Project Overview
This project is a full-stack application that allows users to generate unique images from simple text descriptions (prompts). It demonstrates my ability to work with and deploy powerful generative models. The tool bridges the gap between a complex AI model and a user-friendly interface, showcasing skills in both back-end AI development and front-end design.
Tech Stack & Tools
- Python: For the back-end logic, including the AI model.
- Flask/FastAPI: To create a REST API for serving the model.
- Stable Diffusion/DALL-E: The pre-trained models used for text-to-image generation.
- HTML, CSS, JavaScript: For the front-end user interface.
My Process
Phase 1: API & Model Setup
I started by setting up a back-end server using Flask or FastAPI to create a simple API endpoint. This API was configured to accept a text prompt from the user and forward it to the pre-trained image generation model.
Phase 2: Front-end Development
I then built a clean and simple front-end interface. It included a text input field for the prompt and a button to send the request to the back-end API. A loading state was also implemented to provide feedback to the user while the image was being generated.
Phase 3: Integration & Deployment
The front-end and back-end were connected to create a functional tool. The final step was deploying the application to a cloud service, making it accessible to a wider audience.
Results & Future Work
The final product is a functional image generation tool that demonstrates end-to-end knowledge of building and deploying a generative AI application. It showcases my skills in both front-end and back-end development, a valuable asset for any Generative AI Engineer.
Future Enhancements:
- Add options for fine-tuning generation parameters, such as image size and style.
- Implement a user gallery to showcase previously generated images.
- Explore the use of different generative models to provide users with more options.