Mood Flow

Inspiration

Music is emotional, but music discovery isn't. We wanted to create a smarter way to discover music — one that reacts to how you feel in the moment. MoodFlow bridges that gap with facial recognition and AI-powered playlists.

What it does

MoodFlow detects your current mood through your webcam and generates a Spotify playlist tailored to your emotional state — happy, sad, calm, or energized — in real time.

How we built it

We built a full-stack app using React for the frontend, Node.js + Express for the backend, and Socket.IO for real-time updates. Facial emotion detection powers the mood analysis, and the Spotify Web API delivers music recommendations. OpenAI enhances personalization by suggesting mood-aligned music styles.

Challenges we ran into

Managing real-time emotion tracking without performance issues

Ensuring accurate mood detection across various lighting and expressions

Navigating Spotify’s OAuth and rate limits

Integrating OpenAI while staying responsive to user inputs

Accomplishments that we're proud of

Seamless real-time integration between emotion detection and playlist generation

Clean, modern UI with smooth animations using Framer Motion

Functional, interactive music previews and Spotify links

Full-stack coordination with minimal lag

What we learned

How to work with real-time socket communication

Dealing with cross-origin authentication and API credential security

Fine-tuning UX around something unpredictable like facial expressions

That even small UI flourishes make a big difference in user experience

What's next for Mood Flow

Train a custom emotion model for more accuracy

Add support for wearable inputs like heart rate

Enable collaborative mood-based playlists

Launch a mobile version for on-the-go emotion-to-music syncing

Built With

• html
• javascript
• react