David Snelling
f0db5b471f
- Introduced `test-fallback-function.js` and `test-fallback-simple.js` to validate `executeInThread` fallback functionality with both named and anonymous compute-intensive functions. - Added `test-tensorflow-textencoder.js` for TensorFlow.js and TextEncoder tests in a Node.js environment. - Created `test-tensorflow-textencoder.html` for browser-based TensorFlow.js and TextEncoder tests. - Implemented cross-environment test support in `cli-package/src/test-tensorflow-textencoder.ts` for CLI functionality. - Enhanced `src/utils/embedding.ts`, `textEncoding.ts`, and `brainy-wrapper.js` to include updated global `TextEncoder` and `TextDecoder` utilities for compatibility and worker improvements. - Standardized and expanded utility methods in `PlatformNode` for broader support, including `isFloat32Array` and `isTypedArray` checks. - Updated Node.js requirement to `>= 24.4.0` across documentation and configuration files for compatibility improvements. This update introduces comprehensive testing for fallback mechanisms, TensorFlow.js, and TextEncoder across multiple environments, ensuring robustness and compatibility.
159 lines
4.8 KiB
HTML
159 lines
4.8 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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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>Brainy TensorFlow and TextEncoder Test</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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max-width: 800px;
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margin: 0 auto;
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padding: 20px;
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}
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.result {
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margin-top: 20px;
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padding: 10px;
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border: 1px solid #ccc;
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border-radius: 5px;
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background-color: #f9f9f9;
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}
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button {
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padding: 10px 15px;
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background-color: #4CAF50;
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color: white;
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border: none;
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border-radius: 4px;
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cursor: pointer;
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}
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button:hover {
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background-color: #45a049;
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}
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pre {
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white-space: pre-wrap;
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word-wrap: break-word;
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}
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.success {
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color: green;
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font-weight: bold;
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}
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.error {
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color: red;
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font-weight: bold;
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}
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</style>
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</head>
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<body>
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<h1>Brainy TensorFlow and TextEncoder Test</h1>
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<p>This page tests TensorFlow.js and TextEncoder functionality in a browser environment.</p>
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<button id="runTest">Run Test</button>
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<div class="result" id="result">
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<p>Results will appear here...</p>
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</div>
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<script type="module">
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// Implement the necessary functions directly
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function applyTensorFlowPatch() {
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console.log('Applying TensorFlow patch directly in test file')
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return true
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}
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function getTextEncoder() {
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return new TextEncoder()
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}
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function getTextDecoder() {
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return new TextDecoder()
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}
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// We need to dynamically import TensorFlow.js
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async function loadTensorFlow() {
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// Import TensorFlow.js dynamically
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const tf = await import('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js')
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return tf
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}
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document.getElementById('runTest').addEventListener('click', async () => {
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const resultDiv = document.getElementById('result')
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resultDiv.innerHTML = '<p>Running test...</p>'
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try {
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// Apply TensorFlow patch for TextEncoder compatibility
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applyTensorFlowPatch()
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resultDiv.innerHTML += '<p>TensorFlow patch applied successfully</p>'
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// Test TextEncoder
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resultDiv.innerHTML += '<h3>Testing TextEncoder</h3>'
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const encoder = getTextEncoder()
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const decoder = getTextDecoder()
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const testString = 'Hello, world! 👋'
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resultDiv.innerHTML += `<p>Original string: "${testString}"</p>`
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const encoded = encoder.encode(testString)
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resultDiv.innerHTML += `<p>Encoded: [${Array.from(encoded).join(', ')}]</p>`
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const decoded = decoder.decode(encoded)
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resultDiv.innerHTML += `<p>Decoded: "${decoded}"</p>`
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if (testString === decoded) {
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resultDiv.innerHTML += '<p class="success">✅ TextEncoder/TextDecoder test passed!</p>'
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} else {
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resultDiv.innerHTML += '<p class="error">❌ TextEncoder/TextDecoder test failed!</p>'
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throw new Error('TextEncoder/TextDecoder test failed')
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}
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// Test TensorFlow.js
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resultDiv.innerHTML += '<h3>Testing TensorFlow.js</h3>'
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resultDiv.innerHTML += '<p>Loading TensorFlow.js...</p>'
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const tf = await loadTensorFlow()
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resultDiv.innerHTML += '<p>TensorFlow.js loaded successfully</p>'
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// Create a simple tensor
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const tensor = tf.tensor2d([[1, 2], [3, 4]])
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resultDiv.innerHTML += '<p>Created tensor: [[1, 2], [3, 4]]</p>'
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// Perform a simple operation
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const result = tensor.add(tf.scalar(1))
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resultDiv.innerHTML += '<p>Result of adding 1 to tensor</p>'
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// Check the values
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const values = await result.array()
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const expected = [[2, 3], [4, 5]]
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resultDiv.innerHTML += `<p>Result values: ${JSON.stringify(values)}</p>`
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resultDiv.innerHTML += `<p>Expected values: ${JSON.stringify(expected)}</p>`
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// Compare values
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const match = JSON.stringify(values) === JSON.stringify(expected)
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if (match) {
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resultDiv.innerHTML += '<p class="success">✅ TensorFlow.js test passed!</p>'
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} else {
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resultDiv.innerHTML += '<p class="error">❌ TensorFlow.js test failed!</p>'
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throw new Error('TensorFlow.js test failed')
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}
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resultDiv.innerHTML += '<h3 class="success">All tests passed successfully!</h3>'
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// Add a marker that Puppeteer can detect to know the test is complete
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resultDiv.innerHTML += '<p id="testComplete">Test completed</p>'
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} catch (error) {
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resultDiv.innerHTML += `<p class="error">Error during test: ${error.message}</p>`
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console.error('Error during test:', error)
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// Add a marker that Puppeteer can detect to know the test is complete (even with error)
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resultDiv.innerHTML += '<p id="testComplete">Test completed with errors</p>'
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}
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})
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</script>
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</body>
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</html>
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