README.md

Amazon Rekognition Application Server

Principle

1. Set up a Front-End Machine with a web server to upload a picture, analyze it with Amazon Rekognition and predict your mood

2. Set up a Back-End Machine with a python server for the predictions (ideally in a private subnet, but not in this tutorial)

Network Architecture

1. Create a VPC (CIDR: 192.168.0.0/16)

2. Create a Public Subnet (CIDR: 192.168.0.0/24)

3. Create an Internet Gateway and attach it to the VPC

4. Edit the Route Table

   Public Subnet Route Table
   Destination	Target
   10.0.0.0/16	local
   0.0.0.0/0	IGW

5. Launch two EC2 instances (for example Ubuntu Server 18.04 LTS (HVM), SSD Volume Type - ami-04b9e92b5572fa0d1) with a basic Instance Type (e.g., t2.micro)

6. If a Public IP has not been assigned at launch, then allocate new Elastic IP addresses and associate them to each machine

7. Edit the Security Groups of the instances

   • for the front-end machine, open ports 80 (HTTP) and 443 (HTTPS) for the connection with the web server
   • for the back-end machine, open port 5000 used by Flask Python web framework
   
   

   Front-End Machine Security Group
   	Type	Ports	Protocol	Source
   Inbound	SSH	22	TCP	0.0.0.0/0
   	HTTP	80	TCP	0.0.0.0/0
   	HTTPS	443	TCP	0.0.0.0/0
   Outbound	all traffic	all	all	0.0.0.0/0

   Back-End Machine Security Group
   	Type	Ports	Protocol	Source
   Inbound	SSH	22	TCP	0.0.0.0/0
   	Custom TCP Rule	5000	TCP	0.0.0.0/0
   Outbound	all traffic	all	all	0.0.0.0/0

Front-End Web Server

1. Connect to the machine via ssh

   PS> ssh -i myKeyPair.pem ubuntu@<frontend_public_dns>
   

2. Update the package tool

   $ sudo apt-get update
   

3. Install apache web server

   $ sudo apt-get install apache2
   

4. From local machine, export Files/index.html and Files/static/* to remote machine

   PS> scp -i myKeyPair.pem ./index.html ubuntu@<frontend_public_ip>:/home/ubuntu/
   PS> scp -i myKeyPair.pem ./static/* ubuntu@<frontend_public_ip>:/home/ubuntu/static/
   

5. Move index.html and static/ to the default Ubuntu document root /var/www/html

   $ sudo mv index.html /var/www/html
   $ sudo mv static /var/www/html
   

6. In the static/app.js file, change the target IP (public IP) of the back-end machine (variable backend_ip)

7. We can now access the web application by typing in any browser http:\\<frontend_public_ip>

   

Back-End Python Server

1. Connect to the machine via ssh

   PS> ssh -i myKeyPair.pem ubuntu@<backend_public_dns>
   

2. Update the package tool

   $ sudo apt-get update
   

3. Install Miniconda (and restart the shell)

   $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   $ sh Miniconda3-latest-Linux-x86_64.sh
   $ exec $SHELL
   

   If conda command is not found after installation: $ export PATH=~/miniconda3/bin:$PATH

4. Create a new virtual environment with Python 3, install packages Flask and boto3

   $ conda create -n myenv python=3.6
   $ conda activate myenv
   $ conda install Flask boto3
   

5. Install Flask extension for handling Cross Origin Resource Sharing (CORS)

   conda intall flask-cors
   

6. Configure AWS credentials:

   • create a file ~/.aws/credentials with the Access Key ID and the Secret Access Key, which looks like that:
   
   

   [default]
   aws_access_key_id=XXXXXXXXXXXXXXXXXXXX
   aws_secret_access_key=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
   aws_session_token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
   

   • a file ~/.aws/config with the default region:
   
   

   [default]
   region = us-east-1
   

7. From local machine, export Python script rekognition.py to remote backend machine

   PS> scp -i myKeyPair.pem ./rekognition.py ubuntu@<backend_public_ip>:/home/ubuntu/
   

8. Create an S3 bucket with name image-for-mood-reko to store the picture to analyze (see in rekognition.py)

   Nota: the name of the bucket must be unique across all existing bucket names in Amazon S3, so use your own and update rekognition.py script accordingly

9. Make sure myenv virtual environment is active and then run Flask app

   $ python rekognition.py
   

Results

1. After uploading the picture:

   

2. If you want to know in which mood you are, just click the button (I am angry apparently, but just a little bit!)