From 81ecd7b4e4a807946a10344aecb9591f5d090a48 Mon Sep 17 00:00:00 2001 From: Jaymie Date: Wed, 7 Oct 2026 13:34:12 -0400 Subject: [PATCH 1/4] Release TrainKit 1.3.0 with optional cloud captioning and reliability fixes --- CHANGELOG.md | 29 + CONTRIBUTING.md | 6 +- README.md | 32 +- backend/core/jobs.py | 6 +- backend/models/requests.py | 26 +- backend/pyproject.toml | 1 + backend/routers/caption.py | 17 +- backend/routers/rename.py | 2 - backend/service/cloud_captioning.py | 204 + backend/service/image_captioning.py | 156 +- backend/service/image_rename.py | 28 +- backend/service/manifest.py | 28 +- backend/service/ncnn_upscaling.py | 2 + backend/service/service_manager.py | 6 + backend/tests/test_api.py | 23 + backend/tests/test_cloud_captioning.py | 401 + backend/tests/test_file_safety.py | 102 + backend/tests/test_jobs.py | 41 + backend/tests/test_manifest.py | 55 +- backend/tests/test_ncnn.py | 20 + backend/tests/test_requests.py | 41 + backend/tests/test_service_manager.py | 104 + backend/uv.lock | 619 +- docs/architecture.md | 6 + docs/model-support.md | 8 +- eslint.config.mjs | 2 - package-lock.json | 11954 ++++------------ package.json | 31 +- src/backend-manager.test.ts | 152 + src/backend-manager.ts | 140 +- src/components/App.tsx | 16 +- src/components/trainkit/Navigation.tsx | 14 +- src/components/trainkit/panels/api-panel.tsx | 84 + .../trainkit/panels/caption-panel.tsx | 52 +- .../trainkit/shared/ImageCompareSlider.tsx | 126 - src/components/trainkit/shared/Input.tsx | 14 +- src/components/trainkit/shared/Textarea.tsx | 8 +- src/components/trainkit/shared/fields.test.ts | 22 + src/lib/api.test.ts | 9 + src/lib/api.ts | 4 +- src/lib/use-job-operation.test.ts | 75 + src/lib/use-job-operation.ts | 47 +- src/lib/websocket-context.test.ts | 58 + src/lib/websocket-context.tsx | 34 +- src/main.ts | 56 +- src/path-grants.test.ts | 23 + src/path-grants.ts | 15 + src/preload.ts | 4 + src/provider-settings.test.ts | 92 + src/provider-settings.ts | 135 + src/setup-manager.ts | 3 +- src/types/contracts.test.ts | 33 +- src/types/contracts.ts | 25 +- src/types/electron.d.ts | 5 + src/types/providers.ts | 24 + vite.renderer.config.ts | 3 + vitest.config.ts => vitest.config.mjs | 4 +- 57 files changed, 5749 insertions(+), 9478 deletions(-) create mode 100644 backend/service/cloud_captioning.py create mode 100644 backend/tests/test_cloud_captioning.py create mode 100644 backend/tests/test_requests.py create mode 100644 backend/tests/test_service_manager.py create mode 100644 src/backend-manager.test.ts create mode 100644 src/components/trainkit/panels/api-panel.tsx delete mode 100644 src/components/trainkit/shared/ImageCompareSlider.tsx create mode 100644 src/components/trainkit/shared/fields.test.ts create mode 100644 src/lib/use-job-operation.test.ts create mode 100644 src/lib/websocket-context.test.ts create mode 100644 src/path-grants.test.ts create mode 100644 src/path-grants.ts create mode 100644 src/provider-settings.test.ts create mode 100644 src/provider-settings.ts create mode 100644 src/types/providers.ts rename vitest.config.ts => vitest.config.mjs (54%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 102b08a..39ec134 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,35 @@ All notable TrainKit changes are documented here. TrainKit follows semantic versioning. +## [1.3.0] - 2026-10-07 + +TrainKit 1.3.0 adds optional Claude and OpenAI captioning through your own API keys and improves batch safety and desktop reliability. + +### Added + +- API connections tab with masked key entry, session-only keys by default, optional OS-encrypted persistence, editable vision model IDs, removal, and an explicit connection test that sends no images. +- Caption provider selection with unavailable providers disabled and explicit consent before cloud uploads. +- Anthropic Messages and OpenAI Responses image captioning with resized, metadata-free JPEGs, bounded responses, cancellable requests, and safe errors without automatic paid retries. + +### Changed + +- Local and cloud captioning reuse the same collision-safe batch loop, atomic output writes, and optional resumable manifests. +- Cloud batches stop on provider failures, refusals, or incomplete output; dry runs and fully skipped or completed batches make no provider requests. +- Updated Electron to 44.6.0, Electron Forge to 8, ESLint to 10, Vitest to 5, and Lucide to 1; refreshed both dependency lockfiles and raised the development Node.js minimum to 22.17. +- Removed unused comparison-slider and import-lint dependencies. TypeScript stays on 5.9 to match the supported range of the ESLint parser. + +### Fixed + +- Failed model switches no longer retain a cleaned-up service in the model cache. +- Queued cancellation prevents the runner from starting, and missed job events are recovered after reconnecting. +- Caption model controls stay consistent during loading, unloading, and completed runs; duplicate submissions and cancellation errors are handled centrally. +- Backend spawn failures no longer hang shutdown, and empty error responses produce useful messages. +- New output directories and generated manifests cannot escape selected folders through existing junctions. +- Rename batches reject invalid images, restrict duplicate detection to supported images, and use deterministic tie-breakers for natural ordering. +- Single-channel NCNN output is converted to RGB and invalid tiled overlap is rejected before processing. +- Failed atomic text writes remove their temporary files, and damaged provider records do not discard another provider's remembered key. +- Development setup preserves test tools and excludes backend environments from frontend file watching; the setup failure close button remains visible. + ## [1.2.1] - 2026-07-15 TrainKit 1.2.1 fixes NCNN graph compatibility, makes manifests opt-in, and adds individual-image workflows across every operation. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5a156e9..40296f2 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,7 +4,7 @@ Thanks for improving TrainKit. Keep changes focused, add regression coverage for ## Development setup -Install Node.js 22.12+, uv, Python 3.12, and the Microsoft Visual C++ x64 Redistributable on Windows. Then run: +Install Node.js 22.17+, uv, Python 3.12, and the Microsoft Visual C++ x64 Redistributable on Windows. Then run: ```powershell npm ci @@ -30,6 +30,8 @@ npm run verify:package ## Project invariants - The renderer never receives the backend token or its port. +- Configured API keys stay in the main process and per-job authenticated backend headers; never log them or include them in manifests. Persistence requires explicit opt-in and OS encryption. +- Cloud captioning requires explicit upload consent and must not automatically retry paid requests. Tests use mocked transports, not real keys or paid calls. - Renderer filesystem access must flow through narrow IPC and a user-selected path grant. - Backend HTTP and WebSocket access remains authenticated and loopback-only. - Resume manifests are untrusted and must remain scoped to the requested input/output roots. @@ -46,7 +48,7 @@ User-visible changes must update `CHANGELOG.md` under the target version. The re ## Maintainer release setup -Tagged releases must match `package.json` exactly (for example, version `1.2.1` requires tag `v1.2.1`). Releases are unsigned by default. To Authenticode-sign the packaged executable, configure both GitHub Actions secrets: +Tagged releases must match `package.json` exactly (for example, version `1.3.0` requires tag `v1.3.0`). Releases are unsigned by default. To Authenticode-sign the packaged executable, configure both GitHub Actions secrets: - `WINDOWS_CERTIFICATE_BASE64`: Base64-encoded PFX code-signing certificate. - `WINDOWS_CERTIFICATE_PASSWORD`: PFX password. diff --git a/README.md b/README.md index 4b2f25d..502195d 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ MIT License Latest release Windows - Electron 43 + Electron 44 Python 3.12 PyTorch 2.11

@@ -23,21 +23,21 @@ Architecture

-TrainKit prepares individual images or complete image folders without uploading them to a hosted service. Every run is deterministic, cancellable, and collision-aware; resumable manifest files are optional. +TrainKit prepares individual images or complete image folders with local captioning, upscaling, tagging, and renaming. Optional Claude and OpenAI captioning uses your own API keys and requires consent before uploading images. Batch planning is deterministic, cancellable, and collision-aware; resumable manifest files are optional. -## What's new in 1.2.1 +## What's new in 1.3.0 -- Automatic NCNN input/output blob discovery instead of hard-coded `in0` and `out0` names. -- Individual-image selection for captioning, upscaling, tagging, and renaming. -- Manifests disabled by default, with explicit save, dry-run, and resume options. -- First processing errors remain visible even when no manifest is written. -- A single standard Windows ZIP distribution with no installer. +- Claude and OpenAI captioning through your own API keys, alongside local models. +- An API tab with session keys, optional OS-encrypted storage, editable vision model IDs, and a key connection test. +- Explicit cloud upload consent, bounded image requests, cancellation, and safe failures without automatic paid retries. +- Recovered progress after reconnecting, safer model switching and shutdown, and fixes for batch file handling. +- Updated Electron and packaging dependencies with a clean dependency audit. -See the [complete 1.2.1 changelog](CHANGELOG.md#121---2026-07-15) for details. +See the [complete 1.3.0 changelog](CHANGELOG.md#130---2026-10-07) for details. ## Features -- **Captioning:** local standard Transformers multimodal-chat, BLIP, and InstructBLIP models; auto-detection, explicit preload/unload, prompt control, and token-level cancellation. +- **Captioning:** local standard Transformers multimodal-chat, BLIP, and InstructBLIP models, or Claude/OpenAI vision APIs. Local models support auto-detection, preload/unload, prompt control, and token-level cancellation. - **Upscaling:** safe `.safetensors` models through Spandrel, or NCNN `.param` + `.bin` models through the Python bindings with automatic blob discovery, CPU/Vulkan, and tiled inference. - **Tagging:** local Hugging Face image-classification models with threshold/top-K controls and scored JSON, training-text, or paired sidecars. - **Renaming:** natural input ordering, configurable zero padding, optional visual-duplicate filtering, and atomic copies. @@ -61,7 +61,7 @@ An NVIDIA GPU is recommended for Transformers and Spandrel workloads. NCNN can r ## Build from source -Requirements: Git, Node.js 22.12 or newer, uv, Python 3.12 (uv can install it), and the Visual C++ x64 Redistributable on Windows. +Requirements: Git, Node.js 22.17 or newer, uv, Python 3.12 (uv can install it), and the Visual C++ x64 Redistributable on Windows. ```powershell git clone https://github.com/LightCyan01/TrainKit.git @@ -92,11 +92,19 @@ Normal runs do not write a manifest. Enable **Save resumable manifest** when you Dry runs always write a manifest because the plan is their output. A resume uses the same operation and input/output paths, preserves completed and skipped items, retries failed items, and updates the selected manifest. Manifests are treated as untrusted input: paths outside the selected roots are rejected. +## Cloud captioning + +Open **API**, paste an Anthropic or OpenAI API key, choose a vision model available to your account, and save. Keys stay in memory for the session unless you select **Remember on this computer**, which uses OS encryption. Remembered credentials are stored under Electron's user-data directory and are tied to your operating-system account. Saved keys are never returned to the renderer. **Test key** checks authentication without sending images; it does not verify access to every model. API billing is separate from ChatGPT and Claude subscriptions. + +In **Caption**, choose the configured provider, select your images and output folder, enter an instruction, and confirm upload consent. Images are converted to JPEG, metadata is removed, and the longest edge is capped at 1568 pixels. Your instruction and resized images are sent to the selected provider; its data and billing policies apply. OpenAI requests use `store: false`, which does not replace the provider's retention policy. Captions remain local UTF-8 sidecars. + +Cloud requests run sequentially and are not automatically retried. Authentication, quota, rate-limit, refusal, incomplete-response, or transport failures stop the batch safely. Save a manifest if you want to resume pending work. Cancellation stops pending requests and prevents publishing their captions, but cannot undo provider work or charges already incurred. Dry runs, already-completed resumes, and skipped outputs make no provider requests. Local mode remains the default. + See [model support](docs/model-support.md) for model layouts and NCNN assumptions, [architecture](docs/architecture.md) for the desktop/backend trust boundaries, and the [changelog](CHANGELOG.md) for complete release history. ## Project status -Version 1.2.1 completes the original processing roadmap and improves NCNN compatibility, input selection, and output control. New feature proposals and model-compatibility reports are welcome through GitHub issues. +Version 1.3.0 adds optional cloud captioning and improves batch and desktop reliability. New feature proposals and model-compatibility reports are welcome through GitHub issues. ## Contributing and security diff --git a/backend/core/jobs.py b/backend/core/jobs.py index b9c78c7..46ec874 100644 --- a/backend/core/jobs.py +++ b/backend/core/jobs.py @@ -153,8 +153,12 @@ async def reserve(self, operation: str): async def _execute(self, record: JobRecord, cancelled: Event, runner: Runner): context = JobContext(self, record, cancelled) - await self.update(record.job_id, status="running", message=f"Starting {record.operation}") try: + context.raise_if_cancelled() + await self.update( + record.job_id, status="running", message=f"Starting {record.operation}" + ) + context.raise_if_cancelled() manifest_path = await runner(context) context.raise_if_cancelled() await self.update( diff --git a/backend/models/requests.py b/backend/models/requests.py index 4e0c85c..0008883 100644 --- a/backend/models/requests.py +++ b/backend/models/requests.py @@ -3,7 +3,7 @@ from datetime import datetime from typing import Literal -from pydantic import BaseModel, ConfigDict, Field, field_validator +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator CollisionPolicy = Literal["fail", "skip", "overwrite", "rename"] JobOperation = Literal["rename", "caption", "upscale", "tag"] @@ -38,13 +38,35 @@ class UpscaleRequest(BatchRequest): ncnn_scale: int = Field(default=4, ge=1, le=16) ncnn_use_vulkan: bool = True + @model_validator(mode="after") + def validate_tiling(self) -> UpscaleRequest: + if self.use_tiling and self.tile_overlap >= self.tile_size: + raise ValueError("Tile overlap must be smaller than tile size") + return self + class CaptionRequest(BatchRequest): - caption_model_path: str + caption_model_path: str = "" + provider: Literal["local", "anthropic", "openai"] = "local" + cloud_model: str = Field(default="", max_length=200) + cloud_consent: bool = False prompt: str = Field(min_length=1, max_length=8000) adapter: Literal["auto", "multimodal", "blip", "instructblip"] = "auto" max_new_tokens: int = Field(default=256, ge=1, le=2048) + @model_validator(mode="after") + def validate_provider(self) -> CaptionRequest: + if self.provider == "local": + if not self.caption_model_path.strip(): + raise ValueError("Local captioning requires a model folder") + else: + self.cloud_model = self.cloud_model.strip() + if not self.cloud_model: + raise ValueError("Cloud captioning requires a model ID") + if not self.dry_run and not self.cloud_consent: + raise ValueError("Cloud captioning requires consent to upload images") + return self + class TagRequest(BatchRequest): tag_model_path: str diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 63faf0b..a46caf1 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -8,6 +8,7 @@ dependencies = [ "accelerate>=1.11.0", "difpy>=4.2.1", "fastapi>=0.139.0,<0.140", + "httpx>=0.28.1,<1", "ncnn>=1.0.20260526", "numpy>=2.3.0", "pillow>=12.3.0,<13", diff --git a/backend/routers/caption.py b/backend/routers/caption.py index 42b6481..15c02ec 100644 --- a/backend/routers/caption.py +++ b/backend/routers/caption.py @@ -2,13 +2,14 @@ from pathlib import Path -from fastapi import APIRouter, Depends +from fastapi import APIRouter, Depends, Header from core.dependencies import ( get_connection_manager, get_job_manager, get_service_manager, ) +from core.exceptions import TrainKitException from core.jobs import JobManager from core.websocket import ConnectionManager from models import CaptionRequest, JobResponse, ModelStatusRequest, PreloadRequest @@ -19,10 +20,24 @@ @router.post("/caption", response_model=JobResponse, status_code=202) async def caption( request: CaptionRequest, + provider_key: str | None = Header(default=None, alias="x-trainkit-provider-key"), jobs: JobManager = Depends(get_job_manager), services=Depends(get_service_manager), ): + if request.provider != "local" and not request.dry_run: + if not provider_key or not provider_key.strip(): + raise TrainKitException( + "Configure the provider API key first", 400, "provider_key_missing" + ) + provider_key = provider_key.strip() + if len(provider_key) > 8192 or any(not 33 <= ord(char) <= 126 for char in provider_key): + raise TrainKitException("Invalid provider API key", 400, "provider_key_invalid") + async def run(context): + if request.provider != "local": + from service.cloud_captioning import process_cloud_caption_batch + + return await process_cloud_caption_batch(request, context, provider_key or "") service = services.get_caption_service( Path(request.caption_model_path), request.adapter, request.max_new_tokens ) diff --git a/backend/routers/rename.py b/backend/routers/rename.py index cfc4726..9039a89 100644 --- a/backend/routers/rename.py +++ b/backend/routers/rename.py @@ -13,8 +13,6 @@ async def rename( jobs: JobManager = Depends(get_job_manager), service=Depends(get_rename_service), ): - service.clear_cache() - async def run(context): return await service.process(request, context) diff --git a/backend/service/cloud_captioning.py b/backend/service/cloud_captioning.py new file mode 100644 index 0000000..48f558f --- /dev/null +++ b/backend/service/cloud_captioning.py @@ -0,0 +1,204 @@ +from __future__ import annotations + +import asyncio +import base64 +import json +from io import BytesIO +from pathlib import Path +from threading import Event +from typing import Any + +import httpx +from PIL import Image + +from core.exceptions import JobCancelledError, ProcessingError +from core.jobs import JobContext +from models import CaptionRequest +from service.caption_adapters import SYSTEM_PROMPT +from service.image_captioning import process_caption_batch + +ENDPOINTS = { + "anthropic": "https://api.anthropic.com/v1/messages", + "openai": "https://api.openai.com/v1/responses", +} +REQUEST_TIMEOUT_SECONDS = 60 +MAX_RESPONSE_BYTES = 1024 * 1024 +MAX_IMAGE_BYTES = 4 * 1024 * 1024 + + +def encode_cloud_image(image: Image.Image) -> str: + # ponytail: one 1568px JPEG per request; add resolution controls if captions need finer detail. + with image.copy() as resized: + resized.thumbnail((1568, 1568), Image.Resampling.LANCZOS) + resized.info.clear() + with BytesIO() as buffer: + resized.save(buffer, format="JPEG", quality=90) + if buffer.tell() > MAX_IMAGE_BYTES: + raise ProcessingError("Image is too large for cloud captioning") + return base64.b64encode(buffer.getvalue()).decode("ascii") + + +def parse_cloud_caption(provider: str, response: Any) -> str: + if not isinstance(response, dict): + raise ProcessingError("Provider returned an invalid caption response") + if provider == "anthropic": + reason = response.get("stop_reason") + if reason == "refusal": + raise ProcessingError("Provider refused the image; no caption was written") + if reason != "end_turn": + raise ProcessingError("Provider returned an incomplete caption; no caption was written") + blocks = response.get("content") + text_type = "text" + else: + if ( + response.get("status") != "completed" + or response.get("error") + or response.get("incomplete_details") + ): + raise ProcessingError("Provider returned an incomplete caption; no caption was written") + output = response.get("output") + if not isinstance(output, list): + raise ProcessingError("Provider returned an invalid caption response") + blocks = [] + for item in output: + if not isinstance(item, dict): + raise ProcessingError("Provider returned an invalid caption response") + if item.get("type") == "message": + if item.get("role") != "assistant" or item.get("status") != "completed": + raise ProcessingError("Provider returned an incomplete caption") + content = item.get("content") + if not isinstance(content, list): + raise ProcessingError("Provider returned an invalid caption response") + blocks.extend(content) + elif item.get("type") != "reasoning": + raise ProcessingError("Provider returned an invalid caption response") + text_type = "output_text" + if not isinstance(blocks, list) or any(not isinstance(block, dict) for block in blocks): + raise ProcessingError("Provider returned an invalid caption response") + if any(block.get("type") == "refusal" for block in blocks): + raise ProcessingError("Provider refused the image; no caption was written") + texts = [block.get("text") for block in blocks if block.get("type") == text_type] + if not texts or any(not isinstance(text, str) for text in texts): + raise ProcessingError("Provider returned an empty or invalid caption") + caption = "\n".join(texts).strip() + if not caption: + raise ProcessingError("Provider returned an empty caption") + return caption + + +async def _post_caption( + client: httpx.AsyncClient, provider: str, headers: dict[str, str], body: dict[str, Any] +) -> Any: + async with client.stream("POST", ENDPOINTS[provider], headers=headers, json=body) as response: + if not response.is_success: + status = response.status_code + if status in {401, 403}: + message = "Provider rejected access; check the API key and model permissions" + elif status == 429: + message = "Provider rate or quota limit reached; check limits before resuming" + else: + message = f"Provider request failed (HTTP {status}); check settings before resuming" + raise ProcessingError(message) + chunks = bytearray() + async for chunk in response.aiter_bytes(chunk_size=64 * 1024): + chunks.extend(chunk) + if len(chunks) > MAX_RESPONSE_BYTES: + raise ProcessingError("Provider response exceeded the size limit") + try: + return json.loads(chunks) + except (ValueError, UnicodeError): + raise ProcessingError("Provider returned an invalid caption response") from None + + +async def caption_cloud_image( + client: httpx.AsyncClient, + request: CaptionRequest, + key: str, + image: Image.Image, + prompt: str, + cancelled: Event, +) -> str: + if cancelled.is_set(): + raise JobCancelledError() + encoded = await asyncio.to_thread(encode_cloud_image, image) + if cancelled.is_set(): + raise JobCancelledError() + instruction = f"{SYSTEM_PROMPT} Return only the caption text." + if request.provider == "anthropic": + headers = {"x-api-key": key, "anthropic-version": "2023-06-01"} + body = { + "model": request.cloud_model, + "max_tokens": request.max_new_tokens, + "system": instruction, + "messages": [ + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": encoded, + }, + }, + {"type": "text", "text": prompt}, + ], + } + ], + } + else: + headers = {"authorization": f"Bearer {key}"} + body = { + "model": request.cloud_model, + "max_output_tokens": request.max_new_tokens, + "store": False, + "instructions": instruction, + "input": [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{encoded}", + "detail": "auto", + }, + {"type": "input_text", "text": prompt}, + ], + } + ], + } + pending = asyncio.create_task(_post_caption(client, request.provider, headers, body)) + try: + async with asyncio.timeout(REQUEST_TIMEOUT_SECONDS): + while not pending.done(): + if cancelled.is_set(): + raise JobCancelledError() + await asyncio.wait({pending}, timeout=0.1) + if cancelled.is_set(): + raise JobCancelledError() + response = await pending + except TimeoutError: + raise ProcessingError("Provider request timed out; no caption was written") from None + except httpx.HTTPError: + raise ProcessingError("Could not reach the provider; no caption was written") from None + finally: + if not pending.done(): + pending.cancel() + await asyncio.gather(pending, return_exceptions=True) + return parse_cloud_caption(request.provider, response) + + +async def process_cloud_caption_batch( + request: CaptionRequest, context: JobContext, key: str +) -> Path | None: + async with httpx.AsyncClient( + timeout=httpx.Timeout(60, connect=10, write=30, pool=10), + follow_redirects=False, + trust_env=False, + ) as client: + + async def caption(image: Image.Image, prompt: str, cancelled: Event) -> str: + return await caption_cloud_image(client, request, key, image, prompt, cancelled) + + return await process_caption_batch(request, context, caption) diff --git a/backend/service/image_captioning.py b/backend/service/image_captioning.py index 61354f6..6ebfd3b 100644 --- a/backend/service/image_captioning.py +++ b/backend/service/image_captioning.py @@ -1,8 +1,11 @@ from __future__ import annotations import asyncio +from collections.abc import Awaitable, Callable from pathlib import Path +from threading import Event +from PIL import Image from transformers import GenerationConfig from core.exceptions import JobCancelledError, ProcessingError @@ -48,71 +51,96 @@ async def load(self): await asyncio.to_thread(self.adapter.load) async def process(self, request: CaptionRequest, context: JobContext) -> Path | None: - load_path = Path(request.load_path).resolve() - save_path = Path(request.save_path).resolve() - if request.resume_manifest_path: - output_manifest = Path(request.resume_manifest_path).resolve() - manifest = BatchManifest.load(output_manifest, expected_operation="caption") - manifest.validate_scope(load_path, save_path) - manifest.validate_destination_suffixes({".txt"}) - manifest.preflight_resume(request.collision_policy) - else: - manifest = build_manifest( - job_id=context.job_id, - operation="caption", - load_path=load_path, - save_path=save_path, - sources=list_images(load_path), - destination_for=lambda source, _index: save_path / f"{source.stem}.txt", - collision_policy=request.collision_policy, - ) - output_manifest = ( - manifest_path(save_path, context.job_id) - if request.save_manifest or request.dry_run - else None - ) - if output_manifest is not None: - manifest.save(output_manifest) - total = len(manifest.items) - completed = sum(item.status in {"completed", "skipped"} for item in manifest.items) - message = "Caption manifest ready" if output_manifest is not None else "Caption plan ready" - await context.progress(completed, total, message, output_manifest) - if request.dry_run: - return output_manifest - await self.load() + async def caption(image: Image.Image, prompt: str, cancelled: Event) -> str: + return await asyncio.to_thread(self.adapter.caption, image, prompt, cancelled) - failures = 0 - first_error: str | None = None - for item in manifest.items: - if item.status in {"completed", "skipped"}: - continue - context.raise_if_cancelled() - try: - image = await asyncio.to_thread(load_rgb_image, Path(item.source)) - caption = await asyncio.to_thread( - self.adapter.caption, image, request.prompt, context.cancelled - ) - await asyncio.to_thread(atomic_write_text, Path(item.destination), caption) - item.status = "completed" - item.error = None - except JobCancelledError: - raise - except Exception as exc: - item.status = "failed" - item.error = str(exc) - failures += 1 - first_error = first_error or f"{Path(item.source).name}: {str(exc)[:500]}" - completed += 1 - if output_manifest is not None: - manifest.save(output_manifest) - await context.progress( - completed, total, f"Captioned {Path(item.source).name}", output_manifest - ) - if failures: - detail = f": {first_error}" if first_error else "" - suffix = "; see the manifest" if output_manifest is not None else "" - raise ProcessingError(f"Captioning failed for {failures} item(s){detail}{suffix}") - return output_manifest + return await process_caption_batch(request, context, caption, prepare=self.load) def cleanup(self): self.adapter.cleanup() + + +async def process_caption_batch( + request: CaptionRequest, + context: JobContext, + caption_image: Callable[[Image.Image, str, Event], Awaitable[str]], + *, + prepare: Callable[[], Awaitable[None]] | None = None, +) -> Path | None: + load_path = Path(request.load_path).resolve() + save_path = Path(request.save_path).resolve() + if request.resume_manifest_path: + output_manifest = Path(request.resume_manifest_path).resolve() + manifest = BatchManifest.load(output_manifest, expected_operation="caption") + manifest.validate_scope(load_path, save_path) + manifest.validate_destination_suffixes({".txt"}) + manifest.preflight_resume(request.collision_policy) + else: + manifest = build_manifest( + job_id=context.job_id, + operation="caption", + load_path=load_path, + save_path=save_path, + sources=list_images(load_path), + destination_for=lambda source, _index: save_path / f"{source.stem}.txt", + collision_policy=request.collision_policy, + ) + output_manifest = ( + manifest_path(save_path, context.job_id) + if request.save_manifest or request.dry_run + else None + ) + if output_manifest is not None: + manifest.save(output_manifest) + total = len(manifest.items) + completed = sum(item.status in {"completed", "skipped"} for item in manifest.items) + message = "Caption manifest ready" if output_manifest is not None else "Caption plan ready" + await context.progress(completed, total, message, output_manifest) + if request.dry_run or completed == total: + return output_manifest + context.raise_if_cancelled() + if prepare is not None: + await prepare() + + failures = 0 + first_error: str | None = None + for item in manifest.items: + if item.status in {"completed", "skipped"}: + continue + context.raise_if_cancelled() + try: + image = await asyncio.to_thread(load_rgb_image, Path(item.source)) + try: + caption = await caption_image(image, request.prompt, context.cancelled) + finally: + image.close() + context.raise_if_cancelled() + await asyncio.to_thread(atomic_write_text, Path(item.destination), caption) + item.status = "completed" + item.error = None + except JobCancelledError: + raise + except Exception as exc: + item.status = "failed" + item.error = ( + str(exc) + if request.provider == "local" or isinstance(exc, ProcessingError) + else "Cloud captioning failed; no caption was written" + ) + if request.provider != "local": + if output_manifest is not None: + manifest.save(output_manifest) + raise ProcessingError(item.error) from None + failures += 1 + first_error = first_error or f"{Path(item.source).name}: {str(exc)[:500]}" + completed += 1 + if output_manifest is not None: + manifest.save(output_manifest) + await context.progress( + completed, total, f"Captioned {Path(item.source).name}", output_manifest + ) + if failures: + detail = f": {first_error}" if first_error else "" + suffix = "; see the manifest" if output_manifest is not None else "" + raise ProcessingError(f"Captioning failed for {failures} item(s){detail}{suffix}") + return output_manifest diff --git a/backend/service/image_rename.py b/backend/service/image_rename.py index 78ede8a..8c2bb18 100644 --- a/backend/service/image_rename.py +++ b/backend/service/image_rename.py @@ -10,13 +10,10 @@ from core.jobs import JobContext from models import RenameRequest from service.manifest import BatchManifest, build_manifest, manifest_path -from utils.file_util import atomic_copy, list_images +from utils.file_util import atomic_copy, list_images, validate_image_path class RenameService: - def __init__(self): - self._duplicates_cache: set[Path] | None = None - async def process(self, request: RenameRequest, context: JobContext) -> Path | None: load_path = Path(request.load_path).resolve() save_path = Path(request.save_path).resolve() @@ -27,10 +24,10 @@ async def process(self, request: RenameRequest, context: JobContext) -> Path | N manifest.validate_destination_suffixes(SUPPORTED_INPUT_EXTENSIONS) manifest.preflight_resume(request.collision_policy) else: - duplicates: set[Path] = set() + sources = list_images(load_path) if request.skip_duplicates and load_path.is_dir(): - duplicates = await asyncio.to_thread(self.skip_duplicates, load_path) - sources = [path for path in list_images(load_path) if path not in duplicates] + duplicates = await asyncio.to_thread(self.skip_duplicates, sources) + sources = [path for path in sources if path not in duplicates] def destination(source: Path, index: int) -> Path: number = str(index).zfill(request.zero_pad) @@ -70,6 +67,7 @@ def destination(source: Path, index: int) -> Path: continue context.raise_if_cancelled() try: + await asyncio.to_thread(validate_image_path, Path(item.source)) await asyncio.to_thread(atomic_copy, Path(item.source), Path(item.destination)) item.status = "completed" item.error = None @@ -90,17 +88,17 @@ def destination(source: Path, index: int) -> Path: raise ProcessingError(f"Rename failed for {failures} item(s){detail}{suffix}") return output_manifest - def skip_duplicates(self, load_path: Path) -> set[Path]: - if self._duplicates_cache is not None: - return self._duplicates_cache - dif = difPy.build(str(load_path), recursive=False) + def skip_duplicates(self, sources: list[Path]) -> set[Path]: + for source in sources: + validate_image_path(source) + if len(sources) < 2: + return set() + dif = difPy.build( + [str(source) for source in sources], recursive=False, limit_extensions=False + ) search = difPy.search(dif) duplicates: set[Path] = set() for duplicate_group in search.result.values(): for entry in duplicate_group: duplicates.add(Path(entry[0]).resolve()) - self._duplicates_cache = duplicates return duplicates - - def clear_cache(self): - self._duplicates_cache = None diff --git a/backend/service/manifest.py b/backend/service/manifest.py index ef04caf..a7afff9 100644 --- a/backend/service/manifest.py +++ b/backend/service/manifest.py @@ -135,11 +135,14 @@ def natural_key(path: Path) -> list[str | int]: def sorted_files(paths: Iterable[Path]) -> list[Path]: - return sorted(paths, key=natural_key) + return sorted(paths, key=lambda path: (natural_key(path), path.name.casefold(), str(path))) def manifest_path(save_path: Path, job_id: str) -> Path: - return save_path / ".trainkit" / "manifests" / f"{job_id}.json" + destination = save_path / ".trainkit" / "manifests" / f"{job_id}.json" + if not _is_within(destination, save_path): + raise InvalidPathError("Manifest path escapes the selected output directory") + return destination def _path_key(path: Path) -> str: @@ -233,14 +236,19 @@ def build_manifest( def atomic_write_text(path: Path, contents: str): path.parent.mkdir(parents=True, exist_ok=True) - with NamedTemporaryFile( - "w", encoding="utf-8", dir=path.parent, prefix=f".{path.name}.", delete=False - ) as handle: - handle.write(contents) - handle.flush() - os.fsync(handle.fileno()) - temporary = Path(handle.name) - os.replace(temporary, path) + temporary = None + try: + with NamedTemporaryFile( + "w", encoding="utf-8", dir=path.parent, prefix=f".{path.name}.", delete=False + ) as handle: + temporary = Path(handle.name) + handle.write(contents) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + if temporary is not None: + temporary.unlink(missing_ok=True) def atomic_write_json(path: Path, value: object): diff --git a/backend/service/ncnn_upscaling.py b/backend/service/ncnn_upscaling.py index 2867372..e0d057d 100644 --- a/backend/service/ncnn_upscaling.py +++ b/backend/service/ncnn_upscaling.py @@ -120,6 +120,8 @@ def _process_tile(self, image: Image.Image) -> Image.Image: output_array = output_array.transpose(1, 2, 0) if output_array.ndim == 2: output_array = np.repeat(output_array[:, :, None], 3, axis=2) + if output_array.shape[2] == 1: + output_array = np.repeat(output_array, 3, axis=2) output_array = output_array[:, :, :3] if output_array.max(initial=0) <= 1.5: output_array *= 255.0 diff --git a/backend/service/service_manager.py b/backend/service/service_manager.py index d489366..0072477 100644 --- a/backend/service/service_manager.py +++ b/backend/service/service_manager.py @@ -32,6 +32,8 @@ def get_caption_service( if self._caption_service is None or self._caption_key != key: if self._caption_service is not None: self._caption_service.cleanup() + self._caption_service = None + self._caption_key = None self._caption_service = ImageCaptioningService( model=model_path, adapter=adapter, @@ -74,6 +76,8 @@ def get_tag_service(self, model_path: Path): if self._tag_service is None or self._tag_model_path != resolved: if self._tag_service is not None: self._tag_service.cleanup() + self._tag_service = None + self._tag_model_path = None self._tag_service = ImageTaggingService(resolved) self._tag_model_path = resolved return self._tag_service @@ -135,6 +139,8 @@ def factory(): if self._upscale_service is None or self._upscale_key != key: if self._upscale_service is not None: self._upscale_service.cleanup() + self._upscale_service = None + self._upscale_key = None self._upscale_service = factory() self._upscale_key = key return self._upscale_service diff --git a/backend/tests/test_api.py b/backend/tests/test_api.py index 342d06a..82de85a 100644 --- a/backend/tests/test_api.py +++ b/backend/tests/test_api.py @@ -1,4 +1,5 @@ from fastapi.testclient import TestClient +from PIL import Image from main import app from service.service_manager import ServiceManager @@ -56,3 +57,25 @@ def test_model_status_does_not_echo_user_path(monkeypatch, tmp_path): "gpu_memory_reserved_gb": 0, "gpu_memory_total_gb": 0, } + + +def test_cloud_caption_http_boundary_requires_consent_and_key_but_allows_dry_run(tmp_path): + source = tmp_path / "image.png" + Image.new("RGB", (2, 2), "blue").save(source) + body = { + "provider": "openai", + "cloud_model": "gpt-4.1-mini", + "load_path": str(source), + "save_path": str(tmp_path / "output"), + "prompt": "Describe the image", + } + headers = {"x-trainkit-token": "test-token"} + with TestClient(app) as client: + assert client.post("/caption", json=body).status_code == 401 + assert client.post("/caption", json=body, headers=headers).status_code == 422 + missing = client.post("/caption", json={**body, "cloud_consent": True}, headers=headers) + assert missing.status_code == 400 + assert missing.json()["error"]["code"] == "provider_key_missing" + dry_run = client.post("/caption", json={**body, "dry_run": True}, headers=headers) + assert dry_run.status_code == 202 + assert dry_run.json()["operation"] == "caption" diff --git a/backend/tests/test_cloud_captioning.py b/backend/tests/test_cloud_captioning.py new file mode 100644 index 0000000..97fe021 --- /dev/null +++ b/backend/tests/test_cloud_captioning.py @@ -0,0 +1,401 @@ +import asyncio +import base64 +import json +from io import BytesIO +from threading import Event + +import httpx +import pytest +from PIL import Image +from pydantic import ValidationError + +from core.exceptions import InvalidPathError, JobCancelledError, ProcessingError, TrainKitException +from models import CaptionRequest +from routers.caption import caption +from service import cloud_captioning +from service.image_captioning import process_caption_batch +from service.manifest import BatchManifest + +TEST_KEY = "test-provider-secret" + + +class FakeContext: + job_id = "cloud-job" + + def __init__(self): + self.cancelled = Event() + self.updates = [] + + def raise_if_cancelled(self): + if self.cancelled.is_set(): + raise JobCancelledError() + + async def progress(self, current, total, message, manifest_path=None): + self.raise_if_cancelled() + self.updates.append((current, total, message, manifest_path)) + + +def make_request(tmp_path, **options): + source = tmp_path / "input" + source.mkdir(exist_ok=True) + Image.new("RGB", (2, 2), "blue").save(source / "image.bmp") + return CaptionRequest( + **{ + "provider": "openai", + "cloud_model": "gpt-4.1-mini", + "cloud_consent": True, + "load_path": str(source), + "save_path": str(tmp_path / "output"), + "prompt": "Describe this image", + **options, + } + ) + + +def success_response(provider="openai", text="A blue square."): + if provider == "anthropic": + return {"stop_reason": "end_turn", "content": [{"type": "text", "text": text}]} + return { + "status": "completed", + "output": [ + { + "type": "message", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": text}], + } + ], + } + + +def install_transport(monkeypatch, handler): + client_type = httpx.AsyncClient + clients = [] + + def create_client(**options): + clients.append(options) + return client_type(transport=httpx.MockTransport(handler), **options) + + monkeypatch.setattr(cloud_captioning.httpx, "AsyncClient", create_client) + return clients + + +def test_caption_request_requires_provider_settings_and_upload_consent(tmp_path): + legacy = CaptionRequest( + caption_model_path="model", load_path="input", save_path="output", prompt="Describe it" + ) + assert legacy.provider == "local" + for options in ({"provider": "local"}, {"cloud_model": " "}, {"cloud_consent": False}): + with pytest.raises(ValidationError): + make_request(tmp_path, **options) + assert make_request(tmp_path, dry_run=True, cloud_consent=False).dry_run + assert "api_key" not in CaptionRequest.model_fields + + +@pytest.mark.parametrize("provider", ["openai", "anthropic"]) +async def test_provider_contract_and_shared_atomic_batch(monkeypatch, tmp_path, provider): + request = make_request(tmp_path, provider=provider, save_manifest=True) + original = (tmp_path / "input" / "image.bmp").read_bytes() + calls = [] + + def handler(sent): + calls.append(sent) + body = json.loads(sent.content) + assert sent.method == "POST" + assert str(sent.url) == cloud_captioning.ENDPOINTS[provider] + assert body["model"] == request.cloud_model + assert TEST_KEY not in sent.content.decode() + if provider == "anthropic": + assert sent.headers["x-api-key"] == TEST_KEY + assert sent.headers["anthropic-version"] == "2023-06-01" + assert body["max_tokens"] == 256 + content = body["messages"][0]["content"] + encoded = content[0]["source"]["data"] + assert content[0]["source"]["media_type"] == "image/jpeg" + assert content[1] == {"type": "text", "text": request.prompt} + else: + assert sent.headers["authorization"] == f"Bearer {TEST_KEY}" + assert body["store"] is False + assert body["max_output_tokens"] == 256 + content = body["input"][0]["content"] + encoded = content[0]["image_url"].removeprefix("data:image/jpeg;base64,") + assert content[1] == {"type": "input_text", "text": request.prompt} + with Image.open(BytesIO(base64.b64decode(encoded))) as image: + assert image.format == "JPEG" and image.mode == "RGB" + return httpx.Response(200, json=success_response(provider)) + + clients = install_transport(monkeypatch, handler) + manifest_path = await cloud_captioning.process_cloud_caption_batch( + request, FakeContext(), TEST_KEY + ) + assert (tmp_path / "output" / "image.txt").read_text() == "A blue square." + assert (tmp_path / "input" / "image.bmp").read_bytes() == original + assert len(clients) == len(calls) == 1 + assert clients[0]["follow_redirects"] is False and clients[0]["trust_env"] is False + assert manifest_path and TEST_KEY not in manifest_path.read_text() + assert BatchManifest.load(manifest_path).items[0].status == "completed" + + +def test_cloud_image_resize_strips_metadata_without_modifying_source(): + with Image.new("RGB", (4000, 2000), "blue") as image: + image.info["comment"] = b"private metadata" + image.info["exif"] = b"private metadata" + encoded = cloud_captioning.encode_cloud_image(image) + assert image.size == (4000, 2000) and image.info["comment"] == b"private metadata" + with Image.open(BytesIO(base64.b64decode(encoded))) as uploaded: + assert uploaded.size == (1568, 784) + assert "comment" not in uploaded.info and not uploaded.getexif() + + +@pytest.mark.parametrize("mode", ["dry_run", "skip", "resume"]) +async def test_cloud_plans_without_requests(monkeypatch, tmp_path, mode): + request = make_request(tmp_path, dry_run=True, cloud_consent=False) + + def handler(_sent): + pytest.fail("A planned or completed batch must not contact the provider") + + install_transport(monkeypatch, handler) + manifest_path = await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), "") + assert manifest_path and not (tmp_path / "output" / "image.txt").exists() + if mode == "dry_run": + return + destination = tmp_path / "output" / "image.txt" + destination.write_text("Existing caption") + options = {"dry_run": False, "cloud_consent": True} + if mode == "skip": + options["collision_policy"] = "skip" + else: + manifest = BatchManifest.load(manifest_path) + manifest.items[0].status = "completed" + manifest.save(manifest_path) + options["resume_manifest_path"] = str(manifest_path) + await cloud_captioning.process_cloud_caption_batch( + request.model_copy(update=options), FakeContext(), TEST_KEY + ) + assert destination.read_text() == "Existing caption" + + +async def test_collision_preflight_prevents_cloud_charge(monkeypatch, tmp_path): + request = make_request(tmp_path) + destination = tmp_path / "output" / "image.txt" + destination.parent.mkdir() + destination.write_text("Existing caption") + install_transport(monkeypatch, lambda _sent: pytest.fail("Collision must precede request")) + with pytest.raises(InvalidPathError, match="collision"): + await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY) + assert destination.read_text() == "Existing caption" + + +@pytest.mark.parametrize( + "provider,response", + [ + ("openai", {"status": "incomplete", "output": []}), + ( + "openai", + { + "status": "completed", + "output": [ + { + "type": "message", + "status": "completed", + "role": "assistant", + "content": [{"type": "refusal", "refusal": TEST_KEY}], + } + ], + }, + ), + ("openai", success_response(text=" ")), + ( + "anthropic", + {"stop_reason": "max_tokens", "content": [{"type": "text", "text": TEST_KEY}]}, + ), + ("anthropic", {"stop_reason": "refusal", "content": [{"type": "text", "text": TEST_KEY}]}), + ("anthropic", {"stop_reason": "end_turn", "content": []}), + ("anthropic", {"stop_reason": "model_context_window_exceeded", "content": []}), + ], +) +async def test_invalid_cloud_output_preserves_destination_and_safe_manifest( + monkeypatch, tmp_path, provider, response +): + request = make_request( + tmp_path, provider=provider, save_manifest=True, collision_policy="overwrite" + ) + Image.new("RGB", (2, 2)).save(tmp_path / "input" / "later.png") + destination = tmp_path / "output" / "image.txt" + destination.parent.mkdir() + destination.write_text("Existing caption") + calls = [] + + def handler(sent): + calls.append(sent) + return httpx.Response(200, json=response) + + install_transport(monkeypatch, handler) + with pytest.raises(ProcessingError) as error: + await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY) + assert TEST_KEY not in str(error.value) and len(calls) == 1 + assert destination.read_text() == "Existing caption" + assert not (tmp_path / "output" / "later.txt").exists() + saved = tmp_path / "output" / ".trainkit" / "manifests" / "cloud-job.json" + data = json.loads(saved.read_text()) + assert data["items"][0]["status"] == "failed" + assert data["items"][1]["status"] == "pending" + assert TEST_KEY not in saved.read_text() + + +@pytest.mark.parametrize("status", [401, 403, 429, 500, 302]) +async def test_http_errors_do_not_retry_or_leak_provider_response(monkeypatch, tmp_path, status): + request = make_request(tmp_path, save_manifest=True) + calls = [] + + def handler(sent): + calls.append(sent) + return httpx.Response(status, text=TEST_KEY, headers={"location": "https://example.com"}) + + install_transport(monkeypatch, handler) + with pytest.raises(ProcessingError) as error: + await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY) + assert TEST_KEY not in str(error.value) and len(calls) == 1 + assert not (tmp_path / "output" / "image.txt").exists() + + +async def test_pending_cloud_request_cancels_and_closes_transport(monkeypatch, tmp_path): + request = make_request(tmp_path, save_manifest=True) + started, closed = asyncio.Event(), asyncio.Event() + + async def handler(_sent): + started.set() + try: + await asyncio.Event().wait() + finally: + closed.set() + + install_transport(monkeypatch, handler) + context = FakeContext() + running = asyncio.create_task( + cloud_captioning.process_cloud_caption_batch(request, context, TEST_KEY) + ) + await asyncio.wait_for(started.wait(), 1) + context.cancelled.set() + with pytest.raises(JobCancelledError): + await asyncio.wait_for(running, 1) + assert closed.is_set() and not (tmp_path / "output" / "image.txt").exists() + + +async def test_total_timeout_cancels_transport_without_retry(monkeypatch, tmp_path): + request = make_request(tmp_path) + closed = asyncio.Event() + + async def handler(_sent): + try: + await asyncio.Event().wait() + finally: + closed.set() + + install_transport(monkeypatch, handler) + monkeypatch.setattr(cloud_captioning, "REQUEST_TIMEOUT_SECONDS", 0.02) + with pytest.raises(ProcessingError, match="timed out"): + await asyncio.wait_for( + cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY), 1 + ) + assert closed.is_set() and not (tmp_path / "output" / "image.txt").exists() + + +async def test_cancel_after_generation_prevents_atomic_publish(tmp_path): + request = make_request(tmp_path) + context = FakeContext() + + async def generate(_image, _prompt, _cancelled): + context.cancelled.set() + return "Must not be published" + + with pytest.raises(JobCancelledError): + await process_caption_batch(request, context, generate) + assert not (tmp_path / "output" / "image.txt").exists() + + +async def test_missing_key_rejected_before_job_start(tmp_path): + class NeverStart: + async def start(self, *_args): + pytest.fail("Missing key must not create a job") + + with pytest.raises(TrainKitException, match="API key"): + await caption(make_request(tmp_path), provider_key=None, jobs=NeverStart(), services=None) + + +async def test_resume_sends_only_pending_images_with_one_client(monkeypatch, tmp_path): + plan = make_request(tmp_path, dry_run=True, cloud_consent=False) + Image.new("RGB", (2, 2), "red").save(tmp_path / "input" / "second.png") + Image.new("RGB", (2, 2), "green").save(tmp_path / "input" / "third.png") + calls = [] + + def handler(sent): + calls.append(sent) + return httpx.Response(200, json=success_response()) + + clients = install_transport(monkeypatch, handler) + manifest_path = await cloud_captioning.process_cloud_caption_batch(plan, FakeContext(), "") + manifest = BatchManifest.load(manifest_path) + manifest.items[0].status = "completed" + manifest.save(manifest_path) + (tmp_path / "output" / "image.txt").write_text("Original caption") + request = plan.model_copy( + update={ + "dry_run": False, + "cloud_consent": True, + "resume_manifest_path": str(manifest_path), + } + ) + await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY) + assert len(calls) == 2 and len(clients) == 2 + assert (tmp_path / "output" / "image.txt").read_text() == "Original caption" + assert all(item.status == "completed" for item in BatchManifest.load(manifest_path).items) + + +async def test_transport_exception_is_sanitized(monkeypatch, tmp_path): + def handler(_sent): + raise httpx.ReadError(f"Sensitive provider detail: {TEST_KEY}") + + install_transport(monkeypatch, handler) + request = make_request(tmp_path, save_manifest=True) + with pytest.raises(ProcessingError, match="Could not reach") as error: + await cloud_captioning.process_cloud_caption_batch(request, FakeContext(), TEST_KEY) + assert TEST_KEY not in str(error.value) + saved = tmp_path / "output" / ".trainkit" / "manifests" / "cloud-job.json" + assert TEST_KEY not in saved.read_text() + + +async def test_route_passes_header_key_only_to_cloud_runner(monkeypatch, tmp_path): + received = [] + + async def process(request, _context, key): + received.append((request.model_dump(), key)) + return None + + class RunImmediately: + async def start(self, operation, runner): + assert operation == "caption" + return await runner(FakeContext()) + + monkeypatch.setattr(cloud_captioning, "process_cloud_caption_batch", process) + await caption( + make_request(tmp_path), provider_key=TEST_KEY, jobs=RunImmediately(), services=None + ) + assert received[0][1] == TEST_KEY + assert TEST_KEY not in json.dumps(received[0][0]) + + +async def test_shared_local_batch_keeps_per_image_failure_behavior(tmp_path): + request = make_request(tmp_path, provider="local", caption_model_path="model") + Image.new("RGB", (2, 2), "red").save(tmp_path / "input" / "second.png") + calls = [] + + async def generate(image, _prompt, _cancelled): + calls.append(image.getpixel((0, 0))) + if len(calls) == 1: + raise RuntimeError("Local model input failed") + return "A red square." + + with pytest.raises(ProcessingError, match="Local model input failed"): + await process_caption_batch(request, FakeContext(), generate) + assert len(calls) == 2 and (tmp_path / "output" / "second.txt").read_text() == "A red square." diff --git a/backend/tests/test_file_safety.py b/backend/tests/test_file_safety.py index 0968f1d..59abf0c 100644 --- a/backend/tests/test_file_safety.py +++ b/backend/tests/test_file_safety.py @@ -1,7 +1,14 @@ +from multiprocessing.dummy import Pool as ThreadPool from pathlib import Path +from unittest.mock import Mock +import pytest from PIL import Image +from core.jobs import JobManager +from models import RenameRequest +from service.image_rename import RenameService +from service.manifest import BatchManifest from utils.file_util import atomic_copy, atomic_save_image, list_images @@ -27,3 +34,98 @@ def test_image_listing_accepts_an_individual_image(tmp_path: Path): Image.new("RGB", (2, 2), "green").save(source) assert list_images(source) == [source] + + +@pytest.mark.asyncio +@pytest.mark.parametrize("resume", [False, True]) +async def test_rename_rejects_invalid_images_from_folders_and_resume_manifests( + tmp_path: Path, resume: bool +): + source = tmp_path / "input" + output = tmp_path / "output" + source.mkdir() + image = source / "broken.png" + image.write_bytes(b"not an image") + manifest_path = tmp_path / "resume.json" + if resume: + BatchManifest( + job_id="previous", + operation="rename", + load_path=str(source), + save_path=str(output), + collision_policy="fail", + items=[{"source": str(image), "destination": str(output / "00001.png")}], + ).save(manifest_path) + request = RenameRequest( + load_path=str(source), + save_path=str(output), + resume_manifest_path=str(manifest_path) if resume else None, + ) + service = RenameService() + manager = JobManager() + + async def runner(context): + return await service.process(request, context) + + created = await manager.start("rename", runner) + await manager.tasks[created["job_id"]] + job = manager.get(created["job_id"]) + + assert job["status"] == "failed" + assert "Invalid or unsafe image broken.png" in job["error"] + assert not (output / "00001.png").exists() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("extension", ["png", "bmp"]) +async def test_rename_duplicate_search_ignores_unsupported_images( + monkeypatch, tmp_path: Path, extension: str +): + source = tmp_path / "input" + output = tmp_path / "output" + source.mkdir() + image = Image.new("RGB", (4, 4), "red") + for name in ("a.tiff", f"b.{extension}", f"c.{extension}"): + image.save(source / name) + # Keep the real difPy algorithm while avoiding extra worker processes in the test. + monkeypatch.setattr("difPy.dif.Pool", ThreadPool) + request = RenameRequest(load_path=str(source), save_path=str(output), skip_duplicates=True) + manager = JobManager() + + async def runner(context): + return await RenameService().process(request, context) + + created = await manager.start("rename", runner) + await manager.tasks[created["job_id"]] + job = manager.get(created["job_id"]) + + assert job["status"] == "completed" + assert job["total"] == 1 + assert [path.name for path in output.iterdir()] == [f"00001.{extension}"] + with Image.open(output / f"00001.{extension}") as copied: + assert copied.getpixel((0, 0)) == (255, 0, 0) + + +@pytest.mark.asyncio +async def test_rename_validates_images_before_duplicate_search(monkeypatch, tmp_path: Path): + source = tmp_path / "input" + source.mkdir() + (source / "broken.png").write_bytes(b"not an image") + Image.new("RGB", (4, 4), "red").save(source / "valid.png") + duplicate_build = Mock() + monkeypatch.setattr("difPy.build", duplicate_build) + request = RenameRequest( + load_path=str(source), save_path=str(tmp_path / "output"), skip_duplicates=True + ) + manager = JobManager() + + async def runner(context): + return await RenameService().process(request, context) + + created = await manager.start("rename", runner) + await manager.tasks[created["job_id"]] + job = manager.get(created["job_id"]) + + assert job["status"] == "failed" + assert "Invalid or unsafe image broken.png" in job["error"] + duplicate_build.assert_not_called() diff --git a/backend/tests/test_jobs.py b/backend/tests/test_jobs.py index f4bbb2d..5b55a86 100644 --- a/backend/tests/test_jobs.py +++ b/backend/tests/test_jobs.py @@ -53,6 +53,47 @@ async def runner(context): assert manager.get(created["job_id"])["status"] == "cancelled" +@pytest.mark.asyncio +async def test_cancelling_a_queued_job_skips_its_runner(): + connections = FakeConnections() + manager = JobManager(connections) # type: ignore[arg-type] + runner_called = False + + async def runner(_context): + nonlocal runner_called + runner_called = True + + created = await manager.start("upscale", runner) + await manager.cancel(created["job_id"]) + await manager.tasks[created["job_id"]] + + assert not runner_called + assert manager.get(created["job_id"])["status"] == "cancelled" + assert "running" not in [event["status"] for event in connections.events] + + +@pytest.mark.asyncio +async def test_cancellation_during_start_notification_skips_its_runner(): + class CancellingConnections(FakeConnections): + async def send_job(self, job): + await super().send_job(job) + if job["status"] == "running": + await manager.cancel(job["job_id"]) + + manager = JobManager(CancellingConnections()) # type: ignore[arg-type] + runner_called = False + + async def runner(_context): + nonlocal runner_called + runner_called = True + + created = await manager.start("upscale", runner) + await manager.tasks[created["job_id"]] + + assert not runner_called + assert manager.get(created["job_id"])["status"] == "cancelled" + + @pytest.mark.asyncio async def test_model_maintenance_blocks_jobs_and_releases_reservation(): manager = JobManager(FakeConnections()) # type: ignore[arg-type] diff --git a/backend/tests/test_manifest.py b/backend/tests/test_manifest.py index d69399f..57f048e 100644 --- a/backend/tests/test_manifest.py +++ b/backend/tests/test_manifest.py @@ -3,7 +3,13 @@ import pytest from core.exceptions import InvalidPathError -from service.manifest import BatchManifest, build_manifest +from service.manifest import ( + BatchManifest, + atomic_write_text, + build_manifest, + manifest_path, + sorted_files, +) def test_manifest_naturally_sorts_and_zero_collision_renames(tmp_path: Path): @@ -218,3 +224,50 @@ def test_resume_rechecks_destinations_created_after_original_run(tmp_path: Path) loaded.preflight_resume("overwrite") assert loaded.items[0].status == "pending" + + +def test_natural_sort_ties_are_independent_of_enumeration(tmp_path: Path): + files = [tmp_path / "image1.png", tmp_path / "image01.png", tmp_path / "Image1.png"] + assert sorted_files(files) == sorted_files(reversed(files)) + + +def test_failed_atomic_write_preserves_output_and_removes_temporary(tmp_path: Path, monkeypatch): + import service.manifest as module + + output = tmp_path / "caption.txt" + output.write_text("original", encoding="utf-8") + + def fail_replace(*_args): + raise OSError("file locked") + + monkeypatch.setattr(module.os, "replace", fail_replace) + with pytest.raises(OSError, match="file locked"): + atomic_write_text(output, "replacement") + assert output.read_text(encoding="utf-8") == "original" + assert list(tmp_path.iterdir()) == [output] + + +def test_generated_manifest_rejects_directory_link_outside_output(tmp_path: Path): + import os + import subprocess + + output = tmp_path / "output" + outside = tmp_path / "outside" + output.mkdir() + outside.mkdir() + link = output / ".trainkit" + if os.name == "nt": + subprocess.run( + ["cmd", "/c", "mklink", "/J", str(link), str(outside)], check=True, capture_output=True + ) + else: + link.symlink_to(outside, target_is_directory=True) + try: + with pytest.raises(InvalidPathError, match="Manifest path escapes"): + manifest_path(output, "test-job") + assert not list(outside.iterdir()) + finally: + if os.name == "nt": + link.rmdir() + else: + link.unlink() diff --git a/backend/tests/test_ncnn.py b/backend/tests/test_ncnn.py index 9ef9580..aadc1c6 100644 --- a/backend/tests/test_ncnn.py +++ b/backend/tests/test_ncnn.py @@ -1,5 +1,7 @@ from pathlib import Path +from types import SimpleNamespace +import numpy as np import pytest from PIL import Image @@ -52,3 +54,21 @@ def test_ncnn_blob_override_reports_discovered_names(tmp_path: Path): scale=1, use_vulkan=False, ) + + +@pytest.mark.parametrize("output_shape", [(2, 2), (1, 2, 2)]) +def test_ncnn_converts_grayscale_model_output_to_rgb(output_shape): + extractor = SimpleNamespace( + input=lambda *_args: 0, + extract=lambda *_args: (0, np.full(output_shape, 0.5, dtype=np.float32)), + ) + service = object.__new__(NCNNUpscaleService) + service.net = SimpleNamespace(create_extractor=lambda: extractor) + service.input_blob = "input" + service.output_blob = "output" + + output = service._process_tile(Image.new("RGB", (2, 2), "white")) + + assert output.mode == "RGB" + assert output.size == (2, 2) + assert output.getpixel((0, 0)) == (127, 127, 127) diff --git a/backend/tests/test_requests.py b/backend/tests/test_requests.py new file mode 100644 index 0000000..5ef4c17 --- /dev/null +++ b/backend/tests/test_requests.py @@ -0,0 +1,41 @@ +import pytest +from pydantic import ValidationError + +from models import UpscaleRequest + + +@pytest.mark.parametrize("overlap", [64, 65]) +def test_tiled_upscale_rejects_overlap_at_least_tile_size(overlap): + with pytest.raises(ValidationError, match="Tile overlap must be smaller than tile size"): + UpscaleRequest( + upscale_model_path="model.safetensors", + load_path="input", + save_path="output", + tile_size=64, + tile_overlap=overlap, + ) + + +def test_tiled_upscale_accepts_overlap_below_tile_size(): + request = UpscaleRequest( + upscale_model_path="model.safetensors", + load_path="input", + save_path="output", + tile_size=64, + tile_overlap=63, + ) + + assert request.tile_overlap == 63 + + +def test_untiled_upscale_does_not_require_overlap_smaller_than_tile_size(): + request = UpscaleRequest( + upscale_model_path="model.safetensors", + load_path="input", + save_path="output", + use_tiling=False, + tile_size=64, + tile_overlap=512, + ) + + assert request.tile_overlap == 512 diff --git a/backend/tests/test_service_manager.py b/backend/tests/test_service_manager.py new file mode 100644 index 0000000..c3e63bb --- /dev/null +++ b/backend/tests/test_service_manager.py @@ -0,0 +1,104 @@ +import sys +from pathlib import Path +from types import ModuleType + +import pytest + +from core.exceptions import ModelLoadError +from models import UpscaleRequest +from service.service_manager import ServiceManager + + +@pytest.mark.parametrize( + "operation,module_name,class_name,service_attribute,key_attribute", + [ + ( + "caption", + "service.image_captioning", + "ImageCaptioningService", + "_caption_service", + "_caption_key", + ), + ( + "tag", + "service.image_tagging", + "ImageTaggingService", + "_tag_service", + "_tag_model_path", + ), + ( + "spandrel", + "service.image_upscaling", + "ImageUpscaleService", + "_upscale_service", + "_upscale_key", + ), + ( + "ncnn", + "service.ncnn_upscaling", + "NCNNUpscaleService", + "_upscale_service", + "_upscale_key", + ), + ], +) +def test_failed_model_switch_discards_cleaned_service_and_reloads_previous_model( + monkeypatch, + tmp_path: Path, + operation, + module_name, + class_name, + service_attribute, + key_attribute, +): + events = [] + + class FakeService: + def __init__(self, *args, **kwargs): + model_path = ( + args[0] + if args + else kwargs.get("model", kwargs.get("model_path", kwargs.get("model_param_path"))) + ) + events.append(f"load:{model_path.name}") + if model_path.name == "broken": + raise ModelLoadError("Broken model") + self.cleaned = False + + def cleanup(self): + events.append("cleanup") + self.cleaned = True + + module = ModuleType(module_name) + setattr(module, class_name, FakeService) + monkeypatch.setitem(sys.modules, module_name, module) + image_util = ModuleType("utils.image_util") + image_util.get_device = lambda: "cpu" + monkeypatch.setitem(sys.modules, "utils.image_util", image_util) + manager = ServiceManager() + + def get_service(model_path): + if operation == "caption": + return manager.get_caption_service(model_path) + if operation == "tag": + return manager.get_tag_service(model_path) + return manager.get_upscale_service( + UpscaleRequest( + backend=operation, + upscale_model_path=str(model_path), + load_path=str(tmp_path), + save_path=str(tmp_path), + ) + ) + + previous = get_service(tmp_path / "good") + with pytest.raises(ModelLoadError, match="Broken model"): + get_service(tmp_path / "broken") + + assert previous.cleaned + assert getattr(manager, service_attribute) is None + assert getattr(manager, key_attribute) is None + recovered = get_service(tmp_path / "good") + assert recovered is not previous + assert not recovered.cleaned + assert events == ["load:good", "cleanup", "load:broken", "load:good"] diff --git a/backend/uv.lock b/backend/uv.lock index f00a7e3..fbf27bb 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -9,7 +9,7 @@ resolution-markers = [ [[package]] name = "accelerate" -version = "1.14.0" +version = "1.15.0" source = { registry = 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a/docs/architecture.md +++ b/docs/architecture.md @@ -20,6 +20,8 @@ flowchart LR `src/preload.ts` exposes a small typed API. The renderer can request a directory or file picker, operate only within granted paths, submit allowlisted backend routes, and receive sanitized status/job/log events. It does not know the Python port or authentication token. +`src/provider-settings.ts` keeps configured provider keys in the main process. Its IPC returns only presence, model IDs, and persistence status. Session keys are the default; opt-in persistence uses Electron `safeStorage` and rejects Linux's plaintext fallback. Keys are added to authenticated caption requests through an internal header and never enter renderer request bodies, job snapshots, or manifests. Existing path ancestors are resolved before grants are checked, including new folders under junctions. + `src/runtime-paths.ts` is the single authority for packaged paths. `src/setup-manager.ts` installs the locked Python environment beside the packaged backend under `resources/backend`, including its managed Python interpreter. uv's cache and temporary directory also stay there during setup and are removed after success. A marker binds the environment to the desktop version and `uv.lock` SHA-256. Session logs are stored under `logs` beside the executable. Because the packaged runtime is self-contained, TrainKit must be extracted to a folder the current user can write to. Setup checks this before downloading multi-gigabyte dependencies and reports the exact rejected path. @@ -32,10 +34,14 @@ Because the packaged runtime is self-contained, TrainKit must be extracted to a `backend/core/jobs.py` permits one active heavyweight job, assigns explicit job IDs, records terminal states, broadcasts progress after completed work, and supports cooperative cancellation. Caption generation also checks cancellation between generated tokens; tiled upscaling checks between tiles. +WebSocket reconnects recover authoritative job snapshots. The renderer merges snapshots and events by update time so delayed recovery cannot roll back newer progress or terminal states. + ## Data pipeline Every operation accepts one verified image or enumerates a selected folder in deterministic natural order. It creates an in-memory execution plan and resolves collision policy before the first output. The plan is persisted as a schema-versioned manifest only when requested, for dry runs, or while resuming an existing manifest. Writes use a temporary file in the destination directory followed by atomic replacement. +Local and cloud captioning share one batch loop. Cloud calls use a per-job HTTP client, fixed provider endpoints, bounded metadata-free JPEGs and responses, explicit timeouts, and no automatic retries. Pending requests can be cancelled. Provider failures stop the cloud batch; credentials and raw provider errors are excluded from progress, logs, and persisted errors. OpenAI response storage is disabled, while provider retention policies still apply. + Resume accepts only a matching operation and identical input/output paths. Completed and skipped items remain terminal; failed items return to pending. Every source and destination is resolved and checked against the selected input and output directory, including paired tagging outputs. Image safety defaults are 128 MiB per encoded file and 100 million decoded pixels. Supported input extensions are PNG, JPEG, BMP, and WebP. diff --git a/docs/model-support.md b/docs/model-support.md index 84ed03b..3b363f7 100644 --- a/docs/model-support.md +++ b/docs/model-support.md @@ -1,6 +1,6 @@ # Model support -TrainKit uses local model paths. It does not enable Transformers `trust_remote_code`, and Spandrel pickle checkpoints are intentionally rejected. +Local operations use model paths. TrainKit does not enable Transformers `trust_remote_code`, and Spandrel pickle checkpoints are intentionally rejected. Cloud captioning optionally uses a configured Anthropic or OpenAI vision model. ## Caption models @@ -13,6 +13,12 @@ Select a local Hugging Face model directory containing `config.json` plus the mo Compatibility depends on the installed Transformers version and the model using a built-in architecture. Models that require arbitrary repository Python code are not supported. Captions are written as UTF-8 `.txt` sidecars. +### Cloud caption models + +The API tab defaults to `claude-haiku-4-5-20251001` for Anthropic and `gpt-4.1-mini` for OpenAI. The model ID is editable because availability and account permissions vary. Use a model supporting image input and text output on the provider's Messages or Responses API. A key connection test checks authentication, not model-specific permissions. No local caption model or GPU is used for cloud inference, although TrainKit's normal backend runtime is still required. + +Cloud captioning stops on refusals, empty output, and truncated responses instead of saving incomplete training captions. Images and instructions leave your computer only after explicit consent for the selected provider. See the [cloud captioning guide](../README.md#cloud-captioning) for credentials, billing, and data handling. + ## Spandrel upscalers Spandrel models must be a single `.safetensors` file recognized by Spandrel 0.4. TrainKit stores and invokes the returned model descriptor, reads its scale, supports direct or overlapping tiled inference, clamps output to the image range, and writes the selected image format atomically. diff --git a/eslint.config.mjs b/eslint.config.mjs index ff5386b..89cd42b 100644 --- a/eslint.config.mjs +++ b/eslint.config.mjs @@ -1,7 +1,6 @@ import eslint from "@eslint/js"; import tseslint from "@typescript-eslint/eslint-plugin"; import tsparser from "@typescript-eslint/parser"; -import importPlugin from "eslint-plugin-import"; import globals from "globals"; export default [ @@ -42,7 +41,6 @@ export default [ }, plugins: { "@typescript-eslint": tseslint, - import: importPlugin, }, rules: { ...tseslint.configs.recommended.rules, diff --git a/package-lock.json b/package-lock.json index 7de4d54..013c2f3 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,29 +1,29 @@ { "name": "trainkit", - "version": "1.2.1", + "version": "1.3.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "trainkit", - 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^22.12.0 || >=23" } }, "node_modules/yocto-queue": { @@ -10815,18 +5191,12 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/yoctocolors-cjs": { - "version": "2.1.3", - "resolved": "https://registry.npmjs.org/yoctocolors-cjs/-/yoctocolors-cjs-2.1.3.tgz", - "integrity": "sha512-U/PBtDf35ff0D8X8D0jfdzHYEPFxAI7jJlxZXwCSez5M3190m+QobIfh+sWDWSHMCWWJN2AWamkegn6vr6YBTw==", + "node_modules/yoga-layout": { + "version": "3.2.1", + "resolved": "https://registry.npmjs.org/yoga-layout/-/yoga-layout-3.2.1.tgz", + "integrity": "sha512-0LPOt3AxKqMdFBZA3HBAt/t/8vIKq7VaQYbuA8WxCgung+p9TVyKRYdpvCb80HcdTN2NkbIKbhNwKUfm3tQywQ==", "dev": true, - "license": "MIT", - "engines": { - "node": ">=18" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } + "license": "MIT" } } } diff --git a/package.json b/package.json index 6ef3f44..392e1aa 100644 --- a/package.json +++ b/package.json @@ -1,14 +1,14 @@ { "name": "trainkit", "productName": "TrainKit", - "version": "1.2.1", + "version": "1.3.0", "description": "A dataset preparation toolkit for AI image training", - "main": ".vite/build/main.js", + "main": ".vite/build/main.cjs", "scripts": { "start": "electron-forge start", "package": "electron-forge package", "make": "electron-forge make", - "publish": "electron-forge publish", + "publish": "electron-forge release", "lint": "eslint --ext .ts,.tsx .", "typecheck": "tsc --noEmit", "test": "npm run test:frontend && npm run test:backend", @@ -20,7 +20,7 @@ "verify:package": "node scripts/verify-package.mjs" }, "engines": { - "node": ">=22.12.0" + "node": ">=22.17.0" }, "keywords": [], "author": { @@ -29,11 +29,12 @@ }, "license": "MIT", "devDependencies": { - "@electron-forge/cli": "^7.11.2", - "@electron-forge/maker-zip": "^7.11.2", - "@electron-forge/plugin-fuses": "^7.11.2", - "@electron-forge/plugin-vite": "^7.11.2", - "@electron/fuses": "^1.0.0", + "@electron-forge/cli": "^8.0.1", + "@electron-forge/maker-zip": "^8.0.1", + "@electron-forge/plugin-fuses": "^8.0.1", + "@electron-forge/plugin-vite": "^8.0.1", + "@electron/fuses": "^2.1.3", + "@eslint/js": "^10.0.1", "@tailwindcss/postcss": "^4.3.2", "@tailwindcss/vite": "^4.3.2", "@types/react": "^19.2.7", @@ -43,28 +44,26 @@ "@typescript-eslint/parser": "^8.49.0", "@vitejs/plugin-react": "^6.0.3", "autoprefixer": "^10.4.23", - "electron": "43.1.1", - "eslint": "^9.39.1", - "eslint-plugin-import": "^2.32.0", + "electron": "44.6.0", + "eslint": "^10.12.0", "globals": "^17.1.0", "postcss": "^8.5.6", "tailwindcss": "^4.1.18", "typescript": "~5.9.3", "vite": "^8.1.4", - "vitest": "^4.1.10" + "vitest": "^5.0.3" }, "dependencies": { "clsx": "^2.1.1", - "lucide-react": "^0.563.0", + "lucide-react": "^1.52.0", "react": "^19.2.7", - "react-compare-slider": "^3.1.0", "react-dom": "^19.2.7", "tailwind-merge": "^3.4.0", "tw-animate-css": "^1.4.0", "ws": "^8.21.1" }, "overrides": { - "tar": "7.5.20", + "tar": "7.5.22", "tmp": "0.2.7" } } diff --git a/src/backend-manager.test.ts b/src/backend-manager.test.ts new file mode 100644 index 0000000..0d14dcb --- /dev/null +++ b/src/backend-manager.test.ts @@ -0,0 +1,152 @@ +import { EventEmitter } from "node:events"; +import fs from "node:fs"; +import { afterEach, beforeEach, describe, expect, it, vi } from "vitest"; +import { BackendManager } from "./backend-manager"; +import type { JobRecord } from "./types/contracts"; + +const mocks = vi.hoisted(() => ({ + sockets: [] as EventEmitter[], + children: [] as (EventEmitter & { kill: () => boolean })[], + logger: { info: vi.fn(), success: vi.fn(), warning: vi.fn(), error: vi.fn() }, +})); + +vi.mock("electron", () => ({ app: { getVersion: () => "test" }, BrowserWindow: {} })); +vi.mock("./logger", () => ({ getLogger: () => mocks.logger })); +vi.mock("./runtime-paths", () => ({ + getRuntimePaths: () => ({ backendPath: "backend", runtimePath: "runtime", modelCachePath: "cache" }), +})); +vi.mock("node:fs", () => ({ default: { existsSync: () => true, mkdirSync: vi.fn() } })); +vi.mock("node:net", () => ({ + createServer: () => ({ + unref: vi.fn(), + on: vi.fn(), + listen: (_port: number, _host: string, callback: () => void) => callback(), + address: () => ({ port: 12345 }), + close: (callback: () => void) => callback(), + }), +})); +vi.mock("node:child_process", () => ({ + spawn: () => { + const child = Object.assign(new EventEmitter(), { + stdout: new EventEmitter(), stderr: new EventEmitter(), kill: vi.fn(() => true), + }); + mocks.children.push(child); + return child; + }, +})); +vi.mock("ws", async () => { + const { EventEmitter: SocketEvents } = await import("node:events"); + return { default: class extends SocketEvents { + constructor() { + super(); + mocks.sockets.push(this); + } + close() { this.emit("close"); } + } }; +}); + +const runningJob: JobRecord = { + job_id: "abc", + operation: "caption", + status: "running", + current: 0, + total: 1, + percent: 0, + message: "working", + manifest_path: null, + error: null, + created_at: "2026-07-14T00:00:00.000001+00:00", + updated_at: "2026-07-14T00:00:01.000001+00:00", +}; + +describe("backend request and event recovery", () => { + beforeEach(() => { + vi.clearAllMocks(); + mocks.sockets.length = 0; + mocks.children.length = 0; + vi.stubGlobal("fetch", vi.fn().mockImplementation(async (url: string) => { + const data = url.endsWith("/health") ? { status: "ok", version: "test" } : { jobs: [runningJob] }; + return { ok: true, status: 200, json: async () => data, text: async () => JSON.stringify(data) }; + })); + }); + afterEach(() => { vi.useRealTimers(); vi.unstubAllGlobals(); }); + + async function readyManager() { + const manager = new BackendManager(); + await manager.start(); + expect(await manager.waitForReady()).toBe(true); + return manager; + } + + it("forwards an internal provider key while preserving its own authentication token", async () => { + const manager = await readyManager(); + const headers = { "x-trainkit-provider-key": "test-key", "X-Trainkit-Token": "untrusted" }; + await manager.request("/caption", "POST", {}, headers); + const request = vi.mocked(fetch).mock.calls.at(-1)?.[1]; + const sentHeaders = new Headers(request?.headers); + expect(sentHeaders.get("x-trainkit-provider-key")).toBe("test-key"); + expect(sentHeaders.get("content-type")).toBe("application/json"); + expect(sentHeaders.get("x-trainkit-token")).toMatch(/^[a-f0-9]{64}$/); + }); + + it("recovers current jobs on the initial socket and after a reconnect", async () => { + vi.useFakeTimers(); + const manager = await readyManager(); + const listener = vi.fn(); + manager.onEvent(listener); + mocks.sockets[0].emit("open"); + await vi.advanceTimersByTimeAsync(0); + expect(listener).toHaveBeenCalledWith({ type: "job", ...runningJob }); + + const completed = { ...runningJob, status: "completed", updated_at: "2026-07-14T00:00:02.000001+00:00" }; + vi.mocked(fetch).mockResolvedValue({ + ok: true, status: 200, text: async () => JSON.stringify({ jobs: [completed, { status: "invalid" }] }), + } as Response); + mocks.sockets[0].emit("close"); + await vi.advanceTimersByTimeAsync(1_000); + mocks.sockets[1].emit("open"); + await vi.advanceTimersByTimeAsync(0); + expect(listener).toHaveBeenLastCalledWith({ type: "job", ...completed }); + expect(listener).toHaveBeenCalledTimes(2); + }); + + it("can stop after a failed spawn has closed without emitting exit", async () => { + vi.useFakeTimers(); + const manager = new BackendManager(); + await manager.start(); + const child = mocks.children[0]; + child.emit("error", new Error("spawn rejected")); + child.emit("close", -1, null); + expect(manager.getStatus()).toBe("error"); + expect(manager.getLastError()).toBe("Backend process error: spawn rejected"); + const stopped = vi.fn(); + void manager.stop().then(stopped); + await vi.advanceTimersByTimeAsync(0); + expect(stopped).toHaveBeenCalledOnce(); + expect(child.kill).not.toHaveBeenCalled(); + expect(manager.getLastError()).toBe("Backend process error: spawn rejected"); + }); + + it("finishes shutdown if a failed child closes while stop is waiting", async () => { + vi.useFakeTimers(); + const manager = new BackendManager(); + await manager.start(); + const child = mocks.children[0]; + child.emit("error", new Error("spawn rejected")); + const stopped = vi.fn(); + void manager.stop().then(stopped); + child.emit("close", -1, null); + await vi.advanceTimersByTimeAsync(0); + expect(stopped).toHaveBeenCalledOnce(); + expect(manager.getLastError()).toBe("Backend process error: spawn rejected"); + }); + + it("reports a cache-directory failure as a startup error", async () => { + vi.mocked(fs.mkdirSync).mockImplementationOnce(() => { throw new Error("cache permission denied"); }); + const manager = new BackendManager(); + await expect(manager.start()).rejects.toThrow("cache permission denied"); + expect(manager.getStatus()).toBe("error"); + expect(manager.getLastError()).toBe("cache permission denied"); + expect(mocks.children).toHaveLength(0); + }); +}); diff --git a/src/backend-manager.ts b/src/backend-manager.ts index d174a61..f99ff7d 100644 --- a/src/backend-manager.ts +++ b/src/backend-manager.ts @@ -7,7 +7,7 @@ import { app, BrowserWindow } from "electron"; import WebSocket from "ws"; import { getLogger } from "./logger"; import { getRuntimePaths } from "./runtime-paths"; -import { isJobEvent, isLogEvent, type BackendEvent } from "./types/contracts"; +import { isJobEvent, isJobRecord, isLogEvent, type BackendEvent } from "./types/contracts"; export type BackendStatus = "stopped" | "starting" | "running" | "error"; @@ -52,65 +52,68 @@ export class BackendManager { const logger = getLogger(); this.lastError = null; this.setStatus("starting"); - this.port = await this.findAvailablePort(); - this.token = randomBytes(32).toString("hex"); + try { + this.port = await this.findAvailablePort(); + this.token = randomBytes(32).toString("hex"); - const paths = getRuntimePaths(); - const backendPath = paths.backendPath; - if (!fs.existsSync(backendPath)) { - this.lastError = `Backend directory not found: ${backendPath}`; - this.setStatus("error"); - throw new Error(this.lastError); - } + const paths = getRuntimePaths(); + const backendPath = paths.backendPath; + if (!fs.existsSync(backendPath)) { + throw new Error(`Backend directory not found: ${backendPath}`); + } - const runtimePath = paths.runtimePath; - const python = - process.platform === "win32" - ? path.join(runtimePath, ".venv", "Scripts", "python.exe") - : path.join(runtimePath, ".venv", "bin", "python"); - if (!fs.existsSync(python)) { - this.lastError = `Python environment not found: ${python}`; + const runtimePath = paths.runtimePath; + const python = + process.platform === "win32" + ? path.join(runtimePath, ".venv", "Scripts", "python.exe") + : path.join(runtimePath, ".venv", "bin", "python"); + if (!fs.existsSync(python)) { + throw new Error(`Python environment not found: ${python}`); + } + + fs.mkdirSync(paths.modelCachePath, { recursive: true }); + + const args = [ + "-m", + "uvicorn", + "main:app", + "--host", + this.host, + "--port", + String(this.port), + "--no-server-header", + ]; + logger.info("backend", `Starting authenticated backend on ${this.host}:${this.port}`); + logger.info("backend", `Backend source: ${backendPath}`); + logger.info("backend", `Python environment: ${path.dirname(path.dirname(python))}`); + this.process = spawn(python, args, { + cwd: backendPath, + detached: false, + stdio: "pipe", + windowsHide: true, + env: { + ...process.env, + PYTHONIOENCODING: "utf-8", + PYTHONUTF8: "1", + PYTHONUNBUFFERED: "1", + NO_COLOR: "1", + FORCE_COLOR: "0", + HF_HOME: path.join(paths.modelCachePath, "huggingface"), + HF_HUB_CACHE: path.join(paths.modelCachePath, "huggingface", "hub"), + TORCH_HOME: path.join(paths.modelCachePath, "torch"), + XDG_CACHE_HOME: paths.modelCachePath, + TRAINKIT_BACKEND_TOKEN: this.token, + TRAINKIT_DESKTOP_ORIGIN: DESKTOP_ORIGIN, + TRAINKIT_VERSION: app.getVersion(), + }, + }); + this.setupListeners(); + } catch (error) { + this.lastError = error instanceof Error ? error.message : String(error); this.setStatus("error"); - throw new Error(this.lastError); + logger.error("backend", this.lastError); + throw error; } - - fs.mkdirSync(paths.modelCachePath, { recursive: true }); - - const args = [ - "-m", - "uvicorn", - "main:app", - "--host", - this.host, - "--port", - String(this.port), - "--no-server-header", - ]; - logger.info("backend", `Starting authenticated backend on ${this.host}:${this.port}`); - logger.info("backend", `Backend source: ${backendPath}`); - logger.info("backend", `Python environment: ${path.dirname(path.dirname(python))}`); - this.process = spawn(python, args, { - cwd: backendPath, - detached: false, - stdio: "pipe", - windowsHide: true, - env: { - ...process.env, - PYTHONIOENCODING: "utf-8", - PYTHONUTF8: "1", - PYTHONUNBUFFERED: "1", - NO_COLOR: "1", - FORCE_COLOR: "0", - HF_HOME: path.join(paths.modelCachePath, "huggingface"), - HF_HUB_CACHE: path.join(paths.modelCachePath, "huggingface", "hub"), - TORCH_HOME: path.join(paths.modelCachePath, "torch"), - XDG_CACHE_HOME: paths.modelCachePath, - TRAINKIT_BACKEND_TOKEN: this.token, - TRAINKIT_DESKTOP_ORIGIN: DESKTOP_ORIGIN, - TRAINKIT_VERSION: app.getVersion(), - }, - }); - this.setupListeners(); } async stop(): Promise { @@ -123,7 +126,7 @@ export class BackendManager { } await new Promise((resolve) => { const timeout = setTimeout(() => child.kill("SIGKILL"), 5_000); - child.once("exit", () => { + child.once("close", () => { clearTimeout(timeout); resolve(); }); @@ -182,18 +185,19 @@ export class BackendManager { requestPath: string, method = "GET", body?: unknown, + extraHeaders: Record = {}, ): Promise> { const normalizedMethod = method.toUpperCase(); if (!ALLOWED_METHODS.has(normalizedMethod) || !ALLOWED_PATH.test(requestPath)) { throw new Error("Backend request is outside the allowed API surface"); } if (!this.isRunning()) throw new Error("Backend is not running"); + const headers = new Headers(extraHeaders); + for (const [name, value] of Object.entries(this.authHeaders())) headers.set(name, value); + if (body !== undefined) headers.set("content-type", "application/json"); const response = await fetch(`${this.getServerUrl()}${requestPath}`, { method: normalizedMethod, - headers: { - ...this.authHeaders(), - ...(body === undefined ? {} : { "content-type": "application/json" }), - }, + headers, body: body === undefined ? undefined : JSON.stringify(body), signal: AbortSignal.timeout( requestPath === "/preload" || requestPath === "/upscale-model-info" @@ -275,6 +279,17 @@ export class BackendManager { headers: this.authHeaders(), }); this.eventSocket = socket; + socket.on("open", () => { + void this.request<{ jobs: unknown[] }>("/jobs").then((response) => { + if (this.eventSocket !== socket || !response.ok || !Array.isArray(response.data?.jobs)) return; + for (const job of response.data.jobs) { + if (!isJobRecord(job)) continue; + for (const listener of this.eventListeners) listener({ ...job, type: "job" }); + } + }).catch((error) => { + getLogger().warning("backend", `Could not recover job state: ${String(error)}`); + }); + }); socket.on("message", (payload) => { try { const event: unknown = JSON.parse(payload.toString()); @@ -332,6 +347,9 @@ export class BackendManager { logger.error("backend", `Process error: ${error.message}`); this.setStatus("error"); }); + child.on("close", () => { + if (this.process === child) this.process = null; + }); child.on("exit", (code, signal) => { logger[code === 0 ? "info" : "error"]( "backend", diff --git a/src/components/App.tsx b/src/components/App.tsx index 26dc145..7c5ea86 100644 --- a/src/components/App.tsx +++ b/src/components/App.tsx @@ -1,4 +1,5 @@ -import { lazy, Suspense, useState } from "react"; +import { lazy, Suspense, useEffect, useState } from "react"; +import type { ProviderSettings } from "@/types/providers"; import { Header } from "./trainkit/Header"; import { Navigation, type ServiceTab } from "./trainkit/Navigation"; import { TitleBar } from "./trainkit/TitleBar"; @@ -19,9 +20,19 @@ const TagPanel = lazy(() => const LogsPanel = lazy(() => import("./trainkit/panels/logs-panel").then((module) => ({ default: module.LogsPanel })), ); +const ApiPanel = lazy(() => import("./trainkit/panels/api-panel").then((module) => ({ default: module.ApiPanel }))); export function App({ isBackendOnline }: { isBackendOnline: boolean }) { const [activeTab, setActiveTab] = useState("caption"); + const [providerSettings, setProviderSettings] = useState(null); + const [providerError, setProviderError] = useState(""); + useEffect(() => { + let cancelled = false; + void window.electronAPI.getProviderSettings().then((settings) => { + if (!cancelled) setProviderSettings(settings); + }).catch(() => { if (!cancelled) setProviderError("Could not load API settings. Restart TrainKit and try again."); }); + return () => { cancelled = true; }; + }, []); return (
@@ -29,11 +40,12 @@ export function App({ isBackendOnline }: { isBackendOnline: boolean }) {
}> -
+
setActiveTab("api")} />
+
diff --git a/src/components/trainkit/Navigation.tsx b/src/components/trainkit/Navigation.tsx index 7acaf17..cd7986b 100644 --- a/src/components/trainkit/Navigation.tsx +++ b/src/components/trainkit/Navigation.tsx @@ -1,8 +1,8 @@ import React, { memo } from "react"; import { cn } from "@/lib/utils"; -import { MessageSquareText, Maximize2, FileEdit, Tags, Terminal } from "lucide-react"; +import { MessageSquareText, Maximize2, FileEdit, Tags, Terminal, KeyRound } from "lucide-react"; -export type ServiceTab = "caption" | "upscale" | "rename" | "tag" | "logs"; +export type ServiceTab = "caption" | "upscale" | "rename" | "tag" | "logs" | "api"; interface NavigationProps { activeTab: ServiceTab; @@ -39,6 +39,12 @@ const tabs: { icon: , description: "Image Classification", }, + { + id: "api", + label: "API", + icon: , + description: "Provider Connections", + }, { id: "logs", label: "LOGS", @@ -67,7 +73,7 @@ export const Navigation = memo(function Navigation({ activeTab, onTabChange }: N key={tab.id} onClick={() => onTabChange(tab.id)} className={cn( - "group relative flex items-center gap-3 px-6 py-4 transition-all duration-200", + "group relative flex items-center gap-3 px-4 py-4 transition-all duration-200", "border-b-2 -mb-px", activeTab === tab.id ? "border-primary bg-primary/10 text-foreground" @@ -123,7 +129,7 @@ export const Navigation = memo(function Navigation({ activeTab, onTabChange }: N ))} {/*label */} -
+
Select Operation diff --git a/src/components/trainkit/panels/api-panel.tsx b/src/components/trainkit/panels/api-panel.tsx new file mode 100644 index 0000000..e50e772 --- /dev/null +++ b/src/components/trainkit/panels/api-panel.tsx @@ -0,0 +1,84 @@ +import { useEffect, useState } from "react"; +import { KeyRound, ShieldCheck, PlugZap, Trash2 } from "lucide-react"; +import { CLOUD_PROVIDERS, PROVIDER_DEFAULTS, type CloudProvider, type ProviderSettings } from "@/types/providers"; +import { Button, Input } from "../shared"; + +interface ApiPanelProps { + settings: ProviderSettings | null; + onChange: (settings: ProviderSettings) => void; + error: string; +} + +function ProviderCard({ provider, settings, onChange }: Omit & { provider: CloudProvider }) { + const status = settings?.providers[provider]; + const [key, setKey] = useState(""); + const [model, setModel] = useState(PROVIDER_DEFAULTS[provider].model); + const [remember, setRemember] = useState(false); + const [busy, setBusy] = useState(false); + const [message, setMessage] = useState(""); + const [error, setError] = useState(""); + + useEffect(() => { + if (status) { setModel(status.model); setRemember(status.remembered && settings.encryptionAvailable); } + }, [status?.model, status?.remembered, settings?.encryptionAvailable]); + + const perform = async (action: "save" | "remove" | "test") => { + setBusy(true); setMessage(""); setError(""); + try { + if (action === "test") { + await window.electronAPI.testProviderKey(provider); + setMessage("Key accepted. Check that your account can use the selected vision model."); + } else { + const next = action === "save" + ? await window.electronAPI.setProviderSettings(provider, { key: key.trim(), model: model.trim(), remember }) + : await window.electronAPI.removeProviderKey(provider); + onChange(next); setKey(""); + setMessage(action === "save" ? "Settings saved. This provider is available in Caption." : "Key removed."); + } + } catch (caught) { + setError(caught instanceof Error ? caught.message : "Could not update API settings."); + } finally { setBusy(false); } + }; + + return ( +
+
+
+

{PROVIDER_DEFAULTS[provider].label}

+ + + {status?.configured ? status.remembered ? "REMEMBERED" : "SESSION KEY" : "NO KEY"} + +
+ + +

Use a vision model available to your API account. A ChatGPT or Claude subscription does not include API credits.

+ +
+ + + +
+
+ {message &&

{message}

} + {error &&

{error}

} +
+
+ ); +} + +export function ApiPanel({ settings, onChange, error }: ApiPanelProps) { + return ( +
+
+

API CONNECTIONS

Your keys. Your models. One caption workflow.

+

Local captioning stays on your computer. Cloud captioning sends resized images and your instruction to the selected provider and uses your API credits. Each cloud run requires your consent. Testing a key sends no images.

+ {(error || settings?.warning) &&

{error || settings?.warning}

} +
{CLOUD_PROVIDERS.map((provider) => )}
+
+
+ ); +} diff --git a/src/components/trainkit/panels/caption-panel.tsx b/src/components/trainkit/panels/caption-panel.tsx index ec1036b..b992670 100644 --- a/src/components/trainkit/panels/caption-panel.tsx +++ b/src/components/trainkit/panels/caption-panel.tsx @@ -5,14 +5,17 @@ import { IMAGE_EXTENSIONS } from "@/lib/config"; import { useJobOperation } from "@/lib/use-job-operation"; import { useWebSocket } from "@/lib/websocket-context"; import type { CollisionPolicy } from "@/types/contracts"; +import { CLOUD_PROVIDERS, PROVIDER_DEFAULTS, type CaptionProvider, type ProviderSettings } from "@/types/providers"; import { BatchOptions, Button, Input, JobProgress, ModelStatus, Textarea } from "../shared"; import { ImagePreview } from "../shared/ImagePreview"; type CaptionAdapter = "auto" | "multimodal" | "blip" | "instructblip"; -export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean }) { +export function CaptionPanel({ isBackendOnline, providerSettings, onOpenApi }: { isBackendOnline: boolean; providerSettings: ProviderSettings | null; onOpenApi: () => void }) { const { addFrontendLog } = useWebSocket(); const { job, isActive, start, cancel } = useJobOperation("caption"); + const [provider, setProvider] = useState("local"); + const [cloudConsent, setCloudConsent] = useState(false); const [modelPath, setModelPath] = useState(""); const [adapter, setAdapter] = useState("auto"); const [loadPath, setLoadPath] = useState(""); @@ -27,6 +30,9 @@ export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean }) const [memory, setMemory] = useState(0); const [loadingModel, setLoadingModel] = useState(false); const [error, setError] = useState(""); + const cloudSettings = provider === "local" ? null : providerSettings?.providers[provider]; + const controlsDisabled = isActive || loadingModel; + useEffect(() => { setCloudConsent(false); }, [provider, loadPath, prompt, cloudSettings?.model, cloudSettings?.configured, job?.job_id]); useEffect(() => { let cancelled = false; @@ -66,7 +72,7 @@ export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean }) return () => { cancelled = true; }; - }, [modelPath, adapter, isBackendOnline]); + }, [modelPath, adapter, isBackendOnline, job?.status]); const browseModel = async () => { const selected = await window.electronAPI.openDirectory(); @@ -104,16 +110,20 @@ export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean }) } }; const unload = async () => { - await apiRequest("/unload", { method: "POST" }); - setModelLoaded(false); - setMemory(0); + setLoadingModel(true); setError(""); + try { + await apiRequest("/unload", { method: "POST" }); + setModelLoaded(false); setMemory(0); + } catch (caught) { + setError(caught instanceof Error ? caught.message : String(caught)); + } finally { setLoadingModel(false); } }; const run = async () => { setError(""); try { await start("/caption", { - caption_model_path: modelPath, - adapter, + provider, + ...(provider === "local" ? { caption_model_path: modelPath, adapter } : { cloud_model: cloudSettings?.model, cloud_consent: cloudConsent }), load_path: loadPath, save_path: savePath, prompt, @@ -128,7 +138,7 @@ export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean }) }; const canRun = - isBackendOnline && modelValid && loadPath && savePath && prompt.trim() && !isActive; + isBackendOnline && (provider === "local" ? modelValid : cloudSettings?.configured && (dryRun || cloudConsent)) && loadPath && savePath && prompt.trim() && !controlsDisabled; return (
@@ -137,14 +147,25 @@ export function CaptionPanel({ isBackendOnline }: { isBackendOnline: boolean })

IMAGE CAPTIONING

-

LLaVA/Qwen-style multimodal, BLIP, and InstructBLIP adapters

+

Local vision models or your own Claude and OpenAI API keys

+
+ + +
+ {provider === "local" ? <>
- +
- - + +
+ :
+

{cloudSettings?.model || "Add an API key in settings"}

+

Images are resized to at most 1568 px and metadata is removed before upload. Captions are saved locally. Provider usage charges apply.

+ +
}