Build AI-Powered Applications with Python Flask & Amazon Bedrock: Step-by-Step Guide

Actors and Components

  1. User:

    • Interacts with the application through the Web Interface (frontend).

  2. Frontend:

    • Web Interface:
      • Displays a user-friendly interface.
      • Collects input (prompt) from the user.
      • Sends the input to the backend for processing.

  3. Backend:

    • Flask Application:
      • Processes user input and communicates with Amazon Bedrock API.
      • Handles requests and returns AI-generated responses to the frontend.

  4. Amazon Bedrock:

    • Foundation Models:
      • Provides the AI capabilities (e.g., text generation, summarization).
      • Responds to API requests made by the Flask application.

  5. Deployment:

    • Two deployment options:
      • AWS Elastic Beanstalk: A managed service for deploying and scaling web applications.
      • AWS EC2: A virtual server in the cloud for custom deployment and management.

Workflow

  1. User Interaction:

    • The user inputs a prompt via the Web Interface.

  2. Request Handling:

    • The Web Interface sends the prompt to the Flask backend.

  3. AI Processing:

    • The Flask application forwards the request to the Amazon Bedrock API.
    • Bedrock invokes the foundation model to process the prompt.

  4. Response Generation:

    • The processed response is sent back to the Flask application.

  5. Response Delivery:

    • Flask delivers the AI-generated response to the Web Interface for display to the user.

  6. Deployment:

    • The application can be deployed using either Elastic Beanstalk or EC2 for scalability and accessibility.

This architecture ensures a seamless user experience while leveraging Amazon Bedrock's AI capabilities.


Here’s a comprehensive guide to building AI-driven applications using Python Flask and Amazon Bedrock, from scratch to deployment.

1. Overview

Amazon Bedrock allows developers to build and scale AI applications using foundation models (FMs) from providers like Anthropic, Stability AI, Cohere, and more, without managing infrastructure. Flask is a lightweight Python framework for building web applications.

2. Prerequisites

  1. AWS Account: Ensure access to Amazon Bedrock (check availability in your region).
  2. Python Environment:
    • Install Python 3.8+.
    • Install Flask: pip install flask.
  3. AWS SDK:
    • Install Boto3: pip install boto3.
  4. IDE: Use VS Code, PyCharm, or any preferred editor.
  5. AWS CLI: Configure it using aws configure.

3. Architecture

  1. Frontend: A simple web interface for user interaction.
  2. Backend: Flask application to handle requests and interact with Amazon Bedrock.
  3. AI Model: Use Amazon Bedrock to process AI-related tasks.
  4. Deployment: Deploy the application on AWS (Elastic Beanstalk or EC2).

4. Steps to Build the Application

Step 1: Set Up Flask Project

Create a Flask project structure:

ai_app/
├── app.py
├── templates/
│   └── index.html
├── static/
│   ├── style.css
│   └── script.js
└── requirements.txt

app.py

from flask import Flask, render_template, request, jsonify
import boto3
import os

app = Flask(__name__)

# Initialize Amazon Bedrock client
def get_bedrock_client():
    return boto3.client(
        'bedrock',
        region_name='us-east-1',  # Replace with your region
        aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
        aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
    )

@app.route("/")
def index():
    return render_template("index.html")

@app.route("/generate", methods=["POST"])
def generate():
    try:
        prompt = request.json.get("prompt")
        bedrock_client = get_bedrock_client()
        
        # Call Bedrock to generate text
        response = bedrock_client.invoke_model(
            modelId="anthropic.claude-v2",  # Replace with your desired model
            contentType="application/json",
            accept="application/json",
            body={"prompt": prompt, "maxTokens": 100}
        )
        generated_text = response["body"].read().decode("utf-8")
        return jsonify({"response": generated_text})
    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(debug=True)

requirements.txt

flask
boto3

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AI-Driven App</title>
    <link rel="stylesheet" href="/static/style.css">
</head>
<body>
    <h1>AI Text Generator</h1>
    <form id="ai-form">
        <textarea id="prompt" placeholder="Enter your prompt"></textarea>
        <button type="submit">Generate</button>
    </form>
    <div id="response"></div>
    <script src="/static/script.js"></script>
</body>
</html>

script.js

document.getElementById("ai-form").addEventListener("submit", async (e) => {
    e.preventDefault();
    const prompt = document.getElementById("prompt").value;
    const responseDiv = document.getElementById("response");

    responseDiv.innerHTML = "Generating...";
    const response = await fetch("/generate", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ prompt }),
    });

    const data = await response.json();
    responseDiv.innerHTML = data.response || data.error;
});

style.css

body {
    font-family: Arial, sans-serif;
    text-align: center;
    margin: 0;
    padding: 0;
}

h1 {
    margin: 20px;
}

form {
    margin: 20px;
}

textarea {
    width: 300px;
    height: 100px;
}

button {
    padding: 10px 20px;
    margin-top: 10px;
}

#response {
    margin: 20px;
    font-weight: bold;
}

5. Deploy the Application

Option 1: Elastic Beanstalk

  1. Initialize: Run eb init and configure your environment.
  2. Deploy: Run eb deploy to deploy your application.

Option 2: EC2

  1. Launch EC2 Instance: Choose a Python-compatible AMI.
  2. Setup Flask:
    sudo yum install python3
pip3 install flask boto3
  3. Run the App: Use nohup python3 app.py & to keep it running.

6. Testing

  1. Navigate to the deployed app URL.
  2. Enter a prompt in the text area.
  3. Click "Generate" to receive a response from Amazon Bedrock.

7. Enhancements

  1. Authentication: Secure API with AWS IAM roles or tokens.
  2. Advanced Models: Experiment with different Bedrock models.
  3. Scalability: Use AWS Lambda and API Gateway for a serverless backend.
  4. UI Improvements: Enhance the frontend with frameworks like React or Vue.js.

This approach ensures an end-to-end AI-driven application leveraging Amazon Bedrock's foundation models and Flask's simplicity for rapid development.


🎉 Master Python Web Development with Flask! 🚀

Want to build sleek, powerful web applications with Python? Look no further! 📚 "Building Web Apps with Python and Flask" by Malhar Lathkar is your ultimate guide to becoming a web development pro!

🔥 What’s Inside?

  • Core Flask Features: URL routing, templates, static files, cookies, and sessions.
  • Advanced Topics: Flask extensions, database integrations, and RESTful API deployment.
  • Flask Tools: Leverage Jinja2, Werkzeug, and more!
  • Build Scalable Apps: Learn blueprints, design patterns, and modular structures.

💡 Whether you're a Python enthusiast, a beginner, or a tech startup looking to scale, this guide is packed with practical insights to turbocharge your development skills.

📈 Why This Book?

  • Expert-led coverage from industry pro Malhar Lathkar.
  • Real-world projects to practice your skills.
  • Learn to deploy fully functional apps and APIs!

🚀 Ready to Build the Future of Web Apps? 💥 Grab your copy of "Building Web Apps with Python and Flask" today and take your Python web development journey to the next level! 🌐

Don’t miss out – unlock your web development potential NOW!

Popular posts from this blog

Learn Java 8 streams with an example - print odd/even numbers from Array and List

Image
Example 1: Java 8 program to print odd numbers from a List   import java.util.Arrays; import java.util.List; import java.util.stream.Collectors; /*Java 8 Program to find Odd Numbers from a List*/ public class DriverClass { public static void main( String [] args) { List < Integer > numbers = Arrays. asList( 1 , 4 , 8 , 40 , 11 , 22 , 33 , 99 ); List < Integer > oddNumbers = numbers.stream(). filter(o -> o % 2 != 0 ). collect(Collectors.toList()); System.out.println(oddNumbers); } } 1. Initialize List using Arrays.asList() method. Java's built-in Arrays.asList() method converts an array into a list. This method takes an array as an argument and returns a List object. 2.  Java List interface provides stream() method which returns a sequential Stream with list of Integer as its source. 3. The Java stream filter(...
Read more

Java Stream API - How to convert List of objects to another List of objects using Java streams?

Image
In this section, we will show you how to convert a List of objects to another List of objects in Java using the Java streams map(). The ‘map’ method maps each element to its corresponding result. Java Stream API   The Java Stream API provides a functional programming approach over the object oriented capabilities of Java. It is used for processing collections of objects. The Stream in Java can be defined as a sequence of elements from a source List, Set or Array. Most of the stream operations return a Stream. This avails engender a chain of stream operations(stream pipe-lining). The streams withal support the aggregate or terminal operations on the elements. for example, finding the sum or maximum element or finding the average etc...Stream operations can either be executed sequentially or parallel. when performed parallelly, it is called a parallel stream.   Stream map() Method   The Java 8 Stream map() is an intermediate operation. It converts Stream<obj1> to Str...
Read more

Registration and Login with Spring Boot + Spring Security + Thymeleaf

Image
In this section, we will learn how to create user registration and login using Spring boot, Spring security, Thymeleaf, JPA, and H2DB. The GitHub repository link is provided at the end of this tutorial. You can download the source code. Technologies used: Spring Boot 2.5.5  Spring Data JPA  Spring Security  Thymeleaf  Maven 3+  Java 17  H2 Database   User Interface User Registration Authentication Failed Authentication success Sign out Final Project Structure: Complete pom.xml: <? xml version ="1.0" encoding ="UTF-8" ?> < project xmlns ="http://maven.apache.org/POM/4.0.0" xmlns: xsi ="http://www.w3.org/2001/XMLSchema-instance" xsi :schemaLocation ="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd" > < modelVersion >4.0.0</ modelVersion > < parent > < groupId >org.springframework.boot</ groupId > < artifactId >spring-boot-starter-pare...
Read more

Java, Spring Boot Mini Project - Library Management System - Download

Image
Hello everyone, Hope you are doing well. Here, we provide a Library Management System mini-project for beginners, it's free for download. Maybe you can build more on top of this project and create your own product, that's all about you. The source code download link is provided at the end of this post. 1. Benefit of this project You could cover the following technologies and features by learning and debugging this mini project. Java 17 Java Stream Java Lamda Expressions Java Records Functional programming Object-oriented programming var Keyword in Java Generics in Java Java Switch Spring boot 2.6.4 Spring Security Thymeleaf template engine OpenCSV Spring Data JPA Server Side Pagination H2 Database  Bootstrap HTML CSS and more... 2. Objective Of Spring Boot On Library Management System The main objective of this project is to manage the details of the books, author, category, and publisher. Only Admin/Librarian will manage all these activities. Also, they can export the data in ...
Read more

ReactJS, Spring Boot JWT Authentication Example

Image
In this tutorial, we’ll create a user registration & login example using ReactJS, Spring Boot, Spring Security, and JWT authentication. You could download the source code from our Github repository, the download link is provided at the end of this tutorial. Technologies used Backend Technologies: Java 17 Spring Boot 2.7.0 Spring Security Spring Data JPA JWT H2 Database Frontend Technologies: React 17.0.1 Axios 0.27.2 Redux 4.0.5 Bootstrap 4.5.2 ReactJS - SpringBoot - JWT - Flow Backend Project Directory: Frontend Project Directory: Following is the screenshot of our application - User Registration: User Signin: Profile View: Access Resource: We will build two projects:  1. Backend:   spring-boot-security-jwt 2. Frontend: react-redux-jwt Project 1: spring-boot-security-jwt Pom.xml <?xml version = "1.0" encoding = "UTF-8" ?> <project xmlns = "http://maven.apache.org/POM/4.0.0" xmlns:xsi = "http://www.w3.org/2001/XMLSchema-instance"...
Read more

Top 5 Java ORM tools - 2024

Image
  The term ORM refers to object-relational mapping, which uses objects to adapt programming languages to database systems while allowing them to function with SQL and object-oriented programming ideas. Any kind of database management system that can accomplish object mapping to the table in the virtual system is suitable for the implementation of ORM. Java has a wide variety of ORM tools, some of which are mentioned below: 1. Hibernate Hibernate is a lightweight, open-source, non-invasive Java ORM (Object-Relational Mapping) framework that allows developers to create objects that function independently of database software and create persistence logic in any Java or JavaEE application. It offers an abstraction layer, so developers don't have to bother about implementations; Hibernate handles these tasks internally, such as connecting to databases and creating queries to carry out CRUD operations, among other things. Persistence logic development is done with it. Persistence logic r...
Read more

Java - Blowfish Encryption and decryption Example

Image
Blowfish is an encryption method developed by Bruce Schneier in 1993 as an alternative to the DES encryption method. It is significantly faster than DES and provides good encryption speed, although no effective cryptanalysis technique has been found to date. It is one of the first secure block ciphers that is not protected by any patents and is therefore freely available for anyone to use. This is a symmetric block cipher algorithm. Block size: 64 bits Key size: variable size from 32 to 448 bits number of subsections: 18 [P-array] number of rounds: 16 number of substitution blocks: 4 [each with 512 records of 32 bits each] develop  Example 1: Encryption and decryption using Blowfish import java.io.UnsupportedEncodingException; import java.nio.charset.Charset; import java.security.InvalidKeyException; import java.security.NoSuchAlgorithmException; import java.util.Base64; import javax.crypto.BadPaddingException; import javax.crypto.Cipher; import javax.crypto.IllegalBlockSiz...
Read more

Spring boot video streaming example-HTML5

Image
Hello everyone, Today we will learn how to stream MP4 video using Spring Boot and Spring Web Flux.  The Source code download link is provided at the end of this post. New, Quarkus Practice User Interface   Project Structure: G radle(build.gradle) plugins { id 'org.springframework.boot' version '2.5.4' id 'io.spring.dependency-management' version '1.0.11.RELEASE' id 'java' } group = 'com.knf.demo' version = '0.0.1-SNAPSHOT' sourceCompatibility = '11' repositories { mavenCentral() } dependencies { implementation 'org.springframework.boot:spring-boot-starter-webflux' testImplementation 'org.springframework.boot:spring-boot-starter-test' testImplementation 'io.projectreactor:reactor-test' } test { useJUnitPlatform() } Service (VideoStreamingService.java) package  com.knf.demo.service ; import org.springframework.beans.factory.annotation. Autowired ; import org.springframework...
Read more

Google Cloud Storage + Spring Boot - File Upload, Download, and Delete

Image
In this section, we will learn  how to  create a simple spring boot application to perform different file operations such as upload, download, list, and delete files from the Google Cloud Storage. 1.  A little bit of Background Google Cloud Storage Cloud Storage is a managed service for storing unstructured data. Store any amount of data and retrieve it as often as you like. More Info - click here! Spring Boot Spring Boot makes it easy to create stand-alone, production-grade Spring-based Applications that you can "just run".  More Info - click here! 2. Create a GCP Project First, Sign into the Google console at  https://console.cloud.google.com . You can create a new project by first selecting the project dropdown in the top left and selecting " New Project ". Next, specify your GCP  Project name  and  Project ID . Then  Click on the " CREATE " button. Copy " Project ID " and keep it for future purposes. 3. Create a Google Cloud Storage bucke...
Read more