Zedoman/Dynamic_Emotion-Based_Playlist_Generator

Avradeep Nayak

Completed work

AI Developer

Web Developer

Docker

Flask

Python

Sample Python/Flask Application

A Python-based application that dynamically generates playlists based on the user's emotional state and inputs, using APIs and machine learning techniques.

🚀 Getting Started

Open Using Daytona Daytona My_Project

To quickly start working on this project, use Daytona to set up a standardized development environment.

Install Daytona: Follow the Daytona installation guide to install Daytona on your machine: Daytona Installation Guide

Create the Workspace:

daytona create https://github.com/Zedoman/Dynamic_Emotion-Based_Playlist_Generator

This command will set up the workspace with all necessary files and configurations.

Set up Dependencies:

Navigate to the project directory:

cd Dynamic_Emotion-Based_Playlist_Generator

Start the Application::

To start the application, use the following command:

python app.py

✨ Features

Personalized Playlist Generation: Users can input their favorite artists, preferred genre, and language to generate a playlist tailored to their tastes.

Emotion-Based Playlist: Based on the user's inputs, TuneTailor can suggest songs that align with their emotional preferences, ensuring the playlist matches their mood.

Customizable Playlist Size: Users can specify how many songs they want in their playlist, making it easy to create short or long playlists (up to 60 songs).

Genre and Language Preferences: Users can narrow down their playlist to specific genres (e.g., Hip-Hop, Jazz) and languages (e.g., English, Spanish), making the playlist more suited to their cultural or emotional context.

User-Centered Customization: The app is built around users’ preferences, offering them the ability to fine-tune their playlist with precise details like the number of songs and specific artist genres.

Tech Stack

Emotion Detection: Python, OpenCV, DeepFace

Backend: Node.js with Express

Music Data: Spotify API for track metadata and playback

Benchmarking: Daytona

Frontend: React with Tailwind CSS

Backend Framework: Python (Flask or similar framework)

Machine Learning: scikit-learn, TensorFlow/Keras (possibly)

External APIs: Spotify, Last.fm, or similar music services (via Spotipy)

Development Environment: Daytona (for setting up a standardized environment)

Containerization: Docker

Data Handling: pandas, numpy

Video

Screen.Recording.2024-12-11.at.23.47.16.mov

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Completed work

Posted Mar 20, 2025

A Python-based application that dynamically generates playlists based on the user's emotional state and inputs. Developed for the Daytona Organization.

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Timeline

Nov 20, 2024 - Dec 5, 2024