<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/b972fc299e6346be9fe1573c7673c477&quot; frameborder=&quot;0&quot; width=&quot;1728&quot; height=&quot;1296&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1296</height><width>1728</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1296</thumbnail_height><thumbnail_width>1728</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/b972fc299e6346be9fe1573c7673c477-60b71267e69eed08.gif</thumbnail_url><duration>294.604</duration><title>Applied AI Music System Demo for Focus 🎵</title><description>Hi, I’m Uriel Gadu, and I’m walking you through my Applied AI Music System in Python. You pick a genre, mood, and energy level, and the tool scores tracks and uses RAG to generate contextual explanations for each choice. I demo Lo-Fi Chill at 0.3, Rock intense at 0.9, and Space Jazz happy at 0.5, showing a reliability guardrail fallback when Space Jazz is unknown. It is production ready, with audit logs and specialized tests, and I ran TestSystem.py with a 100 percent pass rate. No viewer action was requested.</description></oembed>