watchdock 0.1.2

A local, self-hosted file monitoring and organization tool using AI

WatchDock

A local, self-hosted, always-on "watchdog" tool that automatically organizes your files using AI.

Features

• 🔍 Monitors folders - Watch one or more folders on your laptop for new or modified files
• 🤖 AI-powered analysis - Uses local or cloud AI to understand file content
• 📁 Auto-organization - Automatically renames, tags, and moves files to the correct archive location
• ⚙️ Configurable - Customize watched folders, AI providers, and organization rules
• 🖥️ Native GUI - Cross-platform desktop application (Windows, macOS, Linux)
• 💻 CLI Mode - Command-line interface for developers
• 🤝 HITL Mode - Human-In-The-Loop mode for approval before organizing files
• 📚 Few-Shot Learning - Provide examples to train the AI on your preferences
• 🔄 Always-on - Runs continuously in the background

Installation

Via pip (Recommended)

pip install watchdock

From Source

1. Clone or download this repository
2. Install dependencies:

pip install -r requirements.txt
pip install -e .

Quick Start

Option 1: Native GUI (Recommended for non-developers)

1. Install WatchDock:

pip install -e .
# Or: pip install watchdock

2. Launch the GUI:

watchdock-gui
# Or: python -m watchdock.gui_main
# Or: watchdock --gui

3. Configure WatchDock through the GUI:

   • Set up watched folders
   • Configure AI provider and API keys
   • Set archive preferences
   • Add few-shot examples (optional)

4. Run WatchDock (from command line):

watchdock
# Or: python main.py

Option 2: Command Line (For Developers)

1. Install WatchDock:

pip install -e .

2. Initialize configuration:

watchdock --init-config
# Or: python main.py --init-config

This creates a default configuration file at ~/.watchdock/config.json

3. Edit the configuration file to:

   • Add your AI API keys (if using cloud AI)
   • Configure watched folders
   • Set archive preferences

4. Run WatchDock:

watchdock
# Or: python main.py

Creating Standalone Executables

To create standalone executables (.app on macOS, .exe on Windows) for distribution:

1. Install PyInstaller:

pip install pyinstaller

2. Create executable:

# For GUI application
pyinstaller --name=WatchDock --windowed --onefile watchdock/gui_main.py

# For CLI application
pyinstaller --name=watchdock --onefile watchdock/main.py

The executables will be in the dist/ folder.

Operation Modes

WatchDock supports two operation modes:

Auto Mode (Default)

Files are automatically analyzed and organized without user intervention. Perfect for fully automated workflows.

HITL Mode (Human-In-The-Loop)

Files are analyzed and proposed actions are queued for your approval. You can:

• Review each proposed action in the GUI
• Approve or reject individual actions
• Approve all pending actions at once
• Use CLI commands to manage pending actions

CLI Commands for HITL Mode:

# List all pending actions
watchdock --list-pending

# Approve a specific action
watchdock --approve <action_id>

# Reject a specific action
watchdock --reject <action_id>

GUI: Use the "Pending Actions" tab to review and manage pending actions.

Configuration

The configuration file (~/.watchdock/config.json by default) contains:

Watched Folders

{
  "watched_folders": [
    {
      "path": "/Users/yourname/Downloads",
      "enabled": true,
      "recursive": false,
      "file_extensions": null
    }
  ]
}

AI Configuration

WatchDock supports multiple AI providers:

• OpenAI - Cloud-based (requires API key)
• Anthropic - Cloud-based (requires API key)
• Ollama - Local AI (no API key needed)

Example for OpenAI:

{
  "ai_config": {
    "provider": "openai",
    "api_key": "your-api-key-here",
    "model": "gpt-4",
    "temperature": 0.3
  }
}

Example for Ollama (local):

{
  "ai_config": {
    "provider": "ollama",
    "model": "llama2",
    "base_url": "http://localhost:11434/v1"
  }
}

Archive Configuration

{
  "archive_config": {
    "base_path": "/Users/yourname/Documents/Archive",
    "create_date_folders": true,
    "create_category_folders": true,
    "move_files": true
  }
}

How It Works

1. Monitoring: WatchDock monitors specified folders using the watchdog library
2. Detection: When a new file appears or is modified, it's detected
3. Analysis: The file is analyzed using AI to understand its content
4. Organization: Based on the analysis, the file is:
   • Categorized (e.g., Documents, Images, Videos)
   • Renamed with a clean, descriptive name
   • Tagged with relevant keywords
   • Moved to an organized archive structure

File Organization Structure

Files are organized in the archive like this:

Archive/
├── 2024-01/
│   ├── Documents/
│   │   ├── project_proposal.pdf
│   │   └── meeting_notes.txt
│   ├── Images/
│   │   └── screenshot_2024.png
│   └── Videos/
│       └── presentation_recording.mp4

Few-Shot Examples

You can provide examples to help the AI understand your organization preferences. This is especially useful for:

• Custom category names
• Specific naming conventions
• Tag preferences
• Domain-specific file types

Examples can be added through the GUI or by editing ~/.watchdock/few_shot_examples.json:

[
  {
    "file_name": "IMG_20240101_123456.jpg",
    "category": "Photos",
    "suggested_name": "2024-01-01_family_photo.jpg",
    "tags": ["family", "photo", "2024"],
    "description": "Family photo from January 2024"
  }
]

Logging

WatchDock logs to both:

• Console output
• watchdock.log file in the current directory

Requirements

• Python 3.8+
• Internet connection (for cloud AI providers) or local AI setup (Ollama)

License

MIT License