Building a Dynamic Emotion-Based Playlist Generator Using Python and Daytona (TuneTailor)

GITHUB LINK: https://github.com/Zedoman/Dynamic_Emotion-Based_Playlist_Generator

_TuneTailor _
Have you ever wanted music to perfectly match your mood? ? Whether you're feeling upbeat, melancholic, or relaxed, music has a unique way of complementing our emotional states. In this post, I’ll walk you through building a Dynamic Emotion-Based Playlist Generator using Python, Daytona, and popular music APIs.

✨ Features of TuneTailor:
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.

? Getting Started with Daytona
To get started, you can use Daytona to quickly create a workspace and set up the development environment. Daytona allows us to easily manage dependencies and replicate the setup across multiple machines.

Install Daytona
Follow the Daytona installation guide to install Daytona on your system.
https://github.com/daytonaio/daytona/

Create the Workspace:
daytona create https://github.com/Zedoman/Dynamic_Emotion-Based_Playlist_Generator
This command will create the workspace and set up the repository files.

Install Dependencies:
Once you have the workspace set up, install the necessary Python dependencies:
pip install -r requirements.txt

Run the Application:
To start the application, you can use the following command:
python app.py

Alternatively, you can use Docker to spin up the application in a containerized environment:
docker-compose up

?️ Tech Stack
Python: For backend development and emotion classification logic.
Flask: A lightweight web framework for serving the playlist generation API.
Machine Learning: scikit-learn for emotion recognition.
Spotify API: Integration using Spotipy to fetch music data based on emotions.
Docker: For containerizing the app and standardizing the development environment.
Daytona: For easy setup and managing the development environment.
? Why Build This?
The Dynamic Emotion-Based Playlist Generator combines machine learning and API integration to create a personalized music experience. By analyzing a user’s emotional input, it curates playlists that match their feelings, whether they’re looking for something relaxing or energetic.

It’s an interesting project for anyone who wants to experiment with emotion recognition, API integrations, and music recommendation systems.

I hope this project inspires you to explore the endless possibilities of emotion-driven systems. What feature would you like to see in a system like this? Let me know in the comments below!

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