This repository includes a Django-based API to serve a deep learning model previously trained. A simple front end is provided to give non-power users the possibility to interact via UI.

The model used is an Emotion Classifier trained with audio files of the RAVDESS dataset. More info here: https://github.com/marcogdepinto/Emotion-Classification-Ravdess

The vision of this project is to show that artificial intelligence applications can be shipped to production, consumed by users and have a real impact. This is just a research project, but hope it can inspire someone to build something big :)

The user journey start on the index page at /index/ where it is possible to choose if

1. Upload a new file on the server;
2. Delete a file from the server (WIP);
3. Make a prediction on a file already on the server;

Choosing Upload your audio file the user will be redirected to a modified home page. The user will be asked to pick a file from his computer. The UI will confirm if the operation has been successful.

Choosing Make your prediction the user will be redirected to a modified home page. In this page, it will be possible to see a list of the files already on the server. Following the path media/{filename} it will be also possible to listen to the audio file.

After clicking on Submit, the user will be redirected to a modified home page that will include the prediction made by the Keras model for the file selected.

There is a short demo of the first version on YouTube: https://youtu.be/86HhxTRL3_c . The UI has been updated since then, as now manages all the actions extending the index templates with the action templates. The above pictures are updated with the new workflow.

DB creation

You need PostgreSQL installed on your machine. To facilitate the configurations and if you are not familiar with PostgreSQL commands, I suggest to use a Db manager with UI: in my case, I use pgAdmin.

After the installation of PostgreSQL, use pgAdmin to create a django-emotion-classification database and a App_filemodel table.

The App_filemodel table can be created with the following script:

CREATE DATABASE django-emotion-classification;

CREATE USER marco WITH PASSWORD 'test';

CREATE TABLE App_filemodel (
   id INT PRIMARY KEY NOT NULL,
   file TEXT NOT NULL,
   timestamp DATE NOT NULL,
   path TEXT NOT NULL
);

GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA django-emotion-classification TO marco;

ALTER USER marco CREATEDB; -- This is to run the automatic tests, otherwise you will get an "unable to create database" error when running python manage.py test

Please note the above script is made with the data available in my settings.py, but you can change it according to your needs.

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql_psycopg2',
        'NAME': 'django-emotion-classification',
        'USER': 'marco',
        'PASSWORD': 'test',
        'HOST': 'localhost',
        'PORT': '',
        'OPTIONS': {'sslmode': 'disable'},
    }
}

How to start the server and try it

1. git clone https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API.git
2. $ pip install -r requirements.txt
3. Open a terminal window, cd into the project folder and run python manage.py runserver.

How to run the tests

python manage.py test

Other important topics

The Keras model is stored in the models folder.

gitmedia folder includes the pictures used for this README.

media folder includes the audio files loaded using the server.

If you do not know how Django works, you can skip to the App/views.py file to review the high level logic of the API.

User Stories

What is the plan for the future and what it is currently ongoing: https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/projects/2