Let’s create a generative AI-enabled web application from scratch using Python Flask. The application will provide a cloud security tip of the day by integrating with Amazon Bedrock using the amazon.titan-text-premier-v1:0 model.
This blog is an introduction to building a generative AI web application using Amazon Bedrock. The application provides a daily cloud security tip, however please note that detailed security aspects about Amazon Bedrock will be covered in the future. Stay tuned for more updates!
Prerequisites:
- An AWS account with access to Amazon Bedrock with `amazon.titan-text-premier-v1:0` foundation model enabled.
- Python installed on your system.
- Basic knowledge of Flask.
Steps:
- Create a new project directory:
mkdir cloud-security-tips
cd cloud-security-tips- Set up a virtual environment:
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`- Install Flask and Boto3 (AWS SDK for Python):
python3 -m pip install flask boto3- Create a filename
app.pyto create python3 flask app:
Use AWS secret manager or open source secret managers instead of hardcoded credentials.
touch app.pyfrom flask import Flask, render_template, request, jsonify
import boto3
import os
app = Flask(__name__)
# Load AWS credentials from environment variables
AWS_ACCESS_KEY = "AKIA2SAMPLE" #changethis
AWS_SECRET_KEY = "0nu-SAMPLEKEY-rJpP" #changethis
AWS_REGION = 'us-east-1' # Change to your region
# Initialize the Amazon Bedrock client
bedrock_client = boto3.client('bedrock-runtime',
aws_access_key_id=AWS_ACCESS_KEY,
aws_secret_access_key=AWS_SECRET_KEY,
region_name=AWS_REGION)
# Home route
@app.route('/')
def index():
return render_template('index.html')
# API route to get cloud security tip
@app.route('/get_tip', methods=['GET'])
def get_tip():
model_id = 'amazon.titan-text-premier-v1:0'
prompt = "Provide a cloud security tip of the day"
response = bedrock_client.converse(
modelId=model_id,
messages=[{"role": "user", "content": [{"text": prompt}]}]
)
tip = response['output']['message']['content'][0]['text']
return jsonify({'tip': tip})
if __name__ == '__main__':
app.run(debug=True)- Create a
templatesdirectory and anindex.htmlfile inside it:
mkdir templates
touch index.html<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Cloud Security Tips</title>
<style>
body { font-family: Arial, sans-serif; background-color: #f0f0f0; text-align: center; padding: 50px; }
#tip { font-size: 1.5em; margin-top: 20px; }
button { padding: 10px 20px; font-size: 1em; }
</style>
</head>
<body>
<h1>Cloud Security Tip of the Day</h1>
<button onclick="getTip()">Get Tip</button>
<div id="tip">Click the button to get today's cloud security tip!</div>
<script>
async function getTip() {
const response = await fetch('/get_tip');
const data = await response.json();
document.getElementById('tip').innerText = data.tip;
}
</script>
</body>
</html>- Run the Flask application:
python3 app.py- Open your browser and navigate to
http://127.0.0.1:5000. You should see the web page with a button to get the cloud security tip of the day.
Source code: https://github.com/justmorpheus
Explanation
- app.py:
– Imports necessary modules for setting up flask.
– Setup credentials (for testing), in production/reality do not hard-code credentials.
Conclusion
By following these steps, you can build and run a generative AI web application using Python Flask and Amazon Bedrock.
In case of issues associated with
converse method not available, refer to github issues.
