Building a Serverless Image Recognition App with AWS Rekognition and JavaScript

AI Project Engine
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Objective: The objective of this project is to build a serverless web application that leverages the power of AWS Rekognition to analyze and categorize images. The application will allow users to upload images, which will then be processed by AWS Rekognition to extract information such as objects, scenes, and facial attributes. The results will be displayed to the user in an intuitive and visually appealing manner.

Learning Outcomes: By completing this project, you will learn how to:

  1. Set up a serverless backend using AWS services, including S3 and Lambda.
  2. Integrate a third-party API, in this case, AWS Rekognition, into your application.
  3. Implement client-side image processing using JavaScript.
  4. Develop a responsive user interface to handle image uploads and display analysis results.
  5. Gain hands-on experience with AWS SDK for JavaScript, including configuring and utilizing various AWS services.
  6. Understand and implement best practices for security and error handling in a cloud-based application.

Steps and Tasks: Step 1: Set up AWS Account and Services

  • Create an AWS account if you don’t have one already.
  • Set up AWS services: S3, Lambda, API Gateway, and IAM.

Step 2: Configure AWS SDK and API Access

  • Install the AWS SDK for JavaScript.
  • Set up AWS credentials and configure the SDK.
  • Enable CORS (Cross-Origin Resource Sharing) for your API.

Step 3: Build the User Interface

  • Create an HTML file for the user interface.
  • Add a file input element for image uploads.
  • Implement JavaScript functions for handling user interactions.

Step 4: Process and Upload Images

  • Write JavaScript code to process user-selected images.
  • Generate a unique key for each image.
  • Use the AWS SDK to upload the processed image to your S3 bucket.

Step 5: Create a Serverless Function

  • Set up a new Lambda function in the AWS Management Console.
  • Configure the function to receive HTTP requests from API Gateway.

Step 6: Implement Image Analysis

  • Write the code to analyze the uploaded image using AWS Rekognition.
  • Extract relevant information from the analysis results.

Step 7: Return Analysis Results

  • Configure the Lambda function to return the analysis results.
  • Handle any errors that may occur during the analysis process.

Step 8: Display Results in the User Interface

  • Update your HTML and JavaScript code to display the analysis results.
  • Handle different types of analysis results, such as object detection, scene detection, and facial analysis.

Step 9: Test and Deploy Your Application

  • Test your application locally to ensure everything is working correctly.
  • Deploy your application to AWS using the AWS Command Line Interface (CLI) or the AWS Management Console.

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 analysis results should be displayed to the user in a clear and understandable manner.
  • The application should handle errors gracefully and provide informative error messages.
  • The user interface should be responsive and visually appealing.
  • The application should be deployed and fully functional on AWS.

Resources and Learning Materials:

  1. AWS Documentation
  2. AWS SDK for JavaScript Documentation
  3. Serverless Stack - A comprehensive guide to building serverless applications.
  4. MDN Web Docs - A great resource for learning web development technologies.
  5. FreeCodeCamp - Offers a wide range of free coding courses, including web development and AWS.

Need a little extra help?

Step 2: Configure AWS SDK and API Access To configure the AWS SDK for JavaScript, you first need to install it using npm (Node Package Manager), which is a package manager for JavaScript. Open your command line interface and navigate to your project directory. Then, run the following command to install the AWS SDK:

npm install aws-sdk

Once the installation is complete, you can configure the SDK with your AWS credentials. You will need your Access Key ID and Secret Access Key, which can be obtained from the AWS Management Console.

Create a new file in your project directory called config.js and add the following code:

// config.js
window._config = {
  region: 'YOUR_REGION',
  s3Bucket: 'YOUR_S3_BUCKET',
  rekognitionBucket: 'YOUR_REKOGNITION_BUCKET',
  apiGateway: 'YOUR_API_GATEWAY_URL',
  accessKeyId: 'YOUR_ACCESS_KEY_ID',
  secretAccessKey: 'YOUR_SECRET_ACCESS_KEY'
};

Replace 'YOUR_REGION' with the AWS region you are using (e.g., ‘us-east-1’), 'YOUR_S3_BUCKET' with the name of your S3 bucket, 'YOUR_REKOGNITION_BUCKET' with the name of the bucket you will use for storing Rekognition analysis results, and 'YOUR_API_GATEWAY_URL' with the URL of your API Gateway.

Next, open your HTML file and add the following script tag before your own JavaScript code:

<script src="https://sdk.amazonaws.com/js/aws-sdk-2.966.0.min.js"></script>
<script src="config.js"></script>
<script src="your-script.js"></script>

Replace 'your-script.js' with the filename of your own JavaScript file.

Now, you can access your AWS configuration in your JavaScript code using the window._config object. For example, to access your Access Key ID, you can use window._config.accessKeyId.

To enable CORS for your API, you need to add the appropriate headers to your API’s responses. This can be done using an AWS Lambda function. We will cover this in more detail in the next steps.

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