This was a project we designed for the Georgia Tech Hacklytics 2025 Hackathon.
Our current stack includes the following models:
| Model | Origin | Link | Purpose and Details |
|---|---|---|---|
| GPT-4o | OpenAI | OpenAI | |
| GPT-4o mini | OpenAI | OpenAI | |
| StableDesign | Github & Replicate API | Comprised of multiple other layered models including | |
| YOLO 8m | Ultralytics | GitHub & Docs | |
Segment Anything Model (SAM) VIT-H |
Meta Research | GitHub | |
During previous stages of our development and/or in the intended designs we had but weren't able to implement yet, we include the following models in addition to the current:
| Model | Origin | Link | Purpose and Details |
|---|---|---|---|
Contrastive Language-Image Pre-Training (CLIP) Commit dcba3cb |
OpenAI | GitHub | |
| OmniGen | VectorSpaceLab | GitHub & Replicate & Hugging Face | |
| StableDiffusion |
We recommend setting up a conda or ve`nv environment to run this.
Within our pip requirements includes various packages for api calls and model downloads (such as for openai, CLIP, and replicate).
pip install -r requirements.txtThis is used for our content matching phase for product matching purposes.
Download Page: https://googlechromelabs.github.io/chrome-for-testing/#stable
Ultralytics's You Only Look Once 8m (Yolo8m) and Meta (Facebook) Research's Segment Anything Model (SAM) models are required for our content extraction phase. They're used for furniture and decor item identification within our generated design plans.
| Model Name | Direct Download Link | Docs and Details |
|---|---|---|
| YOLO8m | Ultralytics Direct Download | Ultralytics YOLO8m Docs |
| VIT-H SAM | Meta's Direct Download | Facebook Research's Github SAM Docs |
streamlit run app.py