Project Description: SecondBite transforms the way restaurants handle surplus food by combining computer vision, AI-driven shelf-life prediction, and streamlined distribution. At its core is an image-classifier model that instantly recognizes 13 categories—from grains, proteins, and dairy to fast foods like burgers and pizza—using a simple webcam snapshot. Once the food type is identified, staff enter its production date in a standard YYYY-MM-DD format. SecondBite then consults a lightweight AI agent (via a ChatGPT extension) to predict how many days the item will remain fresh under typical storage conditions. This prediction, together with predefined safety thresholds for each food group, determines whether the item is still “Fresh” or has “Expired.”
If the item is fresh, SecondBite calculates a discounted price—typically 50% off the original—and presents the restaurant with two clear, one-click options: put it on sale at the reduced rate or mark it for donation. A built-in database logs every decision, generating real-time inventory reports and sustainability metrics. Managers can instantly see how many meals were saved, total revenue generated from discounted sales, and the aggregate environmental impact in terms of food waste prevented and corresponding CO₂ emissions avoided.
By automating classification, shelf-life calculation, pricing, and distribution, SecondBite not only helps restaurants recoup lost revenue but also advances key Sustainable Development Goals—Zero Hunger (SDG 2), Responsible Consumption (SDG 12), and Climate Action (SDG 13). It’s a practical, scalable solution that turns today’s leftover meals into tomorrow’s community benefits.
Project Theme: Climate Action and Responsible Consumption
