🌟 Inspiration

As international students, we faced significant challenges in identifying recyclable items due to cultural differences. We believe this struggle extends beyond international students to the broader community. Even with trash cans and logos, identification remains an issue.

💡 What it does

Our innovative web application simplifies the process. Users can snap a picture of an object and the machine learning algorithm will analyze it. The program identifies the object's material, type, and provides recycling information.

🚀 How we built it

We harnessed the power of Machine Learning pre-trained models from Hugging Face, fined-tuned to our needs. Streamlit served as our efficient front-end framework, complemented by our growing knowledge of waste sorting.

🛠 Challenges we faced

Our main challenges included finding a suitable trash sorting model and dealing with dependency issues from various libraries.

🏆 Accomplishments that make us proud

Our pride stems from the fact that we have a functional, deployed web page that's accessible to anyone based on the things that we learned in class which shows how powerful they are.

📚 What we learned

WE LEARNED A LOT! We gained invaluable experience in using cutting-edge libraries to create functional AI models and learned how to build web pages using Python and Streamlit.

🚀 What's next for Waste Wizard

Given more time, we envision integrating our prototype into smart sensor cameras within trash cans across Boston. Our goal is to enhance sustainability for future generations and our precious planet.