mirror of
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cb5db59f77e6b77fd9a575996b8514d6760aaf5b
21
Commits
| Author | SHA1 | Message | Date | |
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 SnapOtterandGitHub
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a731c3d1fe |
fix: reliable, self-healing AI feature-bundle installs (#472)
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. |
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SnapOtterandGitHub
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6e3a14ec6b |
fix: remove automatic third-party egress of user data + optional strict offline mode (OSM tiles, Scalar fonts, editor fonts, AI model downloads) (#422)
* 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 |
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SnapOtterandGitHub
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b4375e558d |
fix: harden install queue/dispatcher lifecycle and repair review-sweep regressions (#395)
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 |
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SnapOtterandGitHub
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b37faed95f |
fix: QA sweep - tool routes, security, i18n, a11y, + AI bundle install hardening (#393)
* 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 |
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SnapOtterandGitHub
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3b50bcdc5c |
fix(ai-bundles): repair bundle build + publish pipeline (deepsafe repo, CPU provider, manifest)
Bundle build/publish fixes: CPUExecutionProvider in rembg build, pip/import/arm64 deps, hf-CLI publish to deepsafe/feature-bundles, real manifest sha256+sizes, installer fallback repo. |
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SnapOtter
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1f5b222267 |
test: fix docker test-image env and container-specific test guards
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. |
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SnapOtter
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a4fa3ce2a7 | feat: rewrite install_feature.py for pre-built tar bundles | ||
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SnapOtterandGitHub
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abd1efb46d |
fix: add retry logic to HuggingFace model downloads (#201)
HuggingFace snapshot_download had no retry logic, causing lama-onnx and codeformer-onnx installs to fail on transient network errors. Direct URL downloads already had 3 retries with exponential backoff -- this adds the same pattern to HF downloads (3 attempts, 10s/20s backoff). |
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SnapOtter
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e03c6089af |
feat: support downloadFn-based model manifests and improve install error messages
Add support for models defined via downloadFn/args (rembg_session, hf_snapshot) in bundle verification, recovery, and uninstall paths. Previously only path-based models were tracked, so bundles using rembg or HF snapshot downloads appeared broken after install. Also improve pip install error messages with user-friendly hints for common failures (basicsr build issues, OOM, disk full) and add better error context for rembg session download failures. |
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SnapOtter
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36431bce48 |
fix: improve AI feature install error handling and resource limits
Handle OOM kills (exit code 137) with actionable memory guidance, filter ANSI/progress noise from error output, add --no-cache-dir to pip installs, reduce download concurrency to 2, and bump default container memory from 4g to 6g. |
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SnapOtter
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a329dac004 |
fix: guard against division-by-zero in HR-matting predict and register session in installer
The BiRefNetHRMattingSession.predict normalization crashes when all pixels share the same value (ma == mi). Use a guarded denominator so uniform-alpha inputs produce a zero mask instead of a NaN explosion. Also adds _register_birefnet_hr_matting() to install_feature.py so the HR-matting model can be downloaded during feature installation, matching the existing registration in remove_bg.py. |
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SnapOtter
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4f81b29fbc |
fix: AI feature install failures — missing rembg session and Fastify 415 (#102, #103)
Register custom BiRefNet-matting ONNX session in install_feature.py so
rembg.new_session("birefnet-matting") no longer raises ValueError during
on-demand installs. The session was already registered in remove_bg.py
(runtime) and download_models.py (build-time) but was missed in the
install path, causing background-removal bundle installs to always fail.
Send JSON body on install/uninstall POST requests to avoid Fastify 5's
strict content-type parser rejecting body-less POSTs with 415.
Fix error message extraction to preserve structured {"error": ...} JSON
from the Python script and filter out pthread_setaffinity_np noise.
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SnapOtter
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8bc8b18f90 |
fix: version constant and CPU torch install for Docker release
- Bump APP_VERSION to 1.15.11 (was hardcoded at 1.15.9, causing health endpoint to report wrong version in Docker images) - Fix cpu_fallback_packages() splitting --index-url into separate pip install arguments, breaking torch install on CPU-only amd64 |
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SnapOtter
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bf0307d87d |
fix: QA sweep — 7 bugs fixed, 17 test corrections
Code fixes: - Sidebar state bleed: reset file store on HomePage mount - restore-photo: raise error instead of silently skipping colorize when DDColor model missing - PaddleOCR OOM: cap input images to 2048px before OCR inference - Torch CPU optimization: use --index-url .../whl/cpu on CPU nodes Test fixes: - upscale: add exact:true to scale factor button locators - smart-crop: add exact:true to "Pad to square" locator - colorize: use regex for model button names (Best/Balanced/Fast) - enhance-faces: use .first() for ambiguous percentage display - passport-photo: fix DPI locator, .or() compound, generate fallback - people: update maxUsers assertions for unlimited (0) default - automate: "Save Pipeline" → "Save" matching actual button text - tools.test: add resize to Sharp mock chain for OCR tests |
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SnapOtter
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0309e0f680 |
chore: deploy to Cloudflare Pages and update branding
- Add Cloudflare Pages deployment for landing page (snapotter.com) and docs (docs.snapotter.com) - Create deploy-landing.yml and update deploy-docs.yml workflows - Update CI to ignore apps/landing/** paths - Fix logo transparency (remove white background) across all apps - Recreate social-preview.png with SnapOtter branding - Update all docs URLs from GitHub Pages to docs.snapotter.com - Update VitePress config: light theme default, fix llms.txt paths - Add .vitepress/cache/ and .env.* to gitignore |
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ashim-hq
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f67a03bb36 |
fix: resolve all audit findings — e2e coverage, feature system hardening, visual baselines
- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize, restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal) closing all HIGH/MEDIUM coverage gaps from the test matrix audit - Fix ensureAiDirs() crash on non-Docker environments by gating on isDockerEnvironment() — prevents ENOENT when /data doesn't exist - Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature manifest for broader Python version compatibility - Add Python 3.14 version guard warning in install_feature.py - Remove duplicate torchvision shims from upscale.py and enhance_faces.py (dispatcher.py already handles this at startup) - Remove orphaned tools.batch i18n key and dead pipeline-builder filter - Regenerate 4 visual regression baselines for current UI state - Add data-testid to passport-photo generate button for E2E testability |
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AshimandClaude Opus 4.6
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39e27635c8 |
fix: audit fixes for cross-platform correctness
- Fix NameError in restore.py: face enhancement loop used undefined variable `i`, now uses enumerate() - Fix gpu.py ONNX fallback: previous smoke-test with empty bytes always raised, making GPU detection unreachable via the ONNX path. Now uses nvidia-smi hardware check after confirming CUDA EP is compiled in — works on Linux, Windows, and gracefully fails on macOS - Fix cpu_fallback_packages stripping CUDA-specific index URLs when replacing paddlepaddle-gpu with paddlepaddle for CPU-only systems Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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AshimandClaude Opus 4.6
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01d30cfb61 |
fix: pin torch cu126 for GPU compatibility and fix cross-platform bugs
- Pin torch==2.6.0+cu126 and torchvision==0.21.0+cu126 in feature manifest to prevent NCCL symbol mismatch on CUDA 12.6 base images - Move lpips after torch in install order to prevent wrong version resolution from PyPI - Add einops to upscale-enhance common deps (required by SCUNet) - Update cpu_fallback_packages to handle multi-package CUDA torch entries on amd64 without GPU - Fix gpu.py ONNX CUDA detection: replace hardcoded .so path with cross-platform session smoke-test - Fix os.dup(1) crashes on Windows in upscale, enhance_faces, and noise_removal by wrapping in try/except with sys.stderr fallback - Guard top-level numpy/cv2 imports in colorize.py and restore.py with helpful error messages - Add weights_only=False fallback for torch.load in noise_removal - Fix integration tests to accept 501 for uninstalled AI features and 422 for missing system tools (exiftool, libheif) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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AshimandClaude Opus 4.6
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6edb92c242 |
feat: add output filename suffixes, CPU fallback for GPU packages, and fix e2e tests
- Add tool-specific suffix to output filenames so downloads don't overwrite originals (batch & single-tool routes) - Skip deleting shared models when uninstalling a bundle that shares models with another installed bundle - Auto-detect NVIDIA GPU and swap GPU-only pip packages (onnxruntime-gpu, paddlepaddle-gpu) for CPU equivalents - Refactor docker-compose with YAML anchors and explicit cpu/gpu profiles - Add libheif-plugin-x265 to Dockerfile - Fix install-all queue logic to handle concurrent individual installs and clear stale errors - Unify playwright docker config to use same test dir with API_URL env var - Fix flaky e2e selectors, rename Strip Metadata → Remove Metadata, handle collage custom dropzone, improve fallback test image generation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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ashim-hq
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10bdc24a4a | feat: on-demand AI feature install with progress indicators | ||
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ashim-hq
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7ffbd5e3c6 |
feat: add Python install script for on-demand AI feature bundles
Reads the feature manifest, installs pip packages (common + arch-specific), downloads models in parallel with atomic rename, and writes installed.json. Includes disk space pre-check, NCCL conflict handling, retry logic, and progress reporting via stderr JSON lines. Also updates the feature route to pass manifestPath and modelsDir as CLI arguments. |