Mood-ify

Inspiration

We aimed to create a product with an unconventional approach to mental health. Instead of a traditional website, we integrated the psychology of colours to dynamically influence users' emotions, helping them regulate and navigate their emotional states. This innovative strategy goes beyond the ordinary, offering a unique and engaging avenue for users to enhance their mental well-being.

What it does

Mood-ify is an application designed to create customized environments based on users' emotional states. Through advanced algorithms and user input, the app detects the user's mood and adjusts its interface, features, and content accordingly.

How we built it

Moodify's front end was created using HTML, CSS, and JavaScript to provide an intuitive and user-friendly interface. The AI component leveraged Python and TensorFlow for robust emotion recognition algorithms, while Flask was used to seamlessly connect the front end with the back end, ensuring a fluid and responsive user experience. The chatbot components are built from Python, langchain and streamlit.

Challenges we ran into

Building Mood-ify presented several challenges. One major obstacle was developing accurate emotion recognition algorithms and training them for a long time. Using the proper tools to build the front end of the app became a challenge as we started off with the React framework, in which we decided to pivot to regular HTML, CSS, and JavaScript due to package issues. Connecting the front-end and the back-end components using Flask was a challenge as we needed video access from our device’s camera to display and show how our trained model detected emotions. This obstacle became a...

Accomplishments that we're proud of

With the help of our team members, we are proud to bring a large language AI model into our project that detects emotions using our camera. Working with AI is new to most of our team, and the outcome of our project has met our expectations by using a large language model. We addressed and managed our challenges and made sure that our project included all possible visions of our application. Even with the difficulty of sharing code and version control, we're proud that we managed to combine our code.

What we learned

Moodify provided us with invaluable insights into machine learning and AI implementation. The project served as a comprehensive learning experience, offering hands-on opportunities to explore and collaborate with diverse frameworks essential for transforming our conceptualized idea into a functional reality. We learned new skills such as training AI models, effectively managing datasets, licensing, and harnessing the power of open-source tools.

What's next for Mood-ify

With Mood-ify currently being a website, we plan on bringing our application to mobile devices for a better user experience to reach our audience. We also plan to add more emotion options from the AI model to ensure that the full dataset is utilized and that emotion capture is more accurate. With users entering their data, we are looking forward to presenting them with a summary based on their previous inputs. Lastly, we are open to potential collaborations with mental health organizations for using our product for broader mental health research and awareness.

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