Trashify

Inspiration

Our inspiration came from observing the confusion and inefficiency in current recycling practices. We wanted to create a tool that simplifies and encourages responsible waste disposal, making a tangible impact on the environment.

What it does

Trashify uses AI to revolutionize waste sorting. Users take a photo of their waste, and our app, using TensorFlow-powered image recognition, identifies the material - whether it's plastic, paper, or aluminum - and advises on the correct recycling bin.

How we built it

We built Trashify using a combination of Next.js for the front-end to capture images and a Flask back-end for handling image uploads. The core functionality leverages TensorFlow for image classification, identifying different types of waste materials.

Challenges we ran into

We faced challenges in integrating TensorFlow with our Flask back-end and ensuring accurate image recognition. Fine-tuning the model for diverse waste types and handling real-time image processing were significant hurdles.

Accomplishments that we're proud of

We're proud of creating a functional, user-friendly app that aids in correct waste disposal. Overcoming technical challenges and building a seamless interface between the front-end capture and AI-powered back-end analysis was a major accomplishment.

What we learned

Throughout this project, we learned about advanced TensorFlow applications, the intricacies of full-stack development, and the importance of user experience in environmental tech solutions.

What's next for Trashify

Looking forward, we aim to enhance Trashify's accuracy and broaden the range of recognizable materials. We're also exploring partnerships with waste management organizations and educational initiatives to further our environmental impact.

Built With

• flask
• next.js
• python
• tensorflow
• typescript