DEV Community: Jesse Akowuah

5 Daily Uses of AI in Our Everyday Lives

Jesse Akowuah — Tue, 06 Aug 2024 10:48:09 +0000

Social Media

Social Media has become an integral part of our daily lives as a medium of connecting with our loved ones, making new connections, and being updated on happenings around us. Facebook’s AI helps its users to translate the various posts on their Newsfeed to the user’s preferred language. Twitter’s AI uses the user’s past activity(likes, comments, and retweets) to recommend the most relevant tweets to the timelines of the user. Instagram also uses its AI with the help of relevant tags and other trending information, to help the user find photos and videos of a particular activity, place, event, restaurant, and discovery experience. one other use of AI used by all the various social media platforms is the curbing of cyberbullying by identifying various keywords and phrases that are tagged as hateful speech.

Intelligent Virtual Assistant

IOS has its ‘Siri’ and Android has its ‘Google Assistant’ with Amazon also having ‘Alexa’. all these virtual assistants are powered by AI that’s able to translate your voice commands into binary, a language that computers understand. these commands are then executed such as the playing of music, making calls, creating a remainder, and shopping. these assistants have become popular in recent years and they’re here to stay.

Commuting Recommendations

In these modern times, community from one place to another is not a simple process as everyone wants to avoid traffic, this is where the navigation apps come to play. the most popular one is Google Maps, which uses AI to analyze the current movement of traffic to provide the route to reach your destination within the shortest time. this app also takes information from users and news outlets on reports about construction works and accidents to help make better recommendations.

Spam Filters

Spam’s origins can be traced back to 1864 when the first unsolicited electronic communications were allegedly sent by telegraph. These were dubious investment offers sent to wealthy Americans. Today, this is an issue of the past, the spam filter has to be one of the important aspects of our inbox as it is powered by Al that identifies key phrases and words in past spam messages to use to filter current and future emails to prevent the user from being spammed.

Facial Recognition

About a decade ago, the concept of facial recognition was only a thing found in science fiction movies. Facial recognition is used by both operating systems and software applications. Various operating systems use this for security measures to prevent intruders from accessing your phone, whiles applications such as Tiktok and Snapchat use this AI for facial identification for the usage in their various filters available to users.

Building Your First AWS Web Server: A Beginner's Guide to Cloud Computing

Jesse Akowuah — Sat, 27 Jul 2024 16:29:46 +0000

This project is from one of my Lab sections in my Amalitech AWS reStart Certified Cloud Practitioner Program. It's the perfect project to get your hands dirty with real-world cloud infrastructure.

What We're Building

By the end of this tutorial, you'll have:

A Virtual Private Cloud (VPC) with public and private subnets
A web server running on an EC2 instance
Basic security configurations

Prerequisites

An AWS account (you can create one for free)
Basic understanding of networking concepts (IP addresses, subnets)
Patience and curiosity!

Let's Get Started!

Step 1: Creating Your Virtual Private Cloud (VPC)

Think of a VPC as your own private section of the AWS cloud. It's where all your resources will live.

Log into the AWS Management Console
Search for 'VPC' in the search bar and navigate to the VPC dashboard
Click "Create VPC" and use these settings:
- Resources to create: VPC and more
- Name tag auto-generation: Uncheck
- IPv4 CIDR: 10.0.0.0/16
- IPv6 CIDR block: No IPv6 CIDR block
- Tenancy: Default
- Number of Availability Zones (AZs): 1
- Number of public subnets: 1
- Number of private subnets: 1
Customize subnet CIDR blocks:
- Public subnet in us-west-2a: 10.0.0.0/24
- Private subnet in us-west-2a: 10.0.1.0/24
Set NAT gateways to "In 1 AZ" and VPC endpoints to "None"
Review and create your VPC

Congratulations! You've just set up your own private cloud network.

Step 2: Expanding Your Network

Now, let's add some more subnets to make our network more resilient.

In the VPC dashboard, navigate to "Subnets"
Create two new subnets:
- Public Subnet 2 (CIDR: 10.0.2.0/24)
- Private Subnet 2 (CIDR: 10.0.3.0/24)
Associate these new subnets with the appropriate route tables:
- Public Subnet 2 with the Public Route Table
- Private Subnet 2 with the Private Route Table

Pro tip: Multiple subnets across different Availability Zones increase your application's fault tolerance!

Step 3: Securing Your Infrastructure

Security is crucial in the cloud. Let's set up a basic firewall.

In the VPC dashboard, go to "Security Groups"
Create a new security group:
- Name: Web Security Group
- Description: Enable HTTP access
- VPC: Choose your newly created VPC
Add an inbound rule:
- Type: HTTP
- Source: Anywhere IPv4

This security group will control access to your web server.

Step 4: Launching Your Web Server

Now for the exciting part – launching your web server!

Navigate to the EC2 dashboard
Click "Launch Instance" and use these settings:
- Name: Web Server 1
- AMI: Amazon Linux 2 AMI (HVM)
- Instance type: t3.micro
- Key pair: Create a new key pair or use an existing one
- Network: Your new VPC, Public Subnet 2
- Security group: Web Security Group
In the "Advanced details" section, paste this user data script:

#!/bin/bash
yum install -y httpd mysql php
wget https://aws-tc-largeobjects.s3.us-west-2.amazonaws.com/CUR-TF-100-RESTRT-1/267-lab-NF-build-vpc-web-server/s3/lab-app.zip
unzip lab-app.zip -d /var/www/html/
chkconfig httpd on
service httpd start

Launch your instance!

This script installs and starts a web server automatically when your instance launches.

The Moment of Truth

Once your instance is running and has passed its status checks:

Select your instance in the EC2 dashboard
Copy the "Public IPv4 DNS" value
Paste this into a new browser tab

If you see a welcome page, congratulations! You've successfully set up your first cloud web server!

Building a Movie Recommendation System with Streamlit and Python

Jesse Akowuah — Wed, 17 Jul 2024 16:33:21 +0000

Hey fellow developers! Today, I'm excited to share a cool project I've been working on: a movie recommendation system built with Python and Streamlit. This system suggests movies based on a user's favorite film, making it a fun way to discover new movies to watch. Let's dive into how it works!

The Tech Stack

For this project, we're using:

Python
Streamlit for the web interface
pandas for data handling
scikit-learn for text processing and similarity calculations
TMDb API for fetching movie posters

How It Works

Data Loading: We start by loading movie data from a CSV file using pandas.
Feature Engineering: We combine several movie features (genres, director, tagline, keywords, cast) into a single string for each movie.
Text Vectorization: Using TfidfVectorizer from scikit-learn, we convert our text data into numerical feature vectors.
Similarity Calculation: We use cosine similarity to calculate how similar movies are to each other based on their feature vectors.
User Input: Through the Streamlit interface, users can input their favorite movie and choose how many recommendations they want.
Recommendation Generation: We find the closest match to the user's input, then use our similarity matrix to find and display the most similar movies.
Movie Posters: To make our app more visually appealing, we fetch movie posters from TMDb API.

The Code

Here's a breakdown of the main components:

import streamlit as st
import pandas as pd
import numpy as np
import difflib
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import requests

# Function to fetch movie posters
def fetch_movie_poster(movie_title):
    # ... (implementation details)

# Load and preprocess data
movies_data = pd.read_csv('movies.csv')
selected_features = ['genres', 'director', 'tagline', 'keywords', 'cast']

# Combine features and vectorize
combined_features = movies_data['genres'] + ' ' + movies_data['director'] + ' ' + movies_data['tagline'] + ' ' + movies_data['cast'] + ' ' + movies_data['keywords']
vectorizer = TfidfVectorizer()
feature_vector = vectorizer.fit_transform(combined_features)

# Calculate similarity
similarity = cosine_similarity(feature_vector)

# Streamlit UI
st.title('Movie Recommendation System')
movie_name = st.text_input('Enter the name of your favorite movie:')
num_recommendations = st.slider('How many recommendations would you like?', min_value=1, max_value=30, value=10)

# Generate and display recommendations
if movie_name:
    # ... (recommendation logic)

Running the App

To run this app, make sure you have all the required libraries installed and a movies.csv file with the necessary data. Then, simply run:

streamlit run your_script_name.py

Future Improvements

There are several ways this System could be improved:

Implement user accounts to track viewing history and improve recommendations over time.
Add more data sources to get a broader range of movies and more detailed information.
Incorporate collaborative filtering to consider user ratings and preferences.
Optimize the similarity calculation for larger datasets.

Conclusion

Building this movie recommendation system was a fun way to combine data science concepts with web development. It's a great starting point for more complex recommendation systems and showcases the power of Python libraries like scikit-learn and Streamlit.

I hope you found this interesting! Feel free to try it out, modify the code, and let me know if you have any questions or suggestions for improvements.

Happy coding! 🎬🍿

My First AWS EC2 Project: Hosting a Static Website

Jesse Akowuah — Sat, 13 Jul 2024 16:17:13 +0000

Are you looking to break into cloud computing? Let me share my recent experience setting up a static website on Amazon Web Services (AWS) using an EC2 instance. This project was a great introduction to cloud infrastructure and helped me gain hands-on experience with AWS.

Getting Started with EC2

First, I logged into the AWS console and searched for EC2. After navigating to the Instances section, I clicked "Create Instance." For this project, I chose a Linux server with a t3.micro instance type—perfect for learning as it's part of the AWS Free Tier.

Setting Up the Instance

Once my instance was running, I clicked on the instance ID to access its information page. The next step was connecting to the instance via SSH. I used Git Bash, ensuring I was in the same directory as my downloaded key pair (.pem) file.

To connect, I ran these commands:

chmod 400 keyparfilename.pem
ssh -i keyparfilename.pem ubuntu@ec2-public-IP-address.AWSareaID.compute.amazonaws.com

Installing Necessary Software

After successfully connecting, I switched to root access with sudo su -. Then, I installed and started Nginx:

apt install nginx -y
systemctl start nginx
systemctl enable nginx

I also installed unzip to help with unpacking the website files:

apt install unzip

Deploying the Website

Next, I downloaded and unzipped the website files:

curl "name-of-zipped-file" -o "preferred-file-name.zip"
unzip preferred-file-name.zip

Finally, I copied all the files to the Nginx web directory:

cp -r * /var/www/html/

Viewing the Result

With everything set up, I copied the public IP address from the AWS console and pasted it into a web browser. Voila! My website was live.

Challenges and Learnings

While the process seems straightforward now, I encountered a few hiccups along the way. Understanding the EC2 dashboard took some time, and I initially struggled with SSH connections. However, careful reading of AWS documentation, Youtube videos, our slack channel and some troubleshooting helped me overcome these obstacles.

This project taught me valuable lessons about cloud infrastructure, Linux command line, and web servers. It's amazing to see how quickly you can deploy a website using cloud services!

If you're looking to start your cloud computing journey, I highly recommend trying out a similar project. It's a great way to get hands-on experience and build your confidence in working with AWS.

Screenshot of the Hosted Website
Website template accessed from : https://www.tooplate.com/view/2135-mini-finance

What is Cloud Computing ?

Jesse Akowuah — Wed, 10 Jul 2024 17:06:01 +0000

Tired of Expensive IT Costs and Managing Servers? Cloud Computing Can Be Your Game-Changer!

The concept of cloud computing revolves around the provision of IT services to end users for a fee. Unlike traditional computing where users shoulder the upfront costs for every capability they need, cloud computing offers a more flexible and cost-effective solution.

Imagine a bank requiring a server for its network operations. Traditionally, they'd need a physical location to house the servers, purchase and install them, and dedicate staff to manage them. Cloud computing eliminates all these upfront costs by providing the bank's server needs over the internet.

Cloud Computing Benefits: Numerous Advantages for Businesses

Cloud computing offers a multitude of benefits for businesses of all sizes. Here are some key advantages:

Cost Efficiency: Cloud computing's biggest edge over traditional IT is its ability to save customers money. Since it's essentially "on-demand IT," you avoid the upfront costs of acquiring traditional IT infrastructure.
Accessibility: This computing option allows for service accessibility at various locations across the internet for the same client. This empowers remote work for startups and small companies, fostering a more flexible work environment.
Security: Cloud providers invest heavily in security measures to safeguard your data. Regular backups and disaster recovery features ensure business continuity in case of unforeseen events. For instance, cloud providers like Amazon Web Services (AWS) and Microsoft Azure utilize encryption to safeguard your data at rest and in transit.
Scalability: The cloud allows you to easily scale resources (storage, computing power, and networking) up or down as your needs evolve. No need to invest in expensive hardware upfront; you only pay for what you use. Cloud resources can automatically scale up during peak hours and down during slower periods, optimizing costs and performance.

Cloud Deployment Models: Choosing the Right Fit

Cloud service providers offer various deployment models to cater to different client needs. Here's a breakdown of the fundamental types:

Public Cloud: A cloud deployment model where a third-party manages all the services, and cloud resources are delivered through the internet. Many companies share the same resources from the public cloud.
Private Cloud: A cloud deployment system where a single company exclusively uses the architecture and infrastructure. The infrastructure can be on-premises or hosted by a third party.
Hybrid Cloud: A cloud deployment model that combines both public and private cloud computing models, allowing transfer of data and resources between the two.

Cloud Service Models: A Spectrum of Services

Cloud service providers offer three main service models that clients can choose from:

Infrastructure as a Service (IaaS): Offers all computing resources and infrastructure in a virtual environment accessible to multiple users. Services include storage, databases, servers, and networking. Examples of IaaS include virtual machines, storage, and networking equipment.
Platform as a Service (PaaS): An environment where users can build, compile, and run their programs without managing the underlying infrastructure. This caters primarily to software developers. Examples of PaaS include Heroku, Google App Engine, and Microsoft Azure App Service.
Software as a Service (SaaS): Provides pay-per-use access to application software for end users. Accessible with either a web browser or a lightweight software application. End customers benefit the most from SaaS. Examples of SaaS include Microsoft Office 365, Gmail, Salesforce, Dropbox, Zoom, and Slack.