCR) so 501 guard tests run instead of skipping
- Update noise-removal test mock to include sharp metadata() method
