unsloth/studio/backend/main.py
Michael Han d5cf96d628
Studio: add local speech-to-text dictation engine (#7095)
* Studio: add Voice settings tab (dictation, dictionary, read aloud)

New Voice tab in Settings, placed just before About:

- Dictation: microphone picker, browser STT engine, recognition language,
  and an inline mic test with a live transcript
- Dictation dictionary: entries rewrite matching speech to their exact
  spelling and casing, applied in both dictation paths
- Recent dictations: last 20 final transcripts with copy and clear, so
  text can be recovered if it lands in the wrong place
- Read aloud: optional button on assistant responses with two engines,
  curated system voices (novelty and legacy voices filtered, quality
  ranked, capped at 20) or the TTS audio model loaded in Unsloth via
  /audio/generate (e.g. Orpheus), plus speed, pitch, volume and preview

Settings persist in localStorage (unsloth_voice_settings) and are read
at call time so changes apply without reloading the runtime. Adds en
keys plus the tab label for ja, zh-CN and pt-BR.

* Studio: drop the single option STT engine select, rename TTS option

The STT engine dropdown only had one entry, so it added noise without
giving a real choice. The engine row can come back once local STT
models land. Also renames the TTS engine option Unsloth TTS model to
Load TTS model to make the action clearer.

* Studio: harden Voice settings against edge cases found in simulation

Simulated the feature across Chromium, Firefox and WebKit plus node
level unit runs and backend contract checks. Fixes from the findings:

- Dictionary rewrite used a replacement string, so entries containing
  dollar patterns corrupted transcripts (A$$AP became A$AP, $& injected
  the match). Switched to the callback form of String.replace
- Persisted voice settings now validate types on hydration: non string
  micDeviceId, dictationLanguage and ttsVoiceURI, and non boolean
  ttsEnabled fall back to defaults instead of flowing into the UI
- Dictionary entries are trimmed, capped at 120 chars and re-sanitized
  on hydration
- The Test dictation panel now falls back to the default microphone
  when the saved device is unplugged, matching the composer adapter

Test coverage: 46 unit assertions (dictionary regex edge cases across
unicode, word boundaries and injection, voice curation for simulated
macOS, Windows and Linux voice inventories, corrupt storage merge),
13 backend contract checks against /audio/generate on an isolated
instance, and 60 browser assertions across the three engines covering
rendering, degradation without SpeechRecognition, curation in a real
DOM, dictionary persistence with unicode and dollar entries, the
no-model preview error path and corrupt localStorage recovery.

* Studio: address Voice settings review feedback

Verified each review comment before acting. Confirmed and fixed:

- Editing a dictionary entry was broken in two ways: the store trimmed
  on every keystroke so spaces could not be typed, and clearing the
  field deleted the entry and unmounted the input mid edit. Updates now
  keep the raw value and a blur commit trims or removes the entry
- The unplugged mic fallback checked instanceof DOMException, but a
  cross browser probe showed Firefox and WebKit throw
  OverconstrainedError objects that are not DOMExceptions, so the
  fallback never fired there. Matching on the error name now
- When the browser ended a dictation test on its own (silence timeout),
  the mic stream stayed open. All recognition end paths now stop the
  tracks and save the transcript through a single finalize path
- The studio TTS audio element now releases its WAV data URL as soon as
  playback ends, fails or is cancelled
- Allow microphone now reports insecure contexts (no mediaDevices)
  accurately instead of claiming access was blocked
- Voice tab copy moved into i18n keys per src/i18n/AGENTS.md, so locale
  overlays can translate it; en is the baseline and parity passes
- unsloth_voice_settings added to the Reset all local preferences key
  list so voice preferences obey the reset
- Non default microphones note that the system default is used when the
  browser speech engine cannot bind a specific device, since browsers
  without the start(track) overload ignore the argument silently

Re-ran the full simulation set after the changes: 46 unit assertions,
13 backend contract checks and 60 browser assertions across Chromium,
Firefox and WebKit all pass, plus a dedicated browser probe for the
dictionary editing behavior.

* Studio: use the chat mic icon in Voice settings for consistency

The Voice tab and its buttons used the hugeicons Mic02 glyph while the
chat composer uses a custom filled mic. Extract that composer icon into
a shared lib/mic-icon component, drop the duplicate inline copies in
thread.tsx and shared-composer.tsx, and use it for the Voice tab icon
and the tab's mic buttons so the microphone looks the same everywhere.

* Studio: address second round of Voice settings review feedback

Verified each new comment against the current code first. One item was
already fixed in the previous round (recording transcripts when the
browser ends a dictation test on its own). Confirmed and fixed:

- The microphone row showed a picker with generic names when browsers
  enumerate unlabeled devices before permission, leaving no way to
  grant access from the row. It now branches on whether labels are
  visible and shows Allow microphone otherwise
- Compare chat dictation ignored the selected microphone. It now opens
  the chosen device with the same fallback rules as the main adapter,
  passes the track to recognition where supported and releases the
  stream when recognition ends
- Closing the Voice tab cancelled the shared speechSynthesis even when
  read aloud was playing a chat message. Cleanup now only cancels when
  the tab owns an active preview
- Double clicking Start test could race two recognizers and leak the
  first stream. A starting flag set before the getUserMedia await makes
  start reentrancy safe
- Turning off the read aloud setting mid playback removed the only stop
  control. The stop button now renders whenever a message is speaking
- When an engine lacks the start(track) overload, both dictation paths
  now release the selected device stream before retrying with the
  default microphone instead of holding it open
- Read aloud support no longer requires Web Speech synthesis: the
  Unsloth TTS engine only needs audio playback, so it stays available
  in WebViews without speechSynthesis, with a clear error if the system
  engine is chosen there

Not addressed here: cancelling in flight backend TTS generation on
stop. The route runs generation in a worker thread without a
cancellation path, which is shared pre existing behavior with audio
chat generation and belongs in a backend change.

All suites re-run green: 46 unit, 13 backend contract and 60 browser
matrix assertions across Chromium, Firefox and WebKit, plus probes for
the unlabeled device branch and the double click race.

* Studio: drop empty and duplicate voiceURIs so the Voice tab never renders a crashing Select item

* Studio: guard dictation mic lifecycle in Voice test and Compare composer

Release a microphone opened after the component unmounts, and stop Compare
dictation on a permission or security failure instead of silently recording
from the default device, matching the main chat adapter.

* Studio: fix dictation and read-aloud lifecycle edge cases in Voice settings

- Join final dictation chunks with a space so recorded transcripts do not merge words
- Ignore a stale recognizer onend so a quick stop then restart is not torn down
- Use previewingRef so a double click on TTS preview does not orphan the first request
- Keep the read-aloud stop control visible when a new run starts while a message is spoken
- Stop the dictionary remove button from deleting an adjacent entry on a blur then click race

* Studio: trim redundant Voice settings comments

* Studio: fix Voice preview and Compare dictation edge cases

- Only cancel the shared speechSynthesis for a system-voice preview, so stopping
  a Studio preview no longer stops an unrelated chat read-aloud
- Release the Studio preview audio and its WAV data URL on normal completion
- Iterate every finalized result in Compare dictation so batched phrases are kept
- Cap persisted recent dictations to the last 20 on hydration

* Studio: use clipboard fallback for recents and release failed preview audio

- Copy recent dictations via the copyToClipboard helper so the execCommand
  fallback works in Safari and insecure http LAN contexts
- Release the Studio preview audio when play() rejects, not just on ended/error

* Studio: add local speech-to-text dictation engine

Add an offline dictation engine that transcribes with a local faster-whisper
model, alongside the existing browser (Web Speech) engine. The browser engine
streams audio to Apple or Google speech services and needs internet; the new
engine runs on the server, works offline, and drives any chat model without
evicting it (it loads in the backend process, separate from the model
subprocess). It also gives Firefox dictation, which has no Web Speech support.

Backend: a lazily-loaded, kept-warm faster-whisper sidecar and three routes
under /api/inference/audio (stt/status, stt/load, transcribe). faster-whisper
is torch-free, so this does not disturb the existing model stack.

Frontend: a Dictation engine setting (browser or local model), a curated model
picker with sizes, and MediaRecorder capture posted to the transcribe route.
The model warms automatically when the engine is selected, with live status.

* Studio: stream local STT transcription as you speak

Local dictation showed nothing until you stopped, because the whole clip was
transcribed once on stop. Now the growing recording is re-transcribed on a
fast pass every second and emitted as live interim text, with an accurate
final pass on stop. Partial recordings decode fine, and the model refines
earlier words as more audio arrives.

Adds an interim flag to the transcribe route (beam 1, no VAD) for the fast
preview pass; the final stop uses the accurate path.

* Studio: make local dictation stop instant and reliable

Stopping local dictation waited for a final network transcription before the
session ended, so the stop button did not flip and a second click ended the
session early and dropped the text. Now stop commits the live transcript
immediately, releases the mic at once, and ignores a second stop while
finalizing. Previews run more often so the committed text is current.

* Studio: record local dictation in short clips for reliable streaming

Re-transcribing a growing buffer every second got slower as it grew, flooded
the backend, showed stale words, and could leave the stop button stuck waiting
on a backlog. Record short independent clips instead and transcribe each once,
appending the text as you speak. Work per clip is bounded, so stopping is
prompt (with a hard timeout as a safety net) and long dictations stay smooth.

* Studio: dictate then transcribe once on stop, ChatGPT style

Local STT dictation streamed by re-transcribing the growing clip, which
was quadratic and saturated the backend (multi-second lag), and stop only
halted the recorder without releasing the mic, so it kept recording. Record
the microphone continuously, release it the instant the user stops, and
transcribe the whole clip once. Stopping is immediate and the transcript
lands in about a second. Also add the tiny model for the fastest option.

* Studio: surface dictation and read-aloud failures instead of failing silently

- Compare dictation reports microphone and speech-recognition errors via toast,
  reusing the main chat adapter's describeMediaError and describeSpeechError
- Read-aloud toasts genuine model or synthesis failures while ignoring cancellations

* Studio: ChatGPT-style recording bar for dictation

Clicking the mic now drops the composer into a dedicated recording bar
with a live waveform, a discard (X) and a confirm (tick), instead of a
plain stop button. The tick stops recording and transcribes the clip;
the X throws the recording away and keeps whatever text was already in
the composer. The model adapter taps the mic with an analyser to drive
the waveform, and the router tracks the live session so the X can cancel
it without transcribing.

* Studio: transcribe dictation while speaking, ChatGPT layout

Match ChatGPT's recording layout: the bar now renders in place of the
input with the left plus button kept, the waveform in the middle, and
the discard and confirm buttons together on the right.

Cut the post-confirm delay by transcribing in the background as the user
talks. The audio is split at natural pauses (voice-activity detection off
the same analyser that drives the waveform) and each clip is transcribed
as it is cut, so confirming only has to finish the short final tail. The
model is also warmed when recording starts so the first run never pays a
cold load.

* Studio: ChatGPT waveform, hide tools while dictating, faster STT

Make the recording UI read like ChatGPT: the waveform is now a dense row
of round dots that rise into thin centered bars, and while dictating only
the plus button shows, with the mode badge and tool toggles hidden so the
bar is just the waveform and controls.

Speed up transcription: decode greedily (beam_size=1), which is several
times faster on CPU with negligible accuracy loss on short dictation
clips, and cap background segments at 6s so the final tail after confirm
stays short.

* Studio: finish ChatGPT voice bar and low-latency STT

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: full-width waveform with a timer that freezes on stop

Use the full-width waveform for the recording bar: brighter, bigger bars
that advance on a fixed cadence (keeping peaks between advances) so they
glide instead of racing by, inset from the composer edges. Keep a visible
timer and the green confirm button, matching the ChatGPT reference, and
freeze the timer and waveform the moment the user confirms.

* Studio: fix multilingual local dictation

* Studio: speed up dictation and release local STT

* Studio: harden dictation finalization and STT decoding

* Studio: restore Firefox dictation fallback

* Studio: add dictation history manager

* Studio: manage speech model downloads

* Studio: remove em dash from voice model label

* Studio: move dictation history into Voice

* Studio: source local STT from Unsloth Whisper models

Point the dictation STT sidecar and its Model Hub download entries at
Unsloth's Hugging Face Whisper repos (small, large-v3-turbo, large-v3)
and run them through Transformers, so Studio only ever downloads
Unsloth-uploaded weights. Drop faster-whisper and the Systran/mobiuslabs
repos; keep the Model Hub as the only download path via local_files_only,
and keep PyAV for audio decoding.

Device selection uses float16 on CUDA and float32 on MPS and CPU, since
Whisper's decoder is unstable in float16 on MPS and repeats tokens.

Shorten the model picker labels to name plus download size and update the
STT tests for the new backend.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: smooth dictation waveform and keep pill height

* Studio: align STT model dropdown width and tidy voice copy

* Studio: guide to local engine when browser dictation is offline

* Studio: clarify voice section and STT model copy

* Studio: keep STT warm with training-aware eviction

* Harden STT lifecycle and browser compatibility

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix model discovery test lint

* Harden cross-browser microphone errors

* Harden cross-browser microphone errors

* Surface voice test recognition errors and fall back to Studio TTS

- Voice test now toasts non-abort speech-recognition failures instead of
  ending silently, matching the main and Compare dictation paths.
- Read-aloud routes to the backend model when the runtime lacks Web Speech
  synthesis (audio-only WebView), so it no longer errors immediately.

* Fix reviewed STT lifecycle races

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix read-aloud fallback controls

* Guard read-aloud stop when deleting a non-speaking message

aui.message().stopSpeaking() throws unless this message is the one being
read aloud, so calling it unconditionally rejected the delete handler before
the message was removed. Only stop speech when this message is speaking.

* Cap recent dictation transcript length before persisting

Recent dictations only limited entry count, so a long transcript stored the
full text in the persisted voice settings and a few could exceed the
localStorage quota, throwing synchronously from the uncaught dictation cleanup
path. Truncate each entry on save and on hydration, matching the dictionary cap.

* Studio: keep dictation mic clickable and guide to local model

Register the dictation adapter unconditionally so the mic stays enabled
for any engine and starts working right after switching to the local
model on an already-open thread.

When the browser engine cannot run (Firefox, Brave, non-secure origins),
clicking the mic shows a toast that points to the local speech-to-text
model instead of leaving a disabled button. The toast stacks its action
below the text with a fully rounded button.

* Studio: add bottom padding below the dictation guidance toast button

* Studio: increase bottom padding under the dictation toast button

* Studio: add bottom padding inside the dictation toast button

* Studio: add five Whisper defaults and custom model search

Add private UnslothAI Tiny and Base mirrors to the curated local STT choices while keeping Small as the default. Let users search or paste a Transformers-compatible Whisper repository and validate it end to end.

Keep short dictations in one clip to avoid repeated padded encoder work, then split longer recordings near Whisper's 30-second boundary.

Update hidden model filters and tests, including the CPU-only CI runtime stub for PyAV.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: use public Unsloth Whisper repositories

Point the Tiny and Base dictation defaults to the public unsloth repositories and remove the private mirror references from model filtering and tests.

* Studio: update Whisper download sizes

Reflect the cleaned public Tiny and Base repositories in the curated model labels.

* Studio: right-align STT model size, fix dropdown wheel scroll, refresh sizes

- Show the download size on the right of each model row so long names
  like Whisper Large v3 Turbo no longer hide it
- Update curated Whisper sizes to the safetensors weights actually
  downloaded: Tiny 151 MB, Base 290 MB, Small 967 MB
- Drive the model list scroll from a wheel handler so the mouse wheel
  scrolls it inside the Settings dialog, not just the scrollbar
- Add a search icon and shorten the placeholder to Search model

* Studio: do not search when a dictation model is picked, shrink repo label

- Treat the filled-in model text as a selection, not a query, so choosing
  a model no longer kicks off a Hugging Face search
- Make the repository line under each model name smaller

* Studio: tighten dictation model and local engine descriptions

* Studio: keep model display on pick instead of the query, shrink row text

- Guard the combobox input so selecting a model shows its name and does
  not echo the typed query back or start a search
- Map the item label to the friendly display so picks fill the field
- Reduce the model name and size text in each row

* Studio: show only the model name in the dictation field, shrink size label

- Drop the download size from the search field; the name alone is shown
  once a model is selected, with sizes kept in the dropdown list
- Reduce the size label text in each row

* Studio: clarify the dictation model description

* Studio: drop Hugging Face from the dictation model description

* Studio: move the dictation dictionary to its own Manage subpage

- Replace the inline entry list with a Manage row, matching Dictation
  history, so a long dictionary no longer crowds Voice settings
- Add a DictationDictionaryView subpage that holds the entry editor

* Studio: match STT field font, use best voice for System default

- Bump the dictation model field text to text-sm so it matches the
  engine dropdown next to it
- Resolve the System default read-aloud voice to the top curated voice
  instead of the browser default, which is a robotic legacy voice on macOS

* Studio: rerank read-aloud voices and drop duplicate voice entries

- Rank by vendor quality, then the user's locale, then a preferred list of
  natural voices, so the best voice leads instead of the first alphabetically
- Collapse voices that macOS reports twice under one name and language

* Studio: fold dictionary and recents into the dictation section

- Drop the separate Dictation dictionary and Recent dictations headings;
  their Manage rows now sit under Dictation, split by the row divider
- Shorten the custom spellings description

* Studio: add search and sort to dictation history

- Filter saved dictations by text with a search field
- Sort by newest, oldest, or A to Z; show a no-matches message
- Keep Clear all available regardless of the current filter

* Studio: settle cancelled STT loads before training and fix dictation review items

Wait for a cancelled STT load to exit and release its memory before
reporting it freed for training, so the loader cannot still be inside
from_pretrained()/.to(device) holding VRAM when the training subprocess
starts. A load that finishes before observing the cancel now gets
unloaded so the memory is actually reclaimed.

Clear the accelerator cache before the CPU fallback in load() so a failed
CUDA/MPS load does not strand reserved VRAM once the sidecar is marked
CPU-resident.

Send the saved Hugging Face token when polling STT download progress so a
gated or private repo resolves and shows the correct Load/Downloaded
state instead of reporting missing.

Mark the composer Dictate button as type="button" so clicking it does not
also submit the draft when the composer already has text or attachments.

* Studio: pin dictation settings per session and close STT startup races

Capture the STT model and language when a dictation session starts and
pass them to every queued segment and the warm-up load, so changing the
model or language mid-recording no longer transcribes the same clip with
the wrong model or a model that is not downloaded.

Check the local runtime at the top of transcribe(), before the model
cache lookup and the bounded audio decode, so a server missing PyTorch or
Transformers returns 501 up front instead of decoding a long clip first.

Treat the training startup window as active for STT device selection.
start_training frees VRAM in before_spawn but only assigns _proc later, so
a concurrent STT load could take the GPU that was just cleared. A startup
flag now reports training active from the free until the process is live,
forcing those loads to CPU; a finally clears it on every exit.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: stub the STT runtime check in transcribe orchestration tests

transcribe() now verifies the local runtime up front, so the unit tests
that exercise transcription orchestration must treat the runtime as
present to keep passing where PyTorch, Transformers, and PyAV are not
installed. Stub ensure_stt_available in the shared fixture and restore
the real check in the availability and load-rejection tests.

* Harden custom Whisper dictation models

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Add whisper.cpp dictation engine with per-engine downloads and history rework

Engines
- New GGML STT sidecar that runs a managed whisper-server subprocess with
  idle unload, plus a pinned static build script (scripts/build_whisper_cpp.sh)
- Dictation engine picker now offers Browser, Local transcription
  (whisper.cpp), and Local transcription (Transformers)
- Both local engines serve the same five curated Whisper models and download
  them directly with byte-level progress reported by /audio/stt/status
- Models auto load on selection and when their download finishes
- Unload and training admission account for both engines

Benchmarks (Apple Silicon, greedy, warm, same checkpoints)
- whisper.cpp transcribes 2.4x to 5x faster than Transformers and loads in
  about 0.45s vs 0.86s for Whisper Small
- whisper.cpp GGUF path is unchanged by the Transformers addition
  (load 0.445s -> 0.444s, short clip 0.391s -> 0.347s, long 1.197s -> 1.129s)

Voice settings UI
- Plain curated model select replaces the searchable combobox
- Single download progress bar with transfer rate for both engines
- Dictation history now stores every dictation with Show more pagination,
  a top Clear history action, and links back to the chat it was spoken into
- Archived chats dialog gets the same pagination
- Delete dialog offers deleting a dictation together with its chat

Tests: 88 backend STT tests pass, including new snapshot download coverage.
Frontend typecheck, lint, i18n parity, and production build pass.

* Merge local engines into one option and source GGML models from unslothai

Engine selection
- The dictation engine dropdown is back to two choices: Browser and Local
  transcription. The selected model decides the backend: curated ids run
  GGML checkpoints through whisper.cpp, searched Hugging Face repositories
  run safetensors through Transformers
- Model picker lists the curated models and searches Hugging Face for other
  Whisper repositories, validating them before selection. The trigger is a
  plain button so the selection never renders inside a text input
- /audio/stt/status accepts a model query param so downloaded state works
  for custom repositories; the engine param on load, transcribe, and
  download routes is derived from the model everywhere

Model source
- Curated GGML checkpoints now download from the Unsloth-hosted
  unslothai/whisper-*-GGUF repositories (one repo per model) instead of
  ggerganov/whisper.cpp; cache lookups, progress totals, and in-flight blob
  tracking are per-model

Fixes
- Voice settings and dictation history were not persisting: the quota-safe
  localStorage wrapper was declared after the store that uses it, so the
  persist storage factory failed silently. Every settings write also threw
  mid-click, which kept the model picker popover from closing on selection
- is_model_downloaded now verifies config, preprocessor config, and real
  weight files instead of trusting an offline snapshot lookup, so a partial
  download left by an aborted fetch shows the Download button instead of
  failing to load
- Removed whisper.cpp mentions from user-facing text: the ready status
  shows Loaded instead of the runtime name, picker rows show the source
  repository, and runtime error messages say local transcription runtime

Verified with automated browser sessions and live API checks: selection
closes the picker with no page errors, persisted settings hydrate on
reload, a stale partial snapshot triggers download then loads on MPS and
transcribes, and curated models download from the unslothai repos. 88
backend STT tests, typecheck, lint, i18n parity, and build pass.

* Skip the duplicate source line for custom models in the STT picker

A custom repository's display name is its id, so search results and the
appended current selection rendered the same string twice. The source
line now only renders when it differs from the name; curated rows keep
their name, unslothai source repository, and download size.

* Verify every shard of a sharded checkpoint in the downloaded check

A snapshot holding one of N shards (or a corrupt shard index) passed the
downloaded check and then failed at load. When model.safetensors.index.json
exists, every shard in its weight map must now be present. Found by
simulation; covered by a regression test.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Rename stale _starting references in the pump resilience tests

The startup flag on TrainingBackend was renamed to _spawn_in_progress but
two tests added alongside it still asserted on the old name, failing the
Python 3.11 to 3.13 CI jobs.

* Make the selected model row clearly highlighted in the STT picker

The current selection was a faint background tint. It now uses the accent
background with a medium weight name. Two line rows use a small corner
radius; single line custom repo rows keep the pill shape.

* Address review feedback on STT snapshot checks, VRAM release, and dictation UX

Verify snapshot completeness in the load preflight so a partial download
fails before the audio is decoded, for curated and custom repos alike.
Drop the failed accelerator traceback before the CPU retry so the cache
clear can actually release that memory. Keep unloading the GGUF sidecar
after cancelling an in-flight Transformers load; both engines can hold
memory at once. Allow Auto language with English-only .en checkpoints,
matching the backend which sends no forced language. Keep the discard
button usable while a transcription is pending so a slow or hung request
cannot trap the composer in dictation mode. Stop linking Compare and
settings test dictations to the unrelated active single chat thread.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Move the CPU retry out of the exception handler

On Python 3.10 the interpreter exception state keeps its own reference
to the traceback, so dropping it from the caught exception was not
enough to release the failed accelerator load during the retry. Leaving
the handler before clearing the cache works on every supported version.

* Address review feedback on session handoff, chat pinning, and server lifetime

Starting a dictation from a second entry point now cancels the session
it replaces, so the old recording cannot keep the microphone open or
save a transcript with no discard button pointing at it. The linked
chat is pinned when recording starts, so switching threads while a
transcription finalizes cannot relink the transcript to the newly
opened chat. whisper-server is now bound to Studio's lifetime like the
other long-lived children: PDEATHSIG on Linux, the parent job object on
Windows, and pid adoption so the shutdown sweep reaps it; before this
it survived a Ctrl+C exit as an orphan still holding the model.

* Remove the dictation mic test from Voice settings

The composer dictate button covers the same check, so the test row, its
transcript panel, the unsupported fallback row, and their strings and
search entry are gone.

* Studio STT: gate GGUF whisper-server on training and fix dictation retry and dictionary edits

GGUF (whisper.cpp) sidecar:
- Launch whisper-server with --no-gpu while training is active, mirroring the Transformers sidecar's CPU device choice, so a mid-training dictation cannot reclaim the VRAM training just freed.
- Report is_loading() during whisper-server startup so training VRAM admission accounts for the accelerator memory it is about to bind.
- Require PyAV in is_available() so /audio/stt/status reports the engine unavailable when uploads cannot be decoded, instead of loading fine and then 501ing at transcription.
- Reject a missing model before decoding audio, matching the Transformers download preflight.

Voice settings:
- The download Retry button now restarts the download; the sidecar error is sticky until a new start(), so re-polling alone never cleared it.

Dictation dictionary:
- Tabbing from an emptied entry to its remove button no longer commit-splices the row first, which shifted indices and deleted the wrong entry.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio STT: fix curated GGUF whisper filenames to match hosted repos

The unslothai/whisper-*-GGUF repos host the checkpoint as whisper-<id>.bin,
not ggml-<id>.bin, so every curated dictation download and cached-path
lookup 404'd and the whisper.cpp engine could never load a model. Point
GGML_STT_MODELS at the real filenames and guard the naming with a test.

* Studio STT: validate a custom dictation repo before downloading it

The Transformers STT engine accepts an arbitrary owner/model repo, but the
download route handed it straight to snapshot_download, pulling a possibly large
non-Whisper repository into the shared HF cache. Confirm the repo is a Whisper
checkpoint first with the existing metadata-only validate_remote_model (no
weights); curated ids short-circuit and the GGUF engine (curated-only) is
unaffected. A non-Whisper repo now 422s before any download.

* Studio STT: preempt a still-loading GGUF server for training admission

A whisper-server still in its startup window binds accelerator memory but has no
loaded_model yet, so training admission could miss it and launch into an OOM.
Make the GGUF startup cancellable (cancel_pending_load signals an abort event and
terminates the starting process without the load lock; _wait_for_server observes
it and raises SttLoadCancelledError; wait_for_load_to_settle blocks on the lock
until the killed server is reaped), and always fold the GGUF sidecar into the
resident-STT summary so a resident Transformers model cannot mask a loading GGUF
server. free_stt_model_for_training now cancels an in-flight load and waits for it
to settle before training claims the memory.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio STT: fall back to Transformers when whisper-server is absent

A curated dictation model (including the default small) hard-pinned the GGUF
engine, but standard installs do not ship whisper-server, so every recording
501'd instead of using the Transformers engine that serves the same checkpoint
-- the GGUF sidecar's own documented contract. Add _resolve_serving_stt_engine:
a GGUF request for a curated id (the only ids GGUF accepts, all Transformers-
servable) downgrades to Transformers when whisper-server is unavailable, applied
consistently to download, load and transcribe (not unload, which targets a
specific engine). The Voice tab likewise falls back to the Transformers status so
the model is not shown unavailable and download is not blocked.

* Studio STT: hide custom Whisper caches from the legacy model pickers

The legacy /cached-models (and /cached-gguf) routes called is_hidden_model with
only the owner/model id, which cannot reach the config-based Whisper check, so a
downloaded custom (non-curated) Whisper checkpoint was still offered as a chat
model. Pass the cached snapshot path so _path_is_whisper_model inspects the repo
config and hides it, matching the discovery route.

* Studio STT: hide GGUF dictation repos, lock-free status, unload fallback, split training eviction

- Hide the curated GGUF dictation repos (unslothai/whisper-*-GGUF) from the chat
  model inventory and pickers, backend and frontend. Only their Transformers
  safetensors companions were hidden; the GGUF repos use a different org and a
  -GGUF suffix and carry a raw .bin with no whisper config.json, so they leaked
  into chat pickers.
- Make the GGUF sidecar loaded_model/device accessors lock-free, mirroring the
  Transformers sidecar. transcribe() holds self._lock across the whole inference
  call, so /audio/stt status polls and training admission previously blocked
  behind an in-flight transcription.
- stt_unload resolves through the serving resolver: a "gguf" pick on a host
  without whisper-server is served by the Transformers fallback, so unload must
  target that engine or the resident model is never freed. Unload also attempts
  every engine even if one raises, so a failure freeing one backend no longer
  skips the other.
- free_stt_model_for_training frees the Transformers and GGUF sidecars under
  independent exception boundaries so a failure unloading one no longer skips
  the other before training claims the memory.

Adds tests/test_stt_review_fixes.py covering all four.

* Studio STT: resolve Auto dictation language for the model engine + snapshot process liveness

- The model dictation adapter sent the raw setting (the literal "auto") to the
  backend, while the browser engine resolves Auto via resolveDictationLanguage.
  A batch of non-English voice notes came back mostly English on Auto. Add
  resolveModelDictationLanguage: only the literal "auto" is resolved to a
  concrete locale, gated so it becomes a language the model AND Whisper can
  honor (mirroring the backend's known-whisper-languages set); an explicit
  language, or a locale Whisper cannot honor, stays unchanged/auto-detect. Wire
  it into both adapter call sites.
- GgmlSttSidecar._process_alive() read self._process twice; a concurrent
  unload() nulls it under the lock while loaded_model/device read lock-free, so
  a null between the two reads called None.poll(). Snapshot once. Adds a
  deterministic regression test.

* studio: tighten comments and docstrings in the dictation modules

* studio: harden dictation model downloads, GGML readiness, and recording paths

Address review findings on the STT dictation feature:

- build_whisper_cpp.sh refuses to delete a whisper.cpp tree under a custom
  Studio home unless it carries the Studio ownership marker, matching the
  setup.sh policy, and marks trees it creates
- _snapshot_is_complete validates every shard of a sharded PyTorch
  (pytorch_model.bin.index.json) checkpoint like the safetensors path, and
  requires tokenizer assets (tokenizer.json or vocab.json + merges.txt)
- custom-repo downloads pin the revision resolved at validation time and
  restrict snapshot_download to the model/tokenizer/config/preprocessor file
  classes Studio loads
- the GGML sidecar holds its port reservation until just before spawning
  whisper-server and only accepts readiness from a responder that both looks
  like whisper.cpp's server and belongs to the still-running managed child,
  probing twice, so mic audio cannot be posted to a foreign local process
- the recording adapter transcribes every non-empty segment; the RMS meter
  only shapes segment boundaries and can no longer discard quiet speech
- Compare-pane dictation can cancel a pending transcription on second click,
  with the button relabeled while finalizing
- localStorage quota recovery halves the dictation history until the save
  fits, so small histories shrink too
- the System default TTS voice resolves to the platform default voice
- new dictation UI imports go through the chat and hub feature barrels

Regression tests cover the build-script gate, sharded PyTorch and tokenizer
completeness, revision pinning and allow patterns, and the whisper-server
readiness probe.

* Fix STT download and voice picker follow-ups

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Add dictation button regression coverage

* Studio: prebuilt whisper.cpp via the shared llama.cpp install core, slim bundles paired to the llama prebuilt (#7294)

* Studio STT: add prebuilt whisper.cpp (whisper-server) installer

New install_whisper_prebuilt.py downloads a per-platform whisper-server
bundle published by the unslothai/whisper.cpp prebuilt CI into the managed
whisper.cpp dir (build/bin/whisper-server) so local dictation needs no
compiler. Mirrors install_node_prebuilt.py / install_llama_prebuilt.py:
host + backend detection, sha256 pins (whisper_prebuilt_pins.json) as the
trust anchor, staging + install lock + atomic swap, traversal-safe extract,
co-located shared libs (RUNPATH=$ORIGIN), an UNSLOTH_WHISPER_PREBUILT_INFO.json
marker with idempotent "already matches", and exit codes 0/1/2/3. Not wired
into setup yet; the pins ship empty so every asset fails closed until the
first fork release is published and its digests are reviewed in.

* Studio STT: install prebuilt whisper.cpp during setup and update

Add a fail-open whisper.cpp block to setup.sh after the llama.cpp section so
`unsloth studio update` (and a fresh install) fetch the prebuilt whisper-server
into the managed whisper.cpp dir the sidecar discovers. It skips a user-set
WHISPER_SERVER_PATH/UNSLOTH_WHISPER_CPP_PATH, honors UNSLOTH_SKIP_WHISPER_INSTALL,
forwards the resolved ROCm gfx, and never aborts setup: a busy install keeps the
existing runtime, and an unavailable prebuilt stays quiet (source build is opt-in
via UNSLOTH_WHISPER_FORCE_COMPILE) since Transformers STT and browser dictation
remain. Register UNSLOTH_WHISPER_PREBUILT_INFO.json as Studio-owned evidence.

* Studio STT: harden whisper-server child env + WSL ROCm detection

- Sidecar spawns whisper-server with a scrubbed child env that prepends the
  binary dir (co-located GPU libs) to the loader path, and on WSL2 ROCm loads
  the system HIP first (HSA_ENABLE_DXG_DETECTION=1) so a bundle's bare-metal HIP
  does not segfault on /dev/dxg. Secret-bearing vars are dropped from the child.
- find_whisper_server_binary now requires an executable, not just a file.
- Installer rocm probe passes HSA_ENABLE_DXG_DETECTION and falls back to
  /opt/rocm/bin/rocminfo so a WSL ROCm host is not misdetected as CPU-only;
  gfx parsing skips the gfx000 CPU agent and generic ISA lines.
- Tests for the child env (secret scrub, lib dir, WSL HIP precedence), the
  executable check, and the WSL rocm detection.

* Studio STT: in-app whisper.cpp prebuilt update stack + ship pins in the wheel

Mirror the llama.cpp update stack for the whisper.cpp prebuilt so Studio can
detect and install a newer whisper-server release from inside the app:
- backend/utils/whisper_cpp_freshness.py: read UNSLOTH_WHISPER_PREBUILT_INFO.json
  and compare the installed release against the newest unslothai/whisper.cpp
  release. Whisper tags are v<upstream>-unsloth.<N>, so is_behind compares a
  (major, minor, patch, serial) key with a strict downgrade guard; 24h cache;
  fail-open.
- backend/utils/whisper_cpp_update.py: run install_whisper_prebuilt.py to fetch
  and atomically swap the newest bundle, unloading the warm GGUF sidecar first.
- backend/routes/whisper.py mounted at /api/whisper (update-status + update).
- pyproject: add whisper_prebuilt_pins.json to studio package-data so the
  installer's trust anchor ships in the wheel (it is a data file, not a .py
  module, so package discovery alone does not include it; node_prebuilt_pins.json
  is listed for the same reason). Without this a pip-installed wheel had no pins
  and the prebuilt install aborted to Transformers STT.
Adds test_whisper_cpp_freshness.py (version parser, is_behind matrix + downgrade
guard, marker layouts, stale decision, fail-open).

* Studio STT: verify whisper prebuilts via the release checksum index, like llama.cpp

Re-align the whisper.cpp prebuilt installer to install_llama_prebuilt.py's trust
model: instead of a committed whisper_prebuilt_pins.json, verify every download
against the release's own whisper-prebuilt-sha256.json checksum index, fetched
from the same GitHub release.

- parse_release_checksums / fetch_release_checksums / expected_sha256_for replace
  the pins layer. The index is validated for schema/component and that its
  release_tag matches the resolved release; an asset absent from it, a release
  that does not publish it, or a manifest sha256 that disagrees with it all fail
  closed to a source build.
- resolve_release_tag now resolves the newest published release at runtime (or an
  explicit --published-release-tag), matching llama and the freshness check;
  removed the pinned-default and the UNSLOTH_WHISPER_ALLOW_UNVERIFIED opt-in.
- Delete studio/whisper_prebuilt_pins.json and drop its pyproject package-data
  entry (nothing to ship now, same as llama which has no committed pins).
- Adds test_install_whisper_prebuilt_checksums.py (index parser, fail-closed on
  uncovered asset, tampered-manifest guard, newest-release resolution).

This is a same-origin checksum (integrity, not authenticity), identical to the
llama.cpp installer; pair releases with GitHub artifact attestations for provenance.

* Resolve whisper prebuilt release via the download host (no GitHub API)

Mirror install_llama_prebuilt.py's fast path: resolve the release tag from
the releases/latest redirect and fetch the manifest + checksum index from
constructed releases/download URLs, so the common install path makes zero
api.github.com calls (unauthenticated api.github.com is capped at 60 req/hour
per IP; the download host is not). Fall back to the GitHub API only on a 404,
malformed asset, or tag mismatch.

* Studio STT: coverage-aware whisper prebuilt selection via a shared core

whisper's select_artifact returned the first os/arch/backend manifest match and
ignored the SM-coverage fields the release manifest already carries, so a
Blackwell B200 (sm_100) was served cuda12-legacy (sms 50-61) -- runnable only via
forward PTX JIT. install_llama_prebuilt.py on the same host correctly picks
cuda13-newer.

Extract the coverage-aware selection into a shared, component-agnostic core under
studio/backend/utils/prebuilt/ (selection + GPU host-capability detection), lifted
from llama's linux_cuda_choice_from_release / _artifact_covers_sms / _sm_range and
generalised over a normalised artifact. whisper's HostInfo now records the GPU
compute caps + driver CUDA version (honoring CUDA_VISIBLE_DEVICES), and
select_artifact routes CUDA/ROCm through the shared selector: every visible SM
must be covered, the tightest-covering profile wins (Blackwell-aware runtime-line
ordering), ROCm matches the gfx target exactly, and an uncovered GPU falls back to
the CPU bundle. CPU/Metal/Vulkan keep first-match. The resolver JSON, exit codes,
and "already matches" contract are unchanged.

On the B200 the installer now resolves cuda13-newer, matching llama.

* Studio STT: gate whisper CUDA selection on the on-disk runtime, like llama

The prebuilt CUDA bundles are dynamically linked and intentionally do NOT ship
libcudart/libcublas -- they load the same runtime the host already has. So the
driver's advertised CUDA version is only an upper bound: a cuda13 bundle still
needs cuda13 runtime libraries present on disk. Port llama's on-disk runtime
scan (detected_linux_runtime_lines / detected_windows_runtime_lines) into the
shared core and intersect it with the driver-compatible lines in
select_cuda_attempts. A host with a cuda13 driver but only cuda12 runtime (e.g.
torch-cuda12) now correctly gets a cuda12 bundle instead of an unloadable cuda13
one; a host with no CUDA runtime at all falls back to CPU.

Fixes a glob bug in the port (any(Path(d).glob(p) for d in dirs) tests generator
truthiness, not a match) that made every major report present; add a real
filesystem test that exercises the scan.

* studio: harden shared prebuilt core to full llama parity

Apply the review findings on the shared coverage-aware prebuilt-consumer
core so whisper.cpp selection is exactly equivalent to the llama.cpp path.

hosts.py: port llama's CUDA_VISIBLE_DEVICES handling. A GPU hidden by an
index/UUID selector now reports has_usable_nvidia False instead of staying
usable, via supports_explicit_visible_device_matching plus the physical /
explicit-match branches, and _select_visible_rows now matches rows the way
llama does (index or UUID, gpu- prefix optional) and skips unmatched tokens
rather than keeping all rows. Adds the Linux /proc/driver/nvidia/gpus
fallback and has_physical_nvidia. Adds parse_macos_version.

runtime_libs.py: the Linux on-disk scan now requires the exact libcudart /
libcublas SONAME (libcudart.so.13), not a libcudart.so.13* glob, so a bare
versioned file without the SONAME symlink no longer counts as loadable.
Hardens the ldconfig parse against an empty left-hand side.

selection.py: fix the Blackwell/torch reordering so it keys on the covering
runtime lines (falls through to the torch preference when the covering lines
were filtered out), matching linux_cuda_choice_from_release. Corrects the
compatible_runtime_lines_for_driver docstring: the bundles do not ship the
CUDA runtime, so the driver version is only an upper bound and the caller
must intersect with the on-disk scan.

install_whisper_prebuilt.py: enforce a macOS artifact's min_os (new
HostInfo.macos_version) so a bundle that cannot load on the host OS version
is dropped. Keep resolver stdout to only the JSON line by leaving logs on
stderr in --resolve-prebuilt mode, and map an unexpected probe failure to
prebuilt_available False instead of a traceback.

Tests: new host-probe suite for the visible-device logic, exact-SONAME
runtime-scan cases, macOS min_os filtering, resolver stdout-only-JSON,
exit-code mapping, and the repo key.

* studio: fix whisper prebuilt selection + launch parity gaps from review

A parallel review surfaced integration defects where the whisper path could
select or launch a bundle that cannot run on a concrete host. Each is fixed to
match install_llama_prebuilt.py.

macOS min_os: the manifest labels macOS requirements as macos-<version>
(e.g. macos-14.0), which the version parser could not read, so the guard was a
no-op and a macOS-13 host would install the macos-14 Metal bundle. Strip the
platform prefix before parsing.

ROCm gfx detection: _detect_rocm_gfx returned the first gfx token and ignored
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES / CUDA_VISIBLE_DEVICES. Since exact
ROCm matching treats that token as the active GPU, a mixed APU + dGPU host
(gfx1151 + gfx1100) with HIP_VISIBLE_DEVICES=1 installed the wrong archive. Route
through a shared pick_rocm_gfx_target (lifted from llama) that parses per-GPU
sections and honors the visibility vars (empty / -1 -> no AMD GPU).

--rocm-gfx override: recording the arch without setting has_rocm left the host on
its CUDA/CPU path so the ROCm bundle was never picked. --rocm-gfx now implies
has_rocm and clears NVIDIA state, like llama's _apply_host_overrides.

CUDA launch env: a CUDA bundle ships the ggml CUDA backend but not
libcudart/libcublas, and the sidecar launch env exposed only the bundle dir, so
on a host whose CUDA runtime lives only in the PyTorch wheels the selection would
gate cuda usable but the server could not load it. Add the CUDA-from-PyTorch
runtime dirs to the child loader path for CUDA bundles (bundle dir still first),
mirroring binary_env.

Also normalize a manifest artifact's supported_sms defensively (parity with
llama's parser) and document that blackwell_min_toolkit_for_caps is retained for
the Phase B llama Windows path.

Not changed (verified parity, not defects): Linux/Windows min_os is enforced
nowhere in llama (macOS only); the resolver is optimistic about the checksum
index and the install path verifies.

* studio: tighten prebuilt-core code comments

* studio: lift shared prebuilt installer core out of the whisper installer

* studio: reuse the llama.cpp prebuilt installer machinery for whisper

* studio: unify llama and whisper prebuilt installers on a shared descriptor core

* studio: consolidate prebuilt installer tests into the shared core suite

Grow tests/studio/install/test_prebuilt_core.py from 62 to 164 tests so every
component-agnostic behavior runs against both descriptors: the full seven
profile CUDA release matrix (multi-GPU, on-disk runtime gating, shuffle
stability, missing SM metadata, dotted SM normalization, no-driver fallback
policy), the ROCm gfx family matrix, macOS min_os gating and its helper,
backend resolution incl. cpu-fallback precedence and Intel-mac auto detect,
checksum-index non-object and plain-lookup cases, the tar symlink/hardlink
extraction guards moved from the llama suite, and the compute-cap, visible
device, runtime-line and Blackwell helper value tables moved verbatim from
the llama characterization suites.

Delete only tests whose exact behavior the master now asserts for the same
component: 40 pure-alias helper cases in test_selection_logic.py (replaced by
value-identical master tables plus an alias-identity pin), 6 extraction moves
and the master-absorbed zip-symlink case in the llama logic suite, 3 routing
twins in test_rocm_support.py already pinned byte-for-byte in
test_selection_logic.py, the 2 Blackwell helper tables in the backend resolve
suite, 28 whisper logic tests and 10 whisper checksum tests re-asserted by
the master whisper parameterization. Wrapper wiring pins, the llama release
plan dialect, fingerprints and every llama-only behavior stay untouched.

* studio: dedupe sidecar and update helpers into the backend prebuilt package

* studio: chain whisper.cpp prebuilt updates onto the llama.cpp update flow

* studio: consume paired slim whisper prebuilts via the llama ggml runtime

* studio: serve every whisper backend from slim prebuilts

* studio: drop the whisper fat per-accelerator selection chain

unslothai/whisper.cpp releases are slim-only from v1.9.1-unsloth.2: one
ggml-less bundle per os/arch, paired to the llama.cpp prebuilt that provides
every ggml backend. Delete the whisper-side fat CUDA/ROCm/metal/vulkan
selection glue; keep slim selection + pairing, link_ggml_runtime, and one
legacy shape, the published fat CPU bundle of an explicitly pinned pre-slim
release. Exit 2 now reads as prebuilt unavailable (whisper never source
builds); setup already treats it that way.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Wire libomp runtime DLL alongside ggml in slim whisper installs

llama's clang-built windows-arm64 ggml-base.dll imports
libomp140.aarch64.dll, shipped in the llama bundle but not a system DLL.
Without it next to whisper-server.exe the loader fails with
STATUS_DLL_NOT_FOUND before main. MSVC x64 links vcomp140.dll from
System32 and Linux ggml uses system libgomp.so.1, so only windows-arm64
was affected. The empty-runtime guard still requires a real ggml
library; libomp alone is not a pairing.

* studio: drop whisper-side fat-selection support structure

Slim whisper bundles are selected per os/arch only; all accelerator
capability comes from the installed llama.cpp prebuilt, whose installer
already did the coverage-aware selection. Remove the machinery that only
existed to pick among fat per-accelerator whisper bundles:

- prebuilt_core: delete the generic CUDA/ROCm coverage selection
  (select_cuda_artifact, select_rocm_artifact, ArtifactView adapters,
  detected_cuda_runtime_lines, the exact-SONAME linux probe) that no
  shipped component routes through; llama keeps its own selection chain
  and whisper shadows select_artifact with the slim-only version.
  select_artifact is now a plain os/arch/backend first-match.
- install_whisper_prebuilt: drop the HostInfo CUDA fields
  (compute_caps, driver_cuda_version, torch_runtime_line) and the torch
  runtime probe that populated them; nothing reachable reads them, and
  the resolver payload sources runtime_line from the artifact.
- whisper_cpp_update: delete the standalone start_update job worker;
  whisper applies only run as the chained phase of the combined
  llama+whisper update. The status payload keeps its job field (idle).
- routes/whisper: drop the progress logger that could never fire.
- tests: remove tests of the deleted paths and tests duplicating the
  descriptor-parameterized core suite or the llama freshness suite.

Contracts unchanged: resolver JSON keys, exit codes, marker fields,
pairing logs, and the pinned pre-slim fat CPU escape hatch.

* Address review feedback on the whisper prebuilt update and install paths

- Pin the chained whisper phase to the release the freshness check
  offered, so the download-host latest pointer cannot reinstall an
  older build in a loop
- Wire the whisper prebuilt install into setup.ps1 (Windows setup
  previously skipped it entirely)
- Treat a non-executable server or missing wired ggml libraries as a
  broken install instead of reporting already matches
- Keep whisper sidecar reloads out of the job-level reload flag and
  resync chat state after a partial chained update that unloaded llama
- Repoint home and profile vars for the whisper-server subprocess at a
  managed scratch dir and drop credential-store pointers
- Clear the prebuilt marker before the opt-in source build overwrite
- Write the prebuilt marker with explicit utf-8 encoding

* Tighten comments in the whisper prebuilt consumer

* Harden the Windows whisper setup phase and the chained update edges

- setup.ps1: honor WHISPER_SERVER_PATH / UNSLOTH_WHISPER_CPP_PATH /
  UNSLOTH_SKIP_WHISPER_INSTALL, run the custom-home ownership guard
  before the atomic install, and forward the release-tag pin and ROCm
  hints like setup.sh
- sidecar: a cpu-selected install launches whisper-server with --no-gpu
  (slim wiring links every llama backend, so the flag is what keeps a
  deliberate CPU choice off the GPU)
- chained update: leave whisper unpinned on macOS (the llama phase can
  walk back there, and a newest-tag pin could be an impossible pairing
  on every retry) and treat installer exit 2 as kept-existing-runtime
  instead of failing the combined job
- job.to_tag now comes only from the llama phase, so a whisper-only
  round cannot report a llama update that never ran

* Fix slim whisper runtime follow-ups

* Address remaining whisper update reviews

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Address remaining prebuilt update reviews

* Fix remaining chained update reviews

* Fix remaining whisper runtime review edges

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>

---------

Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
2026-07-23 01:39:03 -07:00

1628 lines
62 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Main FastAPI application for Unsloth UI Backend
"""
import os
import sys
import threading
from pathlib import Path as _Path
import asyncio
from dataclasses import asdict
from typing import Any, Optional
# Suppress C-level dependency warnings globally
os.environ["PYTHONWARNINGS"] = "ignore"
# Pin GPU index ordering to PCI bus id before any torch import creates a CUDA
# context. Without this, torch/CUDA default to FASTEST_FIRST while nvidia-smi
# (and Unsloth's VRAM probes) use PCI-bus order, so a GPU index chosen from
# nvidia-smi data can resolve to a different physical card via
# CUDA_VISIBLE_DEVICES. setdefault so an explicit user override wins. See
# utils/hardware/hardware.py for the full rationale; set here too so the entry
# process is covered before its heavy ML imports.
os.environ.setdefault("CUDA_DEVICE_ORDER", "PCI_BUS_ID")
# Windows terminals default to the active system code page. Reconfigure
# stdout/stderr before the startup banner so non-ASCII output cannot crash the
# backend process.
if sys.platform == "win32":
for _win_stream in (sys.stdout, sys.stderr):
if _win_stream is not None and hasattr(_win_stream, "reconfigure"):
try:
_win_stream.reconfigure(encoding = "utf-8", errors = "replace")
except Exception:
pass
del _win_stream
_SYSTEM_GPU_CACHE_TTL_SECONDS = 10.0
_system_gpu_cache_lock = threading.Lock()
_system_gpu_cache: Optional[tuple[float, dict[str, Any]]] = None
# ── Windows AMD ROCm DLL injection ──────────────────────────────────────────
# Python 3.8+ ignores PATH for extension modules; register ROCm bin dirs with
# os.add_dll_directory() so amdhip64.dll etc. are found before any torch import.
if sys.platform == "win32":
# Retained at module scope; os.add_dll_directory returns a handle that
# removes the search-path entry when garbage collected.
_ROCM_DLL_HANDLES: list = []
def _add_rocm_dll_dirs() -> None:
candidates = []
# 1. HIP_PATH / ROCM_PATH set by the AMD HIP SDK installer
for _var in ("HIP_PATH", "ROCM_PATH"):
_val = os.environ.get(_var)
if _val:
candidates.append(os.path.join(_val, "bin"))
# 2. AMD installer: C:\Program Files\AMD\ROCm\<ver>\bin, newest first.
_default_root = os.path.join(
os.environ.get("ProgramFiles", r"C:\Program Files"), "AMD", "ROCm"
)
def _ver_key(name: str) -> tuple:
# Numeric tuple key so "10.0" sorts after "7.0"; non-numeric chunks fall back to string
parts = []
for chunk in name.split("."):
try:
parts.append((0, int(chunk)))
except ValueError:
parts.append((1, chunk))
return tuple(parts)
try:
if os.path.isdir(_default_root):
for _ver in sorted(os.listdir(_default_root), key = _ver_key, reverse = True):
_bin = os.path.join(_default_root, _ver, "bin")
if os.path.isdir(_bin):
candidates.append(_bin)
except OSError:
pass
for _d in candidates:
if os.path.isdir(_d):
try:
_ROCM_DLL_HANDLES.append(os.add_dll_directory(_d))
except (OSError, AttributeError):
pass
_add_rocm_dll_dirs()
del _add_rocm_dll_dirs
# ── Windows AMD ROCm: make hipInfo.exe resolvable for subprocess probes ──
# bitsandbytes' get_rocm_gpu_arch() runs `hipinfo.exe` via PATH at import
# time; the AMD torch wheel ships it in the venv Scripts dir, which is on
# PATH only when the venv is activated -- Unsloth launches python directly.
# Without this, every bitsandbytes import logs a scary (but harmless)
# "Could not detect ROCm GPU architecture: [WinError 2]" ERROR + WARNING.
# Gated on the file existing: only AMD ROCm wheels ship hipInfo.exe, so
# NVIDIA/CPU hosts are untouched. os.add_dll_directory above does not help
# here -- subprocess PATH resolution ignores DLL search directories.
_scripts_dir = os.path.dirname(sys.executable)
if os.path.isfile(os.path.join(_scripts_dir, "hipInfo.exe")):
import shutil as _shutil
if not _shutil.which("hipinfo.exe"):
os.environ["PATH"] = _scripts_dir + os.pathsep + os.environ.get("PATH", "")
del _shutil
del _scripts_dir
# ── Windows AMD ROCm: set BNB_ROCM_VERSION before any bitsandbytes import ─
# bitsandbytes derives the rocm<ver>.dll name from torch.version.hip, but the
# wheel ships rocm72.dll, so the server crashes ("Configured ROCm binary not
# found") without this. Detect the shipped DLL (mirrors worker.py); gate on
# the rocm bnb DLL rather than torch.version.hip to avoid importing torch on
# every Windows host.
# Values seeded by the installer's sitecustomize.py are redetectable
# defaults; explicit caller values remain authoritative.
if (
"BNB_ROCM_VERSION" not in os.environ
or os.environ.get("UNSLOTH_BNB_ROCM_VERSION_SOURCE") == "sitecustomize"
):
import glob as _glob
import logging as _logging
_bnb_rocm_ver = None
_found_rocm_bnb = False
try:
import importlib.util as _ilu
_bnb_spec = _ilu.find_spec("bitsandbytes")
# submodule_search_locations (not spec.origin) handles editable installs
if _bnb_spec and _bnb_spec.submodule_search_locations:
import re as _re_bnb
_all_vers_main: list[str] = []
for _pkg_dir in _bnb_spec.submodule_search_locations:
for _dll in _glob.glob(os.path.join(_pkg_dir, "libbitsandbytes_rocm*.dll")):
_found_rocm_bnb = True
_km = _re_bnb.search(
r"libbitsandbytes_rocm(\d+)\.dll", os.path.basename(_dll)
)
if _km:
_all_vers_main.append(_km.group(1))
if _all_vers_main:
_bnb_rocm_ver = max(_all_vers_main, key = lambda v: int(v))
except Exception as _e:
_logging.getLogger(__name__).warning(
"Windows ROCm: BNB DLL detection failed (%s); leaving BNB_ROCM_VERSION as is",
_e,
)
# Only when a ROCm bnb DLL actually exists: HIP_PATH/ROCM_PATH alone
# (HIP SDK on a CUDA/CPU box) must not force a ROCm backend onto a
# non-ROCm bitsandbytes, which raises at import. DLL unparsable -> "72".
if _found_rocm_bnb:
_bnb_rocm_ver_final = _bnb_rocm_ver or os.environ.get("BNB_ROCM_VERSION") or "72"
os.environ["BNB_ROCM_VERSION"] = _bnb_rocm_ver_final
os.environ["UNSLOTH_BNB_ROCM_VERSION_SOURCE"] = "detected"
_logging.getLogger(__name__).info(
"Windows ROCm: set BNB_ROCM_VERSION=%s (from installed BNB wheel)",
_bnb_rocm_ver_final,
)
# Setting BNB_ROCM_VERSION makes bitsandbytes log a benign override notice on
# import; drop only that record so real errors and mismatch warnings show.
if os.environ.get("BNB_ROCM_VERSION"):
import logging as _logging
_logging.getLogger("bitsandbytes.cextension").addFilter(
lambda _r: "environment variable detected" not in _r.getMessage()
)
# ── WSL AMD Strix Halo (gfx1151): enable ROCDXG before any torch import ──────
# In WSL the AMD GPU is reached via the ROCDXG bridge (librocdxg.so over
# /dev/dxg), which HSA loads only when HSA_ENABLE_DXG_DETECTION=1 is set BEFORE
# torch touches the GPU. A worker launched outside a login shell (e.g.
# `wsl.exe -d Ubuntu-24.04 python ...`) misses the installer's persisted env
# and silently falls back to CPU. Set it here, gated to no-op unless BOTH
# /dev/dxg AND librocdxg.so exist -- native Linux ROCm, NVIDIA, macOS and
# Windows are unaffected.
elif sys.platform.startswith("linux") and "HSA_ENABLE_DXG_DETECTION" not in os.environ:
try:
if os.path.exists("/dev/dxg") and any(
os.path.exists(os.path.join(_p, "librocdxg.so"))
for _p in ("/opt/rocm/lib", "/opt/rocm/lib64")
):
os.environ["HSA_ENABLE_DXG_DETECTION"] = "1"
import logging as _logging
_logging.getLogger(__name__).info(
"WSL ROCm: set HSA_ENABLE_DXG_DETECTION=1 (librocdxg bridge present)"
)
except Exception:
pass
# Put backend dir on sys.path so _platform_compat is importable when main.py
# is launched directly (e.g. `uvicorn main:app`).
_backend_dir = str(_Path(__file__).parent)
if _backend_dir not in sys.path:
sys.path.insert(0, _backend_dir)
# `uvicorn main:app` bypasses run.py; seed thread caps here too.
from utils.cpu_threads import configure_cpu_threads
try:
configure_cpu_threads()
except ValueError as exc:
_raw = os.environ.get("UNSLOTH_CPU_THREADS")
raise SystemExit(f"Error: Invalid UNSLOTH_CPU_THREADS value {_raw!r}: {exc}") from None
# Anaconda/conda-forge Python: seed platform._sys_version_cache before any
# library import triggers attrs -> rich -> structlog -> platform crash.
# See: https://github.com/python/cpython/issues/102396
import _platform_compat # noqa: F401
# Direct `uvicorn main:app` launches bypass run.py, so re-export here too
# (mirrors run.py). Required BEFORE the unsloth-zoo import below, whose
# LLAMA_CPP_DEFAULT_DIR binding is import-time.
from utils.paths.storage_roots import studio_root as _studio_root
try:
_LEGACY_STUDIO_ROOT = (_Path.home() / ".unsloth" / "studio").resolve()
except (OSError, ValueError):
_LEGACY_STUDIO_ROOT = _Path.home() / ".unsloth" / "studio"
try:
_STUDIO_ROOT_RESOLVED = _studio_root().resolve()
except (OSError, ValueError):
_STUDIO_ROOT_RESOLVED = _studio_root()
if _STUDIO_ROOT_RESOLVED != _LEGACY_STUDIO_ROOT:
if not os.environ.get("UNSLOTH_STUDIO_HOME"):
os.environ["UNSLOTH_STUDIO_HOME"] = str(_STUDIO_ROOT_RESOLVED)
if not os.environ.get("UNSLOTH_LLAMA_CPP_PATH"):
os.environ["UNSLOTH_LLAMA_CPP_PATH"] = str(_STUDIO_ROOT_RESOLVED / "llama.cpp")
# The studio bundles unsloth_zoo; declare unsloth present (as `import unsloth`
# does) so its lazy submodule imports (export, hardware, mlx) and the
# DiffusionGemma runner never trip the install guard on a clean install.
os.environ.setdefault("UNSLOTH_IS_PRESENT", "1")
import hashlib
import ipaddress
import mimetypes
import re as _re
import shutil
import warnings
from contextlib import asynccontextmanager
from importlib.metadata import PackageNotFoundError, version as package_version
from urllib.parse import urlparse
_STUDIO_INSTALL_ID_RE = _re.compile(r"^[0-9a-f]{64}$")
def _read_studio_install_id() -> str:
"""Per-install opaque id at $STUDIO_HOME/share/studio_install_id.
Returns "" when absent or not a 64-char lowercase-hex token; then
/api/health emits "" and the launcher accepts any healthy backend.
Carries no install-path info (matters when Unsloth runs -H 0.0.0.0)."""
try:
token = (_STUDIO_ROOT_RESOLVED / "share" / "studio_install_id").read_text().strip()
except (OSError, ValueError):
return ""
return token if _STUDIO_INSTALL_ID_RE.fullmatch(token) else ""
_STUDIO_ROOT_ID_CACHE: str = _read_studio_install_id()
def _studio_root_id() -> str:
"""Same-install discriminator for /api/health (cached at import).
Empty when no installer token is present; the launcher treats "" as
"accept any healthy backend"."""
return _STUDIO_ROOT_ID_CACHE
# Fix broken Windows registry MIME types: some installs map .js to text/plain,
# which mimetypes (hence StaticFiles) inherits and browsers reject for ES
# modules. add_type() before StaticFiles forces correct types.
if sys.platform == "win32":
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
# Suppress dependency warnings in production
if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
warnings.filterwarnings("ignore")
# Or be more specific:
# warnings.filterwarnings("ignore", category=DeprecationWarning)
# warnings.filterwarnings("ignore", module="triton.*")
from fastapi import Depends, FastAPI, HTTPException, Query, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, HTMLResponse, Response
from starlette.middleware.gzip import GZipMiddleware
from pathlib import Path
from datetime import datetime
from routes import (
auth_router,
chat_history_router,
data_recipe_router,
datasets_router,
export_router,
inference_router,
inference_studio_router,
mcp_servers_router,
models_router,
providers_router,
rag_router,
training_history_router,
training_router,
)
from routes.llama import router as llama_router
from routes.whisper import router as whisper_router
from routes.preview import router as preview_router
from hub.routes import (
inventory_router as hub_inventory_router,
datasets_router as hub_datasets_router,
token_router as hub_token_router,
)
from picker.routes import templates_router as picker_templates_router
from hub.schemas.downloads import TransportCapabilities
from hub.utils.download_registry import (
get_download_transport_capabilities,
reap_orphan_workers as reap_hub_orphan_workers,
terminate_active_downloads as terminate_hub_downloads,
)
from routes.settings import router as settings_router
from routes.prompts import router as prompts_router
from auth import storage
from auth.authentication import get_current_subject
from utils.hardware import (
detect_hardware,
get_device,
DeviceType,
get_backend_visible_gpu_info,
)
import utils.hardware.hardware as _hw_module
from utils.cache_cleanup import clear_unsloth_compiled_cache
from utils.lifespan_shutdown import run_lifespan_shutdown
from utils.native_path_leases import native_path_leases_supported
from utils.update_status import (
get_studio_install_source_status,
get_studio_update_status,
)
from utils.studio_version import get_studio_version
from utils.api_errors import install_api_error_handlers
def get_unsloth_version() -> str:
try:
return package_version("unsloth")
except PackageNotFoundError:
pass
version_file = _Path(__file__).resolve().parents[2] / "unsloth" / "models" / "_utils.py"
try:
for line in version_file.read_text(encoding = "utf-8").splitlines():
if line.startswith("__version__ = "):
return line.split("=", 1)[1].strip().strip('"').strip("'")
except OSError:
pass
return "dev"
UNSLOTH_VERSION = get_unsloth_version()
STUDIO_VERSION = get_studio_version()
def _load_desktop_owner() -> dict[str, str] | None:
token = os.environ.pop("UNSLOTH_STUDIO_DESKTOP_OWNER_TOKEN", "")
kind = os.environ.pop("UNSLOTH_STUDIO_DESKTOP_OWNER_KIND", "")
if kind != "tauri" or not token:
return None
return {
"kind": "tauri",
"token_sha256": hashlib.sha256(token.encode("utf-8")).hexdigest(),
}
_DESKTOP_OWNER = _load_desktop_owner()
# The Tauri desktop app runs the backend on the owner's own machine, so local
# stdio MCP servers are safe there. setdefault lets an explicit "0" opt out.
if _DESKTOP_OWNER:
os.environ.setdefault("UNSLOTH_STUDIO_ALLOW_STDIO_MCP", "1")
def _desktop_owner() -> dict[str, str] | None:
return _DESKTOP_OWNER
def _start_helper_precache_if_enabled() -> None:
"""Start optional Helper LLM GGUF pre-cache only after explicit opt-in."""
try:
from utils.helper_precache_settings import should_preload_helper_on_startup
if not should_preload_helper_on_startup():
return
except Exception:
return
import threading
def _precache():
try:
from utils.datasets.llm_assist import precache_helper_gguf
precache_helper_gguf()
except Exception:
pass # non-critical
threading.Thread(target = _precache, daemon = True, name = "helper-gguf-precache").start()
def _run_llama_cpp_startup_probes(app: FastAPI) -> None:
"""llama.cpp capability (MTP support) + freshness (release age) probes.
Runs OFF the startup critical path (see _start_llama_cpp_probes_if_enabled).
Both are cached and freshness has a 24h disk TTL, but on a cold/expired cache
the freshness check makes a blocking GitHub request, and on macOS the first
`llama-server --help` exec can stall on Gatekeeper verification -- neither must
ever gate `Application startup complete`. Writes app.state only; nothing reads
those values synchronously at startup (the status routes call
check_prebuilt_freshness directly at request time), so populating them late is
safe.
"""
try:
from core.inference.llama_cpp import LlamaCppBackend
from utils.llama_cpp_freshness import (
check_prebuilt_freshness,
format_stale_warning,
)
_bin = LlamaCppBackend._find_llama_server_binary()
_caps = LlamaCppBackend.probe_server_capabilities(_bin)
app.state.llama_cpp_capabilities = _caps
_freshness = check_prebuilt_freshness(_bin)
app.state.llama_cpp_freshness = _freshness
import structlog as _structlog
_log = _structlog.get_logger(__name__)
if _caps.get("found") and not _caps.get("supports_mtp"):
_msg = (
"llama.cpp prebuilt lacks MTP support "
"(--spec-type mtp/draft-mtp). Run `unsloth studio update`. "
"MTP GGUFs will load without speculative decoding."
)
_log.warning(_msg)
print(f"WARNING: {_msg}", flush = True)
if _freshness.get("stale"):
_msg = format_stale_warning(_freshness)
_log.warning(_msg)
print(f"WARNING: {_msg}", flush = True)
except Exception as _probe_exc:
import structlog as _structlog
_structlog.get_logger(__name__).debug("llama.cpp startup probes failed: %s", _probe_exc)
def _start_llama_cpp_probes_if_enabled(app: FastAPI) -> None:
"""Run the llama.cpp startup probes on a daemon thread, off the startup
critical path so they never delay `Application startup complete`. Skipped
entirely when update checks are disabled, so a fully offline boot makes no
background network calls."""
if os.environ.get("UNSLOTH_DISABLE_UPDATE_CHECK") == "1":
return
threading.Thread(
target = _run_llama_cpp_startup_probes,
args = (app,),
daemon = True,
name = "llama-cpp-startup-probe",
).start()
def _warm_rag_embedder() -> None:
"""Warm RAG embeddings without blocking backend readiness."""
try:
from storage import rag_db
if not rag_db.RAG_AVAILABLE:
return
from core.rag import embeddings
embeddings.warm()
except Exception:
pass
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Startup: detect hardware, seed default admin if needed. Shutdown: clean up compiled cache."""
import time as _time
_lifespan_started = _time.perf_counter()
import structlog as _structlog
_lifespan_log = _structlog.get_logger(__name__)
clear_unsloth_compiled_cache()
# Remove stale .venv_overlay from old versions; switching now uses .venv_t5/.
overlay_dir = Path(__file__).resolve().parent.parent.parent / ".venv_overlay"
if overlay_dir.is_dir():
shutil.rmtree(overlay_dir, ignore_errors = True)
# Detect hardware first — sets the DEVICE global used everywhere.
detect_hardware()
_lifespan_log.info(
"lifespan hardware detection completed in %.1fms",
(_time.perf_counter() - _lifespan_started) * 1000,
)
# Apple Silicon with MLX missing => Train/Export are greyed out (chat-only).
# Reinstall mlx by name on a background thread (off the critical path) and
# re-detect, so a reinstall/update that dropped mlx self-heals. No-op
# elsewhere; opt out with UNSLOTH_DISABLE_MLX_AUTOREPAIR=1.
try:
from utils.mlx_repair import start_mlx_autorepair_if_needed
start_mlx_autorepair_if_needed()
except Exception as _mlx_exc:
import structlog as _structlog
_structlog.get_logger(__name__).debug("mlx autorepair skipped: %s", _mlx_exc)
# Reap workers/runs orphaned by a previous crash before new work starts.
try:
from storage.studio_db import cleanup_orphaned_runs
cleanup_orphaned_runs()
except Exception as exc:
_lifespan_log.warning("cleanup_orphaned_runs failed at startup: %s", exc)
reap_hub_orphan_workers()
# llama.cpp probes: capability (MTP support) + freshness (release age).
# These used to run inline here and could block `Application startup complete`
# for tens of seconds on macOS (cold GitHub freshness cache / slow network, and
# Gatekeeper verifying the unsigned binary on first `--help` exec). They only
# write app.state and nothing reads it synchronously at startup, so run them on
# a daemon thread off the startup critical path (mirrors the helper-precache and
# RAG-warm threads). Default to None until the thread populates them.
app.state.llama_cpp_capabilities = None
app.state.llama_cpp_freshness = None
_start_llama_cpp_probes_if_enabled(app)
try:
from storage.rag_db import reconcile_orphaned_ingestion_jobs
reconcile_orphaned_ingestion_jobs()
except Exception as exc:
_lifespan_log.warning("reconcile_orphaned_ingestion_jobs failed at startup: %s", exc)
_start_helper_precache_if_enabled()
threading.Thread(target = _warm_rag_embedder, daemon = True, name = "rag-embedder-warm").start()
# Idle auto-unload loop (no-op unless the OpenAI auto-unload TTL is set).
from core.inference.llama_keepwarm import idle_unload_loop, sweep_slot_save_dir
sweep_slot_save_dir()
app.state.idle_unload_task = asyncio.create_task(idle_unload_loop())
# Initialize RSA key pair for API key encryption (external providers).
from core.inference.key_exchange import init_key_pair
init_key_pair()
_lifespan_log.info(
"lifespan pre-auth setup completed in %.1fms",
(_time.perf_counter() - _lifespan_started) * 1000,
)
# run_server's pre-bind gate sets suppress_bootstrap_injection when a public
# URL is about to serve with the default credential active: never (re)capture
# the bootstrap password into app.state, or the HTML would hand it out.
_suppress_bootstrap = getattr(app.state, "suppress_bootstrap_injection", False)
if storage.ensure_default_admin():
bootstrap_pw = None if _suppress_bootstrap else storage.get_bootstrap_password()
app.state.bootstrap_password = bootstrap_pw
bootstrap_path = storage.DB_PATH.parent / ".bootstrap_password"
print("\n" + "=" * 60)
print("DEFAULT ADMIN ACCOUNT CREATED")
print(f" username: {storage.DEFAULT_ADMIN_USERNAME}")
print(f" password saved to: {bootstrap_path}")
print(" Open the Unsloth UI to sign in and change it.")
print("=" * 60 + "\n")
else:
app.state.bootstrap_password = (
None if _suppress_bootstrap else storage.get_bootstrap_password()
)
_lifespan_log.info(
"lifespan startup completed in %.1fms",
(_time.perf_counter() - _lifespan_started) * 1000,
)
yield
_idle_task = getattr(app.state, "idle_unload_task", None)
if _idle_task is not None:
_idle_task.cancel()
try:
await _idle_task
except asyncio.CancelledError:
pass
from core.inference.llama_http import aclose as _close_llama_http
await _close_llama_http()
await run_lifespan_shutdown(
terminate_hub_downloads,
clear_unsloth_compiled_cache,
_hw_module,
)
app = FastAPI(
title = "Unsloth UI Backend",
version = UNSLOTH_VERSION,
description = "Backend API for Unsloth UI - Training and Model Management",
lifespan = lifespan,
)
# The MCP surface is opt-in because it can start GPU jobs and write model
# artifacts. Mount it only when explicitly enabled by the Unsloth process.
if os.environ.get("UNSLOTH_STUDIO_ENABLE_MCP") == "1":
from fastmcp.utilities.lifespan import combine_lifespans
from mcp_server import BearerTokenMiddleware, create_studio_mcp
_studio_mcp_app = create_studio_mcp().http_app(path = "/")
_studio_mcp_lifespan = _studio_mcp_app.lifespan
_mcp_token = os.environ.get("UNSLOTH_STUDIO_MCP_TOKEN")
if not _mcp_token:
raise RuntimeError("UNSLOTH_STUDIO_MCP_TOKEN is required when MCP is enabled")
_studio_mcp_app = BearerTokenMiddleware(_studio_mcp_app, _mcp_token)
app.router.lifespan_context = combine_lifespans(lifespan, _studio_mcp_lifespan)
app.mount("/mcp", _studio_mcp_app)
from loggers.config import LogConfig
from loggers.handlers import LoggingMiddleware
logger = LogConfig.setup_logging(
service_name = "unsloth-studio-backend",
env = os.getenv("ENVIRONMENT_TYPE", "production"),
)
app.add_middleware(LoggingMiddleware)
# img/media-src allow any https origin so HF model-card assets render (mirrors
# tauri.conf.json); scripts/frames/connect-src stay same-origin + HF.
from starlette.datastructures import MutableHeaders # noqa: E402
_CSP_SCRIPT_NONCE_HEADER = "x-internal-script-nonce"
_ARTIFACT_PREVIEW_FRAME_PATH = "/api/inference/artifact-preview-frame"
# /content is Colab's working directory — more reliable than env vars, which
# aren't always set depending on Colab runtime version.
import importlib.util as _importlib_util
_IS_COLAB = os.path.isdir("/content") and (
bool(os.environ.get("COLAB_BACKEND_URL"))
or bool(os.environ.get("COLAB_JUPYTER_IP"))
or _importlib_util.find_spec("google.colab") is not None
)
def _build_csp(script_nonce: "str | None" = None) -> str:
script_src = "script-src 'self'"
if script_nonce:
script_src += f" 'nonce-{script_nonce}'"
# Colab parent frames span multi-level *.prod.colab.dev subdomains (CSP
# wildcards match one level only) and null-origin iframes; use '*' since
# Colab is already a sandboxed single-user environment.
frame_ancestors = "*" if _IS_COLAB else "'none'"
# In Colab, the kernel/output scaffolding injects scripts and fetch/WS from
# *.prod.colab.dev and *.googleusercontent.com, so widen script-src and
# connect-src for those. Scripts still use a nonce, not 'unsafe-inline'.
if _IS_COLAB:
script_src += " https://*.prod.colab.dev https://*.googleusercontent.com"
connect_src = (
"'self' blob: data: "
"https://huggingface.co https://datasets-server.huggingface.co "
"https://*.prod.colab.dev wss://*.prod.colab.dev "
"https://*.googleusercontent.com wss://*.googleusercontent.com"
)
else:
connect_src = "'self' https://huggingface.co https://datasets-server.huggingface.co"
return (
"default-src 'self'; "
"img-src 'self' data: blob: https:; "
"media-src 'self' data: blob: https:; "
f"connect-src {connect_src}; "
"style-src 'self' 'unsafe-inline'; "
f"{script_src}; "
"font-src 'self' data:; "
"frame-src 'self'; "
f"frame-ancestors {frame_ancestors}; "
"form-action 'self'; "
"base-uri 'self'"
)
class SecurityHeadersMiddleware:
"""Set baseline security headers; splice per-response inline-script nonces into CSP.
Pure ASGI (not BaseHTTPMiddleware) so streaming responses are not wrapped in
an anyio stream. Header logic mirrors the prior version exactly via
MutableHeaders on the response-start message.
"""
def __init__(self, app):
self.app = app
async def __call__(self, scope, receive, send):
if scope["type"] != "http":
await self.app(scope, receive, send)
return
path = scope.get("path", "")
async def send_wrapper(message):
if message["type"] == "http.response.start":
# ASGI headers are an iterable; coerce to a list so MutableHeaders
# can mutate in place even if a server sends a tuple or omits it.
raw = message.setdefault("headers", [])
if not isinstance(raw, list):
raw = list(raw)
message["headers"] = raw
headers = MutableHeaders(raw = raw)
# Strip the internal nonce hand-off header so it never reaches the client
nonce = headers.get(_CSP_SCRIPT_NONCE_HEADER)
if nonce is not None:
del headers[_CSP_SCRIPT_NONCE_HEADER]
headers.setdefault("Content-Security-Policy", _build_csp(nonce))
# Omit X-Frame-Options in Colab: CSP frame-ancestors handles it, and
# DENY would block serve_kernel_port_as_iframe regardless of CSP.
if not _IS_COLAB and path != _ARTIFACT_PREVIEW_FRAME_PATH:
headers.setdefault("X-Frame-Options", "DENY")
headers.setdefault("X-Content-Type-Options", "nosniff")
headers.setdefault("Referrer-Policy", "no-referrer")
headers.setdefault(
"Permissions-Policy",
"camera=(), microphone=(self), geolocation=()",
)
headers["server"] = "unsloth-studio"
await send(message)
await self.app(scope, receive, send_wrapper)
app.add_middleware(SecurityHeadersMiddleware)
# Cap request bodies on protected POSTs. Upload routes get explicit multipart
# headroom; non-upload routes keep the default body cap.
import json as _json_for_413 # noqa: E402
from utils.upload_limits import ( # noqa: E402
STT_AUDIO_JSON_MAX_BYTES,
STT_AUDIO_RAW_MAX_BYTES,
UNSTRUCTURED_RECIPE_UPLOAD_MAX_BYTES,
default_request_body_limit_bytes,
upload_request_limit_bytes,
)
_BODY_PROTECTED_PREFIXES = (
"/v1/chat/completions",
"/v1/completions",
"/p/",
"/api/inference",
"/api/picker",
"/api/data-recipe",
"/api/datasets",
"/api/hub",
"/api/chat",
"/api/settings",
"/api/train",
"/api/export",
"/mcp",
)
_DATASET_UPLOAD_PASSTHROUGH_PREFIX = "/api/datasets/upload"
_DATA_RECIPE_UNSTRUCTURED_UPLOAD_PASSTHROUGH_PREFIX = (
"/api/data-recipe/seed/upload-unstructured-file"
)
_BODY_UPLOAD_PASSTHROUGH_PREFIXES = (
_DATASET_UPLOAD_PASSTHROUGH_PREFIX,
_DATA_RECIPE_UNSTRUCTURED_UPLOAD_PASSTHROUGH_PREFIX,
)
def _get_upload_passthrough_request_max_bytes(path: str) -> int:
if path.startswith(_DATA_RECIPE_UNSTRUCTURED_UPLOAD_PASSTHROUGH_PREFIX):
return upload_request_limit_bytes(UNSTRUCTURED_RECIPE_UPLOAD_MAX_BYTES)
if path.startswith(_DATASET_UPLOAD_PASSTHROUGH_PREFIX):
return upload_request_limit_bytes()
return default_request_body_limit_bytes()
def _get_request_body_max_bytes(path: str) -> int:
if path.startswith("/api/inference/audio/transcribe/raw"):
return STT_AUDIO_RAW_MAX_BYTES
if path.startswith("/api/inference/audio/transcribe"):
return STT_AUDIO_JSON_MAX_BYTES
return default_request_body_limit_bytes()
async def _send_411(send) -> None:
payload = _json_for_413.dumps(
{"detail": "Content-Length required for upload requests."},
).encode("utf-8")
await send(
{
"type": "http.response.start",
"status": 411,
"headers": [
(b"content-type", b"application/json"),
(b"content-length", str(len(payload)).encode("ascii")),
],
}
)
await send({"type": "http.response.body", "body": payload, "more_body": False})
async def _send_413(send, total_bytes: int, max_bytes: int) -> None:
payload = _json_for_413.dumps(
{"detail": (f"Request body too large ({total_bytes:,} bytes; max {max_bytes:,}).")},
).encode("utf-8")
await send(
{
"type": "http.response.start",
"status": 413,
"headers": [
(b"content-type", b"application/json"),
(b"content-length", str(len(payload)).encode("ascii")),
],
}
)
await send({"type": "http.response.body", "body": payload, "more_body": False})
class MaxBodyMiddleware:
"""Reject oversized bodies on protected POST/PUT/PATCH; raw ASGI so chunked uploads cannot bypass the cap."""
def __init__(
self,
app,
max_bytes_getter,
protected_prefixes: tuple,
request_max_bytes_getter = None,
upload_passthrough_prefixes: tuple = (),
upload_passthrough_max_bytes_getter = None,
):
self.app = app
self.max_bytes_getter = max_bytes_getter
self.protected_prefixes = protected_prefixes
self.request_max_bytes_getter = request_max_bytes_getter
self.upload_passthrough_prefixes = upload_passthrough_prefixes
self.upload_passthrough_max_bytes_getter = upload_passthrough_max_bytes_getter
def _upload_passthrough_max_bytes(self, path: str) -> int:
if self.upload_passthrough_max_bytes_getter is None:
return int(self.max_bytes_getter())
try:
return int(self.upload_passthrough_max_bytes_getter(path))
except TypeError:
try:
return int(self.upload_passthrough_max_bytes_getter())
except Exception:
return int(self.max_bytes_getter())
except Exception:
return int(self.max_bytes_getter())
def _request_max_bytes(self, path: str) -> int:
if self.request_max_bytes_getter is None:
return int(self.max_bytes_getter())
try:
return int(self.request_max_bytes_getter(path))
except Exception:
return int(self.max_bytes_getter())
async def __call__(self, scope, receive, send):
if scope["type"] != "http":
await self.app(scope, receive, send)
return
method = scope.get("method", "").upper()
path = scope.get("path", "")
if method not in ("POST", "PUT", "PATCH") or not any(
path.startswith(p) for p in self.protected_prefixes
):
await self.app(scope, receive, send)
return
max_bytes = self._request_max_bytes(path)
declared = None
for name, value in scope.get("headers", []):
if name == b"content-length":
try:
declared = int(value.decode("latin-1"))
except (ValueError, UnicodeDecodeError):
declared = None
break
if any(path.startswith(p) for p in self.upload_passthrough_prefixes):
upload_max_bytes = self._upload_passthrough_max_bytes(path)
if declared is None:
await _send_411(send)
return
if declared > upload_max_bytes:
await _send_413(send, declared, upload_max_bytes)
return
await self.app(scope, receive, send)
return
if declared is not None and declared > max_bytes:
await _send_413(send, declared, max_bytes)
return
chunks: list = []
total = 0
while True:
msg = await receive()
mtype = msg.get("type")
if mtype == "http.disconnect":
return
if mtype != "http.request":
# Mid-stream unexpected frame: forwarding would corrupt downstream
return
body = msg.get("body", b"") or b""
if body:
total += len(body)
if total > max_bytes:
await _send_413(send, total, max_bytes)
return
chunks.append(body)
if not msg.get("more_body", False):
break
replayed = {"sent": False}
async def replay_receive():
if not replayed["sent"]:
replayed["sent"] = True
return {
"type": "http.request",
"body": b"".join(chunks),
"more_body": False,
}
# After replay, fall through so http.disconnect still propagates.
return await receive()
await self.app(scope, replay_receive, send)
app.add_middleware(
MaxBodyMiddleware,
max_bytes_getter = default_request_body_limit_bytes,
protected_prefixes = _BODY_PROTECTED_PREFIXES,
request_max_bytes_getter = _get_request_body_max_bytes,
upload_passthrough_prefixes = _BODY_UPLOAD_PASSTHROUGH_PREFIXES,
upload_passthrough_max_bytes_getter = _get_upload_passthrough_request_max_bytes,
)
# Tracks in-flight inference requests for idle auto-unload; off -> passthrough.
from core.inference.llama_keepwarm import LlamaKeepWarmMiddleware # noqa: E402
app.add_middleware(LlamaKeepWarmMiddleware)
from starlette.responses import RedirectResponse as _RedirectResponse # noqa: E402
@app.get("/recipes", include_in_schema = False)
@app.get("/recipes/{rest:path}", include_in_schema = False)
async def _recipes_redirect(rest: str = ""):
target = "/data-recipes" + (("/" + rest) if rest else "")
return _RedirectResponse(url = target, status_code = 308)
from utils.host_policy import cors_origins_for_mode # noqa: E402
_cors_origins = cors_origins_for_mode(
api_only = os.environ.get("UNSLOTH_API_ONLY") == "1",
secure = os.environ.get("UNSLOTH_SECURE") == "1",
)
app.add_middleware(
CORSMiddleware,
allow_origins = _cors_origins,
allow_credentials = True,
allow_methods = ["*"],
allow_headers = ["*"],
)
# ============ Register API Routes ============
# Register routers
app.include_router(auth_router, prefix = "/api/auth", tags = ["auth"])
app.include_router(training_router, prefix = "/api/train", tags = ["training"])
app.include_router(models_router, prefix = "/api/models", tags = ["models"])
app.include_router(chat_history_router, prefix = "/api/chat", tags = ["chat"])
app.include_router(inference_router, prefix = "/api/inference", tags = ["inference"])
# Unsloth-only inference endpoints (cancel, etc.) are NOT exposed on the /v1
# OpenAI-compat prefix below.
app.include_router(inference_studio_router, prefix = "/api/inference", tags = ["inference"])
# OpenAI-compatible: mount the inference router at /v1 for external tools.
app.include_router(inference_router, prefix = "/v1", tags = ["openai-compat"])
app.include_router(preview_router, prefix = "/p", tags = ["preview"])
app.include_router(providers_router, prefix = "/api/providers", tags = ["providers"])
app.include_router(settings_router, prefix = "/api/settings", tags = ["settings"])
app.include_router(mcp_servers_router, prefix = "/api/mcp/servers", tags = ["mcp"])
app.include_router(prompts_router, prefix = "/api/prompts", tags = ["prompts"])
app.include_router(datasets_router, prefix = "/api/datasets", tags = ["datasets"])
app.include_router(data_recipe_router, prefix = "/api/data-recipe", tags = ["data-recipe"])
app.include_router(llama_router, prefix = "/api/llama", tags = ["llama"])
app.include_router(whisper_router, prefix = "/api/whisper", tags = ["whisper"])
app.include_router(export_router, prefix = "/api/export", tags = ["export"])
app.include_router(rag_router, prefix = "/api/rag", tags = ["rag"])
app.include_router(training_history_router, prefix = "/api/train", tags = ["training-history"])
app.include_router(hub_inventory_router, prefix = "/api/hub", tags = ["hub"])
app.include_router(hub_datasets_router, prefix = "/api/hub/datasets", tags = ["hub"])
app.include_router(picker_templates_router, prefix = "/api/picker", tags = ["picker"])
app.include_router(hub_token_router, prefix = "/api/hub", tags = ["hub"])
# Re-wrap client-error responses on the /v1/* surface into OpenAI/Anthropic
# error envelopes; non-/v1 paths keep FastAPI's default {"detail": ...} shape.
install_api_error_handlers(app)
# ============ Health and System Endpoints ============
@app.get("/api/liveness")
async def liveness_check():
"""Cheap process liveness for desktop port validation."""
return {
"status": "alive",
"service": "Unsloth UI Backend",
"desktop_protocol_version": 1,
"desktop_manageability_version": 1,
"supports_desktop_auth": True,
"supports_desktop_backend_ownership": True,
"studio_root_id": _studio_root_id(),
**({"desktop_owner": owner} if (owner := _desktop_owner()) else {}),
}
@app.get("/api/health")
async def health_check(request: Request):
"""Liveness plus launcher capability bits; host fingerprint gated on a bearer.
Unauthenticated callers get non-sensitive fields (service, studio_root_id,
chat_only, desktop_*, native_path_leases_supported) to re-adopt a sibling
backend and gate UI before a token exists. version / studio_version /
device_type require a bearer since they fingerprint the host.
"""
base = {
"status": "healthy",
"timestamp": datetime.now().isoformat(),
"service": "Unsloth UI Backend",
"chat_only": _hw_module.CHAT_ONLY,
"desktop_protocol_version": 1,
"desktop_manageability_version": 1,
"supports_desktop_auth": True,
"supports_desktop_backend_ownership": True,
# Opaque per-install id; launchers reject sibling Studios on the same port.
"studio_root_id": _studio_root_id(),
"native_path_leases_supported": native_path_leases_supported(),
**({"desktop_owner": owner} if (owner := _desktop_owner()) else {}),
}
auth = request.headers.get("authorization", "")
if not auth.lower().startswith("bearer "):
return base
try:
from auth.authentication import get_current_subject as _gcs
from fastapi.security import HTTPAuthorizationCredentials
creds = HTTPAuthorizationCredentials(scheme = "Bearer", credentials = auth.split(" ", 1)[1])
# Must await: a bare coroutine is truthy and would skip the auth check
subject = await _gcs(creds)
except HTTPException:
return base
except Exception:
return base
if not subject:
return base
platform_map = {"darwin": "mac", "win32": "windows", "linux": "linux"}
device_type = platform_map.get(sys.platform, sys.platform)
return {
**base,
# Why chat_only is set. This fingerprints the host, so keep it authed.
"chat_only_reason": getattr(_hw_module, "CHAT_ONLY_REASON", None),
"version": UNSLOTH_VERSION,
"studio_version": STUDIO_VERSION,
"device_type": device_type,
# API-screen fields (authed-only; they fingerprint how the host is exposed).
"cloudflare_url": getattr(request.app.state, "cloudflare_url", None),
"server_url": getattr(request.app.state, "server_url", None),
"secure": bool(getattr(request.app.state, "secure", False)),
}
@app.get("/api/studio/install-source")
def studio_install_source(_current_subject: str = Depends(get_current_subject)):
"""Return source-aware install metadata without remote update checks."""
return get_studio_install_source_status(UNSLOTH_VERSION)
@app.get("/api/studio/update-status")
def studio_update_status(_current_subject: str = Depends(get_current_subject)):
"""Return source-aware manual update status for browser-served Unsloth."""
return get_studio_update_status(UNSLOTH_VERSION)
@app.get(
"/api/studio/download-transport-capabilities",
response_model = TransportCapabilities,
)
def studio_download_transport_capabilities(_current_subject: str = Depends(get_current_subject)):
return asdict(get_download_transport_capabilities())
@app.post("/api/shutdown")
async def shutdown_server(request: Request, current_subject: str = Depends(get_current_subject)):
"""Gracefully shut down the Unsloth Studio server.
Called by the frontend quit dialog so users can stop the server from the UI
without the CLI or killing the process manually.
"""
async def _delayed_shutdown():
await asyncio.sleep(0.2) # Let the HTTP response return first
trigger = getattr(request.app.state, "trigger_shutdown", None)
if trigger is not None:
trigger()
else:
# Fallback when not launched via run_server() (e.g. direct uvicorn)
import signal
import os
os.kill(os.getpid(), signal.SIGTERM)
request.app.state._shutdown_task = asyncio.create_task(_delayed_shutdown())
return {"status": "shutting_down"}
def _get_cached_system_gpu_info(logger) -> dict[str, Any]:
"""Return merged GPU visibility/utilization with bounded live-probe churn."""
import time
from utils.hardware import get_backend_visible_gpu_info, get_visible_gpu_utilization
global _system_gpu_cache
now = time.monotonic()
with _system_gpu_cache_lock:
if _system_gpu_cache is not None:
cached_at, cached_gpu_info = _system_gpu_cache
if now - cached_at < _SYSTEM_GPU_CACHE_TTL_SECONDS:
return cached_gpu_info
try:
visibility_info = get_backend_visible_gpu_info() or {"available": False, "devices": []}
except Exception as e:
logger.debug(f"Failed to get GPU visibility info: {e}")
visibility_info = {"available": False, "devices": []}
try:
utilization_info = get_visible_gpu_utilization() or {"devices": []}
except Exception as e:
logger.debug(f"Failed to get GPU utilization info: {e}")
utilization_info = {"devices": []}
util_devices = {d.get("index"): d for d in utilization_info.get("devices", [])}
enriched_devices = []
for dev in visibility_info.get("devices", []):
idx = dev.get("index")
util = util_devices.get(idx, {})
total_vram = util.get("vram_total_gb") or dev.get("memory_total_gb") or 0
# Keep None (usage unknown, e.g. Windows ROCm perf counter) so the UI
# shows unknown, not a fabricated 0 used / full free.
used_vram = util.get("vram_used_gb")
enriched_dev = dict(dev)
enriched_dev["vram_used_gb"] = used_vram
enriched_dev["vram_free_gb"] = (
round(total_vram - used_vram, 2) if total_vram and used_vram is not None else None
)
enriched_dev["vram_utilization_pct"] = util.get("vram_utilization_pct")
enriched_devices.append(enriched_dev)
# Whether GGUF loads accept an explicit gpu_ids pick: /load and
# /validate 400 picks on XPU hosts (no visibility mask speaks torch-xpu
# ordinals) and on Vulkan-only builds (--device pins ggml's own
# ordinals), so the picker must not offer them.
try:
from core.inference.llama_cpp import LlamaCppBackend
from utils.hardware import DeviceType, get_device
gpu_ids_supported = (
get_device() != DeviceType.XPU and not LlamaCppBackend._is_vulkan_backend()
)
except Exception as e:
logger.debug(f"Could not resolve gpu_ids support: {e}")
gpu_ids_supported = True
gpu_info = {
"available": visibility_info.get("available", False),
"devices": enriched_devices,
"gguf_gpu_ids_supported": gpu_ids_supported,
}
_system_gpu_cache = (time.monotonic(), gpu_info)
return gpu_info
@app.get("/api/system")
def get_system_info(current_subject: str = Depends(get_current_subject)):
"""Get system information.
Auth-gated: the response (platform, Python/GPU, memory, ML packages) can
fingerprint a host, which matters in -H 0.0.0.0 / Colab / Tauri-relayed
setups where remote callers can reach /api/system.
"""
import platform
import psutil
import os
import time
import logging
from utils.hardware import get_device, export_capability
from utils.hardware.hardware import _backend_label
logger = logging.getLogger(__name__)
gpu_info = _get_cached_system_gpu_info(logger)
memory = psutil.virtual_memory()
try:
cpu_freq = psutil.cpu_freq()
except Exception as e:
logger.debug(f"Failed to get CPU frequency: {e}")
cpu_freq = None
try:
disk = psutil.disk_usage(os.path.abspath(os.sep))
except Exception as e:
logger.debug(f"Failed to get disk usage: {e}")
disk = None
try:
current_process = psutil.Process(os.getpid())
process_used_mb = round(current_process.memory_info().rss / 1024**2)
except Exception as e:
logger.debug(f"Failed to get current process memory: {e}")
process_used_mb = 0
try:
boot_time = psutil.boot_time()
except Exception as e:
logger.debug(f"Failed to get boot time: {e}")
boot_time = None
# Read versions from metadata so a 3s poll never imports heavy ML libs (or 500s on their import errors).
from importlib.metadata import PackageNotFoundError, version as pkg_version
ml_packages = {}
for pkg in ("torch", "transformers"):
try:
ml_packages[pkg] = pkg_version(pkg)
except PackageNotFoundError:
pass
except Exception as e:
logger.debug(f"Failed to read {pkg} version: {e}")
return {
"platform": platform.platform(),
"python_version": platform.python_version(),
"device_backend": _backend_label(get_device()),
"cpu_count": psutil.cpu_count(logical = True),
"uptime_seconds": max(0, round(time.time() - boot_time)) if boot_time else None,
"cpu": {
"logical_count": psutil.cpu_count(logical = True),
"physical_count": psutil.cpu_count(logical = False),
"usage_percent": psutil.cpu_percent(interval = None),
"frequency_mhz": round(cpu_freq.current, 2)
if cpu_freq and cpu_freq.current is not None
else None,
},
"memory": {
"total_gb": round(memory.total / 1024**3, 2),
"available_gb": round(memory.available / 1024**3, 2),
"percent_used": memory.percent,
"process_used_mb": process_used_mb,
},
"disk": {
"total_gb": round(disk.total / 1e9, 2) if disk else 0,
"free_gb": round(disk.free / 1e9, 2) if disk else 0,
"percent_used": disk.percent if disk else 0,
},
"gpu": gpu_info,
"ml_packages": ml_packages,
# Export capability + torch-aware reason. See /api/system/hardware.
**export_capability(),
}
@app.get("/api/system/gpu-visibility")
async def get_gpu_visibility(current_subject: str = Depends(get_current_subject)):
return get_backend_visible_gpu_info()
@app.get("/api/system/hardware")
def get_hardware_info(
include_details: bool = Query(False), current_subject: str = Depends(get_current_subject)
):
"""Return GPU name, total VRAM, and key ML package versions.
Gated behind auth alongside /api/system -- same fingerprinting concern.
/api/system/gpu-visibility is also auth-gated.
``include_details`` is for About/diagnostics. The default response stays
cheap for callers that only need the primary GPU summary, like training
method auto-selection. Sync def (not async): hardware/detail probes can
shell out, and FastAPI runs sync endpoints in a threadpool.
"""
from utils.hardware import get_gpu_summary, get_package_versions, export_capability
body = {
"gpu": get_gpu_summary(),
"versions": get_package_versions(),
# Export capability + torch-aware reason; the Export UI grays out with the message.
**export_capability(),
}
if include_details:
from utils.llama_cpp_update import get_installed_llama_version
# All backend-visible GPUs (respects CUDA_VISIBLE_DEVICES), so multi-GPU
# hosts list every device -- get_gpu_summary alone reports only the primary.
# Sort by visible_ordinal: the nvidia-smi path returns rows in physical order,
# so under a reordering CUDA_VISIBLE_DEVICES (e.g. "5,3") labeling by array
# index would otherwise disagree with the GPU 0/1 the backend actually sees.
devices = get_backend_visible_gpu_info().get("devices", [])
body["gpus"] = [
{"name": d.get("name"), "vram_total_gb": d.get("memory_total_gb")}
for d in sorted(devices, key = lambda d: d.get("visible_ordinal", 0))
]
body["llama_cpp"] = get_installed_llama_version()
return body
# ============ Serve Frontend (Optional) ============
def _strip_crossorigin(html_bytes: bytes) -> bytes:
"""Remove ``crossorigin`` attributes from script/link tags.
Vite's default ``crossorigin`` forces CORS mode on font loads, which
Firefox HTTPS-Only Mode breaks over plain HTTP; stripping it makes them
same-origin fetches that work on any protocol.
"""
html = html_bytes.decode("utf-8")
html = _re.sub(r'\s+crossorigin(?:="[^"]*")?', "", html)
return html.encode("utf-8")
def _inject_bootstrap(html_bytes: bytes, app: FastAPI):
"""Inject bootstrap credentials when password change is pending.
Returns ``(html_bytes, script_nonce_or_None)``; callers forward the nonce
via ``_CSP_SCRIPT_NONCE_HEADER`` so CSP allows the inline script.
"""
import json as _json
import secrets as _secrets
if not storage.requires_password_change(storage.DEFAULT_ADMIN_USERNAME):
return html_bytes, None
bootstrap_pw = getattr(app.state, "bootstrap_password", None)
if not bootstrap_pw:
return html_bytes, None
payload = _json.dumps(
{
"username": storage.DEFAULT_ADMIN_USERNAME,
"password": bootstrap_pw,
}
)
nonce = _secrets.token_urlsafe(16)
tag = f'<script nonce="{nonce}">window.__UNSLOTH_BOOTSTRAP__={payload}</script>'
html = html_bytes.decode("utf-8")
html = html.replace("</head>", f"{tag}</head>", 1)
return html.encode("utf-8"), nonce
_DEFAULT_PORTS = {"http": 80, "https": 443, "ws": 80, "wss": 443}
def _canonical_origin(scheme: str, netloc: str) -> Optional[tuple[str, str, int]]:
"""Canonicalise an Origin to ``(scheme, host, port)`` for equality.
Browsers strip default ports (RFC 6454 sec 6.1) and scheme/host are
case-insensitive (RFC 3986), so a bare string compare misclassifies
same-origin requests as cross-origin. Returns ``None`` on unparseable input
so callers fall to the safer cross-origin default.
"""
scheme = (scheme or "").strip().lower()
if not scheme or not netloc:
return None
# Strip userinfo (RFC 3986); Origin never carries credentials.
if "@" in netloc:
netloc = netloc.rsplit("@", 1)[1]
# IPv6 hosts use brackets (RFC 3986 sec 3.2.2): ``[::1]:8902``. Bare
# ``partition(":")`` mis-parses these, breaking ``unsloth studio -H ::1``.
if netloc.startswith("["):
close = netloc.find("]")
if close == -1:
return None
host = netloc[1:close]
rest = netloc[close + 1 :]
if rest.startswith(":"):
port_str = rest[1:]
elif rest == "":
port_str = ""
else:
return None
else:
host, _, port_str = netloc.partition(":")
host = host.strip().lower()
if not host:
return None
if port_str:
try:
port = int(port_str)
except ValueError:
return None
else:
port = _DEFAULT_PORTS.get(scheme, 0)
return (scheme, host, port)
def _is_loopback_ip(host: Optional[str]) -> bool:
"""Return whether ``host`` is a loopback IP, including IPv4-mapped IPv6."""
if not host or "%" in host: # a scope id (::1%eth0) is never a plain loopback
return False
try:
ip = ipaddress.ip_address(host)
except (TypeError, ValueError):
return False
mapped = getattr(ip, "ipv4_mapped", None)
return ip.is_loopback or (mapped is not None and mapped.is_loopback)
# A loopback peer carrying any of these is a proxy/tunnel relaying a remote
# client, so the peer is the proxy, not the caller: cloudflared sets
# cf-connecting-ip, reverse proxies set the rest (uvicorn only consumes
# x-forwarded-for, so the others survive to here).
_PROXIED_CLIENT_HEADERS = (
"cf-connecting-ip",
"forwarded",
"x-forwarded-for",
"x-forwarded-host",
"x-real-ip",
)
def _host_header_is_loopback(host_header: Optional[str]) -> bool:
"""Loopback/localhost check on the raw Host header.
Reads the header directly so a malformed or absent Host cannot fall back to
``request.url.hostname``'s (loopback) ASGI server address.
"""
if not host_header:
return False
host = host_header.strip()
if host.startswith("["): # [IPv6] or [IPv6]:port
end = host.find("]")
if end == -1 or (host[end + 1 :] and not host[end + 1 :].startswith(":")):
return False # unclosed bracket or junk after ] (e.g. [::1]evil)
host = host[1:end]
elif host.count(":") == 1: # host:port
host = host.split(":", 1)[0]
host = host.lower().rstrip(".")
return host == "localhost" or _is_loopback_ip(host)
def _is_local_bootstrap_request(request: Request) -> bool:
"""Allow bootstrap injection only through a direct loopback authority."""
client = request.client
if client is None or not _is_loopback_ip(client.host):
return False
if any(request.headers.get(h) is not None for h in _PROXIED_CLIENT_HEADERS):
return False
return _host_header_is_loopback(request.headers.get("host"))
def _is_same_origin_request(request: Request) -> bool:
"""True when Origin is missing or matches request's scheme://host:port.
Missing Origin counts as same-origin (top-level GETs omit it). Both sides
are canonicalised via :func:`_canonical_origin`; callers must emit
``Vary: Origin``.
"""
origin = request.headers.get("origin")
if origin is None:
# Missing header: top-level same-document GETs omit Origin.
return True
# Empty string is not a valid serialised origin (RFC 6454 sec 6.1).
if not origin:
return False
# "null" token (sandboxed iframes, file:// pages) is never same-origin.
if origin == "null":
return False
# ``urlparse`` raises ``ValueError`` on malformed IPv6 brackets; swallow
# so a garbage Origin doesn't 500 the SPA handler.
try:
parsed = urlparse(origin)
except ValueError:
return False
origin_canon = _canonical_origin(parsed.scheme, parsed.netloc)
if origin_canon is None:
return False
try:
self_canon = _canonical_origin(request.url.scheme, request.url.netloc)
except ValueError:
return False
if self_canon is None:
return False
return origin_canon == self_canon
def _should_inject_bootstrap(request: Request) -> bool:
"""Whether to embed the seeded bootstrap password in index.html."""
if not _is_same_origin_request(request):
return False
if _IS_COLAB:
# Single-user notebook proxy: allow autofill, but never a public
# shareable tunnel (a Colab Cloudflare link sets cf-connecting-ip).
return request.headers.get("cf-connecting-ip") is None
return _is_local_bootstrap_request(request)
_IMMUTABLE_ASSET_CACHE_CONTROL = "public, max-age=31536000, immutable"
class ImmutableStaticFiles(StaticFiles):
"""Serve Vite's content-hashed assets without browser revalidation."""
def file_response(
self,
full_path,
stat_result,
scope,
status_code = 200,
):
response = super().file_response(full_path, stat_result, scope, status_code)
response.headers["Cache-Control"] = _IMMUTABLE_ASSET_CACHE_CONTROL
return response
class _AssetGZipMiddleware(GZipMiddleware):
"""Serve range requests uncompressed; gzip + 206 mislabels Content-Range."""
async def __call__(self, scope, receive, send):
if scope["type"] == "http" and any(key == b"range" for key, _ in scope["headers"]):
await self.app(scope, receive, send)
return
await super().__call__(scope, receive, send)
def setup_frontend(app: FastAPI, build_path: Path):
"""Mount frontend static files (optional)"""
if not build_path.exists():
return False
assets_dir = build_path / "assets"
if assets_dir.exists():
assets_app = _AssetGZipMiddleware(
ImmutableStaticFiles(directory = assets_dir),
minimum_size = 1024,
compresslevel = 6,
)
app.mount("/assets", assets_app, name = "assets")
def _build_index_response(request: Request) -> Response:
content = (build_path / "index.html").read_bytes()
content = _strip_crossorigin(content)
# Bootstrap pw goes only to a same-origin, direct-loopback client (or
# Colab's single-user notebook proxy): a wildcard bind must not serve it
# in-page to a LAN or proxied peer. Vary: Origin keeps caches honest.
if _should_inject_bootstrap(request):
content, nonce = _inject_bootstrap(content, app)
else:
nonce = None
headers = {
"Cache-Control": "no-cache, no-store, must-revalidate",
"Vary": "Origin",
}
if nonce:
headers[_CSP_SCRIPT_NONCE_HEADER] = nonce
return Response(
content = content,
media_type = "text/html",
headers = headers,
)
@app.get("/")
async def serve_root(request: Request):
return _build_index_response(request)
@app.get("/{full_path:path}")
async def serve_frontend(request: Request, full_path: str):
# Unknown API paths: raise a real 404 so the api_errors handlers can
# render the correct envelope for /v1/* (and {"detail":...} for /api/*).
# This handler only sees paths NOT matched by a real route. The full
# request path is "/" + full_path.
if full_path in {"api", "v1"} or full_path.startswith(("api/", "v1/")):
raise HTTPException(status_code = 404, detail = "API endpoint not found")
file_path = (build_path / full_path).resolve()
# Block path traversal — resolved path must stay inside build_path
if not file_path.is_relative_to(build_path.resolve()):
return Response(status_code = 403)
if file_path.is_file():
return FileResponse(file_path)
# Serve index.html as bytes — avoids Content-Length mismatch
return _build_index_response(request)
return True