Folder Manager AI

An intelligent, AI-powered file and folder organization tool built with Python. Folder Manager AI uses local Large Language Models (via LM Studio) and a modern, sleek interface to analyze, categorize, and organize your files efficiently.

Features

• Interactive Simulation Mode: The tool operates in 3 distinct phases to give you complete control:
  1. Build Draft: Scans your directory and generates an AI-suggested folder structure.
  2. Refine Draft: Allows you to adjust the generated structure using natural language prompts.
  3. Finalize & Execute: Physically moves files into their new folders once you approve the draft.
• Content-Aware Categorization: Reads metadata and content from Text files, PDFs, and Excel spreadsheets to categorize files accurately.
• Vision Capabilities: Optional image analysis feature allows the AI to categorize images based on visual content.
• Smart Deduplication: Uses cryptographic hashing to detect and delete exact duplicate files.
• Undo Actions: Easily undo the last move operation if you change your mind.
• Modern UI: Dark-themed, responsive user interface built with CustomTkinter.

Requirements

Ensure you have Python 3.8+ installed. The project relies on the following libraries:

• openai (For LM Studio API compatibility)
• customtkinter
• pypdf
• openpyxl
• rich
• pydantic
• python-dotenv

Installation

1. Clone this repository:

   git clone https://github.com/eedali/Folder-Manager.git
   cd Folder-Manager

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have LM Studio running locally and serving an API endpoint (usually http://127.0.0.1:1234/v1).

Usage

You can run the application via the CLI or the GUI.

Graphical Interface (Recommended)

Launch the modern interface:

python gui.py

From the GUI, select your target directory, adjust settings (like recursive scanning, deep document reading, or vision), and start the simulation process.

Command Line Interface

To organize a directory using the CLI:

python main.py --target /path/to/your/folder

Other CLI options:

• --dry-run: Simulate the organization without actually moving files.
• --undo: Undo the last organization operation in the specified target directory.

Configuration

Default settings (like the LM Studio API URL, batch sizes, and max file sizes for deep scanning) can be adjusted in the config.py file or directly through the GUI settings panel.

License

This project is open-source. Feel free to modify and use it for your personal organization workflows.