Introduction to BlocklyML
BlocklyML serves as an innovative No Code platform designed specifically to facilitate the learning and application of Python and Machine Learning for users. This intuitive tool aims to simplify the intricate process of executing standard machine learning tasks. By leveraging BlocklyML, individuals new to Machine Learning or Python can easily take their first steps. The platform is a derivative of Blockly, specifically enhanced and adapted to meet the needs of machine learning and data analytics applications.
Users can explore its capabilities further by accessing the sampleLayouts folder and experimenting with a hands-on demonstration of the tool.
For more detailed guidance, users can refer to the UserGuide.md.
Key Features
Installing BlocklyML
To get started with BlocklyML, users need to clone the repository from GitHub:
git clone https://github.com/chekoduadarsh/BlocklyML
Once cloned, the application can be set up using the Flask method. Users need to install the required dependencies via:
pip install -r requirements.txt
After installing, the application can be launched using:
python app.py
An alternative way to run the application is through Docker. After building the Docker image, the application can be started and accessed via a web browser at http://localhost:5000.
User Interface Features
Shortcuts
In the BlocklyML interface, several shortcut buttons are available in the top right corner to enhance usability. These buttons allow users to:
- Download XML Layout – Save their current setup.
- Upload XML layout – Load a previously saved setup.
- Copy Code – Copy code for external use.
- Launch Google Colab – Open the project on Google Colab.
- Delete – Remove unwanted elements.
- Run (Note: This feature is not supported yet).
Dataframe Viewer
BlocklyML supports complete HTML visualization of dataframes, accessible via a "view" option in the navigation bar, allowing users to efficiently examine their data.
Download Code
Users have the option to download the code in both .py and .ipynb formats from the navigation bar, facilitating seamless transitions between different work environments.
Contribution and Support
BlocklyML encourages community involvement and welcomes any form of contribution. Users are encouraged to report issues or contribute new features via pull requests. For those interested in contributing, guidance is available in the CONTRIBUTING.md.
License and Acknowledgments
This project is distributed under the Apache License, Version 2.0. The project has received considerable support from numerous contributors, making its continued success possible.
For those who wish to show their appreciation, there's an option to "Buy Me A Coffee" for the creator.
BlocklyML is designed to empower both novices and experienced users alike, streamlining the process of applying machine learning to real-world data without needing extensive programming knowledge.