GN Test Back-End API

This API was made to solve the issue of sentiment analysis for emotion detecion in lyrical corpi.

This API serves up 2 recommendation engines that do the following:

• Song Emotion Endpoint

  • Given a string as an input, this makes use of a multiclass ElasticNet regressor to determine the following emotions for a given song:
    • Anger
    • Fear
    • Joy
    • Sadness
    • Surprise

• Similar Songs

• Given a JSON of emotions that a given song could contain, this uses the K-Nearest Neighbors algorithm to retrieve songs that are nearest in distance to the emotional values submitted. This returns:

  • Artist Name
  • Song title

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

To get started with Using this API please refer to : https://github.com/Novandev/gn_api/wiki

Prerequisites

This package requires Python 3 to be installed

Installing

1. Clone the repo

git clone https://github.com/Novandev/gn_api.git

2. Install dependencies from project root folder (Please utilize virtualenv, or pipenv)

pip install -r requirements.txt

3. Run the project

EXPORT FLASK_RUN=app.py

or

python3 app.py

The server by default will run on port 5000

FOr post examples refer to: https://github.com/Novandev/gn_api/wiki/All-API-Endpoints

Running the tests

Explain how to run the automated tests for this system

Deployment

Add additional notes about how to deploy this on a live system

Built With

• Flask - The Python backend Framework
• SK-Learn - Machine Learning Framework
• Pandas - DataFrame and Analysis Framework
• AWS ElasticSearch - Document storage and retrieval
• IndicoIo - Sentiment Analysis API

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

• ** Novan Adams ** - * Data Scientist and App Developer * - Novandev

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• [https://www.gifnote.com/] - Gifnote