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Sahil commited on
Update index.html
Browse files- index.html +29 -40
index.html
CHANGED
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@@ -4,9 +4,7 @@
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<meta http-equiv="Content-Security-Policy" content="default-src 'self'; script-src 'self' 'unsafe-inline' https://js.puter.com; connect-src 'self' https://api.puter.com https://puter.com; style-src 'self' 'unsafe-inline';">
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<title>ContinuumLearner - AI Training Pipeline</title>
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<script src="https://js.puter.com/v2/" crossorigin="anonymous"></script>
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<style>
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* {
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margin: 0;
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@@ -261,29 +259,6 @@
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<script>
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let selectedModel = "puter:gpt-5-nano";
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let sessionCount = 0;
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let puterReady = false;
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// Check if Puter.js is loaded
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function checkPuterLoaded() {
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if (typeof puter !== 'undefined' && puter.ai) {
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puterReady = true;
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addLog('✅ Puter.js loaded successfully', 'success');
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return true;
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}
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addLog('❌ Puter.js not loaded - Check your internet connection', 'error');
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return false;
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}
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// Wait for Puter.js to load
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window.addEventListener('load', function() {
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setTimeout(() => {
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if (!checkPuterLoaded()) {
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addLog('⚠️ Puter.js failed to load. Retrying...', 'error');
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// Retry after 2 seconds
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setTimeout(checkPuterLoaded, 2000);
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}
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}, 1000);
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});
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// Model selector
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document.querySelectorAll('.model-btn').forEach(btn => {
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@@ -302,21 +277,35 @@
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return;
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}
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// Check if Puter.js is available
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if (typeof puter === 'undefined' || !puter.ai) {
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alert('❌ Puter.js is not loaded. Please refresh the page and check your internet connection.');
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addLog('❌ Puter.js not available', 'error');
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return;
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}
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const responseText = document.getElementById('responseText');
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responseText.innerHTML = '<div class="loading"></div> Generating response...';
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try {
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// Get AI response
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addLog(`🔄 Calling ${selectedModel}...`, 'success');
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const response = await puter.ai.chat(userInput, { model: selectedModel });
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responseText.textContent = response;
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addLog(`✅ Generated response using ${selectedModel}`, 'success');
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@@ -332,16 +321,16 @@
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});
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const trainData = await trainResponse.json();
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if (trainData.success) {
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sessionCount++;
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document.getElementById('sessionCount').textContent = sessionCount;
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document.getElementById('bufferedCount').textContent = trainData.buffered;
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addLog(trainData.message, 'success');
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// Clear input
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document.getElementById('userInput').value = '';
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// Auto-save when buffer is full
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if (trainData.buffered >= 10) {
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addLog('Buffer full! Auto-saving to HuggingFace...', 'success');
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@@ -361,7 +350,7 @@
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try {
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const response = await fetch('/api/flush-buffer', { method: 'POST' });
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const data = await response.json();
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if (data.success) {
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addLog(`💾 ${data.message}`, 'success');
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await refreshStats();
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@@ -377,7 +366,7 @@
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try {
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const response = await fetch('/api/dataset-stats');
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const data = await response.json();
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if (data.success) {
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document.getElementById('totalConversations').textContent = data.total_examples;
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document.getElementById('totalTokens').textContent = data.total_tokens.toLocaleString();
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@@ -395,7 +384,7 @@
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entry.className = `log-entry ${type}`;
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entry.textContent = `[${new Date().toLocaleTimeString()}] ${message}`;
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log.insertBefore(entry, log.firstChild);
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// Keep only last 50 entries
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while (log.children.length > 50) {
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log.removeChild(log.lastChild);
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>ContinuumLearner - AI Training Pipeline</title>
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<style>
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* {
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margin: 0;
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<script>
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let selectedModel = "puter:gpt-5-nano";
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let sessionCount = 0;
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// Model selector
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document.querySelectorAll('.model-btn').forEach(btn => {
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return;
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}
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const responseText = document.getElementById('responseText');
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responseText.innerHTML = '<div class="loading"></div> Generating response...';
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try {
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// Get AI response from backend
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addLog(`🔄 Calling ${selectedModel}...`, 'success');
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const generateResponse = await fetch('/api/generate', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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prompt: userInput,
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model: selectedModel
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})
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});
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const generateData = await generateResponse.json();
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if (!generateData.success) {
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responseText.textContent = `Error: ${generateData.error}`;
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addLog(`❌ Error: ${generateData.error}`, 'error');
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if (generateData.error.includes('HF_TOKEN')) {
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addLog('💡 Add HF_TOKEN environment variable to enable AI model training', 'error');
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}
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return;
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}
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const response = generateData.response;
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responseText.textContent = response;
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addLog(`✅ Generated response using ${selectedModel}`, 'success');
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});
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const trainData = await trainResponse.json();
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if (trainData.success) {
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sessionCount++;
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document.getElementById('sessionCount').textContent = sessionCount;
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document.getElementById('bufferedCount').textContent = trainData.buffered;
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addLog(trainData.message, 'success');
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// Clear input
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document.getElementById('userInput').value = '';
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// Auto-save when buffer is full
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if (trainData.buffered >= 10) {
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addLog('Buffer full! Auto-saving to HuggingFace...', 'success');
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try {
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const response = await fetch('/api/flush-buffer', { method: 'POST' });
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const data = await response.json();
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if (data.success) {
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addLog(`💾 ${data.message}`, 'success');
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await refreshStats();
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try {
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const response = await fetch('/api/dataset-stats');
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const data = await response.json();
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if (data.success) {
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document.getElementById('totalConversations').textContent = data.total_examples;
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document.getElementById('totalTokens').textContent = data.total_tokens.toLocaleString();
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entry.className = `log-entry ${type}`;
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entry.textContent = `[${new Date().toLocaleTimeString()}] ${message}`;
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log.insertBefore(entry, log.firstChild);
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// Keep only last 50 entries
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while (log.children.length > 50) {
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log.removeChild(log.lastChild);
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