Facial Emotion Detection Model with AI Remedies and Frequency Heatmap Visualisation

The increased demand for emotionally intelligent systems has resulted in advances in Facial Emotion Recognition (FER) technologies, which improve human-computer interaction. This work describes a real-time FER system that reliably identifies, tracks, and responds to a user's emotional state using face analysis and generative support mechanisms. The suggested system allows users to log in and upload or take a facial image via a live webcam interface. Using the DeepFace library, the system determines the prevalent emotion in the image. A user validation stage follows, allowing for adjustment in the event of erroneous forecasts. Emotion frequency is observed over time and shown as dynamic heatmaps, providing insights into emotional trends for each user session. To expand functionality beyond categorization, the system includes a generative AI-based module based on Google's Gemini, which offers personalized cure ideas such as motivating prompts, wellness tips, or relaxation tactics. These cures are activated in response to the identified emotional state or at the user's request. This hybrid solution, which is largely run locally for privacy reasons, bridges the gap between emotion identification and practical help by providing a personalized and empathic digital experience via an easy Flask-based web interface.