FishBook

Misinformation in fish keeping, often spread by big pet stores for profit, leads to poor tank management. We built a web app to simplify stocking, providing a smarter solution for hobbyists.

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Inspiration

Our group has a passion for keeping fish tanks and aquariums. In high school, Layla, worked at a local fish store. During that time, she learned a lot about the proper way to stock freshwater tanks and saw firsthand how common misconceptions can lead people to make mistakes, which will ultimately end in stressed or possibly deceased fish. This hobby can be difficult to get into because there are so many rules and factors to consider when setting up and maintaining a healthy tank. Unfortunately, misinformation is everywhere, often driven by large pet stores and corporations that profit when fish die and need to be replaced frequently.

The current software solutions designed to help new and experienced aquarium owners are lacking. Many of them have poor user interfaces, making them difficult to navigate and use. For our hackathon project, my team and I wanted to take on this problem. We developed an application that simplifies the process of managing aquarium stocking by improving the user experience and addressing the underlying complexities with a smarter algorithm. Our goal is to provide a tool that helps aquarium enthusiasts, from beginners to experts, avoid the pitfalls of misinformation and enjoy a healthier, more successful aquarium hobby.

What it does

FishBook is a comprehensive web application designed for aquarium enthusiasts, providing an intuitive platform to ensure compatible fish species for their tanks. It helps users make informed decisions through a fish compatibility checker, personalized equipment recommendations, and a detailed fish directory. With a built-in chatbot, users can get instant answers to their fish-related questions at any time, making fishkeeping easier for beginners and experts alike.

How we built it

We developed FishBook using a modern tech stack, including React for the frontend and Spring Boot for the backend. The database is managed with MySQL, and the app is deployed using AWS and Microsoft Azure. LangChain and OpenAI API were integrated to power the chatbot, providing intelligent, data-driven responses to user queries. We also used Lombok to streamline the development process and improve backend code readability.

Challenges we ran into

One of the biggest challenges was ensuring the accuracy of the fish compatibility algorithm. Balancing the numerous factors such as tank size, water conditions, and temperament required extensive testing. We also faced challenges in integrating the AI-powered chatbot smoothly with the fish database to ensure it provided accurate, helpful responses.

What's next for FishBook

Due to time constraints and the number of features we aimed to develop, we had to simplify our stocking algorithm. Managing all the possible tank and stocking combinations is a complex computational problem, and there is currently no perfect or near-perfect solution. With more time and resources, we would like to further research and develop a more robust algorithm, possibly exploring the use of machine learning to improve accuracy and efficiency.

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