AI Phone Case Studio

Inspiration

I wanted to make a tool for a very simple but real problem: people often have a personal idea for a phone case, but it is hard to turn that idea into something that actually looks good on a case. A normal image generator can make a beautiful picture, but it does not tell you whether the picture will fit the camera cutout, whether it feels like a product, or whether it still looks good when printed.

So I built AI Phone Case Studio as a small creative workflow for designing personal phone cases, not just generating random images.

What it does

The app lets a user create phone case artwork from either a text idea or an uploaded reference image. In the demo, I show a few different ways to use it: writing a prompt, using an image as inspiration, generating visual options, previewing the result on a phone case, and comparing the digital preview with real phone case examples.

The important part is the preview. The user is not only looking at a square AI image; they can see the design in the context of a phone case, which makes it much easier to judge whether the design is actually usable.

How it works

The frontend is built with React and Vite, and the backend is built with Flask, SQLAlchemy, and SQLite. The backend takes the user's design request, turns it into a product-specific image prompt, calls the OpenAI Images API, stores the generated result, and sends it back to the studio and gallery.

The v2 version also includes user accounts, private design history, upload-based redesign, generation job tracking, and a cleaner backend structure for routes and services. Codex with GPT-5.6 helped me refactor the backend, connect the frontend workflow, debug the local app, prepare the README, and polish the final demo.

Challenges

The main challenge was making the project feel like a design tool instead of a prompt box. For this type of product, the image itself is only one part of the experience. The app also needs phone model context, preview layout, saved history, and a way to compare results.

Another challenge was privacy. Since users may upload personal images or save personal designs, I kept the gallery user-scoped instead of making everything public by default.

What I am proud of

I am proud that the final demo shows a complete path from idea to product preview. It starts with a simple creative request, produces AI-generated designs, shows them inside the phone case interface, and then connects those previews to real case results.

That makes the project feel more concrete: the output is not just an AI artwork, but a possible object someone could actually use.

What I learned

I learned that generative AI products become much more useful when they are wrapped in a focused workflow. The model can create the image, but the product experience decides whether the image is understandable, editable, private, and useful.

I also learned that Codex is most helpful when I use it continuously during the build: not just for writing code, but for debugging, restructuring, checking edge cases, and preparing the final submission.

What's next

Next, I would improve the product preview, add more phone models, support more precise print-safe layout checks, and make the real-case comparison more systematic. I would also like to add a safer sharing/remix feature so users can get inspiration from other designs without exposing private uploads.

Built With

• flask
• generative-ai
• image-generation
• openai
• openai-images-api
• pillow
• product-design
• python
• react
• redis
• rq
• sqlalchemy
• sqlite
• three.js
• typescript
• vite