Full Stack Helper AI

Automate CRUD code generation, CSS conversion, and UI customization with AI, boosting developer productivity.

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Inspiration

The inspiration behind Full Stack Helper AI comes from the growing need for developers to streamline and accelerate the development process, especially in full-stack projects. The complexity of building CRUD operations, managing databases, handling frontend frameworks, and ensuring consistent UI/UX across various platforms can be time-consuming. With the power of AI, Full Stack Helper AI aims to automate these repetitive tasks, enabling developers to focus on building impactful features rather than spending time on boilerplate code.

What it does

Full Stack Helper AI is an AI-powered tool that assists developers in generating various components of a web application. It simplifies tasks such as:

  1. CRUD Code Generation: Automatically generates CRUD operations for different frameworks like Laravel, Node.js, and NestJS based on user input.
  2. CSS Framework Conversion: Converts custom CSS into different CSS frameworks such as Tailwind, Bootstrap, and Materialize.
  3. Custom UI Generation: Allows developers to choose their desired frontend frameworks (React, Next.js, Vue.js) and apply custom fonts.
  4. Column Definition for Databases: Helps developers define database columns with various attributes (e.g., required, unique, relations), making it easy to generate database models and tables.

How we built it

The project was built using the following technologies and steps:

  1. Backend: The backend is powered by Python, where the AI logic is handled using Llama2 and Llama3 models, accessed via the langchain library. The backend generates the necessary code or outputs based on user input, such as generating CRUD code or converting CSS.
  2. Frontend: We used Streamlit, a Python-based UI framework, to create an interactive web application where users can input data and see AI-generated results in real time.
  3. AI Integration: Langchain is used to orchestrate the interactions with the AI models, allowing for more flexible and complex conversations with the model, including handling structured prompts for generating code or CSS.
  4. Docker: The entire application is containerized using Docker to ensure that it runs consistently across different environments. A Dockerfile builds the prompt_engineering service, which interacts with the AI models.

Challenges we ran into

  1. Model Tuning: Ensuring the AI models (Llama2 and Llama3) understood and responded correctly to structured, domain-specific prompts for generating CRUD operations and CSS conversion was challenging.
  2. UI/UX Design: Designing an intuitive and responsive UI in Streamlit to handle multiple input fields, checkboxes, and dynamic form elements (for things like custom fonts or relationships) required thoughtful layout planning.
  3. Framework Compatibility: Ensuring that the generated CRUD operations and CSS conversions were compatible with the various frontend and backend frameworks added complexity, particularly with different syntax and architecture.
  4. Real-Time Feedback: Implementing a smooth experience for providing real-time feedback from the AI models to the user (e.g., generating code snippets) required optimizing API calls and response times.

Accomplishments that we're proud of

  1. AI Integration: Successfully integrating AI models like Llama2 and Llama3 into a full-stack tool that can generate code and convert CSS frameworks in real time.
  2. Dynamic UI: Creating a dynamic UI using Streamlit that allows users to easily define tables, columns, and frontend frameworks, with seamless user experience across different input types.
  3. Streamlining Development: The tool significantly speeds up the development process for backend code generation (CRUD operations) and frontend styling (CSS framework conversion).
  4. Customizable Features: The ability to add custom requests (e.g., generating slugs from titles, custom CSS properties) and other features makes the project highly adaptable to various use cases.

What we learned

  1. AI-Driven Development: We learned a lot about how AI models like Llama2 and Llama3 can be utilized to automate development tasks and improve productivity.
  2. Streamlit: We gained experience in building responsive and user-friendly interfaces using Streamlit, which is powerful for rapid prototyping and building data apps.
  3. Model Fine-Tuning: We discovered the importance of refining prompts and understanding the capabilities of large language models to get the desired output.
  4. Docker for Consistency: Using Docker for containerization ensured that the app runs seamlessly across different environments, which is essential for deploying and scaling applications.

What's next for Full Stack Helper AI

  1. Enhanced Code Generation: We plan to enhance the code generation feature, adding support for more advanced use cases such as API generation, authentication setup, and more complex database relationships.
  2. Additional Framework Support: We'll expand the list of supported frameworks for both frontend and backend, adding popular frameworks like Angular, Vue, Django, Flask, etc.
  3. Deployment and Hosting: We aim to deploy the application on cloud platforms (e.g., AWS, Azure) and make it accessible for others to use via a public web interface.
  4. User Customization: Future versions will allow users to define more custom rules and preferences (e.g., specific code styling conventions) to generate even more personalized results.
  5. Integration with Version Control: We may add integration with GitHub or GitLab to allow users to push generated code directly to their repositories.

Built With

  • ai
  • docker
  • langchain
  • llama2
  • llama3
  • python
  • sambanova
  • sambanova-ai
  • streamlit
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