DOMINO ML

VISUAL MACHINE LEARNING PIPELINE BUILDER

> LAUNCH LIVE DEMO ↗

A Flask-based visual machine learning pipeline builder that enables users to create ML workflows through an intuitive drag-and-drop interface. Build complete custom ML pipelines visually and generate production-ready Python code automatically. Now featuring a comprehensive LMS (Learning Management System) for data science education.

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VIDEO DEMO

Click the image above to watch the walkthrough

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VISUAL TOUR

1. LANDING EXPERIENCE

2. AUTHENTICATION

3. PROJECT DASHBOARD

4. PIPELINE BUILDER CANVAS

5. VERSION CONTROL

6. IMPORT / EXPORT

7. TEMPLATE GALLERY

8. CLASSROOM MANAGEMENT

9. ASSIGNMENTS & LABS

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FEATURES

CORE

• DRAG & DROP INTERFACE: Visual pipeline builder with vanilla JavaScript.
• COMPONENT LIBRARY: Extensive collection of ML components.
• CODE EXPORT: Generate Python code from visual pipelines.
• TEMPLATE GALLERY: Pre-built ML pipeline templates.
• REAL-TIME VALIDATION: Model structure validation.

EDUCATIVE LMS (NEW)

• CLASSROOM MANAGEMENT: Create digital classrooms, manage students, and distribute invite codes.
• INTERACTIVE LABS: Attach actual ML Pipelines to assignments. Students can "fork" templates and experiment.
• MARKDOWN & LATEX: Rich assignment instructions with full mathematical typesetting support ($E=mc^2$).
• GRADING SYSTEM: Review student pipelines, provide feedback, and assign grades directly.

SCIENTIFIC ZEN UI

• GLASSMORPHISM: Modern, sleek interface designed for focus.
• THEMED MODES: Switch between "Lab Paper" (Light) and "Deep Space" (Dark) themes.
• RESPONSIVE DESIGN: Optimized experiences for desktop and mobile.

ADDITIONAL

• USER AUTHENTICATION: Secure login/signup.
• CLOUD STORAGE: Save models in SQLite database.
• UNDO/REDO SYSTEM: Smart state management for canvas operations.
• PIPELINE VERSIONING: Full version tracking and comparison.

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QUICK START

PREREQUISITES

• Python 3.8+
• pip
• Modern Web Browser

INSTALLATION

1. CLONE

   git clone https://github.com/1mystic/dominoML-flask.git
   cd dominoML-flask

2. VIRTUAL ENV

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. DEPENDENCIES

   pip install -r requirements.txt

4. CONFIGURE

   cp .env.example .env
   # Edit .env with your settings

5. INIT DB

   python
   >>> from app import create_app, db
   >>> app = create_app()
   >>> with app.app_context():
   ...     db.create_all()
   >>> exit()

6. RUN

   python run.py

   Visit http://localhost:5000

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PROJECT STRUCTURE

Domino_ML/
├── app/                  # Flask application
│   ├── data/            # Components & Templates
│   ├── routes/          # Blueprints (Auth, Main, LMS)
│   ├── static/          # CSS/JS Assets
│   └── templates/       # Jinja2 Views
├── migrations/          # DB Migrations
├── config.py            # App Config
└── run.py               # Entry Point

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TECHNOLOGY STACK

BACKEND

• Flask 3.0
• SQLAlchemy (SQLite)
• Flask-Login
• Flask-WTF

FRONTEND

• Jinja2 Templates
• Custom CSS (Scientific Zen Theme)
• Vanilla JavaScript
• Lucide Icons
• Marked.js + MathJax (Educational Content)

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LICENSE

Not yet licensed.

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BUILT WITH 🖤 FOR THE ML COMMUNITY by @1mystic