A personalized mental health chatbot with emotion detection and content suggestion

The availability of AI-driven mental health chatbots presents an expanded opportunity for delivering timely and individual assistance in achieving improved mental health. However, majority of the existing systems depend on text based inputs, which makes chatbots deficient at truly evaluating the emotions of the user. This paper presents a novel method to fill this gap by integrating real-time facial emotion recognition with one of the most advanced language models, LLaMA-2. This paper will use the ”Mental Health Partner” chatbot as a case to illustrate how such technologies can complement each other in order to enhance user experience. Features like real time emotion recognition, secure session management, and data isolation ensures privacy and safety of users’ personal data. The system is also intended to respect modern standards of data protection such as GDPR, and does not infringe upon the user’s privacy but provides a high level of security. By combining verbal inputs with non-verbal emotional cues, the chatbot creates a more human-like interaction. The integration of experiences helps build trust and improve users experience by building a set of interactions that can be seen as supportive and empathetic. The proposed model shows how AI can extend the lights of understanding and assist in mental health support, making it accessible to anyone in need.