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MOODBOOST

Citation

Abstract

An AI-Powered App for the Future. An App For Mental Health — Revolutionizing How we approach Mental Wellness. What is MoodBoost? MoodBoost is an AI-Powered application designed to revolutionize the mental health space by providing personalized, real-time interventions based on user emotional needs. In a time of mental health crises, which affect over 970 million people across the globe (WHO, 2022) with problems like stress, anxiety, and depression, current solutions provide static content and generalized recommendations that are often not enough. MoodBoost fills this gap by weaving in Gemini’s state-of-the-art Natural Language Processing (NLP)—which analyzes user inputs in real time—to allow the app to create contextually relevant affirmations, actionable recommendations, and therapeutic content. The app’s central innovation is its ability to dynamically adjust to users’ emotional states. For instance, a user who logs a ”stressed” mood may receive a curated meditation guide, while one who feels ”motivated” could be offered suggestions for goal-setting activities. This level of personalization is enabled by a sophisticated technical architecture like Frontend Built with Flutter for crossplatform performance on Android and iOS, For Backend, Utilizes Django REST Framework to manage API logic, handle user authentication, and validate data. For Database, The Firebase is used for structured and scalable storage of user profiles, mood logs, and AI generated content. For AI Integration, We are using Gemini, it uses NLP models to process mood data for insights and affirmations — replies are relevant and empathetic. The app’s approach integrates the latest advancements in AI with an emphasis on user-centered design, positioning it as a vital component in the landscape of contemporary mental health care, providing a forward-looking, data-guided experience with the potential to supplant traditional offerings.

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages 64-65).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.

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Type

Thesis