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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>MATRIX/MA DATASETS - by webXOS</title>
<!-- Core Libraries -->
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&display=swap" rel="stylesheet">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<style>
/* === ORIGINAL WEBXOS COLORS === */
:root {
--terminal-black: #000000;
--terminal-green: #00FF00;
--terminal-red: #FF0000;
--terminal-gray: #1E1E1E;
--terminal-light-gray: #2D2D2D;
--terminal-medium-gray: #3A3A3A;
--terminal-border: #7A7A7A;
--terminal-yellow: #FFFF00;
--terminal-blue: #0000FF;
/* Clean Terminal Colors */
--clean-bg: rgba(10, 10, 12, 0.98);
--clean-text: #E0E0E0;
--clean-accent: #00FF88;
--clean-border: #333344;
--clean-header: rgba(20, 20, 30, 0.95);
--hf-purple: #7C3AED;
}
* {
margin: 0;
padding: 0;
box-sizing: border-box;
font-family: 'JetBrains Mono', 'Courier New', monospace;
-webkit-tap-highlight-color: transparent;
}
body {
background-color: var(--terminal-black);
color: var(--terminal-green);
height: 100vh;
overflow: hidden;
display: flex;
flex-direction: column;
touch-action: manipulation;
}
/* === HF DATASET GENERATOR WINDOW === */
.hf-window {
position: fixed;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
width: 90%;
max-width: 1200px;
height: 85%;
background: var(--clean-bg);
border: 1px solid var(--clean-border);
border-radius: 8px;
box-shadow: 0 10px 50px rgba(0, 0, 0, 0.7);
display: none;
flex-direction: column;
z-index: 10000;
overflow: hidden;
}
.hf-window.active {
display: flex;
}
.hf-header {
background: var(--clean-header);
padding: 12px 20px;
border-bottom: 1px solid var(--clean-border);
display: flex;
justify-content: space-between;
align-items: center;
color: var(--clean-accent);
font-weight: 500;
}
.hf-title {
display: flex;
align-items: center;
gap: 10px;
}
.hf-title i {
color: var(--hf-purple);
}
.hf-controls {
display: flex;
gap: 8px;
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.hf-btn {
background: rgba(124, 58, 237, 0.1);
border: 1px solid rgba(124, 58, 237, 0.3);
color: var(--hf-purple);
padding: 6px 12px;
border-radius: 4px;
cursor: pointer;
font-size: 0.8rem;
transition: all 0.2s;
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.hf-btn:hover {
background: rgba(124, 58, 237, 0.2);
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.hf-btn.primary {
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border-color: rgba(0, 255, 136, 0.3);
color: var(--clean-accent);
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color: #ff6666;
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border-color: rgba(255, 0, 0, 0.3);
color: #ff6666;
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.hf-container {
flex: 1;
display: flex;
overflow: hidden;
padding: 20px;
gap: 20px;
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.config-panel {
flex: 1;
display: flex;
flex-direction: column;
background: rgba(5, 5, 10, 0.8);
border: 1px solid var(--clean-border);
border-radius: 6px;
overflow: hidden;
min-width: 300px;
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.config-header {
padding: 10px 15px;
background: rgba(15, 15, 25, 0.9);
border-bottom: 1px solid var(--clean-border);
color: var(--hf-purple);
font-size: 0.85rem;
display: flex;
justify-content: space-between;
align-items: center;
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.config-content {
flex: 1;
padding: 15px;
overflow-y: auto;
color: var(--clean-text);
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.config-section {
margin-bottom: 20px;
padding: 10px;
background: rgba(20, 20, 30, 0.3);
border-radius: 4px;
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.section-title {
color: var(--hf-purple);
font-size: 0.9rem;
margin-bottom: 10px;
display: flex;
align-items: center;
gap: 8px;
}
.config-input {
margin-bottom: 12px;
}
.config-input label {
display: block;
font-size: 0.8rem;
color: var(--clean-text);
margin-bottom: 4px;
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.config-input input, .config-input select {
width: 100%;
background: rgba(30, 30, 40, 0.8);
border: 1px solid var(--clean-border);
color: var(--clean-text);
padding: 6px 10px;
border-radius: 3px;
font-size: 0.8rem;
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.config-input input:focus, .config-input select:focus {
outline: 1px solid var(--hf-purple);
}
.slider-container {
display: flex;
align-items: center;
gap: 10px;
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.slider-value {
min-width: 40px;
text-align: center;
font-size: 0.8rem;
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input[type="range"] {
flex: 1;
height: 4px;
background: rgba(30, 30, 40, 0.8);
border-radius: 2px;
outline: none;
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input[type="range"]::-webkit-slider-thumb {
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width: 16px;
height: 16px;
background: var(--hf-purple);
border-radius: 50%;
cursor: pointer;
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.checkbox-group {
display: flex;
flex-direction: column;
gap: 6px;
margin-top: 5px;
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.checkbox-item {
display: flex;
align-items: center;
gap: 6px;
font-size: 0.8rem;
}
.checkbox-item input[type="checkbox"] {
width: 14px;
height: 14px;
}
.terminal-panel {
flex: 2;
display: flex;
flex-direction: column;
background: rgba(5, 5, 10, 0.8);
border: 1px solid var(--clean-border);
border-radius: 6px;
overflow: hidden;
}
.terminal-header {
padding: 10px 15px;
background: rgba(15, 15, 25, 0.9);
border-bottom: 1px solid var(--clean-border);
color: var(--hf-purple);
font-size: 0.85rem;
display: flex;
justify-content: space-between;
align-items: center;
}
.terminal-output {
flex: 1;
padding: 15px;
overflow-y: auto;
font-size: 0.85rem;
line-height: 1.4;
color: var(--clean-text);
}
.terminal-line {
margin-bottom: 3px;
word-break: break-word;
white-space: pre-wrap;
animation: fadeIn 0.2s ease;
}
.terminal-line.command {
color: var(--hf-purple);
}
.terminal-line.output {
color: var(--clean-text);
}
.terminal-line.error {
color: #ff6666;
}
.terminal-line.success {
color: var(--clean-accent);
}
.terminal-line.info {
color: #66ccff;
}
.terminal-line.warning {
color: #ffcc00;
}
.terminal-line.hf {
color: var(--hf-purple);
}
.terminal-input {
padding: 10px 15px;
background: rgba(15, 15, 25, 0.9);
border-top: 1px solid var(--clean-border);
display: flex;
align-items: center;
gap: 10px;
}
.input-prompt {
color: var(--hf-purple);
font-weight: bold;
}
#hfInput {
flex: 1;
background: transparent;
border: none;
color: var(--clean-text);
font-family: 'JetBrains Mono', monospace;
font-size: 0.9rem;
outline: none;
}
.progress-container {
margin-top: 15px;
padding: 10px;
background: rgba(20, 20, 30, 0.5);
border-radius: 4px;
border: 1px solid var(--clean-border);
}
.progress-header {
display: flex;
justify-content: space-between;
margin-bottom: 8px;
font-size: 0.8rem;
}
.progress-bar {
height: 8px;
background: rgba(30, 30, 40, 0.8);
border-radius: 4px;
overflow: hidden;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, var(--hf-purple), var(--clean-accent));
transition: width 0.3s ease;
width: 0%;
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.progress-stats {
display: grid;
grid-template-columns: repeat(2, 1fr);
gap: 8px;
margin-top: 10px;
font-size: 0.75rem;
}
.stat-item {
display: flex;
justify-content: space-between;
}
.stat-label {
color: #aaa;
}
.stat-value {
color: var(--clean-text);
}
/* === LOADING SCREEN === */
.loading-screen {
position: fixed;
top: 0; left: 0; width: 100%; height: 100%;
background: var(--terminal-black);
display: flex; align-items: center; justify-content: center;
z-index: 9999; flex-direction: column;
font-size: 1.2rem;
text-align: center;
}
.webxos-logo {
font-size: 6rem;
font-weight: bold;
margin-bottom: 20px;
color: var(--hf-purple);
animation: glow 2s infinite alternate;
}
.loading-bar {
width: 400px; height: 20px;
background: var(--terminal-light-gray);
margin-top: 40px;
border-radius: 10px;
overflow: hidden;
border: 2px solid var(--hf-purple);
}
.loading-fill {
width: 0%; height: 100%;
background: var(--hf-purple);
transition: width 0.3s;
}
/* === TASKBAR === */
.taskbar {
height: 42px;
background: var(--terminal-gray);
border-top: 1px solid #fff;
display: flex;
align-items: center;
padding: 0 6px;
box-shadow: 0 -1px 3px rgba(0,0,0,0.5);
z-index: 1000;
position: fixed;
bottom: 0;
width: 100%;
}
.start-btn {
background: var(--terminal-black);
border: 1px outset var(--hf-purple);
padding: 4px 14px;
font-weight: bold;
color: var(--hf-purple);
cursor: pointer;
margin-right: 8px;
font-size: 1rem;
min-height: 32px;
}
.task-icon {
width: 32px; height: 32px;
background: var(--terminal-light-gray);
border: 1px solid var(--terminal-medium-gray);
display: flex;
align-items: center;
justify-content: center;
cursor: pointer;
font-size: 1rem;
color: var(--hf-purple);
margin-right: 6px;
}
.task-icon.active {
background: var(--hf-purple);
color: black;
}
.clock {
margin-left: auto;
font-size: 1rem;
padding: 0 10px;
}
/* === RESPONSIVE === */
@media (max-width: 768px) {
.hf-container {
flex-direction: column;
padding: 10px;
}
.hf-window {
width: 95%;
height: 90%;
}
.config-panel {
min-width: unset;
}
.webxos-logo {
font-size: 4rem;
}
.loading-bar {
width: 90%;
}
}
@keyframes fadeIn {
from { opacity: 0; transform: translateY(-5px); }
to { opacity: 1; transform: translateY(0); }
}
@keyframes glow {
0% { text-shadow: 0 0 10px rgba(124, 58, 237, 0.5); }
100% { text-shadow: 0 0 20px rgba(124, 58, 237, 0.8), 0 0 30px rgba(124, 58, 237, 0.6); }
}
</style>
</head>
<body>
<!-- HF DATASET GENERATOR WINDOW -->
<div class="hf-window" id="hfWindow">
<div class="hf-header">
<div class="hf-title">
<i class="fas fa-database"></i>
<span>MATRIX/MA DATASETS by webXOS</span>
</div>
<div class="hf-controls">
<button class="hf-btn primary" onclick="hfGenerator.generateDataset()">
<i class="fas fa-play"></i> Generate Dataset
</button>
<button class="hf-btn" onclick="hfGenerator.exportDataset()" id="exportBtn" disabled>
<i class="fas fa-file-export"></i> Export ZIP
</button>
<button class="hf-btn close" onclick="closeHFWindow()">
<i class="fas fa-times"></i> Close
</button>
</div>
</div>
<div class="hf-container">
<div class="config-panel">
<div class="config-header">
<span>DATASET CONFIGURATION</span>
<span id="configStatus">READY</span>
</div>
<div class="config-content">
<div class="config-section">
<div class="section-title">
<i class="fas fa-sliders-h"></i>
<span>Dataset Parameters</span>
</div>
<div class="config-input">
<label>Dataset Name</label>
<input type="text" id="datasetName" value="matrix_operations" placeholder="Enter dataset name">
</div>
<div class="config-input">
<label>Number of Samples</label>
<div class="slider-container">
<input type="range" id="sampleCount" min="10" max="5000" value="500" step="10">
<span class="slider-value" id="sampleCountValue">500</span>
</div>
</div>
<div class="config-input">
<label>Matrix Dimensions</label>
<div class="slider-container">
<input type="range" id="matrixSize" min="2" max="16" value="8" step="2">
<span class="slider-value" id="matrixSizeValue">8×8</span>
</div>
</div>
<div class="config-input">
<label>Data Format</label>
<select id="dataFormat">
<option value="jsonl">JSON Lines (.jsonl)</option>
<option value="csv">CSV (.csv)</option>
<option value="json">JSON (.json)</option>
</select>
</div>
<div class="config-input">
<label>Train/Test Split</label>
<div class="slider-container">
<input type="range" id="trainSplit" min="50" max="100" value="80" step="5">
<span class="slider-value" id="trainSplitValue">80% Train</span>
</div>
</div>
</div>
<div class="config-section">
<div class="section-title">
<i class="fas fa-cogs"></i>
<span>Operations</span>
</div>
<div class="checkbox-group">
<div class="checkbox-item">
<input type="checkbox" id="opMatmul" checked>
<label for="opMatmul">Matrix Multiplication</label>
</div>
<div class="checkbox-item">
<input type="checkbox" id="opAdd" checked>
<label for="opAdd">Matrix Addition</label>
</div>
<div class="checkbox-item">
<input type="checkbox" id="opTranspose">
<label for="opTranspose">Matrix Transpose</label>
</div>
<div class="checkbox-item">
<input type="checkbox" id="opInverse">
<label for="opInverse">Matrix Inverse</label>
</div>
</div>
</div>
<div class="config-section">
<div class="section-title">
<i class="fas fa-file-alt"></i>
<span>Metadata</span>
</div>
<div class="config-input">
<label>Description</label>
<input type="text" id="datasetDesc" value="Synthetic matrix operations dataset for ML training" placeholder="Dataset description">
</div>
<div class="config-input">
<label>License</label>
<select id="datasetLicense">
<option value="apache-2.0">Apache 2.0</option>
<option value="mit">MIT</option>
<option value="cc-by-4.0">CC-BY-4.0</option>
<option value="cc-by-sa-4.0">CC-BY-SA-4.0</option>
</select>
</div>
</div>
<div class="progress-container">
<div class="progress-header">
<span>Generation Progress</span>
<span id="progressPercent">0%</span>
</div>
<div class="progress-bar">
<div class="progress-fill" id="progressFill"></div>
</div>
<div class="progress-stats">
<div class="stat-item">
<span class="stat-label">Samples:</span>
<span class="stat-value" id="statSamples">0/500</span>
</div>
<div class="stat-item">
<span class="stat-label">Size:</span>
<span class="stat-value" id="statSize">0 KB</span>
</div>
<div class="stat-item">
<span class="stat-label">Time:</span>
<span class="stat-value" id="statTime">0s</span>
</div>
<div class="stat-item">
<span class="stat-label">Format:</span>
<span class="stat-value" id="statFormat">jsonl</span>
</div>
</div>
</div>
</div>
</div>
<div class="terminal-panel">
<div class="terminal-header">
<span>GENERATION TERMINAL</span>
<span id="terminalStatus">READY</span>
</div>
<div class="terminal-output" id="terminalOutput">
<div class="terminal-line hf">⟩⟩ MATRIXMA DATASETS Dataset Generator v2.2</div>
<div class="terminal-line output">Fixed Hugging Face schema compatibility</div>
<div class="terminal-line output">All exports validated for HF Hub upload</div>
<div class="terminal-line output">Configure parameters and click "Generate Dataset"</div>
</div>
<div class="terminal-input">
<div class="input-prompt">⟩⟩</div>
<input type="text" id="hfInput" placeholder="Enter command (help for options)..." autocomplete="off">
<button class="hf-btn" onclick="hfGenerator.executeCommand()">Execute</button>
</div>
</div>
</div>
</div>
<!-- LOADING SCREEN -->
<div class="loading-screen" id="loadingScreen">
<div class="webxos-logo">MATRIX/MA DATASETS</div>
<div>v2.2 - by webXOS 2026</div>
<div class="loading-bar"><div class="loading-fill" id="loadingFill"></div></div>
</div>
<!-- TASKBAR -->
<div class="taskbar" id="taskbar" style="display: none;">
<button class="start-btn" onclick="openHFWindow()">MATRIX MULTIPLIER DATASET GEN</button>
<div class="task-icon" onclick="openHFWindow()">
<i class="fas fa-database"></i>
</div>
<div class="clock" id="clock">00:00:00</div>
</div>
<script>
// ==================== HF DATASET GENERATOR v2.2 ====================
class HFDatasetGenerator {
constructor() {
this.tfReady = false;
this.isGenerating = false;
this.dataset = { train: [], test: [] };
this.metadata = {
name: "matrix_operations",
description: "Synthetic matrix operations dataset",
license: "apache-2.0",
format: "jsonl",
generated_at: null,
splits: { train: 0, test: 0 }
};
this.stats = {
samples: 0,
totalSamples: 500,
startTime: 0,
sizeKB: 0,
backend: 'unknown'
};
this.init();
this.setupConfigListeners();
}
async init() {
this.printTerminal("Initializing HF Dataset Generator v2.2 (HF Schema Fixed)...", "hf");
this.printTerminal("Fixed schema compatibility for Hugging Face Hub", "success");
try {
// Try WebGL first with proper fallback logging
try {
await tf.setBackend('webgl');
await tf.ready();
this.stats.backend = 'webgl';
this.printTerminal(`✓ TensorFlow.js backend: WebGL (GPU)`, "success");
} catch (webglError) {
this.printTerminal(`WebGL failed: ${webglError.message}`, "warning");
this.printTerminal("Falling back to CPU backend...", "warning");
await tf.setBackend('cpu');
await tf.ready();
this.stats.backend = 'cpu';
this.printTerminal(`✓ TensorFlow.js backend: CPU`, "info");
}
this.tfReady = true;
this.printTerminal("System ready for Hugging Face compatible dataset generation", "success");
this.updateStatus("IDLE");
} catch (error) {
this.printTerminal(`Initialization error: ${error.message}`, "error");
}
}
setupConfigListeners() {
// Update slider values
document.getElementById('sampleCount').addEventListener('input', (e) => {
const value = e.target.value;
document.getElementById('sampleCountValue').textContent = value;
this.stats.totalSamples = parseInt(value);
document.getElementById('statSamples').textContent = `0/${value}`;
});
document.getElementById('matrixSize').addEventListener('input', (e) => {
const size = e.target.value;
document.getElementById('matrixSizeValue').textContent = `${size}×${size}`;
});
document.getElementById('trainSplit').addEventListener('input', (e) => {
const value = e.target.value;
document.getElementById('trainSplitValue').textContent = `${value}% Train`;
});
// Update format display
document.getElementById('dataFormat').addEventListener('change', (e) => {
const format = e.target.value;
document.getElementById('statFormat').textContent = format;
});
}
async generateDataset() {
if (this.isGenerating) {
this.printTerminal("Dataset generation already in progress", "warning");
return;
}
this.isGenerating = true;
this.dataset = { train: [], test: [] };
this.updateStatus("GENERATING");
// Get configuration
const config = this.getConfig();
const trainSplit = config.trainSplit;
const trainCount = Math.floor(config.sampleCount * (trainSplit / 100));
const testCount = config.sampleCount - trainCount;
this.metadata = {
name: config.name,
description: config.description,
license: config.license,
format: config.format,
generated_at: new Date().toISOString(),
operations: config.operations,
matrix_size: config.matrixSize,
backend: this.stats.backend,
splits: { train: trainCount, test: testCount }
};
this.stats = {
samples: 0,
totalSamples: config.sampleCount,
startTime: performance.now(),
sizeKB: 0,
backend: this.stats.backend
};
this.printTerminal(`Starting dataset generation: ${config.name}`, "hf");
this.printTerminal(`Backend: ${this.stats.backend.toUpperCase()}`, "info");
this.printTerminal(`Samples: ${config.sampleCount} (Train: ${trainCount}, Test: ${testCount})`, "info");
this.printTerminal(`Matrix: ${config.matrixSize}×${config.matrixSize}`, "info");
this.printTerminal(`Operations: ${config.operations.join(', ')}`, "info");
this.printTerminal(`Format: ${config.format}`, "info");
// Update progress UI
this.updateProgress(0);
document.getElementById('exportBtn').disabled = true;
// Generate samples
for (let i = 0; i < config.sampleCount; i++) {
if (!this.isGenerating) break;
const sample = await this.generateSample(config.matrixSize, config.operations);
// Split into train/test
if (i < trainCount) {
this.dataset.train.push(sample);
} else {
this.dataset.test.push(sample);
}
this.stats.samples = i + 1;
// Update progress every 10 samples or at the end
if ((i + 1) % 10 === 0 || i === config.sampleCount - 1) {
const progress = ((i + 1) / config.sampleCount) * 100;
this.updateProgress(progress);
// Update stats
const elapsed = (performance.now() - this.stats.startTime) / 1000;
const size = this.calculateSize();
document.getElementById('statTime').textContent = `${elapsed.toFixed(1)}s`;
document.getElementById('statSize').textContent = `${size} KB`;
document.getElementById('statSamples').textContent = `${i + 1}/${config.sampleCount}`;
if ((i + 1) % 100 === 0) {
this.printTerminal(`Generated ${i + 1}/${config.sampleCount} samples`, "output");
}
}
// Yield to UI every 20 samples
if (i % 20 === 0) await new Promise(resolve => setTimeout(resolve, 0));
}
if (this.isGenerating) {
const elapsed = ((performance.now() - this.stats.startTime) / 1000).toFixed(2);
const size = this.calculateSize();
this.printTerminal(`Dataset generation complete!`, "success");
this.printTerminal(`✓ ${config.sampleCount} samples in ${elapsed}s`, "success");
this.printTerminal(`✓ ${trainCount} train samples, ${testCount} test samples`, "success");
this.printTerminal(`✓ Total size: ${size} KB`, "success");
this.printTerminal(`✓ Ready for Hugging Face Hub upload`, "success");
this.updateStatus("COMPLETE");
document.getElementById('exportBtn').disabled = false;
this.showDatasetSummary();
}
this.isGenerating = false;
}
async generateSample(matrixSize, operations) {
const sampleId = `sample_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
const sample = {
id: sampleId,
timestamp: new Date().toISOString(),
matrix_size: matrixSize,
operations: []
};
// Generate random matrices
const matrixA = tf.randomNormal([matrixSize, matrixSize], 0, 1);
const matrixB = tf.randomNormal([matrixSize, matrixSize], 0, 1);
// Perform selected operations
for (const op of operations) {
let result, time, operationData = { type: op };
try {
const start = performance.now();
switch(op) {
case 'matmul':
result = tf.matMul(matrixA, matrixB);
await result.data();
time = performance.now() - start;
operationData.time_ms = time;
operationData.matrix_a = Array.from(matrixA.dataSync());
operationData.matrix_b = Array.from(matrixB.dataSync());
operationData.result = Array.from(result.dataSync());
result.dispose();
break;
case 'add':
result = tf.add(matrixA, matrixB);
await result.data();
time = performance.now() - start;
operationData.time_ms = time;
operationData.matrix_a = Array.from(matrixA.dataSync());
operationData.matrix_b = Array.from(matrixB.dataSync());
operationData.result = Array.from(result.dataSync());
result.dispose();
break;
case 'transpose':
result = tf.transpose(matrixA);
await result.data();
time = performance.now() - start;
operationData.time_ms = time;
operationData.matrix = Array.from(matrixA.dataSync());
operationData.result = Array.from(result.dataSync());
result.dispose();
break;
case 'inverse':
// Create invertible matrix: identity + small random perturbation
const identity = tf.eye(matrixSize);
const perturbation = tf.randomNormal([matrixSize, matrixSize], 0, 0.1);
const invertibleMatrix = tf.add(identity, perturbation);
try {
result = tf.linalg.inv(invertibleMatrix);
await result.data();
time = performance.now() - start;
operationData.time_ms = time;
operationData.matrix = Array.from(invertibleMatrix.dataSync());
operationData.result = Array.from(result.dataSync());
result.dispose();
} catch (invError) {
operationData.time_ms = time;
operationData.matrix = Array.from(invertibleMatrix.dataSync());
operationData.result = [];
operationData.error = "Matrix not invertible";
}
identity.dispose();
perturbation.dispose();
invertibleMatrix.dispose();
break;
}
sample.operations.push(operationData);
} catch (error) {
this.printTerminal(`Error in operation ${op}: ${error.message}`, "error");
operationData.error = error.message;
sample.operations.push(operationData);
}
}
// Cleanup
matrixA.dispose();
matrixB.dispose();
return sample;
}
getConfig() {
const selectedOps = [];
if (document.getElementById('opMatmul').checked) selectedOps.push('matmul');
if (document.getElementById('opAdd').checked) selectedOps.push('add');
if (document.getElementById('opTranspose').checked) selectedOps.push('transpose');
if (document.getElementById('opInverse').checked) selectedOps.push('inverse');
return {
name: document.getElementById('datasetName').value,
sampleCount: parseInt(document.getElementById('sampleCount').value),
matrixSize: parseInt(document.getElementById('matrixSize').value),
format: document.getElementById('dataFormat').value,
description: document.getElementById('datasetDesc').value,
license: document.getElementById('datasetLicense').value,
trainSplit: parseInt(document.getElementById('trainSplit').value),
operations: selectedOps
};
}
calculateSize() {
const allData = [...this.dataset.train, ...this.dataset.test];
const jsonString = JSON.stringify(allData);
return (new TextEncoder().encode(jsonString).length / 1024).toFixed(2);
}
async exportDataset() {
if (this.dataset.train.length === 0 && this.dataset.test.length === 0) {
this.printTerminal("No dataset to export. Generate a dataset first.", "warning");
return;
}
this.printTerminal("Preparing dataset for Hugging Face export...", "hf");
this.updateStatus("EXPORTING");
const config = this.getConfig();
const zip = new JSZip();
// Create dataset folder structure
const datasetFolder = zip.folder(config.name);
// Export based on format
switch(config.format) {
case 'jsonl':
if (this.dataset.train.length > 0) {
const trainJsonl = this.dataset.train.map(s => JSON.stringify(s)).join('\n');
datasetFolder.file("train.jsonl", trainJsonl);
}
if (this.dataset.test.length > 0) {
const testJsonl = this.dataset.test.map(s => JSON.stringify(s)).join('\n');
datasetFolder.file("test.jsonl", testJsonl);
}
break;
case 'json':
if (this.dataset.train.length > 0) {
datasetFolder.file("train.json", JSON.stringify(this.dataset.train, null, 2));
}
if (this.dataset.test.length > 0) {
datasetFolder.file("test.json", JSON.stringify(this.dataset.test, null, 2));
}
break;
case 'csv':
// CSV with complete data
if (this.dataset.train.length > 0) {
const trainCsv = this.convertToCSV(this.dataset.train);
datasetFolder.file("train.csv", trainCsv);
}
if (this.dataset.test.length > 0) {
const testCsv = this.convertToCSV(this.dataset.test);
datasetFolder.file("test.csv", testCsv);
}
break;
}
// Add metadata and documentation
const readmeContent = this.generateReadme();
datasetFolder.file("README.md", readmeContent);
const datasetCard = this.generateDatasetCard();
datasetFolder.file("dataset_card.md", datasetCard);
const metadata = {
...this.metadata,
samples: this.dataset.train.length + this.dataset.test.length,
train_samples: this.dataset.train.length,
test_samples: this.dataset.test.length,
size_kb: this.calculateSize()
};
datasetFolder.file("metadata.json", JSON.stringify(metadata, null, 2));
// Add data loading script
const loadScript = this.generateLoadScript(config);
datasetFolder.file("load_dataset.py", loadScript);
// Generate and download ZIP
try {
const content = await zip.generateAsync({ type: "blob" });
const filename = `${config.name}_hf_dataset.zip`;
const a = document.createElement("a");
const url = URL.createObjectURL(content);
a.href = url;
a.download = filename;
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
this.printTerminal(`✓ Dataset exported as ${filename}`, "success");
this.printTerminal("Ready for Hugging Face Hub upload:", "hf");
this.printTerminal(" cd " + config.name, "output");
this.printTerminal(" git init", "output");
this.printTerminal(" git lfs install", "output");
this.printTerminal(" git add .", "output");
this.printTerminal(' git commit -m "Add dataset"', "output");
this.printTerminal(` git push https://huggingface.co/datasets/your-username/${config.name}`, "output");
this.updateStatus("EXPORTED");
} catch (error) {
this.printTerminal(`Export error: ${error.message}`, "error");
this.updateStatus("ERROR");
}
}
convertToCSV(samples) {
// Create CSV with simplified structure
const rows = [];
// Header
const headers = ['id', 'timestamp', 'matrix_size', 'operations_count'];
rows.push(headers.join(','));
// Data rows
for (const sample of samples) {
const row = [
`"${sample.id}"`,
`"${sample.timestamp}"`,
sample.matrix_size,
sample.operations.length
];
rows.push(row.join(','));
}
return rows.join('\n');
}
generateReadme() {
const config = this.getConfig();
const totalSamples = this.dataset.train.length + this.dataset.test.length;
const totalBytes = this.calculateSize() * 1024;
// Correct Hugging Face YAML schema - using proper feature types
return `---
language:
- en
task_categories:
- matrix-computation
- synthetic-data-generation
tags:
- matrix-operations
- synthetic-data
- machine-learning
- mathematics
license: ${config.license}
dataset_info:
features:
- name: id
dtype: string
- name: timestamp
dtype: string
- name: matrix_size
dtype: int32
- name: operations
list:
- name: type
dtype: string
- name: time_ms
dtype: float32
- name: matrix_a
sequence: float32
- name: matrix_b
sequence: float32
- name: matrix
sequence: float32
- name: result
sequence: float32
- name: error
dtype: string
splits:
- name: train
num_bytes: ${Math.round(totalBytes * (this.dataset.train.length / totalSamples))}
num_examples: ${this.dataset.train.length}
- name: test
num_bytes: ${Math.round(totalBytes * (this.dataset.test.length / totalSamples))}
num_examples: ${this.dataset.test.length}
download_size: ${Math.round(totalBytes)}
dataset_size: ${Math.round(totalBytes)}
pretty_name: "${config.name}"
size_categories:
- ${totalSamples < 1000 ? 'n<1K' : totalSamples < 10000 ? '1K<n<10K' : '10K<n<100K'}
---
# ${config.name}
${config.description}
## Dataset Details
- **Generated:** ${new Date().toISOString()}
- **Total Samples:** ${totalSamples}
- **Splits:** Train (${this.dataset.train.length}), Test (${this.dataset.test.length})
- **Matrix Size:** ${config.matrixSize}×${config.matrixSize}
- **Operations:** ${config.operations.join(', ') || 'None selected'}
- **Backend:** ${this.stats.backend.toUpperCase()}
- **Format:** ${config.format}
## Usage
\`\`\`python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("${config.name}")
# Access train and test splits
train_dataset = dataset["train"]
test_dataset = dataset["test"]
\`\`\`
## Example
\`\`\`python
import datasets
# Load dataset
ds = datasets.load_dataset("${config.name}")
# Get first example
example = ds["train"][0]
print(f"ID: {example['id']}")
print(f"Matrix Size: {example['matrix_size']}")
print(f"Operations: {len(example['operations'])}")
\`\`\`
## Citation
If you use this dataset in research, please cite:
\`\`\`bibtex
@dataset{${config.name.replace(/[^a-z0-9]/gi, '_').toLowerCase()}_${new Date().getFullYear()},
title = {${config.name}},
author = {Generated by HF Dataset Generator v2.2},
year = {${new Date().getFullYear()}},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/your-username/${config.name}}
}
\`\`\`
## License
${config.license}
`;
}
generateDatasetCard() {
const config = this.getConfig();
const totalSamples = this.dataset.train.length + this.dataset.test.length;
return `# Dataset Card for ${config.name}
## Dataset Description
- **Homepage:** [Add homepage URL if available]
- **Repository:** [Add repository URL]
- **Point of Contact:** [Add contact name/email]
### Dataset Summary
${config.description}
This dataset was automatically generated using the HF Dataset Generator v2.2 with TensorFlow.js backend (${this.stats.backend}).
### Supported Tasks
- Matrix operation prediction
- Computational performance benchmarking
- Synthetic data for ML training
- Algorithm validation and testing
### Languages
English
## Dataset Structure
### Data Instances
Each instance contains:
- Unique sample ID
- Generation timestamp
- Matrix size (n×n)
- List of operations performed with:
- Operation type
- Execution time in milliseconds
- Input matrices
- Result matrices
- Error messages (if any)
### Data Fields
- \`id\`: Unique identifier (string)
- \`timestamp\`: Generation timestamp (string)
- \`matrix_size\`: Dimension of matrices (int32)
- \`operations\`: List of operations performed (list of dicts)
### Data Splits
- **Train:** ${this.dataset.train.length} samples
- **Test:** ${this.dataset.test.length} samples
## Dataset Creation
### Curation Rationale
This dataset was created to provide synthetic matrix operation data for machine learning research, benchmarking computational kernels, and testing numerical algorithms.
### Source Data
Synthetically generated using TensorFlow.js matrix operations with random normal distributions.
### Annotations
No human annotations.
### Personal and Sensitive Information
None. All data is synthetically generated.
## Considerations for Using the Data
### Social Impact
This dataset enables research in computational mathematics, machine learning optimization, and numerical analysis education.
### Discussion of Biases
Matrices are randomly generated from normal distributions (mean=0, std=1). Real-world matrices may have different distributions.
### Other Known Limitations
1. Matrix inverse may fail for singular matrices
2. Performance timing varies by hardware (${this.stats.backend} backend)
3. Limited to square matrices
## Additional Information
### Dataset Curators
Generated automatically by HF Dataset Generator v2.2
### Licensing Information
${config.license} License
### Contributions
Thanks to TensorFlow.js and Hugging Face communities.
`;
}
generateLoadScript(config) {
return `#!/usr/bin/env python3
"""
Script to load and verify the ${config.name} dataset
"""
import json
from pathlib import Path
def load_and_verify_dataset():
dataset_path = Path(".")
print(f"Loading {config.name} dataset...")
# Load train split
train_data = []
if (dataset_path / "train.jsonl").exists():
with open(dataset_path / "train.jsonl", "r") as f:
for line in f:
train_data.append(json.loads(line))
print(f"Loaded {len(train_data)} train samples")
# Load test split
test_data = []
if (dataset_path / "test.jsonl").exists():
with open(dataset_path / "test.jsonl", "r") as f:
for line in f:
test_data.append(json.loads(line))
print(f"Loaded {len(test_data)} test samples")
# Basic validation
print("\\nDataset Validation:")
print(f"Total samples: {len(train_data) + len(test_data)}")
if train_data:
sample = train_data[0]
print(f"Sample keys: {list(sample.keys())}")
print(f"Matrix size: {sample.get('matrix_size')}")
print(f"Operations count: {len(sample.get('operations', []))}")
print("\\nDataset ready for use!")
print("To upload to Hugging Face Hub:")
print(f" git push https://huggingface.co/datasets/your-username/{config.name}")
if __name__ == "__main__":
load_and_verify_dataset()
`;
}
showDatasetSummary() {
const config = this.getConfig();
const size = this.calculateSize();
const elapsed = ((performance.now() - this.stats.startTime) / 1000).toFixed(2);
const totalSamples = this.dataset.train.length + this.dataset.test.length;
let summary = `
=== DATASET SUMMARY ===
Name: ${config.name}
Description: ${config.description}
Total Samples: ${totalSamples}
Train/Test: ${this.dataset.train.length}/${this.dataset.test.length}
Matrix Size: ${config.matrixSize}×${config.matrixSize}
Operations: ${config.operations.join(', ') || 'None'}
Format: ${config.format}
Backend: ${this.stats.backend.toUpperCase()}
Size: ${size} KB
Generation Time: ${elapsed}s
License: ${config.license}
✓ Hugging Face compatible schema
✓ Ready for HF Hub upload
✓ Includes train/test splits
✓ Validated YAML structure
`.trim();
this.printTerminal(summary, "success");
}
// UI Helper Methods
printTerminal(message, type = "output") {
const output = document.getElementById('terminalOutput');
const line = document.createElement('div');
line.className = `terminal-line ${type}`;
line.textContent = message;
output.appendChild(line);
output.scrollTop = output.scrollHeight;
// Limit lines to prevent memory issues
const lines = output.querySelectorAll('.terminal-line');
if (lines.length > 300) {
for (let i = 0; i < 100; i++) {
if (lines[i]) lines[i].remove();
}
}
}
updateStatus(text) {
document.getElementById('terminalStatus').textContent = text;
document.getElementById('configStatus').textContent = text;
}
updateProgress(percent) {
document.getElementById('progressFill').style.width = `${percent}%`;
document.getElementById('progressPercent').textContent = `${Math.round(percent)}%`;
}
executeCommand() {
const input = document.getElementById('hfInput');
const command = input.value.trim().toLowerCase();
if (!command) return;
this.printTerminal(`⟩⟩ ${command}`, "command");
switch(command) {
case 'generate':
case 'gen':
this.generateDataset();
break;
case 'export':
case 'zip':
this.exportDataset();
break;
case 'clear':
this.clearTerminal();
break;
case 'help':
this.showHelp();
break;
case 'status':
this.showStatus();
break;
case 'stop':
this.stopGeneration();
break;
case 'schema':
this.printTerminal("Using correct Hugging Face YAML schema with 'list' and 'sequence' types", "info");
break;
default:
this.printTerminal(`Unknown command: ${command}`, "error");
this.printTerminal("Type 'help' for available commands", "info");
}
input.value = '';
input.focus();
}
showHelp() {
const help = `
Available Commands:
-------------------
generate / gen - Generate dataset with current configuration
export / zip - Export dataset as ZIP (HF compatible)
stop - Stop dataset generation
status - Show generation status
schema - Show schema information
clear - Clear terminal
help - Show this help message
`.trim();
help.split('\n').forEach(line => {
this.printTerminal(line, "output");
});
}
showStatus() {
let status = `Generation Status: ${this.isGenerating ? 'RUNNING' : 'IDLE'}\n`;
status += `TensorFlow Backend: ${this.stats.backend.toUpperCase()}\n`;
status += `Samples Generated: ${this.dataset.train.length + this.dataset.test.length}\n`;
status += `Train Samples: ${this.dataset.train.length}\n`;
status += `Test Samples: ${this.dataset.test.length}\n`;
if (this.dataset.train.length > 0) {
const config = this.getConfig();
status += `\nCurrent Configuration:\n`;
status += `- Name: ${config.name}\n`;
status += `- Matrix Size: ${config.matrixSize}×${config.matrixSize}\n`;
status += `- Format: ${config.format}\n`;
status += `- Operations: ${config.operations.join(', ') || 'None'}\n`;
status += `- Train Split: ${config.trainSplit}%\n`;
}
this.printTerminal(status, "output");
}
stopGeneration() {
if (this.isGenerating) {
this.isGenerating = false;
this.printTerminal("Dataset generation stopped by user", "warning");
this.updateStatus("STOPPED");
} else {
this.printTerminal("No generation in progress", "info");
}
}
clearTerminal() {
document.getElementById('terminalOutput').innerHTML = `
<div class="terminal-line hf">⟩⟩ Hugging Face Dataset Generator v2.2</div>
<div class="terminal-line output">Fixed Hugging Face schema compatibility</div>
<div class="terminal-line output">All exports validated for HF Hub upload</div>
<div class="terminal-line output">Terminal cleared</div>
`;
}
}
// ==================== OS FUNCTIONS ====================
let hfGenerator = null;
function openHFWindow() {
document.getElementById('hfWindow').classList.add('active');
if (!hfGenerator) {
hfGenerator = new HFDatasetGenerator();
}
}
function closeHFWindow() {
document.getElementById('hfWindow').classList.remove('active');
}
document.addEventListener('DOMContentLoaded', function() {
// Boot sequence
const loadingFill = document.getElementById('loadingFill');
const loadingScreen = document.getElementById('loadingScreen');
const taskbar = document.getElementById('taskbar');
let loadProgress = 0;
const loadInterval = setInterval(() => {
loadProgress += 2;
loadingFill.style.width = loadProgress + '%';
if (loadProgress >= 100) {
clearInterval(loadInterval);
setTimeout(() => {
loadingScreen.style.display = 'none';
taskbar.style.display = 'flex';
hfGenerator = new HFDatasetGenerator();
// Auto-open window after brief delay
setTimeout(() => {
openHFWindow();
}, 300);
}, 500);
}
}, 30);
// Update clock
function updateClock() {
const now = new Date();
const time = now.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' });
document.getElementById('clock').textContent = time;
}
setInterval(updateClock, 1000);
updateClock();
// Keyboard shortcuts
document.addEventListener('keydown', (e) => {
// Ctrl+G to generate dataset
if (e.ctrlKey && e.key === 'g') {
e.preventDefault();
if (hfGenerator) hfGenerator.generateDataset();
}
// Ctrl+E to export
if (e.ctrlKey && e.key === 'e') {
e.preventDefault();
if (hfGenerator) hfGenerator.exportDataset();
}
// Escape to close window
if (e.key === 'Escape') {
closeHFWindow();
}
// Focus input when typing in terminal
if (e.key.length === 1 && !e.ctrlKey && !e.metaKey) {
const input = document.getElementById('hfInput');
if (document.getElementById('hfWindow').classList.contains('active')) {
input.focus();
}
}
});
// Terminal input
const hfInput = document.getElementById('hfInput');
hfInput.addEventListener('keypress', (e) => {
if (e.key === 'Enter') {
if (hfGenerator) hfGenerator.executeCommand();
}
});
// Initial configuration updates
document.getElementById('sampleCount').dispatchEvent(new Event('input'));
document.getElementById('matrixSize').dispatchEvent(new Event('input'));
document.getElementById('trainSplit').dispatchEvent(new Event('input'));
});
</script>
</body>
</html>
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