Part 2: API

Introduction
Requirements
Set Up

Introduction

This part of the project provides a RESTful API to obtain predictions from MesoNet variants. The API is implemented using Django and the Django REST Framework, making available all the power of Python to you. This also allows the API to scale to an arbitrary level of complexity without breaking a sweat.

It provides a simple admin interface to upload your trained models and simple endpoints that allow a user of the API to obtain information such as a list of all the available models, the size of the dataset and predictions with visualization.

The API has been divided between two Django apps:

classifiers - The classifiers app is the main workhorse of the API, providing the database model and the helper functions to upload models, load data, calculate things like accuracy and make predictions. This is a completely independent app and can easily be integrated into another Django project without breaking things (as long as the dependencies are met).
api - The api app contains all the endpoints that a user of the API can use. It relies on classifiers to do its work.

Requirements

The project is created in Python 3.8.8. The other requirements are as follows:

Django (3.2)
Django REST Framework (3.12.4)
Matplotlib (3.4.1)
ScikitLearn (0.24.1)
Sqlparse (0.4.1)
Tensorflow (2.4.1)

Set Up

To start using the project, follow the following steps. While the steps are specific to Linux, only the commands/methods change for Windows.

Clone the repository from GitHub.

git clone https://github.com/MalayAgr/MesoNet-DeepfakeDetection-API.git

Change working directory to MesoNet-DeepfakeDetection-API/mesonet_api/.
```
cd MesoNet-DeepfakeDetection-API/mesonet_api/
```
Install requirements. It is recommended that you use a virtual environment.
```
pip install -r ../requirements.txt
```
Create a .env file with at least the SECRET_KEY setting. To obtain a secret key, you can use this tool.
```
export SECRET_KEY=<value>
```
There are other settings that you can modify. These are used in the settings.py file of your project. Also note that during deployment, variables in your .env file should NOT be preceded by export.
Make sure there is a directory at MesoNet-DeepfakeDetection-API/mesonet_api/<MEDIA_FOLDER> with the same name as the DATA_ROOT setting. This directory should contain your prediction data and have the following structure (assuming DATA_ROOT is data). Here, MEDIA_FOLDER refers to the folder where user-uploaded content should be stored:
```
└── data/
    ├── real/
    │   ├── img1.png
    │   └── img2.png
    └── forged/
        ├── img1.png
        └── img2.png
```
Note that real and forged can be renamed to change the class labels used in the API.
Add the environment variables to your current environment.
```
source .env
```
Run migrations.
```
python manage.py migrate
```
While you don’t need to, you can first run python manage.py makemigrations as a sanity check to make sure your migrations are up to date.
Run the development server.
```
python manage.py runserver
```
Done!

Table of Contents

Introduction

Requirements

Set Up