This implements an online data visualization platform for our NLP engine DebateStar. The main page highlights our project features, links to Github open source codes and introduces our team members. The visualization page serves as the main interface to interact with our NLP engine. It dynamically presents our model's prediction scores for varied debates.
The backend is built with micro web framework Flask while the frontend is built with modern HTML5/CSS/Javascript and Bootstrap 4 (I used Flask's built-in Jinja template engine for easy deployment). The platform is deployed with AWS EC2, Nginx and Gunicorn.
This web app requires Python 3 and all dependencies in virtual environment venv.
Activate the virtual environment and install dependency packages.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Run the backend Flask app with application.py
python application.py
Since Elastic Beanstalk has some issues with this project's static file directory, we switched to use authentic EC2, Nginx and Gunicorn to deploy our platform.
A few notes on this. First, we created a Ubuntu 18 EC2 instance, download the private
key and modified ~/.ssh/config to easily ssh into that machine. Then we downloaded
pip and venv to create virtual python environment and manage packages.
As in local machine, we activate the virtual environment and install dependency packages.
git clone
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Some machine learning / AI package is very large, so we need to use to save RAM.
pip install -r requirements.txt --no-cache-dir
NLTK package requires seperate download for data files such as stopwords. Do the following:
>>> import nltk
>>> nltk.download()
Also, EC2 resources might need to be scaled up and we can do so by changing the instance type directly.
We chose ngnix as our reverse proxy. It listens on port 80 and routes traffic to
our web app. It can be configured to serve as load balancer.
$ sudo apt-get install nginx
$ sudo cp ~/debatestar-web/conf/nginx/debatestar.net /etc/nginx/sites-enabled/
$ sudo service nginx reload
$ sudo service nginx restart
$ sudo service nginx status
With this, we will be able to use default python application.py to launch our
app in the cloud and visit it through AWS's public IP.
We chose Gunicorn as our production server. It can be started as
gunicorn --bind 127.0.0.1:5000 application:app
ps ax|grep gunicorn # see the process
pkill gunicorn # kill the gunicorn process
Note that Gunicorn process is not easily managed, so we might use supervisor
to monitor them with configuration file in conf folder. Yet for simplicity,
we didn't use supervisor or other process management tool.
We registered our domain name using Route 53, obtained static IP and then connect them together so that the platform can be visted at www.debatestar.net
We use Certbot to enable HTTPS traffic. To do that, we configured the AWS's inbound
rule to allow 80, 440 and 22.
In current stage, we've implemented both front/backend modules and connected our NLP engine. Users can interact with our debate text analyzer. Soon we will complete our demo page so that our model can dynamically update prediction result.
Furthermore, we might want to provide more details about our model such as accuracy, CPU usage and more on the platform.