A PERSONALIZED MOBILE APPLICATION TO GENERATE MUSIC THERAPY USING A LARGE LANGUAGE MODEL AND STORING THE USER’S DATA ON FIREBASE

Mental health support is out of reach to many people, particularly, neurodivergent people, such as those with ASD or ADHD. Conventional music therapy is expensive and needs clinical supervision, although it is effective. In an attempt to fill this gap, I came up with an AI-based music therapy app that provides patient tailored music and therapy suggestions bearing off survey-based information. It takes no wearables, no therapist. The tool is developed using Flutter and the API offered by OpenAI and consists of both an intuitive interface and a smart understanding of the user's feelings. The most important problems were the control of API expenses and the formulation of survey questions; it was overcome with timely optimization and orderly input schemes. The quality and speed of AI answers was tested and it was revealed that the average user gave responses based on the usefulness with 7.35/10 and an average response time of less than 5 seconds. In contrast to the current solutions, my app is free, available, and designed to be used on a daily basis. This project is scalable with real-time support to the users who need flexible and affordable mental health care as it can reduce the cost and increase access to care