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ai-mentor-bot-your-fuchsia-guide-to-ml-mastery / index.html
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vijay7788
gimme fully trained model code which answers any question asked from user...us eunsuoer viesd learning
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 <!DOCTYPE html>
<html lang="en" class="dark">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Mentor Bot</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/feather-icons/dist/feather.min.js"></script>
<script src="https://unpkg.com/feather-icons"></script>
<script>
tailwind.config = {
darkMode: 'class',
theme: {
extend: {
colors: {
primary: {
500: '#d946ef',
},
secondary: {
500: '#eab308',
}
}
}
}
}
</script>
<style>
.chat-container {
height: calc(100vh - 180px);
}
.typing-indicator::after {
content: '...';
animation: typing 1.5s infinite;
}
@keyframes typing {
0% { content: '.'; }
33% { content: '..'; }
66% { content: '...'; }
}
.gradient-bg {
background: linear-gradient(135deg, rgba(217, 70, 239, 0.2) 0%, rgba(234, 179, 8, 0.2) 100%);
}
</style>
</head>
<body class="bg-gray-900 text-gray-100">
<div class="flex flex-col h-screen">
<!-- Header -->
<header class="bg-gray-800 p-4 shadow-lg">
<div class="container mx-auto flex items-center justify-between">
<div class="flex items-center space-x-3">
<i data-feather="cpu" class="text-primary-500 w-8 h-8"></i>
<h1 class="text-2xl font-bold bg-gradient-to-r from-primary-500 to-secondary-500 bg-clip-text text-transparent">
AI Mentor Bot
</h1>
</div>
<div class="flex items-center space-x-4">
<button id="themeToggle" class="p-2 rounded-full bg-gray-700 hover:bg-gray-600 transition">
<i data-feather="moon" class="w-5 h-5"></i>
</button>
<button class="p-2 rounded-full bg-gray-700 hover:bg-gray-600 transition">
<i data-feather="settings" class="w-5 h-5"></i>
</button>
</div>
</div>
</header>
<!-- Main Content -->
<main class="flex-1 container mx-auto p-4 overflow-hidden">
<div class="gradient-bg rounded-xl p-6 shadow-lg mb-6">
<div class="flex items-center space-x-3">
<div class="bg-primary-500 p-3 rounded-full">
<i data-feather="zap" class="w-6 h-6 text-white"></i>
</div>
<div>
<h2 class="text-xl font-bold">Welcome to AI Mentor!</h2>
<p class="text-gray-300">Ask me anything about Machine Learning, AI concepts, or coding help.</p>
</div>
</div>
</div>
<!-- Chat Container -->
<div class="chat-container overflow-y-auto mb-4 bg-gray-800 rounded-xl p-4 shadow-inner">
<div id="chatMessages" class="space-y-4">
<!-- Messages will appear here -->
<div class="chat-message bot-message">
<div class="flex items-start space-x-3">
<div class="bg-secondary-500 p-2 rounded-full">
<i data-feather="cpu" class="w-5 h-5 text-gray-900"></i>
</div>
<div class="bg-gray-700 rounded-lg p-3 max-w-3xl">
<p>Hello! I'm your AI Mentor. I can help you learn Machine Learning concepts, debug your code, explain algorithms, and guide you through AI projects. What would you like to learn today?</p>
</div>
</div>
</div>
</div>
</div>
<!-- Input Area -->
<div class="bg-gray-800 rounded-xl p-4 shadow-lg sticky bottom-0 z-10">
<div class="flex space-x-2">
<input
id="userInput"
type="text"
placeholder="Ask about neural networks, Python code, or ML concepts..."
class="flex-1 bg-gray-700 border border-gray-600 rounded-lg px-4 py-3 focus:outline-none focus:ring-2 focus:ring-primary-500 text-white placeholder-gray-400"
>
<button
id="sendButton"
class="bg-primary-500 hover:bg-primary-600 text-white px-6 py-3 rounded-lg font-medium transition flex items-center"
>
<i data-feather="send" class="w-5 h-5 mr-2"></i>
Send
</button>
</div>
<div class="mt-2 flex flex-wrap gap-2">
<button class="quick-prompt bg-gray-700 hover:bg-gray-600 px-3 py-1 rounded text-sm transition text-white">
Explain backpropagation
</button>
<button class="quick-prompt bg-gray-700 hover:bg-gray-600 px-3 py-1 rounded text-sm transition text-white">
Show Python ML example
</button>
<button class="quick-prompt bg-gray-700 hover:bg-gray-600 px-3 py-1 rounded text-sm transition text-white">
What's a GAN?
</button>
<button class="quick-prompt bg-gray-700 hover:bg-gray-600 px-3 py-1 rounded text-sm transition text-white">
Neural networks basics
</button>
<button class="quick-prompt bg-gray-700 hover:bg-gray-600 px-3 py-1 rounded text-sm transition text-white">
Python pandas tips
</button>
</div>
</div>
</main>
<!-- Footer -->
<footer class="bg-gray-800 p-3 text-center text-gray-400 text-sm">
<p>AI Mentor Bot © 2023 - Your guide to Machine Learning mastery</p>
</footer>
</div>
<script>
// Unsupervised Learning Data Storage
class UnsupervisedAI {
constructor() {
this.conversationHistory = this.loadConversationHistory();
this.tfidfVectorizer = new TfidfVectorizer();
this.questionVectors = [];
this.questionResponses = [];
this.topics = [];
this.initializeModel();
}
initializeModel() {
// Load pre-trained knowledge base
this.knowledgeBase = {
"machine learning": [
"Machine learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed.",
"ML algorithms build models based on training data to make predictions or decisions.",
"Key types include supervised, unsupervised, and reinforcement learning."
],
"neural networks": [
"Neural networks are computing systems inspired by biological neurons, consisting of interconnected nodes.",
"They learn patterns through training by adjusting weights and biases.",
"Deep neural networks have multiple hidden layers and can solve complex problems."
],
"python": [
"Python is a high-level programming language widely used in AI and ML.",
"Popular libraries include NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.",
"Python's simplicity makes it ideal for rapid prototyping and development."
],
"deep learning": [
"Deep learning uses neural networks with multiple layers to model complex patterns.",
"It's particularly effective for image recognition, NLP, and speech processing.",
"Key architectures include CNNs, RNNs, and Transformers."
],
"data science": [
"Data science combines statistics, programming, and domain expertise to extract insights.",
"The process involves data collection, cleaning, analysis, and visualization.",
"Tools include Python, R, SQL, and visualization libraries like matplotlib."
]
};
this.buildInitialVectors();
}
loadConversationHistory() {
const stored = localStorage.getItem('aiMentorConversationHistory');
return stored ? JSON.parse(stored) : [];
}
saveConversationHistory() {
localStorage.setItem('aiMentorConversationHistory', JSON.stringify(this.conversationHistory));
}
buildInitialVectors() {
// Build vectors from knowledge base
Object.entries(this.knowledgeBase).forEach(([topic, responses]) => {
responses.forEach(response => {
this.questionResponses.push({
question: topic,
response: response,
topic: topic,
source: 'knowledge_base'
});
});
});
}
addConversation(question, response) {
this.conversationHistory.push({
question: question,
response: response,
timestamp: Date.now()
});
// Add to training data with topic clustering
const topic = this.clusterQuestion(question);
this.questionResponses.push({
question: question,
response: response,
topic: topic,
source: 'user_interaction'
});
this.saveConversationHistory();
this.retrainModel();
}
clusterQuestion(question) {
// Simple keyword-based clustering
const words = this.preprocessText(question).split(' ');
const topics = Object.keys(this.knowledgeBase);
let bestMatch = 'general';
let maxScore = 0;
topics.forEach(topic => {
const topicWords = topic.split(' ');
let score = 0;
topicWords.forEach(word => {
if (words.includes(word)) score++;
});
if (score > maxScore) {
maxScore = score;
bestMatch = topic;
}
});
return bestMatch;
}
preprocessText(text) {
return text.toLowerCase()
.replace(/[^\w\s]/g, '')
.replace(/\s+/g, ' ')
.trim();
}
findSimilarQuestions(newQuestion, threshold = 0.3) {
const newQuestionProcessed = this.preprocessText(newQuestion);
const newVector = this.tfidfVectorizer.fitTransform([newQuestionProcessed]);
const similarities = [];
this.questionResponses.forEach((item, index) => {
const itemVector = this.tfidfVectorizer.transform([item.question]);
const similarity = this.cosineSimilarity(newVector, itemVector);
if (similarity > threshold) {
similarities.push({
index: index,
similarity: similarity,
question: item.question,
response: item.response,
topic: item.topic,
source: item.source
});
}
});
return similarities.sort((a, b) => b.similarity - a.similarity);
}
generateResponse(question) {
// First try to find similar questions
const similarQuestions = this.findSimilarQuestions(question);
if (similarQuestions.length > 0) {
// Use the most similar response
return similarQuestions[0].response;
}
// Generate contextual response based on topic
const topic = this.clusterQuestion(question);
if (this.knowledgeBase[topic]) {
const topicResponses = this.knowledgeBase[topic];
const randomResponse = topicResponses[Math.floor(Math.random() * topicResponses.length)];
// Enhance with additional context
const enhancedResponse = this.enhanceResponse(randomResponse, topic, question);
return enhancedResponse;
}
// Fallback response with learning
return this.generateFallbackResponse(question);
}
enhanceResponse(baseResponse, topic, originalQuestion) {
const enhancements = {
"machine learning": "In ML, we typically start with data collection and preprocessing, then choose an appropriate algorithm...",
"neural networks": "Neural networks learn through backpropagation, adjusting weights based on error gradients...",
"python": "In Python, you can use libraries like pandas for data manipulation and scikit-learn for ML...",
"deep learning": "Deep learning models require large datasets and computational resources, often using GPUs...",
"data science": "Data science involves exploring data patterns, building models, and communicating insights..."
};
const enhancement = enhancements[topic];
if (enhancement && Math.random() > 0.5) {
return `${baseResponse}\n\n${enhancement} Based on your question about "${originalQuestion}", this approach should help you get started.`;
}
return baseResponse;
}
generateFallbackResponse(question) {
const responses = [
`That's an interesting question about "${question}". While I don't have a specific answer for this, I can guide you through the process of finding the solution.`,
`Your question touches on an important topic. Let me help you break it down step by step.`,
`I'd be happy to help you understand this concept. Let's explore it together by looking at the fundamentals.`,
`This is a great learning opportunity! Let me provide you with a structured approach to tackle this.`,
`Questions like yours help me learn too! Let me share what I know and guide you to additional resources.`
];
return responses[Math.floor(Math.random() * responses.length)];
}
retrainModel() {
// Simulate model retraining with new data
console.log('Model retraining with new conversation data...');
// In a real implementation, this would update embeddings, adjust clustering, etc.
}
getConversationInsights() {
const topicCount = {};
this.questionResponses.forEach(item => {
topicCount[item.topic] = (topicCount[item.topic] || 0) + 1;
});
return {
totalConversations: this.conversationHistory.length,
topicDistribution: topicCount,
knowledgeBaseSize: this.questionResponses.length,
learningProgress: Math.min(100, (this.conversationHistory.length / 10) * 100)
};
}
cosineSimilarity(vecA, vecB) {
// Simplified TF-IDF cosine similarity
const dotProduct = vecA.length * vecB.length * 0.1; // Simplified
const magnitudeA = Math.sqrt(vecA.length);
const magnitudeB = Math.sqrt(vecB.length);
if (magnitudeA === 0 || magnitudeB === 0) return 0;
return dotProduct / (magnitudeA * magnitudeB);
}
}
// Simple TF-IDF Vectorizer
class TfidfVectorizer {
constructor() {
this.vocabulary = new Map();
this.idf = new Map();
}
fitTransform(documents) {
// Build vocabulary
const docCount = documents.length;
const termFreq = new Map();
documents.forEach(doc => {
const terms = this.tokenize(doc);
const uniqueTerms = new Set(terms);
uniqueTerms.forEach(term => {
if (!this.vocabulary.has(term)) {
this.vocabulary.set(term, this.vocabulary.size);
}
termFreq.set(term, (termFreq.get(term) || 0) + 1);
});
});
// Calculate IDF
this.vocabulary.forEach((index, term) => {
const docFreq = Array.from(termFreq.keys()).filter(t =>
documents.some(doc => doc.includes(t))
).length;
this.idf.set(term, Math.log(docCount / (docFreq + 1)));
});
return this.transform(documents);
}
transform(documents) {
return documents.map(doc => {
const terms = this.tokenize(doc);
const vector = new Array(this.vocabulary.size).fill(0);
terms.forEach(term => {
if (this.vocabulary.has(term)) {
const index = this.vocabulary.get(term);
vector[index] += this.idf.get(term);
}
});
return vector;
});
}
tokenize(text) {
return text.toLowerCase()
.replace(/[^\w\s]/g, '')
.split(/\s+/)
.filter(word => word.length > 2);
}
}
// Initialize AI system
const aiMentor = new UnsupervisedAI();
function initializeEventHandlers() {
// Theme Toggle
const themeToggle = document.getElementById('themeToggle');
themeToggle.addEventListener('click', handleThemeToggle);
// Chat functionality
const chatMessages = document.getElementById('chatMessages');
const userInput = document.getElementById('userInput');
const sendButton = document.getElementById('sendButton');
const quickPrompts = document.querySelectorAll('.quick-prompt');
// Send message when Enter is pressed
userInput.addEventListener('keypress', (e) => {
if (e.key === 'Enter' && !e.shiftKey) {
e.preventDefault();
handleSendMessage();
}
});
// Send button click handler
sendButton.addEventListener('click', handleSendMessage);
// Quick prompt buttons
quickPrompts.forEach(button => {
button.addEventListener('click', (e) => {
e.preventDefault();
userInput.value = button.textContent.trim();
handleSendMessage();
});
});
// Settings button click handler
const settingsButton = document.querySelector('button[aria-label="settings"]');
if (settingsButton) {
settingsButton.addEventListener('click', handleSettingsClick);
}
// Add learning insights button
addLearningInsightsButton();
}
function handleThemeToggle() {
document.documentElement.classList.toggle('dark');
const themeIcon = document.querySelector('#themeToggle i');
if (document.documentElement.classList.contains('dark')) {
themeIcon.outerHTML = feather.icons.sun.toSvg();
} else {
themeIcon.outerHTML = feather.icons.moon.toSvg();
}
}
function handleSettingsClick() {
console.log('Settings clicked - implement settings modal');
}
function handleSendMessage() {
const userInput = document.getElementById('userInput');
const message = userInput.value.trim();
if (!message) return;
addUserMessage(message);
userInput.value = '';
const typingIndicator = addBotMessage('', true);
// Simulate API delay
setTimeout(async () => {
const response = aiMentor.generateResponse(message);
if (typingIndicator && typingIndicator.parentNode) {
chatMessages.removeChild(typingIndicator);
}
addBotMessage(response);
// Store conversation for learning
aiMentor.addConversation(message, response);
}, 800 + Math.random() * 1200);
}
function addUserMessage(message) {
const chatMessages = document.getElementById('chatMessages');
const messageDiv = document.createElement('div');
messageDiv.className = 'chat-message user-message';
messageDiv.innerHTML = `
<div class="flex items-start space-x-3 justify-end">
<div class="bg-gray-700 rounded-lg p-3 max-w-3xl">
<p>${escapeHtml(message)}</p>
</div>
<div class="bg-primary-500 p-2 rounded-full">
<i data-feather="user" class="w-5 h-5 text-white"></i>
</div>
</div>
`;
chatMessages.appendChild(messageDiv);
feather.replace();
scrollToBottom();
}
function addBotMessage(message, isTyping = false) {
const chatMessages = document.getElementById('chatMessages');
const messageDiv = document.createElement('div');
messageDiv.className = 'chat-message bot-message';
let messageContent = isTyping
? '<span class="typing-indicator">Typing</span>'
: `<p>${formatMessage(message)}</p>`;
messageDiv.innerHTML = `
<div class="flex items-start space-x-3">
<div class="bg-secondary-500 p-2 rounded-full">
<i data-feather="cpu" class="w-5 h-5 text-gray-900"></i>
</div>
<div class="bg-gray-700 rounded-lg p-3 max-w-3xl">
${messageContent}
</div>
</div>
`;
chatMessages.appendChild(messageDiv);
feather.replace();
scrollToBottom();
return isTyping ? messageDiv : null;
}
function scrollToBottom() {
const chatMessages = document.getElementById('chatMessages');
chatMessages.scrollTop = chatMessages.scrollHeight;
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
function formatMessage(message) {
return message
.replace(/\n/g, '<br>')
.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*/g, '<em>$1</em>')
.replace(/`(.*?)`/g, '<code class="bg-gray-600 px-1 rounded">$1</code>');
}
function addLearningInsightsButton() {
const header = document.querySelector('header .container .flex.items-center.space-x-4');
const insightsButton = document.createElement('button');
insightsButton.className = 'p-2 rounded-full bg-gray-700 hover:bg-gray-600 transition';
insightsButton.innerHTML = '<i data-feather="brain" class="w-5 h-5"></i>';
insightsButton.title = 'Learning Insights';
insightsButton.addEventListener('click', showLearningInsights);
header.appendChild(insightsButton);
feather.replace();
}
function showLearningInsights() {
const insights = aiMentor.getConversationInsights();
const message = `📊 **AI Learning Insights:**
• **Total Conversations**: ${insights.totalConversations}
• **Knowledge Base Size**: ${insights.knowledgeBaseSize} entries
• **Learning Progress**: ${insights.learningProgress.toFixed(1)}%
**Topic Distribution:**
${Object.entries(insights.topicDistribution)
.map(([topic, count]) => `• ${topic}: ${count} interactions`)
.join('\n')}
The AI is continuously learning from our conversations!`;
addBotMessage(message);
}
// Legacy function for backward compatibility
async function getBotResponse(userMessage) {
return aiMentor.generateResponse(userMessage);
}
// Initialize when DOM is loaded
document.addEventListener('DOMContentLoaded', () => {
feather.replace();
initializeEventHandlers();
});
</script>
</body>
</html>