Deploying an AI Spam Detection App

Stephanie Ekejiuba — Thu, 04 Sep 2025 11:41:18 +0000

AI Powered Spam Detection Web App Deployed on AWS EC2 with Flask & Gunicorn

Introduction

Spam emails continue to plague inboxes worldwide, affecting productivity and exposing users to phishing and malware. To address this problem, I developed an AI-based Spam Detection Web Application using Python and Flask. The app utilizes natural language processing (NLP) and machine learning to classify messages as spam or not spam with high accuracy.

In this project, I not only built the core spam classifier but also deployed the full stack application on an AWS EC2 instance, making it publicly accessible via the web. I also implemented a production-ready environment using Gunicorn, a WSGI HTTP server for Python applications.

This article outlines the development process, deployment architecture, tools used, and the steps I followed to bring the app live.

Project Goals

✅ Build an AI model capable of detecting spam emails

✅ Create a web-based UI using Flask

✅ Deploy the application on a cloud server (AWS EC2)

✅ Make the server production ready using Gunicorn

✅ Demonstrate understanding of cloud infrastructure, Python backend, and deployment best practices

How the Spam Detection Works

At the heart of the application is a machine learning model trained on labeled email data. The model uses techniques such as:

Text preprocessing: Removing noise, stop words, tokenization

TF-IDF Vectorization: Converting text into numerical form

Multinomial Naive Bayes Classifier: A popular algorithm for text classification

When users input a message into the web form, the model predicts whether the message is spam or ham (not spam) in real-time.

Tech Stack

Layer Technology
Frontend HTML/CSS
Backend Python, Flask
ML/NLP Scikit-learn, Pandas, NLTK
Deployment AWS EC2 (Ubuntu 20.04), Gunicorn
Package Manager pip
Git Repository GitHub

App Features

Simple, clean UI for user input
Real-time spam classification
RESTful architecture
Lightweight and responsive
Publicly accessible via EC2 public IP

The app is hosted on an Ubuntu EC2 instance with Flask handling the app logic and Gunicorn ensuring the app can handle production traffic efficiently.

Step by Step Deployment Process

Launching EC2 Instance
Selected Ubuntu 20.04 AMI
t2.micro instance (free tier)
Set up key pair (.pem) and security group

Allowed ports: 22 (SSH), 80 (HTTP), 5000 (Flask Dev)

Installing System Dependencies

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip -y

Cloning the Flask App

git clone <your-github-repo-url>
cd spamemail

Fixing Windows-style line endings (if needed)

sudo apt install dos2unix -y
dos2unix requirements.txt

Installing Python Dependencies

pip3 install -r requirements.txt

Running the App in Development

python3 app.py

Access at: http://your-ec2-ip:5000

Running with Gunicorn for Production

pip3 install gunicorn
gunicorn --workers 3 --bind 0.0.0.0:5000 app:app

Note: Replace app with your filename: flask app object if your file or Flask object has a different name.

Security Considerations

For development, port 5000 was opened to the public.
For production, the app can be routed through Nginx on port 80 with HTTPS using Let's Encrypt.
SSH access was secured with a private key.

Testing & Validation

Tested on multiple test cases and known spam messages
Verified model accuracy and false positive rates
Load tested the Gunicorn server for concurrent requests

Lessons Learned

Handling OS level issues like CRLF line endings on Linux
Setting up and securing cloud infrastructure on AWS
Working with WSGI servers like Gunicorn for Python deployment
Enhancing app performance and fault tolerance Repository

GitHub: your repo link

Next Steps

Integrate with email clients to automatically flag spam
Add user authentication and database support
Deploy behind an Nginx reverse proxy for better performance
Add HTTPS support for secure connections
Dockerize the application for portability

Final Thoughts
This project helped me bridge the gap between machine learning development and real world deployment. From building a robust spam classifier to deploying it in the cloud, I’ve gained hands-on experience in both AI and DevOps fundamentals. It’s a great addition to my portfolio and a stepping stone toward more scalable AI solutions.

Preview of the Application
The application interface is simple and intuitive. Users can paste email content into a text box and receive immediate feedback indicating whether the content is spam or not.

check out the screenshot of what the app looks like

(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0vml4qhcy2ibh00lsduh.png)

(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ihwwkaai2bfqafw18axp.png)

DEV Community: Stephanie Ekejiuba

Deploying an AI Spam Detection App