unsloth/studio/backend/main.py
Daniel Han eb8b0dee2e
Studio: make stop button actually stop generation (#5069)
* Studio: make stop button actually stop generation

The UI stop button routes through assistant-ui's cancelRun, which aborts
the frontend fetch. Four issues combined to let llama-server keep decoding
long after the user clicked stop:

1. request.is_disconnected() does not fire reliably behind proxies
   (e.g. Colab) that don't propagate fetch aborts.
2. llama-server defaults n_predict to n_ctx when max_tokens is not sent,
   so a cancelled request keeps producing tokens up to 262144.
3. The httpx.Client pool keeps TCP keep-alive, so even a cleanly closed
   stream reuses the same connection and llama-server's liveness poll
   never sees a disconnect.
4. No explicit backend route to cancel - every cancel path relied on
   is_disconnected.

Changes:
- Add POST /api/inference/cancel keyed by session_id/completion_id, with
  a registry populated for the lifetime of each streaming response.
- Have the frontend (chat-adapter.ts) POST /inference/cancel on
  AbortController abort, alongside the existing fetch teardown.
- Send max_tokens=4096 + t_max_predict_ms=120000 as defaults on every
  outbound chat completion to llama-server; honoured by user overrides.
- Disable httpx keep-alive on the streaming client so connection close
  reaches llama-server and its 1s liveness check fires.

No behaviour changes for non-streaming paths or for existing callers
that already pass max_tokens/session_id.

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* studio: harden stop-button cancel path and scope cancel route

- Require at least one identifier for /api/inference/cancel so a missing
  thread id cannot silently cancel every in-flight generation.
- Scope /cancel to a dedicated studio_router so it is not exposed under
  the /v1 OpenAI-compat prefix as a surprise endpoint.
- Store a set of cancel events per key in _CANCEL_REGISTRY so concurrent
  requests on the same session_id do not overwrite each other, and
  deduplicate in _cancel_by_keys so the cancelled count reflects unique
  requests.
- Always send session_id with chat completions (not only when tools are
  enabled) so non-tool GGUF streams register under it and are reachable
  from /cancel.
- Register the non-GGUF stream_chunks path in the cancel registry too,
  so transformers-based stop-button works behind proxies that swallow
  fetch aborts.
- Only apply the 2-minute t_max_predict_ms wall-clock cap when the
  caller did not pass max_tokens, so legitimate long generations on
  slow CPU/macOS/Windows supported installs are not silently truncated.
- Remove the abort listener on normal stream completion so reused
  AbortSignals cannot fire a spurious cancel POST after the fact.

* studio: close cancel-race and stale-cancel gaps in stop path

- Register the cancel tracker before returning StreamingResponse so a
  stop POST that arrives during prefill / warmup / proxy buffering
  finds an entry in _CANCEL_REGISTRY. Cleanup now runs via a Starlette
  BackgroundTask instead of a finally inside the async generator body.
- Add a per-run cancel_id on the frontend (crypto.randomUUID) and in
  ChatCompletionRequest so /api/inference/cancel matches one specific
  generation. Removes the stale-cancel bug where pressing stop then
  starting a new run in the same thread would cancel the retry.
- Apply t_max_predict_ms unconditionally in all three llama-server
  payload builders (previously gated on max_tokens=None, which made it
  dead code for UI callers that always send params.maxTokens). Raise
  the default to 10 minutes so slow CPU / macOS / Windows installs are
  not cut off mid-generation.
- Make _cancel_by_keys refuse empty input (return 0) so a future
  internal caller can not accidentally mass-cancel every in-flight
  request.
- Accept cancel_id (primary), session_id, and completion_id on the
  /api/inference/cancel route. Unify the three streaming sites on the
  same _cancel_keys / _tracker variable names.
- Annotate _CANCEL_REGISTRY as dict[str, set[threading.Event]].

* Add review tests for PR #5069

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

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* studio: harden stop-button cancel semantics and wall-clock cap

- Make /inference/cancel match cancel_id EXCLUSIVELY when supplied.
  Previously the handler iterated ('cancel_id','session_id','completion_id')
  and unioned matches, so a stale cancel POST carrying {cancel_id:old,
  session_id:thr} would still cancel a later run on the same thread via
  the shared session_id. cancel_id is now a per-run exclusive key;
  session_id / completion_id are only used as fallbacks when cancel_id
  is absent.

- Close the early-cancel race. If /inference/cancel lands before the
  streaming handler reaches _TrackedCancel.__enter__() (stop clicked
  during prefill / warmup / proxy buffering), the cancel was silently
  dropped. Stash unmatched cancel_ids in _PENDING_CANCELS with a 30 s
  TTL; _TrackedCancel.__enter__() now replays any matching pending
  cancel by set()-ing the event immediately after registration.

- Make t_max_predict_ms = _DEFAULT_T_MAX_PREDICT_MS conditional on
  max_tokens is None at all three llama-server payload sites. The cap
  is a safety net for callers who leave max_tokens unset (otherwise
  llama-server defaults n_predict to n_ctx, up to 262144). Callers who
  set an explicit max_tokens are already self-limiting and must not be
  silently truncated at 10 minutes on slow CPU / macOS / Windows
  legitimate long generations.

- Guard each StreamingResponse return with try/except BaseException so
  _tracker.__exit__ runs even if StreamingResponse construction or any
  preceding statement raises between _tracker.__enter__() and the
  BackgroundTask attachment. Prevents a registry leak on that narrow
  window.

* studio: close TOCTOU race and restore wall-clock backstop on UI path

- Close TOCTOU race in the pending-cancel mechanism. The previous fix
  split cancel_inference's (cancel_by_keys + remember_pending_cancel)
  and _TrackedCancel.__enter__'s (register + consume_pending) into
  four separate lock acquisitions. Under contention a cancel POST
  could acquire-then-release the lock, find the registry empty, and
  stash ONLY AFTER __enter__ had already registered and consumed an
  empty pending map -- silently dropping the cancel. Both call sites
  now do their work inside a single _CANCEL_LOCK critical section, via
  the new atomic helper _cancel_by_cancel_id_or_stash() and an
  inlined consume-pending step in __enter__. Reproduced the race under
  forced interleaving pre-fix; 0/2000 drops post-fix under parallel
  stress.

- Apply t_max_predict_ms UNCONDITIONALLY at all three llama-server
  payload sites. The previous iteration gated the cap on
  `max_tokens is None`, which turned out to be dead code on the
  primary Studio UI path: chat-adapter.ts sets
  maxTokens=loadResp.context_length after every model load, so every
  chat request carries an explicit max_tokens and the wall-clock
  safety net never fired. The cap's original purpose is to bound
  stuck decodes regardless of the token budget; it must always apply.

- Raise _DEFAULT_T_MAX_PREDICT_MS from 10 minutes to 1 hour. 10
  minutes was too aggressive for legitimate slow-CPU chat responses
  (a 4096-token reply at 2 tok/s takes ~34 min); 1 hour accommodates
  that and still catches genuine zombie decodes.

- Prune _PENDING_CANCELS inside _cancel_by_keys as well, so stashed
  entries expire proportionally to overall cancel traffic rather than
  only to cancel_id-specific POSTs.

* studio: trim verbose comments and docstrings in cancel path

* studio/llama_cpp: drop upstream PR hashes from benchmark comment

* Add review tests for Studio stop button

* Consolidate review tests for Studio stop button

* Align cancel-route test with exclusive cancel_id semantics

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

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* studio: move cancel cleanup to generator finally; drop dead helper

- Move _tracker.__exit__ from Starlette BackgroundTask into each
  streaming generator's finally block. Starlette skips the background
  callback when stream_response raises (OSError / ClientDisconnect),
  which leaked _CANCEL_REGISTRY entries on abrupt disconnect.
- Check cancel_event.is_set() at the top of each GGUF while loop so a
  pending-replay cancel falls through to final_chunk + [DONE] instead
  of propagating GeneratorExit out of _stream_with_retry.
- Remove unused _remember_pending_cancel; _cancel_by_cancel_id_or_stash
  superseded it.

* Add review tests for Studio stop-button

* studio: wire audio-input stream into cancel registry

- Register cancel_event with _TrackedCancel on the audio-input streaming
  path so POST /api/inference/cancel can stop whisper / audio-input GGUF
  runs. Previously the registry stayed empty on this branch, so the stop
  button returned {"cancelled":0} and the decode ran to completion.
- Apply the same finally-based cleanup and pre-iteration cancel-event
  check used on the other three streaming paths.
- Update the _CANCEL_REGISTRY block comment to list cancel_id as the
  primary key (was stale "session_id preferred").

* Consolidate review tests for Studio stop-button cancel flow

- Merge the 6 behavioral tests from test_stream_cleanup_on_disconnect.py
  (finally cleanup on normal/exception/aclose, pre-set cancel_event
  pattern, and its regressions) into test_stream_cancel_registration_timing.py,
  which is the PR's existing file covering the same area.
- Extend structural invariants to include audio_input_stream alongside the
  three GGUF / Unsloth streaming generators: no _tracker.__enter__ inside
  the async gen body, cleanup via try/finally, no background= on
  StreamingResponse.
- Delete test_stream_cleanup_on_disconnect.py (now empty).

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

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* studio: make cancel-via-POST interrupt Unsloth and audio-input streams

Close two remaining gaps in the stop-button cancellation wiring:

- stream_chunks (Unsloth path): add a top-of-loop cancel_event check and
  call backend.reset_generation_state() so cancel POSTs flush GPU state
  and close the SSE cleanly instead of relying on request.is_disconnected
  (which does not fire through proxies like Colab's).
- audio_input_stream: run the synchronous audio_input_generate() via
  asyncio.to_thread so blocking whisper chunks do not freeze the event
  loop, matching the pattern already used by the GGUF streaming paths.

* Add review tests for Studio stop-button cancel flow

* Consolidate review tests for Studio stop-button cancel flow

- Delete standalone test_cancel_registry.py at repo root: tests duplicated
  test_cancel_atomicity.py / test_cancel_id_wiring.py and re-implemented
  registry primitives inline (scaffolding).
- Extend tests/studio/test_stream_cancel_registration_timing.py with
  regression guards for the iter-1 cancel-loop fixes:
    structural: each streaming generator checks cancel_event in its loop;
                audio_input_stream offloads next() via asyncio.to_thread;
                stream_chunks cancel branch calls reset_generation_state().
    runtime:    Unsloth loop breaks on external cancel and resets state;
                audio loop stays responsive under blocking next();
                both loops emit zero tokens on pre-set cancel (replay path).

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* studio: extend stop-path to passthrough streams; tighten wall-clock cap

- Lower _DEFAULT_T_MAX_PREDICT_MS from 1 hour to 10 minutes so the
  wall-clock backstop actually bounds runaway decodes when cancel
  signaling fails.
- Wire _TrackedCancel and cancel_event.is_set() into
  _openai_passthrough_stream and _anthropic_passthrough_stream and
  disable httpx keepalive so stop requests from /v1 and /v1/messages
  tool-calling clients reach llama-server.
- Apply t_max_predict_ms to the tool-passthrough request body so the
  backstop covers passthrough paths as well.
- Symmetric pre-registration stash for session_id/completion_id
  cancels (_cancel_by_keys_or_stash) so early cancels by those keys
  replay on later registration like cancel_id.
- Drop dead except BaseException guards around StreamingResponse()
  at four streaming sites; cleanup lives in the generator's finally.

* studio: harden cancel registry against ghost-cancel and leak paths

- Revert the session_id/completion_id stash in the fallback cancel
  helper. session_id is thread-scoped and reused across runs, so
  stashing it on an unmatched POST would fire cancel_event for the
  user's next unrelated request via _TrackedCancel.__enter__.
  cancel_id remains the only per-run unique key that gets stashed.
- Default max_tokens to _DEFAULT_MAX_TOKENS in the tool-passthrough
  body. Mirror the direct GGUF path so OpenAI/Anthropic passthrough
  callers who omit max_tokens get the same zombie-decode cap instead
  of relying on the wall-clock backstop alone.
- Wrap _openai_passthrough_stream setup with an outer try/except
  BaseException. The inner except httpx.RequestError does not catch
  asyncio.CancelledError at await client.send, which would otherwise
  leave _tracker registered in _CANCEL_REGISTRY indefinitely.
- Frontend stop POST uses plain fetch + manual Authorization header
  instead of authFetch. A 401 on the cancel POST no longer refreshes
  tokens or redirects the user to the login page mid-stop.

* Add review tests for Studio stop-button cancel flow

* studio: trim comments on stop-button review changes

Collapse multi-paragraph rationale blocks on the cancel registry,
_openai_passthrough_stream, and the frontend onAbortCancel handler
into one-line explanations of why the non-obvious behaviour exists.
Drop authFetch import that became unused when the cancel POST
switched to plain fetch.

* Consolidate review tests for Studio stop-button cancel flow

Move review-added tests out of test_cancel_dispatch_edges.py into the
existing PR test files that already cover the same areas:
- backend registry fan-out / exclusivity / idempotency / falsy-keys
  edge cases moved into tests/studio/test_cancel_atomicity.py
- frontend plain-fetch (not authFetch) + manual Authorization header
  moved into tests/studio/test_cancel_id_wiring.py
Delete the now-empty test_cancel_dispatch_edges.py.

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

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* Studio: stop default-capping responses at 4096 tokens (follow-up to #5069) (#5174)

* Studio: stop default-capping responses at 4096 tokens

Follow-up to #5069. The 4096 default introduced for runaway-decode
defense silently truncates any caller that omits max_tokens. The
Studio chat UI sets params.maxTokens = loadResp.context_length after
a GGUF load, so it's fine, but every other consumer is not:

- OpenAI-API direct callers (/v1/chat/completions, /v1/responses,
  /v1/messages, /v1/completions) where the OpenAI default is
  effectively unlimited per response. langchain, llama-index, raw
  curl, and the openai SDK all rely on that.
- Reasoning models. Qwen3 / gpt-oss reasoning traces routinely exceed
  4096 tokens before the model emits a single visible content token.
  The user sees the trace cut off mid-thought.
- Long-form generation ("write a chapter", "produce a full SVG").

Reproduced on this branch: gemma-4-E2B-it-GGUF Q8_0, prompt asking
for a 10000-word story, no max_tokens in the request:

    finish_reason: stop  (misleading -- should be 'length')
    content_chars: 19772
    content_tail: ...'a comforting, yet immense, pressure.\n\n*"'

Body ended mid-sentence on a stray opening quote, right at the 4096
token mark.

After this patch the same request returns 38357 chars ending with
'...held in a perfect, dynamic equilibrium.' -- a natural stop, not
a truncation.

Implementation: rename the constant to _DEFAULT_MAX_TOKENS_FLOOR and
set it to 32768. Each call site now uses the model's effective
context length when known, falling back to the floor:

    default_cap = self._effective_context_length or _DEFAULT_MAX_TOKENS_FLOOR

The 10-minute t_max_predict_ms wall-clock backstop from #5069 is
preserved as the second line of defense.

Plumbed _build_passthrough_payload + _build_openai_passthrough_body
through the routes layer so the Anthropic and OpenAI passthrough
paths also respect the model's context length.

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

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---------

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

* Studio: cancel passthrough streams during llama-server prefill + route through apiUrl for Tauri

Three reviewer-flagged correctness gaps in the stop-button mechanism.

1) `_openai_passthrough_stream` could not honor cancel during prefill.
   The cancel check ran inside the `async for raw_line in lines_iter`
   body, so a cancel POST that arrived before llama-server emitted the
   first SSE line was unobservable until prefill completed. With a long
   prompt under proxy/Colab conditions -- the exact target scenario for
   this PR -- that left the model decoding for a long time after the
   user clicked Stop. Add an asyncio watcher task that closes `resp` as
   soon as `cancel_event` is set, raising in `aiter_lines` so the
   generator can exit. The watcher polls a threading.Event because the
   cancel registry is keyed by threading.Event for the synchronous
   /cancel handler.

2) `_anthropic_passthrough_stream` had the same blocking-prefill pattern.
   Same fix.

3) The frontend's stop-button cancel POST used a bare relative
   `fetch("/api/inference/cancel", ...)`, which targets the webview
   origin in Tauri production builds (where the backend is at
   `http://127.0.0.1:8888`). Route through the existing `apiUrl()`
   helper from `lib/api-base.ts` to match every other Studio call.
   Browser/dev builds get the empty base, so behavior is unchanged
   there.

Verified via temp/pr_simulation/sim_5069_prefill_cancel.py: cancel
during prefill terminates within ~250ms on both passthrough paths
(was 145s+ on the Anthropic path before this change), and the standard
non-passthrough chat path still cancels with no regression.

* Studio: log cancel-body parse errors instead of silently swallowing

Reviewer-flagged defensive logging gap. The bare `except Exception: pass`
in `cancel_inference` would mask malformed payloads that hint at a buggy
client or a transport issue. Log at debug so future investigation isn't
left guessing whether `body={}` came from a missing body or a parse
failure. Behavior is unchanged: an unparseable body still falls through
to the empty-dict path and the cancel call returns `{"cancelled": 0}`.

* Studio: Anthropic passthrough cancel parity with OpenAI passthrough

Two reviewer-flagged consistency gaps in the cancel surface for
/v1/messages.

1) Anthropic passthrough did not register cancel_id, so a per-run cancel
   POST (the cleanest Studio-style cancel path) silently missed when
   the route hit `_anthropic_passthrough_stream`. The OpenAI passthrough
   has registered (cancel_id, session_id, completion_id) since this PR
   was first opened; mirror that here. Also add `cancel_id` to
   `AnthropicMessagesRequest` so the route handler can plumb it through.

2) The cancel handler's fallback key list checked only completion_id
   and session_id, never message_id. Anthropic clients that send their
   native `id` (returned in the SSE message_start event) for cancel had
   no way to hit the registry. Add message_id to the fallback list.

Verified via temp/pr_simulation/sim_5069_prefill_cancel.py: P2 now
cancels by cancel_id in 137ms (was hanging pre-fix), and the new P2b
case cancels by message_id in 77ms. P1 (OpenAI) and P3 (standard chat)
still pass with no regression.

---------

Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
2026-04-24 10:09:25 -07:00

420 lines
14 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
from pathlib import Path as _Path
# Suppress annoying C-level dependency warnings globally
os.environ["PYTHONWARNINGS"] = "ignore"
# Ensure backend dir is 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)
# Fix for Anaconda/conda-forge Python: seed platform._sys_version_cache before
# any library imports that trigger attrs -> rich -> structlog -> platform crash.
# See: https://github.com/python/cpython/issues/102396
import _platform_compat # noqa: F401
import mimetypes
import shutil
import warnings
from contextlib import asynccontextmanager
from importlib.metadata import PackageNotFoundError, version as package_version
# Fix broken Windows registry MIME types. Some Windows installs map .js to
# "text/plain" in the registry (HKCR\.js\Content Type). Python's mimetypes
# module reads from the registry, and FastAPI/Starlette's StaticFiles uses
# mimetypes.guess_type() to set Content-Type headers. Browsers enforce strict
# MIME checking for ES module scripts (<script type="module">) and will refuse
# to execute .js files served as text/plain — resulting in a blank page.
# Calling add_type() *before* StaticFiles is instantiated ensures the correct
# types are used regardless of the OS registry.
if sys.platform == "win32":
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
# Suppress annoying dependency warnings in production
if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
warnings.filterwarnings("ignore")
# Alternatively, you can be more specific:
# warnings.filterwarnings("ignore", category=DeprecationWarning)
# warnings.filterwarnings("ignore", module="triton.*")
from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, HTMLResponse, Response
from pathlib import Path
from datetime import datetime
# Import routers
from routes import (
auth_router,
data_recipe_router,
datasets_router,
export_router,
inference_router,
inference_studio_router,
models_router,
training_history_router,
training_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
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()
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Startup: detect hardware, seed default admin if needed. Shutdown: clean up compiled cache."""
# Clean up any stale compiled cache from previous runs
clear_unsloth_compiled_cache()
# Remove stale .venv_overlay from previous versions — no longer used.
# Version switching now uses .venv_t5/ (pre-installed by setup.sh).
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 DEVICE global used everywhere
detect_hardware()
from storage.studio_db import cleanup_orphaned_runs
try:
cleanup_orphaned_runs()
except Exception as exc:
import structlog
structlog.get_logger(__name__).warning(
"cleanup_orphaned_runs failed at startup: %s", exc
)
# Pre-cache the helper GGUF model for LLM-assisted dataset detection.
# Runs in a background thread so it doesn't block server startup.
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).start()
if storage.ensure_default_admin():
bootstrap_pw = 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 Studio UI to sign in and change it.")
print("=" * 60 + "\n")
else:
app.state.bootstrap_password = storage.get_bootstrap_password()
yield
# Cleanup
_hw_module.DEVICE = None
clear_unsloth_compiled_cache()
# Create FastAPI app
app = FastAPI(
title = "Unsloth UI Backend",
version = UNSLOTH_VERSION,
description = "Backend API for Unsloth UI - Training and Model Management",
lifespan = lifespan,
)
# Initialize structured logging
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)
# CORS middleware
_api_only = os.environ.get("UNSLOTH_API_ONLY") == "1"
_cors_origins = ["*"]
if _api_only:
_cors_origins = [
"tauri://localhost", # Linux/macOS Tauri webview
"http://tauri.localhost", # Windows Tauri webview
"http://localhost", # dev fallback
]
_cors_origin_regex = None
else:
_cors_origin_regex = None
app.add_middleware(
CORSMiddleware,
allow_origins = _cors_origins,
allow_origin_regex = _cors_origin_regex,
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(inference_router, prefix = "/api/inference", tags = ["inference"])
# Studio-only inference endpoints (cancel, etc.) are intentionally NOT
# exposed on the /v1 OpenAI-compat prefix below.
app.include_router(inference_studio_router, prefix = "/api/inference", tags = ["inference"])
# OpenAI-compatible endpoints: mount the same inference router at /v1
# so external tools (Open WebUI, SillyTavern, etc.) can use the
# standard /v1/chat/completions path.
app.include_router(inference_router, prefix = "/v1", tags = ["openai-compat"])
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(export_router, prefix = "/api/export", tags = ["export"])
app.include_router(
training_history_router, prefix = "/api/train", tags = ["training-history"]
)
# ============ Health and System Endpoints ============
@app.get("/api/health")
async def health_check():
"""Health check endpoint"""
platform_map = {"darwin": "mac", "win32": "windows", "linux": "linux"}
device_type = platform_map.get(sys.platform, sys.platform)
return {
"status": "healthy",
"timestamp": datetime.now().isoformat(),
"service": "Unsloth UI Backend",
"version": UNSLOTH_VERSION,
"device_type": device_type,
"chat_only": _hw_module.CHAT_ONLY,
"desktop_protocol_version": 1,
"supports_desktop_auth": True,
}
@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 needing to use the CLI or kill the process manually.
"""
import asyncio
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"}
@app.get("/api/system")
async def get_system_info():
"""Get system information"""
import platform
import psutil
from utils.hardware import get_device
from utils.hardware.hardware import _backend_label
visibility_info = get_backend_visible_gpu_info()
gpu_info = {
"available": visibility_info["available"],
"devices": visibility_info["devices"],
}
# CPU & Memory
memory = psutil.virtual_memory()
return {
"platform": platform.platform(),
"python_version": platform.python_version(),
# Use the centralized _backend_label helper so the /api/system
# endpoint reports "rocm" on AMD hosts instead of "cuda", matching
# the /api/hardware and /api/gpu-visibility endpoints.
"device_backend": _backend_label(get_device()),
"cpu_count": psutil.cpu_count(),
"memory": {
"total_gb": round(memory.total / 1e9, 2),
"available_gb": round(memory.available / 1e9, 2),
"percent_used": memory.percent,
},
"gpu": gpu_info,
}
@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")
async def get_hardware_info():
"""Return GPU name, total VRAM, and key ML package versions."""
from utils.hardware import get_gpu_summary, get_package_versions
return {
"gpu": get_gpu_summary(),
"versions": get_package_versions(),
}
# ============ Serve Frontend (Optional) ============
def _strip_crossorigin(html_bytes: bytes) -> bytes:
"""Remove ``crossorigin`` attributes from script/link tags.
Vite adds ``crossorigin`` by default which forces CORS mode on font
subresource loads. When Studio is served over plain HTTP, Firefox
HTTPS-Only Mode does not exempt CORS font requests -- causing all
@font-face downloads to fail silently. Stripping the attribute
makes them regular same-origin fetches that work on any protocol.
"""
import re as _re
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) -> bytes:
"""Inject bootstrap credentials into HTML when password change is required.
The script tag is only injected while the default admin account still
has ``must_change_password=True``. Once the user changes the password
the HTML is served clean — no credentials leak.
"""
import json as _json
if not storage.requires_password_change(storage.DEFAULT_ADMIN_USERNAME):
return html_bytes
bootstrap_pw = getattr(app.state, "bootstrap_password", None)
if not bootstrap_pw:
return html_bytes
payload = _json.dumps(
{
"username": storage.DEFAULT_ADMIN_USERNAME,
"password": bootstrap_pw,
}
)
tag = f"<script>window.__UNSLOTH_BOOTSTRAP__={payload}</script>"
html = html_bytes.decode("utf-8")
html = html.replace("</head>", f"{tag}</head>", 1)
return html.encode("utf-8")
def setup_frontend(app: FastAPI, build_path: Path):
"""Mount frontend static files (optional)"""
if not build_path.exists():
return False
# Mount assets
assets_dir = build_path / "assets"
if assets_dir.exists():
app.mount("/assets", StaticFiles(directory = assets_dir), name = "assets")
@app.get("/")
async def serve_root():
content = (build_path / "index.html").read_bytes()
content = _strip_crossorigin(content)
content = _inject_bootstrap(content, app)
return Response(
content = content,
media_type = "text/html",
headers = {"Cache-Control": "no-cache, no-store, must-revalidate"},
)
@app.get("/{full_path:path}")
async def serve_frontend(full_path: str):
if full_path in {"api", "v1"} or full_path.startswith(("api/", "v1/")):
return {"error": "API endpoint not found"}
file_path = (build_path / full_path).resolve()
# Block path traversal — ensure resolved path stays 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
content = (build_path / "index.html").read_bytes()
content = _strip_crossorigin(content)
content = _inject_bootstrap(content, app)
return Response(
content = content,
media_type = "text/html",
headers = {"Cache-Control": "no-cache, no-store, must-revalidate"},
)
return True