This content originally appeared on DEV Community and was authored by Stanley Wanjohi
Artificial intelligence is no longer something out of the alien world but a normality, making its way into web development. The ability to apply AI to web development opens numerous possibilities for developing prestigious smart applications that are responsive to the user’s behaviours while providing a personalized interface. In this article, we will discuss the application of AI in web development and supply a couple of samples.
Web Development and Why it Matters with the Help of AI
AI improves web development by automating work to be done, adding interactions, and providing suggestive information. Some of the core benefits include: Some of the core benefits include:
Automation of Repetitive Tasks: AI-based applications help perform repetitive tasks such as testing, debugging and even code generation. This ability enables developers to tackle other pressing issues in web development.
Enhanced User Experience: By leveraging AI in the platforms, it is possible to find user patterns and provide content, suggestions and interactivities to fulfil their wants and needs.
Better Analytics and Decision-Making: AI can analyze large volumes of data within the shortest time possible, enabling developers to make the right decisions depending on the website performance, conversion rates and other parameters.
Now that we know different types of AI to use in web development, here are some code examples of their use.
The use of a basic AI-Chatbot by writing JavaScript
AI technology has become widely incorporated into Web Development, and one of the most frequently used Web development is a chatbot. Chatbots help in customer support service to their machine, enable answering of frequently asked questions and more.
Example of how to create a simple AI-powered chatbot using JavaScript
<!DOCTYPE html>
<html>
<head>
<title>Simple AI Chatbot</title>
</head>
<body>
<div>
<h2>AI Chatbot</h2>
<div id="chatbox"></div>
<input type="text" id="userInput" placeholder="Type a message">
<button onclick="getResponse()">Send</button>
</div>
<script>
const responses = {
"hi": "Hello! How can I assist you today?",
"bye": "Goodbye! Have a great day!",
"help": "Sure, what do you need help with?",
"default": "Sorry, I don't understand that."
};
function getResponse() {
const userInput = document.getElementById("userInput").value.toLowerCase();
const chatbox = document.getElementById("chatbox");
let response = responses[userInput] || responses["default"];
chatbox.innerHTML += `<p>User: ${userInput}</p>`;
chatbox.innerHTML += `<p>Bot: ${response}</p>`;
}
</script>
</body>
</html>
This AI concept only works through the command line responding to predefined inputs ‘hi’, ‘bye’, and ‘help’. It helps you understand the initial response of AI to any user’s input. There is a potential to make this chatbot much smarter using machine learning models or the writing API function called natural language processing, NLP, i.e., Dialogflow.
Personalizing User Experience with AI and Python (Flask)
It is part of the more modern web design that a site works in a way that meets the individual needs of the users. Self-organizing page content depends on the type of visitor, geographical location, or interests. In this example, for the sake of the demonstration, we will be using Flask, a Python-based microweb framework, to implement the recommendation system.
Install Flask
# Install Flask
pip install Flask
Flask app that recommends articles based on user preferences
from flask import Flask, render_template, request
app = Flask(__name__)
articles = {
"tech": ["AI in Web Development", "The Future of Cloud Computing"],
"health": ["Healthy Living Tips", "Meditation for Beginners"],
"sports": ["Top Football Players", "The History of the Olympics"]
}
@app.route('/')
def index():
return render_template('index.html')
@app.route('/recommend', methods=['POST'])
def recommend():
user_preference = request.form.get('preference')
recommendations = articles.get(user_preference, ["No recommendations available"])
return render_template('recommend.html', recommendations=recommendations)
if __name__ == '__main__':
app.run(debug=True)
HTML form to capture user input
<!-- index.html -->
<!DOCTYPE html>
<html>
<head>
<title>AI Recommendations</title>
</head>
<body>
<h1>Choose Your Preference</h1>
<form action="/recommend" method="POST">
<select name="preference">
<option value="tech">Tech</option>
<option value="health">Health</option>
<option value="sports">Sports</option>
</select>
<button type="submit">Get Recommendations</button>
</form>
</body>
</html>
If a user chooses, the Flask app shall give the next articles of their choice. You could take this even further by feeding these machine learning models the user data to begin making more accurate predictions of the user’s preferences.
Image recognition in web development
AI does not only occur through typing. Image recognition is an important technique employed in web apps for facial recognition, object detection and even automation of naming and tagging images.
Through TensorFlow.JS, there are opportunities to implement machine learning models in a browser for image recognition. Here’s a quick example of using a pre-trained model to identify objects in an image: Here’s a quick example of using a pre-trained model to identify objects in an image:
<!DOCTYPE html>
<html>
<head>
<title>Image Recognition with TensorFlow.js</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet"></script>
</head>
<body>
<h1>Image Recognition</h1>
<input type="file" accept="image/*" onchange="loadImage(event)">
<img id="image" width="400" alt="Upload an image">
<script>
let model;
async function loadModel() {
model = await mobilenet.load();
console.log('Model loaded');
}
function loadImage(event) {
const image = document.getElementById('image');
image.src = URL.createObjectURL(event.target.files[0]);
identifyImage(image);
}
async function identifyImage(image) {
const predictions = await model.classify(image);
alert(predictions[0].className + " - " + predictions[0].probability.toFixed(2));
}
loadModel();
</script>
</body>
</html>
This app does not have many features and is quite basic, though the software that underpins this app is TensorFlow.js and the MobileNet model to classify the uploaded user’s images. The object will be detected when the image is uploaded and gives a confidence score.
Conclusion
There are many proposed ways to implement AI technology in website development to improve user interaction and make developmental processes efficient. Whether you are developing applications such as chatbots, recommendation systems or image recognition elements, AI tools and libraries help integrate intelligence into applications. Therefore, any developer has to keep in touch with these trends as AI technology advances.
This content originally appeared on DEV Community and was authored by Stanley Wanjohi
Stanley Wanjohi | Sciencx (2024-08-22T00:54:28+00:00) Mastering AI in Web Development: How to Boost Efficiency and Create Smarter Apps. Retrieved from https://www.scien.cx/2024/08/22/mastering-ai-in-web-development-how-to-boost-efficiency-and-create-smarter-apps/
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