About

The gap between accessible and affordable mental health services and the increasing prevalence of mental health issues among youth, especially in educational settings, underscores the urgent need for innovative solutions. Our project is motivated by the desire to bridge this gap and offer early, insightful, and accessible psychological assessments based on the Kinetic-house-tree-person drawings theories through leveraging image classification and generative AI models. 

Our mission is to empower every young person to flourish emotionally and psychologically by harnessing the synergy of cutting-edge AI and dedicated human expertise. We are committed to developing innovative, accessible solutions that enhance mental health interventions, ensuring that children receive the compassionate care they need to thrive in today’s world.

Two layers of models are applied in our application with the first one to perform a multi-label classification task and the second one to provide mental health analysis based on the labels identified from the first layer. We took the ensemble approach to optimize the results from both CNN image classification models and the LLM(Large Language Model) to identify the labels. The second layer model is enabled by a customized GPT-4 assistant that retrieves knowledge from pre-uploaded documents and outputs text in the desired format based on the prompts we crafted.