Deploying an AI Spam Detection App on AWS EC2

#aws #cloud #ai #python

Overview
In the digital age, spam emails are a persistent nuisance, cluttering inboxes and posing security risks. To combat this, we can leverage artificial intelligence to create a spam detection application. In this blog post, we will guide you through the process of deploying an AI spam detection app built with Python and Flask on an AWS EC2 instance. This application utilizes machine learning to classify emails as spam or not spam, providing a practical solution to a common problem.

What You Will Learn

How to set up an AWS EC2 instance
How to install necessary software and dependencies
How to deploy a Flask application using Gunicorn
How to configure security settings for your application

Prerequisites
Before we dive into the deployment process, ensure you have the following:

AWS Account: If you don’t have one, you can create a free-tier account. Create an AWS account here
Basic Knowledge of Terminal Commands: Familiarity with command-line interfaces will be helpful.

Step 1: Launch the Ubuntu EC2 Instance
1) Log in to your AWS Management Console.
2) Navigate to the EC2 Dashboard.
3) Click Launch Instance.

4) Select an Ubuntu Server AMI (e.g., Ubuntu 20.04 LTS).

5) Choose an Instance Type (e.g., t2.micro for free tier).

6) Create a key pair (.pem)

7) Configure security groups:

Allow SSH (port 22).
Add a rule for HTTP (port 80).

8) Launch the instance and connect via EC2 Instance Connect

Step 2: Update the Instance
Once connected to your EC2 instance, it’s a good practice to update the package lists and upgrade the installed packages:

sudo apt update
sudo apt upgrade -y

Step 3: Install Python and Pip
1) Next, we need to install Python and Pip, which are essential for running our Flask application:

sudo apt install python3-pip -y

2) Verify the installation:

python3 --version
pip --version

Step 4: Set Up the Flask App
1) Clone the Flask App Repository: Use Git to clone the repository containing the spam detection app. Replace with the actual URL of your GitHub repository.

git clone <repository-url>

2) Navigate to the project folder (replace with your actual folder name):

cd <folder-name>

3) Check the requirements.txt File: Open the requirements.txt file to ensure it lists all necessary dependencies.

nano requirements.txt

4) Convert Line Endings: If you encounter issues with the requirements.txt file (e.g., it appears encrypted), convert it to Unix-style line endings:

file requirements.txt
sudo apt install dos2unix -y
dos2unix requirements.txt

5) Install the dependencies:

pip install -r requirements.txt

Step 5: Run the Flask App (Development Mode)
To test the application, you can run it in development mode:

python3 app.py

By default, Flask runs on port 5000. You can verify that the app is running by navigating to http://<your-ec2-public-ip>:5000 in your web browser.

Step 6: Open Port 5000 in the Security Group
To allow access to your app, you need to open port 5000 in the security group:

1) Go to the EC2 Dashboard in AWS.
2) Select your instance and navigate to the Security tab.
3) Click on the Security Group link.
4) Edit the Inbound Rules to allow TCP traffic on port 5000.

Step 7: Set Up a Production-Ready Server with Gunicorn (optional)
To run your app on a production-ready server, you can use Gunicorn:

1) Install Gunicorn:

pip install gunicorn

2) Run the app with Gunicorn:

gunicorn --workers 3 --bind 0.0.0.0:5000 app:app

Replace app:app with your actual module and app name if different.

Conclusion
We have successfully deployed your AI spam detection application on AWS EC2! You can now access it via your EC2 public IP. For further enhancements, consider implementing HTTPS and using a reverse proxy like Nginx for better performance and security.

Feel free to check out the screenshot of what the app looks like here

Feel free to ask questions or drop your comments😊