video1.0<iframe src="https://www.loom.com/embed/50c24368995847fc91bf70cbe25b74cf" frameborder="0" width="1728" height="1296" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12961728Loomhttps://www.loom.com12961728https://cdn.loom.com/sessions/thumbnails/50c24368995847fc91bf70cbe25b74cf-f2e6753be3341e74.gif301.974In this video, I present my evolved music recommender system, which now utilizes an agentic algorithm to provide accurate song recommendations based on mood and energy levels. I demonstrate the AI's capabilities by answering questions about my day and the type of music I'm craving, leading to personalized suggestions like 'Red Bone' by Childish Gambino and 'Swimming Pools' by Kendrick Lamar. I've also implemented a rock style enhancement system that includes fun facts about the artists to add depth to the recommendations. Through rigorous testing with simulated personas, I achieved an average reliability score of 4.5 out of 5, showcasing the system's consistency. I encourage viewers to explore how this system bridges the gap between human emotions and music recommendations.