Web Application for Emotion-Based Music Player using Streamlit

Abstract

Abstract: Music is a language that doesn’t speak in particular words, it speaks in emotions. Music has certain qualities or properties that impact our feelings. We've numerous music recommendation systems that recommend music based on the previous search history, previous listening history, or ratings provided by users. Our project is all about developing a recommendation system that recommends music based on the user’s current mood. This approach is more effective than the existing ones and eases user’s work of first searching and creating a specific playlist. A user’s emotion or mood can be identified by his/her facial expressions. These expressions can be deduced from the live feed via the system’s camera. Machine Learning come up with various methods and techniques through which human emotions can be detected. Support Vector Machine (SVM) algorithm is used to develop this system. The SVM is used as a classifier to classify different emotional states such as happy, sad, etc. The facial feature displacements in the live feed are used as input to the SVM classifier. Based on the output of the SVM classifier, the user is redirected to a corresponding playlist. Streamlit Framework is used to build this web application. It helps us to create beautiful web apps for data science and machine learning in less time.