MusMoo

A Python application that detects user emotions via AI and recommends personalized songs based on emotional state.

Overview

When launched, MusMoo engages users with thoughtful questions to gauge their emotional state. Leveraging an advanced emotion detection model, it then curates mood-based music recommendations and fetches them using the 'ytmusicapi' library. The suggestions are saved in TXT files within the /recommendations folder, ready to be used for creating YouTube Music playlists.

Features

• Personalized emotional state detection
• Mood-based song recommendations
• Songs output in .txt with one song URL per line

Getting Started

To use MusMoo, follow these simple steps:

1. Clone the repository to your local machine.
2. Install dependencies using pip install -r requirements.txt OR pip install -r requirements-gpu.txt (recommended if you have a GPU).
3. Run the application and answer the prompted questions to receive personalized music recommendations.

Future Roadmap

As MusMoo progresses through development, the following enhancements are planned:

• Fine-tuning recommendation engine for enhanced accuracy
• Better and less questions for emotion detection
• Direct playlist creation from the application
• Robust error handling for improved user experience
• Implementation of an intuitive graphical user interface (GUI)

Authors

• @devparanjay

Citations

• Hsu, Lun-Kai & Tseng, Wen-Sheng & Kang, Li-Wei & Wang, Yu-Chiang Frank. (2013). Seeing through the expression: Bridging the gap between expression and emotion recognition. Proceedings - IEEE International Conference on Multimedia and Expo. 1-6. 10.1109/ICME.2013.6607638.
  https://www.researchgate.net/publication/261160494_Seeing_through_the_expression_Bridging_the_gap_between_expression_and_emotion_recognition
• Hepach, R., Kliemann, D., Grüneisen, S., Heekeren, H. R., & Dziobek, I. (2011). Conceptualizing emotions along the dimensions of valence, arousal, and communicative frequency - implications for social-cognitive tests and training tools. Frontiers in psychology, 2, 266.
  https://doi.org/10.3389/fpsyg.2011.00266, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196197/

Acknowledgments

• ytmusicapi
• Sam Lowe

License

This project is licensed under the [Aferro GPL 3.0] License - see the LICENSE file for more details. In short, any usage and derivation of this application and its code comes with the following limitations -

• License and copyright notice
• State changes
• Disclose source
• Network use is distribution
• Same license