Building a Serverless Image Recognition App with AWS and Python

AI Project Engine
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Objective: The objective of this project is to build a full-stack application that leverages the power of AWS services, specifically AWS Lambda and AWS Rekognition, to create a serverless image recognition app. The app will allow users to upload images, which will then be processed by AWS Rekognition to identify objects, scenes, and faces in the images. The results of the image analysis will be displayed to the user, providing an interactive and informative experience.

Learning Outcomes: By completing this project, you will gain a strong understanding of:

  • How to set up and configure AWS services, including AWS Lambda, AWS API Gateway, and AWS S3.
  • The principles and best practices of serverless architecture.
  • How to integrate third-party APIs, such as AWS Rekognition, into your applications.
  • The process of handling and processing user-uploaded files securely.
  • Building a responsive front-end interface using HTML, CSS, and JavaScript.
  • Deploying a full-stack application to the web.

Steps and Tasks:

  1. Set up AWS Account and Services

    • Create an AWS account if you don’t have one.
    • Set up AWS Lambda, AWS API Gateway, and AWS S3 services.
    • Configure AWS credentials on your local machine.

  2. Create the Serverless Backend

    • Define a new AWS Lambda function for image processing.
    • Configure the function to handle HTTP requests.
    • Implement the image processing logic using the AWS Rekognition API.
    • Set up environment variables for your AWS credentials and API keys.
    • Enable CORS (Cross-Origin Resource Sharing) on the AWS API Gateway.

  3. Build the Frontend Interface

    • Create a new HTML file for the frontend.
    • Add a form element to the HTML file for image uploads.
    • Implement JavaScript code to handle form submissions and file uploads.
    • Display a loading spinner while the image is being processed.
    • Show the results of the image analysis on the frontend.

  4. Securely Deploy the Application

    • Generate a unique API key in the AWS API Gateway.
    • Restrict access to the API using the generated API key.
    • Configure the AWS S3 bucket to only allow uploads from your application.
    • Implement error handling and validation in your code.

  5. Deploy the Full-Stack Application

    • Use the AWS Command Line Interface (CLI) to package and deploy your Lambda function.
    • Upload your HTML, CSS, and JavaScript files to a hosting service.
    • Test the application to ensure everything is working correctly.

Evaluation: You can evaluate your project based on the following criteria:

  • The application should allow users to upload images.
  • The uploaded images should be processed using AWS Rekognition.
  • The results of the image analysis should be displayed to the user.
  • The application should handle errors and provide appropriate feedback.
  • The frontend interface should be visually appealing and responsive.
  • The application should be securely deployed, with access restricted to authorized users.

Resources and Learning Materials:

Need a little extra help?

  1. Set up AWS Account and Services

To get started, you’ll need to set up an AWS account and create the necessary services. Follow the steps below:

  • Create an AWS account by visiting the AWS website and following the registration process.
  • Once you have an account, log in to the AWS Management Console.
  • Search for the services mentioned, such as AWS Lambda, AWS API Gateway, and AWS S3, and create new instances of each.
  • For AWS Lambda, you can choose to create a function from scratch and select a runtime environment like Python.
  • For AWS API Gateway, create a new REST API and define a POST method for image uploads.
  • For AWS S3, create a new bucket to store the uploaded images.

To configure your AWS services, you’ll need to set up your local environment with the necessary credentials. Here’s how:

  • Install the AWS Command Line Interface (CLI) on your machine.
  • Open a terminal or command prompt and run the aws configure command.
  • You’ll be prompted to enter your AWS Access Key ID and Secret Access Key, which can be found in the AWS IAM console.
  • You can leave the default region and output format as-is.
  1. Create the Serverless Backend

The serverless backend will consist of an AWS Lambda function that processes the uploaded images using the AWS Rekognition API. Follow the steps below to create this backend:

  • Define a new AWS Lambda function and give it a meaningful name.
  • Set the runtime to Python 3.x.
  • In the function code, you’ll need to install the boto3 library, which is the AWS SDK for Python, and its dependencies. You can do this by including the libraries in a deployment package.
  • Configure the function to handle HTTP requests by adding an API Gateway trigger.
  • Implement the image processing logic by using the boto3 library to call the AWS Rekognition API. You can refer to the AWS documentation for examples and guidance on how to use the Rekognition API.
  • Remember to handle any errors that may occur during the image processing.
  • You’ll also need to set up environment variables for your AWS credentials and API keys. You can access these environment variables in your Lambda function using the os.environ dictionary.

To enable cross-origin resource sharing (CORS) on the AWS API Gateway, follow these steps:

  • Open your API in the AWS API Gateway console.
  • Select the appropriate resource and choose “Enable CORS” from the “Actions” menu.
  • This will add the necessary headers to your API’s responses, allowing it to be accessed from a different domain.
  1. Build the Frontend Interface

The frontend interface will allow users to upload images and display the results of the image analysis. Follow the steps below to build the frontend:

  • Create a new HTML file and add a form element to it.
  • The form should have an input of type “file” to allow users to select an image to upload.
  • You’ll also need to add some JavaScript code to handle the form submission and file upload.
  • In the JavaScript code, you’ll need to make an HTTP POST request to your API using the Fetch API or a similar library.
  • When the user submits the form, you should display a loading spinner or some other indication that the image is being processed.
  • Once you receive the response from the server, you can parse the JSON data to extract the results of the image analysis.
  • Finally, you can display the results to the user in a meaningful way.
  1. Securely Deploy the Application

Security is an important aspect of any application. In this step, you’ll learn how to securely deploy your application by generating an API key, restricting access to the API, and configuring the S3 bucket to only allow uploads from your application.

To generate an API key and restrict access to the API, follow these steps:

  • In the AWS API Gateway console, select your API and go to the “API Keys” section.
  • Generate a new API key and associate it with the appropriate usage plan.
  • Go to the “Stages” section and add the API key to the desired stage.
  • You can then configure the API to require the API key for all requests or only for uploads.

To configure the S3 bucket to only allow uploads from your application, you’ll need to update the bucket’s CORS configuration. You can do this by adding the following CORS rules:

[
    {
        "AllowedHeaders": ["*"],
        "AllowedMethods": ["PUT"],
        "AllowedOrigins": ["your-frontend-domain.com"],
        "ExposeHeaders": []
    }
]

This configuration allows PUT requests from your frontend domain and allows all headers. You can update the values as needed.

  1. Deploy the Full-Stack Application

To deploy your serverless backend, you’ll need to use the AWS CLI. Follow the steps below:

  • Open a terminal or command prompt and navigate to the directory containing your Lambda function code.
  • Run the following command to package your function: aws cloudformation package --template-file template.yaml --output-template-file packaged.yaml --s3-bucket your-unique-bucket-name
  • This command will create a new CloudFormation template (packaged.yaml) that references your function code and uploads it to the specified S3 bucket.
  • Finally, you can use the AWS CLI or the AWS Management Console to deploy the packaged CloudFormation template.

For the frontend of your application, you can host the HTML, CSS, and JavaScript files on any static file hosting service. Some popular options include GitHub Pages, Netlify, and AWS S3.

To test your application, simply open the hosted HTML file in a web browser and try uploading some images. Make sure to test different scenarios, such as images with multiple faces or no faces, to ensure your application handles them correctly.

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@joy.b has been assigned as the mentor. View code along.