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‎.github/workflows/build.yml‎

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‎.github/workflows/ci.yml‎

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‎.github/workflows/release.yml‎

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name: Release
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on:
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push:
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tags:
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- "v*"
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permissions:
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contents: write
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jobs:
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build-windows:
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runs-on: windows-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Setup Node.js
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uses: actions/setup-node@v4
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with:
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node-version: "22"
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cache: "npm"
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- name: Setup Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.12"
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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enable-cache: true
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- name: Install frontend dependencies
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run: npm ci
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- name: Install backend dependencies
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run: |
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cd backend
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uv sync
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shell: pwsh
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- name: Get version
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id: get_version
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run: |
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$version = (Get-Content package.json | ConvertFrom-Json).version
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echo "version=$version" >> $env:GITHUB_OUTPUT
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shell: pwsh
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- name: Lint
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run: npm run lint
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- name: Build and package
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run: npm run make
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- name: Zip installer folder
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run: |
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Compress-Archive -Path "out/make/squirrel.windows/x64/*" -DestinationPath "out/TrainKit-Windows-Installer.zip"
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shell: pwsh
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- name: Create Release
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uses: softprops/action-gh-release@v2
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with:
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tag_name: ${{ github.ref_name }}
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name: TrainKit ${{ github.ref_name }}
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draft: false
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prerelease: false
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files: |
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out/TrainKit-Windows-Installer.zip
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out/make/zip/win32/x64/TrainKit-win32-x64-${{ steps.get_version.outputs.version }}.zip
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body: |
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## TrainKit ${{ github.ref_name }}
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### Downloads
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- **TrainKit-Windows-Installer.zip** - Windows installer (extract, run Setup.exe)
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- **TrainKit-win32-x64-${{ steps.get_version.outputs.version }}.zip** - Portable version (extract, run TrainKit.exe)
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### Requirements
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- [uv](https://docs.astral.sh/uv/) must be installed for first-time setup
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- Python 3.12 will be automatically downloaded during setup

‎.gitignore‎

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eggs/
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.eggs/
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lib/
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!/src/lib/
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lib64/
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parts/
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sdist/

‎README.md‎

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<p align="center">
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<img src="https://i.ibb.co/SXZsB2fk/icon.png" alt="TrainKit Logo">
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</p>
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<h1 align="center">TrainKit</h1>
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<p align="center">
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A dataset preparation toolkit for AI image training
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</p>
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<p align="center">
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<a href="https://github.com/LightCyan01/TrainKit/blob/master/LICENSE">
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<img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License">
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</a>
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<a href="https://github.com/LightCyan01/TrainKit/releases">
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<img src="https://img.shields.io/github/v/release/LightCyan01/TrainKit?include_prereleases" alt="Release">
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</a>
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<img src="https://img.shields.io/badge/platform-Windows-lightgrey" alt="Platform">
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<img src="https://img.shields.io/badge/Electron-39-47848F?logo=electron" alt="Electron">
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<img src="https://img.shields.io/badge/Python-3.12-3776AB?logo=python&logoColor=white" alt="Python">
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<img src="https://img.shields.io/badge/PyTorch-2.7-EE4C2C?logo=pytorch&logoColor=white" alt="PyTorch">
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</p>
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---
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## About
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TrainKit streamlines the tedious process of preparing image datasets for AI model training.
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It provides GPU-accelerated tools for captioning, upscaling, and organizing your images wrapped in a modern desktop interface.
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## Features
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- **🖼️ Image Captioning** — Generate captions using local LLaVA-based models (e.g., JoyCaption) with customizable prompts
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- **⬆️ Image Upscaling** — Upscale images using [Spandrel](https://github.com/chaiNNer-org/spandrel) supported architectures
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- **📁 Batch Renaming** — Rename and organize image files with flexible naming patterns.
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## Requirements
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- **Windows 10/11**
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- **NVIDIA GPU** (8GB+ VRAM recommended for captioning)
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- **Python 3.12**
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- **Node.js 18+**
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## Installation
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### From Release
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Download the latest release from the [Releases](https://github.com/LightCyan01/TrainKit/releases) page.
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### From Source
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```bash
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# Clone the repository
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git clone https://github.com/LightCyan01/TrainKit.git
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cd TrainKit
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# Install frontend dependencies
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npm install
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# Install backend dependencies
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cd backend
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uv sync
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# Start the app
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npm start
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```
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## Usage
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1. **Launch TrainKit** — The backend server starts automatically
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2. **Select a panel** — Choose Caption, Upscale, or Rename from the sidebar
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3. **Configure paths** — Set your input folder, output folder, and model path
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4. **Start processing** — Click the action button and monitor progress in the logs panel
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### Captioning
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TrainKit supports LLaVA-based captioning models.
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### Upscaling
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TrainKit uses [Spandrel](https://github.com/chaiNNer-org/spandrel) for upscaling. See the [full list of supported architectures](https://github.com/chaiNNer-org/spandrel?tab=readme-ov-file#model-architecture-support).
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## Development
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```bash
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# Start in development mode
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npm start
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# Build for production
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npm run make
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```
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## Roadmap
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- [ ] NCNN support
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- [ ] Additional captioning model architectures
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- [ ] Image tagging
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## Contributing
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Contributions are welcome! Please open an issue first to discuss what you would like to change.
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## License
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TrainKit is licensed under the MIT license. See the [LICENSE](LICENSE) file for details.

‎backend/.python-version‎

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3.10
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3.12

‎backend/.setup_complete‎

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2026-01-26T09:17:17.575Z

‎backend/config/image_formats.py‎

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from typing import Dict, Set, List
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SUPPORTED_INPUT_EXTENSIONS: Set[str] = {
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'.png', '.jpg', '.bmp', '.tiff', '.webp'
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'.png', '.jpg', '.jpeg', '.bmp', '.webp'
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}
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SUPPORTED_OUTPUT_FORMATS: Dict[str, Dict[str, str]] = {
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"jpg": {"name": "JPEG", "extension": ".jpg", "pil_format": "JPEG"},
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"png": {"name": "PNG", "extension": ".png", "pil_format": "PNG"},
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"bmp": {"name": "Bitmap", "extension": ".bmp", "pil_format": "BMP"},
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"tiff": {"name": "TIFF", "extension": ".tiff", "pil_format": "TIFF"},
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"webp": {"name": "WebP", "extension": ".webp", "pil_format": "WEBP"}
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}
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def get_supported_format() -> List[Dict[str, str]]:
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return [
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{"value": key, "label":info["name"], "extension": info["extension"]}
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{"value": key, "label": info["name"], "extension": info["extension"]}
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for key, info in SUPPORTED_OUTPUT_FORMATS.items()
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]

‎backend/core/__init__.py‎

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from .websocket import ConnectionManager
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from .dependencies import get_connection_manager, get_service_manager, get_rename_service
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from .exceptions import (
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TrainKitException,
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ModelLoadError,
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ProcessingError,
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InvalidPathError,
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trainkit_exception_handler,
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)
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__all__ = [
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"ConnectionManager",
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"get_connection_manager",
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"get_service_manager",
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"get_rename_service",
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"TrainKitException",
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"ModelLoadError",
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"ProcessingError",
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"InvalidPathError",
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"trainkit_exception_handler",
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]

‎backend/core/dependencies.py‎

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from .websocket import ConnectionManager
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from service.service_manager import ServiceManager
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from service.image_rename import RenameService
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def get_connection_manager() -> ConnectionManager:
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return ConnectionManager.get_instance()
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def get_service_manager() -> ServiceManager:
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return ServiceManager.get_instance()
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def get_rename_service() -> RenameService:
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return RenameService()
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# future ncnn integration point:
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# def get_upscale_backend(backend: str = "pytorch"):
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# if backend == "ncnn":
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# from service.ncnn_upscale import NCNNUpscaleService
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# return NCNNUpscaleService.get_instance()
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# return get_service_manager()

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