As students, we often rely on caffeine to power through our daily routines. However, excessive caffeine intake can take a toll on our health. This is where Caf-Fiend steps in to help. Caf-Fiend is a web-based solution designed to assist students in managing their caffeine consumption effectively. By offering insights into caffeine intake history and daily limits, Caf-Fiend empowers you to make informed choices about your health.
Caf-Fiend combines augmented reality (AR), artificial intelligence (AI), and user input to create a comprehensive caffeine management tool. It not only tracks your caffeine intake in real-time but also offers personalized recommendations to ensure you stay within healthy caffeine limits. With options for weekly, monthly, and yearly caffeine logging, you can gain valuable insights into your long-term caffeine habits. Whether you're a coffee lover, a tea enthusiast, or an energy drink aficionado, Caf-Fiend's user-friendly interface helps you navigate your caffeine history effortlessly.
Our journey to create Caf-Fiend began with the design phase in Figma. We then translated our vision into a web application using HTML and CSS. Additionally, we incorporated a database to retrieve caffeine content information for various beverages.
Originally, we aimed to develop an iOS app but encountered challenges while setting up Flutter. This led us to switch our focus to a web-based solution that aligned with our skills. We also faced issues with Git and GitHub, particularly related to merging, pulling, and pushing from repositories.
Despite the initial hurdles, we're delighted with the final product we've created. While we didn't fully implement AI and AR as initially planned, we successfully built a web application with both frontend and backend components. Overcoming our GitHub challenges and mastering Figma were significant accomplishments during this project.
Our experience with Git was a valuable lesson in collaboration and version control. We tackled problems related to merging files, repository sharing, and Git installation. This project taught us effective problem-solving and file-sharing practices.
In the future, we envision expanding Caf-Fiend's capabilities. Implementing a scanner interface using AI APIs to scan drink labels for caffeine information would enhance user experience. We also plan to develop a mobile app, enabling users to access their caffeine intake data conveniently through a widget on their home screens.