waste.0

Landing Page
Dashboard 1
Dashboard 2
Dashboard 3
Databricks 1
Databricks 2
AWS
Mongodb
Clerk

Inspiration

The inspiration for Waste.0 stemmed from the alarming scale of food waste within the food industry, where perfectly good food often goes to waste due to poor inventory management and missed opportunities for donation. With many communities facing food insecurity, we saw an opportunity to bridge the gap between food businesses with surplus stock and donation centers in need of resources. Notably, 30-40% of the entire food supply is wasted. By leveraging AI technology, we aimed to create a solution that not only helps businesses save money but also makes a meaningful impact by reducing food waste and aiding those in need.

What it Does

Waste.0 is a web application that uses AI-driven insights to help food business owners and donation centers minimize food waste. Business owners can upload CSV files detailing their inventory, and the app provides data visualizations showing surplus amounts, upcoming expiration dates, and suggestions for optimal next orders to avoid future overstock. Additionally, Waste.0 offers the option for businesses to donate food approaching its expiration, ensuring it can still be used for good rather than going to waste. Donation centers can access the platform to see when donations are available nearby, facilitating prompt collection and redistribution of food.

Main Features

Predict the Amount of Orders to Minimize Surplus: Leveraging AI, the app forecasts optimal order quantities to prevent overstocking. Spoilage Prediction: The system predicts spoilage timelines, allowing businesses to take proactive measures with their inventory. Donate to Charities: Businesses can easily donate surplus food, connecting them with local charities to help combat food insecurity.

How We Built It

We built Waste.0 using a combination of technologies:

Next.js for the frontend framework to deliver a seamless user experience. Streamlit to create real-time data visualizations within the app. MongoDB for database management to store inventory data. Databricks for AI-powered analytics and predictions, utilizing models like Dynamic Linear Regression and Random Forest Regressor for spoilage predictions and optimized order suggestions. AWS for scalable cloud services, supporting high availability and security. Clerk for user authentication and access control. TypeScript for reliable and scalable code across the stack. GitHub for version control.

Challenges We Ran Into

One of the main challenges we faced was integrating AI models into the inventory prediction system while ensuring accurate and timely feedback for business owners. We were also new to technologies like Streamlit, Databricks, AWS, and Cloudflare, which added a learning curve as we built a robust system. Handling large CSV files and optimizing the backend for quick processing was particularly tricky, requiring us to ensure efficient data processing while maintaining high performance. Additionally, designing a user interface that effectively serves both food business owners and donation centers required careful attention to usability and functionality.

Accomplishments That We're Proud Of

We are particularly proud of successfully integrating AI-powered features that not only help reduce food waste but also allow businesses to make informed decisions about future stock. We’re also proud of the seamless connection between our Next.js frontend and the backend, along with implementing real-time, intuitive visualizations with Streamlit.

What We Learned

This project taught us how to effectively combine several cutting-edge technologies, including Databricks for AI and MongoDB for data management. We learned to handle large datasets efficiently and gained deeper insights into the impact that small optimizations can have on user experience. The project also reinforced the importance of clear communication and team collaboration, especially during a hackathon.

What's Next for Waste.0

Looking ahead, we plan to expand Waste.0 with features like automatic alerts for near-expiration food items and more robust AI suggestions for stock management. We also aim to onboard more donation centers and create a partnership network for faster food redistribution. Additionally, we hope to refine the user experience and continue optimizing our AI models to make Waste.0 the go-to solution for minimizing food waste in the food industry.