Developed by Turunçgil AI, Dekupai is a state-of-the-art desktop application built on ElectronJS that leverages the cutting-edge BiRefNet (Bilateral Reference Network) deep learning model for extremely high-resolution, pixel-perfect background extraction and image matting.

With a beautiful, modern Glassmorphic interface themed with glowing neon citrus accents, Dekupai bridges the gap between complex AI segmentation workflows and elite creator utilities, enabling instant edge-accurate transparent subject isolation, visual cropping, auto-trimming, custom background compositing, and professional asset exporting.

✨ Features

🍊 Turunçgil Brand Accentuation: Stunning glassmorphic dark theme styled with heavy back-filters (backdrop-filter: blur(28px)) and organic glowing neon orange and golden orbs.
🧠 BiRefNet Neural Engine: State-of-the-art segmentation that handles intricate visual details (e.g. hair, thin lines, translucent materials) using a lightweight CPU-optimized local inference pipeline.
⚙️ Integrated Environment Setup: An automated initial setup overlay that checks, builds, and configures an isolated Python virtual environment (.venv) and downloads requirements automatically.
📐 Interactive Cropper Studio: Full integration with Cropper.js enabling precise bounding boxes, standard aspect presets (Square, Story, Cinema, Portrait), 90-degree rotations, and horizontal/vertical mirroring.
⚡ Native Auto-Trim: A high-performance client-side pixel iteration algorithm that scans the active transparent alpha borders and instantly snaps the crop boundaries tight around your subject.
🎨 Creative Background Compositor:
- Transparent Mode: Keeps alpha transparency (perfect for png stickers and web assets).
- Solid Colors: Burns clean flat fills with quick-selection swatches and a custom color wheel picker.
- Premium Gradients: Composes gorgeous, pre-curated linear gradients behind your isolated subject in real-time.
💾 Lossless Export Studio: Composite and save final assets to PNG (transparent), JPEG, or WebP formats with configurable quality parameter sliders using native system dialogs and helpful "Show in Folder" explorer shortcut notifications.

📂 Directory Tree

The project structure is organized cleanly as follows:

Dekupai/
├── backend/
│   ├── requirements.txt   # Python dependency library list (torch, transformers, timm, einops, etc.)
│   ├── setup.py           # Automated virtual environment builder and dependency installer
│   └── process.py         # BiRefNet background segmentation processor (runs local AI inference)
├── node_modules/          # Electron & frontend packages (Ignored in Git)
├── .git/                  # Git tracking files
├── .venv/                 # Python isolated environment (Ignored in Git)
├── .gitignore             # Git ignore instructions
├── package.json           # Node project scripts, metadata, and dependencies
├── package-lock.json      # Node dependency lock file
├── main.js                # Electron main process (IPC handlers, dialogs, python executors)
├── preload.js             # Secure sandbox context bridge mapping IPC communications
├── index.html             # Main HTML5 layout and inline SVG vectors
├── styles.css             # Glassmorphism styling variables, colors, and layout configurations
├── renderer.js            # Renderer logic, drag-drop loops, cropper lifecycles, and export generators
└── README.md              # Project documentation (This file)

🚀 Getting Started

Prerequisites

Ensure you have the following installed on your machine:

Node.js (v18 or above recommended)
Python (v3.10 to v3.12 recommended)
Git

Installation & Run

Move into the project directory:
```
cd dekupai
```
Install Node Dependencies:
```
npm install
```
Boot the Application:
```
npm start
```
Automatic AI Configuration: On your very first launch, the DEKUPAI Initialization Wizard will display. Click Configure Engine (Automatic). The system will build a local virtual environment (.venv), upgrade pip, and install PyTorch CPU version, Hugging Face transformers, and runtime model dependencies.

All progress is streamed in real-time straight to your dashboard progress bar and system terminal window!

Packaging & Building

To package and compile Dekupai into standalone production binaries or installers for Windows and macOS, we use the integrated electron-builder engine:

Build for Windows (NSIS Standalone Installer .exe):
```
npm run build:win
```
Compiles a single-file executable setup installer inside the dist/ directory.
Build for macOS (Standalone DMG Volume .dmg):
```
npm run build:mac
```
Compiles the installer package for Apple Systems inside the dist/ directory. (Note: You can compile macOS builds on Windows using target overrides, but native signed builds are best run on a macOS host).
Compile for Both Platforms Simultaneously:
```
npm run build:all
```

📖 Operational Guide

1. Load an Image

Drag and drop any PNG, JPG/JPEG, or WebP file directly onto the large glowing dragzone.
Or, click Browse Local Files and pick an image from your explorer.

2. Extract Background

Click Extract Subject in the right-side control tower.
The loader box will show real-time progress ([STATUS] Loading model..., [STATUS] Running background removal...).
Note: During the first extraction, the system will download the BiRefNet model weights (~200MB) from Hugging Face. This occurs only once and is cached for instant future runs.

3. Crop & Edit

Toggle between the Drag Mode (navigate) and Crop Selection buttons at the bottom.
Apply aspect constraints, rotate, or flip your image.
Click Auto-Trim to instantly snap the bounding box securely around your transparent subject's non-empty pixels!

4. Style & Export

Select your background fill in Section 3: Transparent, Solid, or Gradient.
In Section 4, pick your export format (PNG, JPEG, WebP) and adjust the quality slider.
Click Export Masterpiece, choose your directory, and save.
Click Show in File Explorer in the success toast to find your exported file instantly!