DishReactions is a free, offline app that records customer reactions per order and shows a live chart of which dish gets the best reviews.
*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle What I Built DishReactions — a local-first app for small restaurants that records customer reaction on every order and shows a live chart of which dish gets the best reviews. Problem: Restaurants guess popularity from sales alone. Sales don't tell you if customers actually loved the dish or just ordered it…
DishReactions is a free, offline app designed to help small restaurants understand which dishes their customers love the most. The app records customer reactions after every order, using a rating system from 1 to 5 stars and an emoji reaction. This data is then displayed in a live chart, highlighting the dish that receives the highest average rating.
The simplicity of the app allows restaurants to quickly identify popular dishes and make informed decisions about what to promote. Instead of relying solely on sales data, which can be misleading, DishReactions provides a clear, data-driven view of customer preferences.
The app is designed to be used locally on a restaurant's device, without the need for a server or internet connection. This ensures privacy for customers' feedback and eliminates the cost of paying for an API. The app also allows for customization, such as changing dishes, language, and branding, giving restaurant owners full control over the tool.
One of the first users of DishReactions was a restaurant owner in Karachi. After using the app for a week, the owner was able to see that their Biryani was actually more popular than their Karahi, despite Karahi selling more. This insight helped the restaurant adjust their menu promotion strategy, leading to increased customer satisfaction and potentially higher profits.
Looking ahead, the developer envisions expanding DishReactions to include a feature that can automatically summarize customer comments, providing even more insights into what customers love or dislike about specific dishes. This could be achieved through local open-source models, further emphasizing the app's commitment to privacy, cost-effectiveness, and offline usability.
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