- 📖 About the Project
- 💻 Getting Started
- 👥 Authors
- 🔭 Future Features
- 🤝 Contributing
- ⭐️ Show your support
- 🙏 Acknowledgements
- ❓ FAQ
- 📝 License
Artsmith is a web application that allows users to generate art using a generative AI model. Users can create an account, purchase credits, and use those credits to generate art. The application also allows users to view their generated art and download it. The application is built using Ruby on Rails and integrates a generative AI model to generate art. The application also uses Stripe as a payment gateway to allow users to purchase credits. The generative AI model used in the application is StableDiffusion and both the model and the application are deployed on EC2 instances on AWS.
Client
Server
Database
- Generative AI Model Integration
- Custom User Authentication
- Stripe Payment Gateway
- Responsive Design
- Cloud Deployment Mastery
- Due to the expensive nature of a compute intensive gpu instance, we are unable to provide a live demo at this time considering the fact that this is just a demo project. However, the link to the video demo is available below.
To get a local copy up and running, follow these steps.
To get a local copy up and running follow these simple example steps.
- Have a computer and internet connection
- Have
Ruby
installed on your computer - Have a basic knowledge of
Ruby
andOOP
concept - Have a general understanding of what testing is
- Have
visual-studio code
or any other code editor installed on your computer.
- In order to get a copy of this project you need to download it from https://github.com/OmarMWarraich/ror-gen-ai.git
- Extract the zipped file and open it in your code editor
- Run the command bellow in your terminal to get all required files
bundle install
yarn
- Run the command bellow in your terminal bin/dev
you can run one of the following command in your terminal
- Run testing
rspec spec
- Run linters
> Rubocop --color
> Rubocop -A
👤 Omar Warraich
- GitHub: @Omar Warraich
- Twitter: @omarwarraich1
- LinkedIn: o-va
- Add Image to Image Generation
Contributions, issues, and feature requests are welcome!
Feel free to check the issues page.
Give a ⭐️ if you like this project!
I would like to thank Phil Smy for his guidance and support throughout the project.
This project is MIT licensed.