🟡 Combo: AWS WebApp, API Explorer, GPT Exploration

stemaway · June 1, 2023, 3:13pm

Code Explanation Engine: Integrating OpenAI with Serverless AWS Architecture and React

Objective

The primary objective of this project is to create an AI-powered web application called the “Code Explanation Engine”. This application allows users to input any piece of code, which will then be interpreted and explained using the OpenAI GPT API. The key focus is not on developing the underlying machine learning capabilities but on effectively integrating this powerful API into a full-stack application.

This project provides you with an in-depth experience of working with third-party APIs, specifically the OpenAI GPT API, and offers exposure to scripting for API integration. Additionally, it provides hands-on learning in deploying a serverless architecture on Amazon Web Services (AWS), a crucial skill for hosting modern web applications.

Another focus of the project is to develop a user-friendly front-end interface using React, emphasizing the creation of intuitive user experiences. This project serves as a practical exercise in advanced web application design and deployment, API utilization, cloud hosting solutions, and the integration of artificial intelligence in a user-facing application.

Learning Outcomes

By the end of this project, you will:

Understand how to set up and configure AWS services, including API Gateway and Lambda functions.
Learn how to integrate a third-party API, such as the OpenAI GPT API, into an application.
Gain knowledge of serverless architecture and its advantages for scalable applications.
Develop skills in handling API requests and responses, including data formatting and error handling.
Enhance front-end development skills by creating an intuitive user interface with React.
Learn how to deploy a full-stack application using AWS services.

Tip: If you find the steps somewhat daunting, we recommend mirroring the steps presented in the mentor’s demo (if available) before trying your hand at the code independently.

Tip: Try each step with the help of the resources listed. If you are stuck, take a look at the example code snippets provided.

General Understanding and Conceptualization

Before diving into the implementation, it’s important to understand the key technologies and concepts involved in this project:

AWS Services: Familiarize yourself with AWS services such as Lambda, API Gateway, and S3. The AWS Documentation is a great resource to understand these services and how to use them.
OpenAI API: Learn how to use the OpenAI GPT-3 API to generate text. The OpenAI Documentation provides comprehensive information on using their API.
Serverless Architecture: Understand the concept of serverless architecture, where you can build and run applications without thinking about servers. Serverless Architecture by Martin Fowler provides a deep dive into this concept.
React: Get comfortable with React for building the front-end interface. The React Documentation is the official resource.

Step 1: Set Up AWS Account and Services

Create an AWS Free Tier Account

First, you need to create an AWS account if you don’t already have one. Visit the AWS website and follow the Creating an AWS Account guide. After registration, log into your account and navigate to the AWS Management Console.

Create an API with API Gateway

Search for API Gateway in the list of services and click on it.
Choose to create a new REST API.

Note: REST (Representational State Transfer) is an architectural style used for web development. RESTful APIs can be interacted with using standard HTTP methods like GET, POST, PUT, DELETE, etc.

The REST API will act as an interface between your front-end application and the back-end Lambda function. You’ll define endpoints and methods on this API to trigger specific actions in your Lambda function.

Optional: As you gain more experience with AWS, you might explore other types of APIs, such as WebSocket APIs for real-time communication or HTTP APIs for simpler, cost-effective options.

Create an AWS Lambda Function

Go back to the services list and search for Lambda.
Click on Create Function.
Choose Author from scratch.
Provide a function name and select a runtime (Node.js or Python).
Under Permissions, choose or create an appropriate execution role (see the Appendix for guidance).

Install and Configure AWS CLI

To streamline your interactions with AWS, you can set up the AWS Command Line Interface (AWS CLI). This allows you to manage AWS services from the command line, making it easier to script and automate tasks.

Install the AWS CLI following the Installing the AWS CLI guide.
Configure the AWS CLI with your credentials as per the Configuring the AWS CLI instructions.

Setting Up AWS Account and Services:

Step 2: Implement Serverless Backend

Set Up AWS Lambda Function

Your Lambda function will process incoming requests from the front-end interface.

In your Lambda function configuration, navigate to the Environment variables section.
Add a new environment variable with Key: OPENAI_API_KEY and Value: your OpenAI API key.
This allows your function to access the OpenAI API key securely without hardcoding it.

Note: Ensure that your Lambda function has the necessary execution role permissions. For this project, the AWSLambdaBasicExecutionRole is sufficient (see the Appendix).

Initial Lambda Function Code

Below are starter code snippets for both Node.js and Python runtimes.

Example Code

JavaScript (Node.js)

exports.handler = async (event) => {
    try {
        const body = JSON.parse(event.body);
        const userInput = body.userInput;

        // TODO: Call GPT API and return response

        return {
            statusCode: 200,
            body: JSON.stringify({ message: 'Success' }),
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*', // Required for CORS support
            },
        };
    } catch (error) {
        console.error('Error processing request:', error);
        return {
            statusCode: 500,
            body: JSON.stringify({ message: 'Error processing request' }),
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        };
    }
};

Python

import json

def lambda_handler(event, context):
    try:
        body = json.loads(event['body'])
        user_input = body['userInput']

        # TODO: Call GPT API and return response

        return {
            'statusCode': 200,
            'body': json.dumps({ 'message': 'Success' }),
            'headers': {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',  # Required for CORS support
            },
        }
    except Exception as error:
        print(f'Error processing request: {error}')
        return {
            'statusCode': 500,
            'body': json.dumps({ 'message': 'Error processing request' }),
            'headers': {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        }

Notes:

The event object contains the HTTP request data.
We parse the request body to extract userInput.
We set CORS headers in the response to allow requests from your front-end application.

Integrate OpenAI GPT API

Now, replace the // TODO section with code that calls the OpenAI GPT API.

Node.js Implementation

Node.js Code Example

First, install the OpenAI SDK:

npm install openai

Then, update your Lambda function code:

const { Configuration, OpenAIApi } = require('openai');

exports.handler = async (event) => {
    try {
        const body = JSON.parse(event.body);
        const userInput = body.userInput;

        const configuration = new Configuration({
            apiKey: process.env.OPENAI_API_KEY,
        });
        const openai = new OpenAIApi(configuration);

        const response = await openai.createCompletion({
            model: 'text-davinci-003',
            prompt: `Explain the following code:\n\n${userInput}`,
            temperature: 0.5,
            max_tokens: 150,
        });

        const explanation = response.data.choices[0].text.trim();

        return {
            statusCode: 200,
            body: JSON.stringify({ explanation }),
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        };
    } catch (error) {
        console.error('Error processing request:', error.response ? error.response.data : error.message);
        return {
            statusCode: 500,
            body: JSON.stringify({ message: 'Error processing request' }),
            headers: {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        };
    }
};

Notes:

We use the openai package to interact with the OpenAI API.
The prompt is formatted to ask the model to explain the provided code.
We handle errors gracefully and log them for debugging.

Python Implementation

Python Code Example

First, install the OpenAI package:

pip install openai

Then, update your Lambda function code:

import json
import os
import openai

def lambda_handler(event, context):
    try:
        body = json.loads(event['body'])
        user_input = body['userInput']

        openai.api_key = os.environ['OPENAI_API_KEY']

        response = openai.Completion.create(
            engine='text-davinci-003',
            prompt=f"Explain the following code:\n\n{user_input}",
            temperature=0.5,
            max_tokens=150,
        )

        explanation = response.choices[0].text.strip()

        return {
            'statusCode': 200,
            'body': json.dumps({ 'explanation': explanation }),
            'headers': {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        }
    except Exception as error:
        print(f'Error processing request: {error}')
        return {
            'statusCode': 500,
            'body': json.dumps({ 'message': 'Error processing request' }),
            'headers': {
                'Content-Type': 'application/json',
                'Access-Control-Allow-Origin': '*',
            },
        }

Notes:

We set the OpenAI API key from the environment variable.
The prompt asks the model to explain the provided code.
We return the explanation in the response body.

Route Incoming POST Requests to Your Lambda Function

Configure API Gateway to route incoming POST requests to your Lambda function.

In the API Gateway console, select your API.
Under Resources, create a new resource or select an existing one.
Click on Actions and choose Create Method.
Select POST and click the checkmark.
In the Integration Type, choose Lambda Function.
Select your Lambda function and save.

Enable CORS:

In the API Gateway console, select your API.
Click on Actions and choose Enable CORS.
Review the settings and confirm.

Deploy the API:

Click on Actions and select Deploy API.
Choose a Deployment Stage (e.g., ‘prod’).

Note: Make a note of the Invoke URL; you’ll need it for your front-end application.

Implementing Serverless Backend Resources:

Step 3: Build Front-End Interface

Using React, you’ll create a user-friendly interface where users can input code and receive explanations.

Set Up React Application

Initialize a new React project using create-react-app:

npx create-react-app code-explanation-engine

Navigate to your project directory:

cd code-explanation-engine

Start the development server:

npm start

Create Components

CodeExplainer Component

Example Code

import React, { useState } from 'react';

const CodeExplainer = () => {
  const [codeInput, setCodeInput] = useState('');
  const [explanation, setExplanation] = useState('');
  const [loading, setLoading] = useState(false);

  const handleFormSubmit = async (e) => {
    e.preventDefault();
    setExplanation('');
    setLoading(true);

    try {
      const response = await fetch('https://<your-api-gateway-url>', {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({ userInput: codeInput }),
      });

      if (!response.ok) {
        throw new Error('Network response was not ok');
      }

      const data = await response.json();
      setExplanation(data.explanation);
    } catch (error) {
      console.error('Error:', error);
      setExplanation('An error occurred while processing your request.');
    } finally {
      setLoading(false);
    }
  };

  return (
    <div>
      <h1>Code Explanation Engine</h1>
      <form onSubmit={handleFormSubmit}>
        <textarea
          value={codeInput}
          onChange={(e) => setCodeInput(e.target.value)}
          placeholder="Enter your code here"
          rows="10"
          cols="50"
        ></textarea>
        <br />
        <button type="submit" disabled={loading}>
          {loading ? 'Explaining...' : 'Explain Code'}
        </button>
      </form>
      {explanation && (
        <div>
          <h2>Explanation:</h2>
          <p>{explanation}</p>
        </div>
      )}
    </div>
  );
};

export default CodeExplainer;

Notes:

We use fetch to send a POST request to the API Gateway endpoint.
We send the code input in the request body under the key userInput, matching what the Lambda function expects.
We handle loading states and errors gracefully.
We display the explanation returned from the backend.

Update Front-End and Back-End Data Structures

Ensure that the keys used in the request and response match between the front-end and back-end.

In the front-end, we send { userInput: codeInput }.
In the back-end, we extract userInput from the request body.
The back-end returns { explanation: explanation }.
The front-end reads data.explanation.

Task: Review your code to ensure consistency in data structures and key names to prevent mismatches.

Hint

The mismatch occurs in two places:

Request Body Key: Ensure that the key in the request body sent from the front-end matches what the back-end expects.
- Front-end sends { userInput: codeInput }.
- Back-end reads body['userInput'].
Response Body Key: Ensure that the key in the response body from the back-end matches what the front-end expects.
- Back-end returns { 'explanation': explanation }.
- Front-end reads data.explanation.

Step 4: Test and Debug

Thoroughly test your application:

Functionality: Test with different code snippets to ensure the explanations are accurate.
Error Handling: Test how the application handles invalid input or API errors.
User Experience: Ensure the interface is intuitive and responsive.

Debugging Tips:

Lambda Logs: Use AWS CloudWatch to view logs from your Lambda function for debugging server-side issues.
Browser Console: Check the console for any client-side errors.
Network Tab: Use browser developer tools to inspect network requests and responses.

Testing Resources:

React Testing Library: Useful for testing React components.
Jest: JavaScript testing framework.

Step 5: Deploy the Application

Deploy Front-End to AWS S3

Build the React App:

npm run build

Create an S3 Bucket:
- Go to the AWS S3 console and create a new bucket (e.g., code-explanation-engine).
- Enable Static website hosting in the bucket properties.
- Set the Index document to index.html.
Upload Build Files:
- Upload the contents of the build folder to your S3 bucket.
Set Bucket Policy:
- Configure the bucket policy to allow public read access to the bucket contents.

Deployment Resources:

Update API Endpoint

Ensure that the API Gateway endpoint is correctly configured in your front-end code and that CORS settings allow requests from your domain.

Appendix

Selecting a Role

Creating an Execution Role for Lambda Function:

Go to the IAM service in AWS.
Click on Roles and then Create role.
Select Lambda as the service.
Attach the AWSLambdaBasicExecutionRole policy.
Name the role (e.g., lambda-basic-execution-role) and create it.

Assign the Role to Your Lambda Function:

In your Lambda function configuration, under Permissions, click Edit.
Choose Use an existing role and select the role you just created.
Save changes.

Note: This role grants permissions for the Lambda function to write logs to CloudWatch Logs.

Remember: Always follow the principle of least privilege. Only grant permissions that are necessary for your Lambda function.

Final Notes

Security: Do not expose your OpenAI API key in your code or in public repositories.
Costs: Be aware of potential costs associated with AWS services and OpenAI API usage.
Optimization: Consider implementing caching or rate limiting if you expect high traffic.

Happy Coding!

stemaway · June 21, 2023, 10:55pm

🟡 Combo: AWS WebApp, API Explorer, GPT Exploration

Code Explanation Engine: Integrating OpenAI with Serverless AWS Architecture and React

Objective

Learning Outcomes

General Understanding and Conceptualization

Step 1: Set Up AWS Account and Services

Create an AWS Free Tier Account

Create an API with API Gateway

Create an AWS Lambda Function

Install and Configure AWS CLI

Step 2: Implement Serverless Backend

Set Up AWS Lambda Function

Initial Lambda Function Code

JavaScript (Node.js)

Python

Integrate OpenAI GPT API

Node.js Implementation

Python Implementation

Route Incoming POST Requests to Your Lambda Function

Step 3: Build Front-End Interface

Set Up React Application

Create Components

CodeExplainer Component

Update Front-End and Back-End Data Structures

Step 4: Test and Debug

Step 5: Deploy the Application

Deploy Front-End to AWS S3

Update API Endpoint

Appendix

Final Notes

Recording of AWS Mentor Webinar (06/21)