Website Projects

Practical AI projects for websites, learn AI skills

Here are some practical AI projects you can integrate into your website, courtesy of Grok. These projects will not only enhance a website, but they can also help build a portfolio as an aspiring AI engineer. These projects are tied to familiar technologies like Python and Flask, common add-ins like Bootstrap, and AI technologies like machine learning. The projects are actionable, with a mix of beginner-friendly and intermediate challenges.

You can find a summary of the projects and links to the technologies employed at the bottom of the page.

AI-Powered Chatbot with Context Awareness

Upgrade the basic chatbot to maintain conversation history and respond contextually. Users get a smarter assistant (e.g., "What"s AI?" ? "Can you explain neural networks?" keeps the thread). Add to the bottom-right of a site for visitor support.

How to Build

Tech: Python, Flask, Hugging Face Transformers.
Steps: Modify the Flask backend to store chat history:

python

from flask import Flask, request, jsonify

from transformers import AutoModelForCausalLM, AutoTokenizer



app = Flask(__name__)

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")

model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")

chat_history = []



@app.route('/chat', methods=['POST'])

def chat():

user_input = request.json.get('message')

chat_history.append(user_input)

input_text = " || ".join(chat_history[-3:]) + tokenizer.eos_token # Last 3 messages

input_ids = tokenizer.encode(input_text, return_tensors="pt")

response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)

response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

chat_history.append(response)

return jsonify({'response': response})



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Update the frontend to display the full conversation. For an enhancement, add English/Spanish toggle using i18next.
Skills Gained: NLP, API design, state management.

Content Recommendation Engine

Suggest articles, tools, or resources based on user behavior or preferences. Personalizes the site experience (e.g., "Liked an AI ethics article? Here"s more!"). Perfect for a blog or resource hub.

How to Build

Tech: Python, scikit-learn (basic ML), JavaScript.
Steps: Collect data (e.g., page titles or tags like "AI Basics," "Deep Learning"). Use a simple collaborative filtering model:

python

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity



articles = ["AI Basics", "Deep Learning Explained", "Ethics in AI"]

vectorizer = TfidfVectorizer()

tfidf_matrix = vectorizer.fit_transform(articles)

similarity = cosine_similarity(tfidf_matrix)



def recommend(index):

similar = similarity[index]

return [articles[i] for i in similar.argsort()[-2:][::-1]]



print(recommend(0)) # Suggests based on "AI Basics"

Serve via Flask and display in a Bootstrap card on the site:

html

<div class="card">

<div class="card-body">

<h5 class="card-title">Recommended for You</h5>

<p id="recommendation"></p>

</div>

</div>

<script>

fetch('/recommend?index=0')

.then(response => response.json())

.then(data => document.getElementById('recommendation').textContent = 
data.join(', '));

</script>

Enhancement: Track clicks with JavaScript to refine recommendations over time.
Skills Gained: Machine learning, data processing, front-end integration.

Sentiment Analysis Feedback Tool

Sentiment analysis tools use natural language processing to analyse text data, determining the emotional tone behind it.  This project involves user feedback comments and AI to analyze sentiment (positive, negative, neutral). Gives site owners insights into user opinions automatically. Add to a "Contact" or "Feedback" page.

How to Build

Tech: Python, NLTK/VADER or Hugging Face, Bootstrap form.
Steps: Use a pre-trained sentiment model:

python

from flask import Flask, request, jsonify

from transformers import pipeline



app = Flask(__name__)

sentiment_analyzer = pipeline("sentiment-analysis")



@app.route('/feedback', methods=['POST'])

def feedback():

text = request.json.get('feedback')

result = sentiment_analyzer(text)[0]

return jsonify({'sentiment': result['label'], 'score': result['score']})



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Add a form to the site:

html

<form id="feedback-form" class="mt-3">

<textarea class="form-control" id="feedback" placeholder="Your 
thoughts..."></textarea>

<button type="submit" class="btn btn-primary mt-2">Submit</button>

<p id="result"></p>

</form>

<script>

document.getElementById('feedback-form').addEventListener('submit', async (e) => 
{

e.preventDefault();

const feedback = document.getElementById('feedback').value;

const response = await fetch('/feedback', {

method: 'POST',

headers: { 'Content-Type': 'application/json' },

body: JSON.stringify({ feedback })

});

const data = await response.json();

document.getElementById('result').textContent = `Sentiment: ${data.sentiment} 
(${data.score})`;

});

</script>

Enhancement: Store results in a database (e.g., SQLite) for later analysis.
Skills Gained: NLP, UI design, data storage.

Image Recognition Upload Tool

Let users upload images, and use AI to identify objects or categorize them. Interactive and showcases computer vision. Could be a demo feature on an AI-focused site.

How to Build

Tech: Python, Flask, TensorFlow (pre-trained model like MobileNet), HTML file upload.
Steps:Set up Flask to handle uploads:

python

from flask import Flask, request, jsonify

from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, 
preprocess_input, decode_predictions

from PIL import Image

import numpy as np



app = Flask(__name__)

model = MobileNetV2(weights='imagenet')



@app.route('/upload', methods=['POST'])

def upload():

file = request.files['image']

img = Image.open(file).resize((224, 224))

img_array = np.array(img)[np.newaxis, ...]

img_array = preprocess_input(img_array)

preds = model.predict(img_array)

results = decode_predictions(preds, top=3)[0]

return jsonify [{'label': label, 'probability': float(prob)} for _, label, prob 
in results])



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Add an upload form:

html

<form id="upload-form" enctype="multipart/form-data" class="mt-3">

<input type="file" class="form-control" id="image" accept="image/*">

<button type="submit" class="btn btn-primary mt-2">Analyze</button>

<ul id="results"></ul>

</form>

<script>

document.getElementById('upload-form').addEventListener('submit', async (e) => {

e.preventDefault();

const formData = new FormData();

formData.append('image', document.getElementById('image').files[0]);

const response = await fetch('/upload', { method: 'POST', body: formData });

const data = await response.json();

const results = document.getElementById('results');

results.innerHTML = data.map(r => `<li>${r.label}: 
${r.probability.toFixed(2)}</li>`).join('');

});

</script>

Enhancement: Add multilingual labels (e.g., Spanish translations of results).
Skills Gained: Computer vision, file handling, API integration.

AI-Generated Content Preview

Generate short text snippets (e.g., article intros) using AI when users click a button. Shows off generative AI in action. Great for a blog or creative section.

How to Build

Tech: Python, Flask, Hugging Face (e.g., GPT-2).
Steps: Backend with generative AI:

python

from flask import Flask, jsonify

from transformers import pipeline



app = Flask(__name__)

generator = pipeline('text-generation', model='gpt2')



@app.route('/generate', methods=['GET'])

def generate():

text = generator("AI is transforming", max_length=50, 
num_return_sequences=1)[0]['generated_text']

return jsonify({'text': text})



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Frontend button:

html

<button class="btn btn-primary" onclick="generateText()">Generate AI 
Text</button>

<p id="generated"></p>

<script>

async function generateText() {

const response = await fetch('/generate');

const data = await response.json();

document.getElementById('generated').textContent = data.text;

}

</script>

Enhancement: Add a dropdown for English/Spanish output.
Skills Gained: Generative AI, UI interactivity.
Integration with Website
Navbar: Add links to these features in the Bootstrap navbar (e.g., "Chat," "Recommendations," "Feedback").

Styling: Use Bootstrap classes for consistency (e.g., btn, card, form-control).
Deployment: Host on Heroku or AWS.

AI-Powered Quiz Generator

Create a dynamic quiz where AI generates questions and evaluates answers based on a topic (e.g., AI basics). Engages users interactively, perfect for educational or fun sites. Add to a "Learn AI" section.

How to Build

Tech: Python, Flask, Hugging Face (text generation), Bootstrap.
Steps: Backend to generate questions:

python

from flask import Flask, jsonify

from transformers import pipeline



app = Flask(__name__)

generator = pipeline('text-generation', model='gpt2')



@app.route('/quiz', methods=['GET'])

def quiz():

prompt = "Generate a question about artificial intelligence: "

question = generator(prompt, max_length=50, 
num_return_sequences=1)[0]['generated_text'].split(': ')[1]

return jsonify({'question': question, 'answer': 'Yes'}) # Simplified answer 
logic



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Frontend with quiz UI:

html

<div class="container mt-3">

<h3>AI Quiz</h3>

<p id="question"></p>

<input type="text" class="form-control" id="answer" placeholder="Your answer">

<button class="btn btn-primary mt-2" onclick="checkAnswer()">Submit</button>

<p id="result"></p>

</div>

<script>

async function loadQuestion() {

const response = await fetch('/quiz');

const data = await response.json();

document.getElementById('question').textContent = data.question;

}

async function checkAnswer() {

const userAnswer = document.getElementById('answer').value;

const result = userAnswer.toLowerCase().includes('yes') ? 'Correct!' : 'Try 
again!';

document.getElementById('result').textContent = result;

}

loadQuestion();

</script>

Enhancement: Use NLP to evaluate free-text answers (e.g., compare to a keyword list) or add multilingual questions.
Skills Gained: Generative AI, user interaction, basic NLP.

Real-Time Text Summarizer

Let users paste an article or text, and AI generates a short summary instantly. Saves time for visitors, showcases NLP power. Useful for a news or blog section.

How to Build

Tech: Python, Flask, Hugging Face (summarization model).
Steps: Backend with summarization:

python

from flask import Flask, request, jsonify

from transformers import pipeline



app = Flask(__name__)

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")



@app.route('/summarize', methods=['POST'])

def summarize():

text = request.json.get('text')

summary = summarizer(text, max_length=100, min_length=30, 
do_sample=False)[0]['summary_text']

return jsonify({'summary': summary})



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Frontend form:

html

<div class="container mt-3">

<textarea class="form-control" id="input-text" rows="5" placeholder="Paste text 
here..."></textarea>

<button class="btn btn-primary mt-2" onclick="summarize()">Summarize</button>

<p id="summary"></p>

</div>

<script>

async function summarize() {

const text = document.getElementById('input-text').value;

const response = await fetch('/summarize', {

method: 'POST',

headers: { 'Content-Type': 'application/json' },

body: JSON.stringify({ text })

});

const data = await response.json();

document.getElementById('summary').textContent = data.summary;

}

</script>

Enhancement: Add a "Summarize in Spanish" option by switching models or translating output.
Skills Gained: NLP, text processing, UI design.

AI-Driven Search Autocomplete

Enhance a website search bar with AI-powered suggestions as users type. Improves navigation, feels snappy and smart. Upgrade the navbar search from our Bootstrap example.

How to Build

Tech: Python, Flask, simple ML (or pre-trained embeddings), JavaScript.
Steps: Backend with basic autocomplete (using a word list for simplicity):

python

from flask import Flask, request, jsonify



app = Flask(__name__)

suggestions = ["AI Basics", "Deep Learning", "Neural Networks", "Ethics in AI"]



@app.route('/autocomplete', methods=['GET'])

def autocomplete():

query = request.args.get('query', '').lower()

matches = [s for s in suggestions if query in s.lower()]

return jsonify(matches[:3])



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Frontend search bar (add to navbar):

html

<form class="d-flex ms-auto">

<input class="form-control me-2" type="text" id="search" placeholder="Search" 
oninput="suggest()">

<div id="suggestions" class="dropdown-menu show"></div>

</form>

<script>

async function suggest() {

const query = document.getElementById('search').value;

if (query.length < 2) return;

const response = await fetch(`/autocomplete?query=${query}`);

const data = await response.json();

const suggestions = document.getElementById('suggestions');

suggestions.innerHTML = data.map(s => `<a 
class="dropdown-item">${s}</a>`).join('');

}

</script>

Enhancement: Use a pre-trained embeddings model (e.g., SentenceTransformers) to suggest based on meaning, not just keywords.
Skills Gained: Real-time processing, UI responsiveness, basic ML.

Voice-Activated AI Assistant

Add a voice interface where users can speak to an AI (e.g., ask questions) via the website. Futuristic and accessible, great for hands-free use. A standout feature for tech demos.

How to Build

Tech: JavaScript (Web Speech API), Flask, Hugging Face.
Steps: Backend (reuse chatbot logic from earlier). Frontend with voice input:

html

<div class="container mt-3">

<button class="btn btn-primary" onclick="startListening()">Start 
Talking</button>

<p id="transcript"></p>

<p id="response"></p>

</div>

<script>

const recognition = new (window.SpeechRecognition || 
window.webkitSpeechRecognition)();

recognition.lang = 'en-US'; // Or 'es-ES' for Spanish

recognition.onresult = async (event) => {

const transcript = event.results[0][0].transcript;

document.getElementById('transcript').textContent = `You said: ${transcript}`;

const response = await fetch('/chat', {

method: 'POST',

headers: { 'Content-Type': 'application/json' },

body: JSON.stringify({ message: transcript })

});

const data = await response.json();

document.getElementById('response').textContent = `AI: ${data.response}`;

};

function startListening() { recognition.start(); }

</script>

Enhancement: Add text-to-speech (e.g., SpeechSynthesis) so the AI talks back.
Skills Gained: Speech recognition, API integration, accessibility.

AI-Personalized Theme Switcher

Use AI to suggest and apply website themes (e.g., light/dark) based on user preferences or time of day. Subtle but smart personalization. Enhances user experience across the site.

How to Build

Tech: JavaScript, Flask, simple ML (or rule-based for simplicity).
Steps: Backend to predict preference:

python

from flask import Flask, jsonify

import datetime



app = Flask(__name__)



@app.route('/theme', methods=['GET'])

def theme():

hour = datetime.datetime.now().hour

theme = 'dark' if 18 <= hour <= 6 else 'light' # Night = dark

return jsonify({'theme': theme})



if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)

Frontend to apply theme:

html

<nav class="navbar navbar-expand-lg" id="navbar">

<a class="navbar-brand" href="#">AI World</a>

<button class="btn btn-secondary ms-auto" onclick="loadTheme()">Switch 
Theme</button>

</nav>

<script>

async function loadTheme() {

const response = await fetch('/theme');

const data = await response.json();

const navbar = document.getElementById('navbar');

navbar.className = `navbar navbar-expand-lg navbar-${data.theme} 
bg-${data.theme}`;

document.body.style.backgroundColor = data.theme === 'dark' ? '#333' : '#fff';

}

loadTheme();

</script>

Enhancement: Use a basic ML model (e.g., decision tree) trained on user clicks to predict preferences.
Skills Gained: UI manipulation, basic ML, time-based logic.

Integration Tips
Navbar: Add these as menu items (e.g., "Quiz," "Summarize," "Search") in our Bootstrap navbar.

Multilingual: Extend with English/Spanish using i18next or manual toggles.
Portfolio Boost: Deploy these on a site or GitHub to show off your AI engineering chops.

Summary

AI-Powered Chatbot with Context Awareness
Upgrade a basic chatbot to maintain conversation history and respond contextually. Users get a smarter assistant (e.g., What is AI? or Can you explain neural networks).
Skills Gained: NLP, API design, state management.

Content Recommendation Engine
Suggest articles, tools, or resources based on user behavior or preferences. Personalizes the site experience (e.g., Liked an AI ethics article? Here"s more!).
Skills Gained: Machine learning, data processing, front-end integration.

Sentiment Analysis Feedback Tool
Let users submit feedback and use AI to analyze sentiment (positive, negative, neutral). Gives site owners insights into user opinions automatically.
Skills Gained: NLP, UI design, data storage.

Image Recognition Upload Tool
Let users upload images, and use AI to identify objects or categorize them. Interactive and showcases computer vision. Could be a demo feature on an AI-focused site.
Skills Gained: Computer vision, file handling, API integration.

AI-Generated Content Preview
Generate short text snippets (e.g., article intros) using AI when users click a button. Shows off generative AI in action. Great for a blog or creative section.
Skills Gained: Generative AI, UI interactivity.

AI-Powered Quiz Generator
Create a dynamic quiz where AI generates questions and evaluates answers based on a topic (e.g., AI basics). Engages users interactively, perfect for educational or fun sites.
Skills Gained: Generative AI, user interaction, basic NLP.

Real-Time Text Summarizer
Let users paste an article or text, and AI generates a short summary instantly. Saves time for visitors, showcases NLP power. Useful for a news or blog section.
Skills Gained: NLP, text processing, UI design.

AI-Driven Search Autocomplete
Enhance a website search bar with AI-powered suggestions as users type. Improves navigation, feels snappy and smart. Upgrade the navbar search from Bootstrap CSS.
Skills Gained: Real-time processing, UI responsiveness, basic ML.

Voice-Activated AI Assistant
Add a voice interface where users can ask questions to an AI via the website. Futuristic and accessible, great for hands-free use. A great feature for tech demos.
Skills Gained: UI manipulation, basic ML, time-based logic.

AI-Personalized Theme Switcher
Use AI to suggest and apply website themes, light or dark, based on user preferences or time of day. Subtle but smart personalization. Enhances user experience across the site.
Skills Gained: UI manipulation, basic ML, time-based logic.

 

Links

Python programming language

Flask web framework

Hugging Face platform for machine learning

Scikit-learn machine learning in Python

JavaScript programming language

Bootstrap CSS provider

TensorFlow platform for machine learning