File Organizer Desktop

This project is a cross-platform file organizer application built using React and Tauri. It allows users to organize their files efficiently with the help of AI classification.

Project Structure

file-organizer-desktop
├── src
│   ├── App.tsx          # Main React component managing the application state and UI
│   ├── main.tsx         # Entry point for the React application
│   ├── components       # Directory for reusable components
│   │   └── index.ts     # Exports various components for modularity
│   └── types            # Directory for TypeScript types and interfaces
│       └── index.ts     # Type definitions for the application
├── index.html           # Main HTML file for the application
├── package.json         # npm configuration file with dependencies and scripts
├── tsconfig.json        # TypeScript configuration file
├── vite.config.ts       # Vite configuration for development and production builds
├── src-tauri            # Directory for Tauri backend
│   ├── src
│   │   └── main.rs      # Main Rust file for Tauri application
│   ├── Cargo.toml       # Rust project configuration file
│   └── tauri.conf.json   # Tauri application configuration
└── README.md            # Documentation for the project

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd file-organizer-desktop
   

2. Install dependencies:

   npm install
   

3. Run the application:

   npm run dev
   

Building the Application

Prerequisites for Building

Before building the Tauri application, ensure you have the following installed:

• Node.js (v16 or higher)

• Rust and Cargo (latest stable version)

  To install Rust and Cargo:

  1. Visit https://rustup.rs/ or run the appropriate command for your platform:

     Windows:

     # Download and run rustup-init.exe from https://rustup.rs/
     # Or use winget:
     winget install Rustlang.Rustup
     

     macOS/Linux:

     curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
     

  2. Follow the on-screen instructions (typically just press Enter to accept defaults)

  3. IMPORTANT: After installation, you must restart your terminal (close and reopen) for the changes to take effect.

     Alternatively, on Windows PowerShell, you can manually add Cargo to your current session:

     $env:PATH += ";$env:USERPROFILE\.cargo\bin"
     

     On macOS/Linux, run:

     source $HOME/.cargo/env  # macOS/Linux
     

  4. Verify installation:

     rustc --version
     cargo --version
     

• Platform-specific dependencies:

  • macOS: Xcode Command Line Tools
  • Linux: See Tauri prerequisites for Linux
  • Windows: Microsoft Visual Studio C++ Build Tools

Development Build

To run the application in development mode with hot-reloading:

npm run tauri dev

Production Build

To create an optimized production build:

npm run tauri build

This will create platform-specific installers in the src-tauri/target/release/bundle/ directory:

• macOS: .app bundle and .dmg installer in bundle/macos/
• Windows: .msi installer in bundle/msi/ and .exe in bundle/nsis/
• Linux: .deb, .AppImage, or other formats in bundle/deb/, bundle/appimage/, etc.

Build Output Locations

After building, you can find the compiled application at:

• Development: src-tauri/target/debug/
• Production: src-tauri/target/release/
• Installers: src-tauri/target/release/bundle/

Customizing the Build

You can customize the build configuration by editing:

• src-tauri/tauri.conf.json - Tauri app configuration (app name, version, window settings, etc.)
• src-tauri/Cargo.toml - Rust dependencies and metadata
• vite.config.ts - Frontend build configuration

Code Signing (macOS/Windows)

For distribution, you'll need to sign your application:

• macOS: Configure your Apple Developer certificate in tauri.conf.json under tauri.bundle.macOS
• Windows: Configure code signing certificate in tauri.conf.json under tauri.bundle.windows

Refer to the Tauri documentation for detailed signing instructions.

Usage

Basic Operation

• Use the application to select a directory and organize files based on AI classification.
• Review and edit proposed file names and categories before applying changes.

LLM Provider Options

The application supports multiple AI providers for file classification:

• Managed Local LLM: Run a local LLM server automatically managed by the app. No manual setup required - select a model and the server will be downloaded and started automatically.
• LM Studio: Local AI server. Start LM Studio and load a model first (default: http://localhost:1234)
• Ollama: Local AI server. Install and run Ollama with a model like llama2 or mistral (default: http://localhost:11434)
• OpenAI: Cloud service requiring API key from platform.openai.com
• Anthropic (Claude): Cloud service requiring API key from console.anthropic.com
• Groq: Fast cloud inference requiring API key from console.groq.com
• Google Gemini: Google AI service requiring API key from ai.google.dev
• Custom: Any OpenAI-compatible API endpoint

Scan Control Features

The application now supports advanced scan control:

Pause/Resume Scan

• While scanning is in progress, click the "Pause" button to temporarily halt the process
• The scan state is preserved, showing progress and partial results
• Click "Resume" to continue from where you left off
• Progress bar updates to show paused state

Stop Scan

• Click "Stop" during scanning or while paused to permanently halt the process
• When stopped, the application will:
  • Display a preview of all files scanned up to that point
  • Automatically send current results to LM Studio for category optimization
  • Allow you to review and approve the partial results
  • Show "Stopped at user request" in the progress indicator

New Scan

• After completing or stopping a scan, use "New Scan" to start fresh
• This resets all scan state and clears previous results

LM Studio Integration

• Configure your LM Studio base URL (e.g., http://localhost:1234/v1)
• Select your preferred model for file classification
• The app automatically sends partial results to LM Studio for optimization when scans are stopped
• Category optimization suggestions are applied automatically and logged in the status area

Real-time Feedback

• Status area shows detailed progress of file reading and classification
• Progress bar displays current file count and percentage completed
• Scan state indicators (Scanning, Paused, Stopped, Completed) provide clear status

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.