https://github.com/marcogdepinto/deep-learning-model-deploy-with-django

Serving a keras model (neural networks) in a website with the python Django-REST framework.
https://github.com/marcogdepinto/deep-learning-model-deploy-with-django

api api-rest artificial-intelligence artificial-neural-networks deep-learning deep-neural-networks django django-application django-framework django-rest-framework keras keras-model keras-models keras-neural-networks keras-tensorflow prediction python python-3 python3 tensorflow

Last synced: 3 months ago
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Serving a keras model (neural networks) in a website with the python Django-REST framework.

• Host: GitHub
• URL: https://github.com/marcogdepinto/deep-learning-model-deploy-with-django
• Owner: marcogdepinto
• License: gpl-3.0
• Created: 2019-08-09T16:45:16.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T23:38:11.000Z (almost 2 years ago)
• Last Synced: 2024-10-11T07:04:39.862Z (3 months ago)
• Topics: api, api-rest, artificial-intelligence, artificial-neural-networks, deep-learning, deep-neural-networks, django, django-application, django-framework, django-rest-framework, keras, keras-model, keras-models, keras-neural-networks, keras-tensorflow, prediction, python, python-3, python3, tensorflow
• Language: Python
• Homepage:
• Size: 29.9 MB
• Stars: 95
• Watchers: 5
• Forks: 38
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Deep learning model deploy with Django

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)

**Executive Summary**

This project is a Django-REST API that offers the consumption of a deep learning model using a simple front end. The model adopted in this work is the previous version of an Emotion Classifier trained with audio files of the [RAVDESS dataset](https://zenodo.org/record/1188976#.XvO2WZMza3c). To know more, see [this repository](https://github.com/marcogdepinto/emotion-classification-from-audio-files). 

# How does this work?

**User Journey**

The user journey starts 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;

![Picture1](https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/blob/master/gitmedia/index.png)

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. 

![Picture2](https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/blob/master/gitmedia/fileuploadv2.png)

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.

![Picture3](https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/blob/master/gitmedia/fileselectionv2.png)

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.

![Picture4](https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/blob/master/gitmedia/predict.png)

**See the App in action!**

The current version of the application is deployed on Heroku: https://emotion-classification-ravdess.herokuapp.com/index/ . Please note the performances are not good as the machine used is a free tier.

# Developers stuff

**DB creation**

PostgreSQL needs to be installed. To facilitate the configuration, I suggest to use a Db manager with UI, like [pgAdmin](https://www.pgadmin.org/). 

After the installation of PostgreSQL, it is possible to 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 the ```settings.py```, but it is possible to change it if needed.

```

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. 

It is possible to have an overview of the application even without knowing how Django works by looking at the ``App/views.py`` file.

**User Stories**

https://github.com/marcogdepinto/Django-Emotion-Classification-Ravdess-API/projects/2