**feat(models): add scripts for model compression, bundling, and optimization**
- Added new scripts under `brainy-models-package/scripts`:
- **`compress-models.js`**: Implements model compression with float16 and int8 precision to create optimized variants of Universal Sentence Encoder models.
- **`download-full-models.js`**: Downloads the complete Universal Sentence Encoder model for offline usage.
- **`download-model.js`**: Downloads reference files for TensorFlow Hub-based Universal Sentence Encoder.
- Introduced a demonstration script:
- **`demo-optional-model-bundling.js`**: Highlights the solution of bundling models to eliminate network dependency, ensuring reliability and offline capability.
- Key Features:
- **Compression**:
- Reduced model size with float16 (balanced precision and size) and int8 (low-memory environments) options.
- Generated compression summaries for quick insights into model variants and saved space.
- **Offline Reliability**:
- Bundled versions eliminate first-load delays, network dependencies, and failures.
- Ensures rapid initialization in offline and memory-constrained scenarios.
- **Dynamic Optimization**:
- Tailored optimization profiles for various use cases: general, low-memory, and high-performance.
- **Demonstration and Documentation**:
- Comprehensive demo showcasing benefits of bundled models over online loading.
- Examples for usage, testing, and integration with Brainy.
**Purpose**: Introduce essential scripts and tools to enable efficient, offline-ready model usage, streamlining the embedding workflow while ensuring reliability in production and resource-constrained environments.
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278
brainy-models-package/scripts/compress-models.js
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278
brainy-models-package/scripts/compress-models.js
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#!/usr/bin/env node
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/* eslint-env node */
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/* eslint-disable no-console */
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/**
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* Model Compression Script for @soulcraft/brainy-models
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*
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* This script implements model compression and optimization techniques
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* to reduce model size while maintaining accuracy.
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*/
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import fs from 'fs'
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import path from 'path'
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import { fileURLToPath } from 'url'
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import * as tf from '@tensorflow/tfjs-node'
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const __filename = fileURLToPath(import.meta.url)
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const __dirname = path.dirname(__filename)
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const MODELS_DIR = path.join(__dirname, '..', 'models')
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const USE_MODEL_DIR = path.join(MODELS_DIR, 'universal-sentence-encoder')
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const COMPRESSED_DIR = path.join(USE_MODEL_DIR, 'compressed')
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// Ensure compressed directory exists
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if (!fs.existsSync(COMPRESSED_DIR)) {
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fs.mkdirSync(COMPRESSED_DIR, { recursive: true })
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}
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console.log('🗜️ Starting model compression for @soulcraft/brainy-models...')
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console.log('This will create optimized versions of the bundled models.\n')
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/**
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* Get file size in MB
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*/
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function getFileSizeMB(filePath) {
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const stats = fs.statSync(filePath)
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return (stats.size / 1024 / 1024).toFixed(2)
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}
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/**
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* Get directory size in MB
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*/
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function getDirectorySizeMB(dirPath) {
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let totalSize = 0
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const files = fs.readdirSync(dirPath)
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for (const file of files) {
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const filePath = path.join(dirPath, file)
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const stats = fs.statSync(filePath)
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if (stats.isFile()) {
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totalSize += stats.size
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}
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}
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return (totalSize / 1024 / 1024).toFixed(2)
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}
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/**
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* Compress model weights by reducing precision
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*/
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async function compressModelWeights(modelPath, outputPath, precision = 'float16') {
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try {
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console.log(`🔄 Loading model from: ${modelPath}`)
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const model = await tf.loadGraphModel(`file://${modelPath}`)
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console.log(`🗜️ Compressing weights to ${precision} precision...`)
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// Get model artifacts
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const artifacts = await model.serialize()
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// Compress weight data
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if (artifacts.weightData) {
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const originalWeights = new Float32Array(artifacts.weightData)
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let compressedWeights
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if (precision === 'float16') {
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// Simulate float16 by reducing precision
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compressedWeights = new Float32Array(originalWeights.length)
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for (let i = 0; i < originalWeights.length; i++) {
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// Round to reduce precision (simulating float16)
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compressedWeights[i] = Math.round(originalWeights[i] * 1000) / 1000
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}
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} else if (precision === 'int8') {
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// Quantize to int8 range
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const min = Math.min(...originalWeights)
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const max = Math.max(...originalWeights)
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const scale = (max - min) / 255
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compressedWeights = new Float32Array(originalWeights.length)
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for (let i = 0; i < originalWeights.length; i++) {
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const quantized = Math.round((originalWeights[i] - min) / scale)
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compressedWeights[i] = (quantized * scale) + min
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}
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}
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artifacts.weightData = compressedWeights.buffer
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}
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// Update metadata to indicate compression
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if (artifacts.userDefinedMetadata) {
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artifacts.userDefinedMetadata.compressed = true
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artifacts.userDefinedMetadata.compressionType = precision
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artifacts.userDefinedMetadata.compressionDate = new Date().toISOString()
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}
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// Save compressed model
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await tf.io.fileSystem(outputPath).save(artifacts)
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console.log(`✅ Compressed model saved to: ${outputPath}`)
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model.dispose()
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return true
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} catch (error) {
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console.error(`❌ Error compressing model: ${error.message}`)
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return false
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}
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}
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/**
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* Create optimized model variants
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*/
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async function createOptimizedVariants() {
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try {
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const originalModelPath = path.join(USE_MODEL_DIR, 'model.json')
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if (!fs.existsSync(originalModelPath)) {
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console.error('❌ Original model not found. Please run "npm run download-models" first.')
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process.exit(1)
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}
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console.log('📊 Original model size:', getDirectorySizeMB(USE_MODEL_DIR), 'MB')
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// Create float16 compressed version
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const float16Path = path.join(COMPRESSED_DIR, 'float16')
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if (!fs.existsSync(float16Path)) {
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fs.mkdirSync(float16Path, { recursive: true })
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}
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console.log('\n🗜️ Creating float16 compressed version...')
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const float16Success = await compressModelWeights(
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originalModelPath,
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path.join(float16Path, 'model.json'),
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'float16'
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)
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if (float16Success) {
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console.log('📊 Float16 model size:', getDirectorySizeMB(float16Path), 'MB')
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}
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// Create int8 quantized version
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const int8Path = path.join(COMPRESSED_DIR, 'int8')
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if (!fs.existsSync(int8Path)) {
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fs.mkdirSync(int8Path, { recursive: true })
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}
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console.log('\n🗜️ Creating int8 quantized version...')
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const int8Success = await compressModelWeights(
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originalModelPath,
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path.join(int8Path, 'model.json'),
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'int8'
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)
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if (int8Success) {
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console.log('📊 Int8 model size:', getDirectorySizeMB(int8Path), 'MB')
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}
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// Create compression summary
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const compressionSummary = {
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originalSize: getDirectorySizeMB(USE_MODEL_DIR),
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variants: {
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float16: {
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available: float16Success,
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size: float16Success ? getDirectorySizeMB(float16Path) : null,
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compressionRatio: float16Success ?
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(parseFloat(getDirectorySizeMB(USE_MODEL_DIR)) / parseFloat(getDirectorySizeMB(float16Path))).toFixed(2) : null
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},
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int8: {
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available: int8Success,
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size: int8Success ? getDirectorySizeMB(int8Path) : null,
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compressionRatio: int8Success ?
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(parseFloat(getDirectorySizeMB(USE_MODEL_DIR)) / parseFloat(getDirectorySizeMB(int8Path))).toFixed(2) : null
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}
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},
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createdAt: new Date().toISOString()
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}
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fs.writeFileSync(
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path.join(COMPRESSED_DIR, 'compression-summary.json'),
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JSON.stringify(compressionSummary, null, 2)
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)
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console.log('\n📋 Compression Summary:')
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console.log(`Original: ${compressionSummary.originalSize} MB`)
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if (float16Success) {
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console.log(`Float16: ${compressionSummary.variants.float16.size} MB (${compressionSummary.variants.float16.compressionRatio}x smaller)`)
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}
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if (int8Success) {
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console.log(`Int8: ${compressionSummary.variants.int8.size} MB (${compressionSummary.variants.int8.compressionRatio}x smaller)`)
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}
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console.log('\n✨ Model compression completed successfully!')
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console.log('Compressed models are available for applications requiring smaller file sizes.')
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} catch (error) {
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console.error('❌ Error during compression:', error)
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process.exit(1)
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}
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}
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/**
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* Optimize model for specific use cases
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*/
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async function optimizeForUseCase(useCase = 'general') {
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console.log(`\n🎯 Optimizing model for use case: ${useCase}`)
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const optimizations = {
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general: {
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description: 'Balanced performance and size',
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precision: 'float16',
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batchSize: 32
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},
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'low-memory': {
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description: 'Minimal memory footprint',
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precision: 'int8',
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batchSize: 1
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},
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'high-performance': {
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description: 'Maximum inference speed',
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precision: 'float32',
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batchSize: 64
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}
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}
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const config = optimizations[useCase] || optimizations.general
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console.log(`📝 Optimization config: ${config.description}`)
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console.log(` Precision: ${config.precision}`)
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console.log(` Batch size: ${config.batchSize}`)
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// Create optimization metadata
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const optimizationMetadata = {
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useCase,
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config,
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createdAt: new Date().toISOString(),
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recommendations: {
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'low-memory': 'Use int8 quantized model for memory-constrained environments',
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'high-performance': 'Use original float32 model with larger batch sizes',
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'general': 'Use float16 model for balanced performance'
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}
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}
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fs.writeFileSync(
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path.join(COMPRESSED_DIR, `optimization-${useCase}.json`),
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JSON.stringify(optimizationMetadata, null, 2)
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)
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console.log(`✅ Optimization profile created for ${useCase}`)
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}
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// Main execution
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async function main() {
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try {
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await createOptimizedVariants()
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await optimizeForUseCase('general')
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await optimizeForUseCase('low-memory')
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await optimizeForUseCase('high-performance')
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console.log('\n🎉 All optimizations completed successfully!')
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} catch (error) {
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console.error('❌ Compression failed:', error)
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process.exit(1)
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}
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}
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main().catch(console.error)
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177
brainy-models-package/scripts/download-full-models.js
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177
brainy-models-package/scripts/download-full-models.js
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#!/usr/bin/env node
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/* eslint-env node */
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/* eslint-disable no-console */
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/**
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* Download Full Models Script for @soulcraft/brainy-models
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*
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* This script downloads the complete Universal Sentence Encoder model
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* and saves it locally for offline use, providing maximum reliability.
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*/
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import fs from 'fs'
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import path from 'path'
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import { fileURLToPath } from 'url'
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import * as tf from '@tensorflow/tfjs-node'
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import * as use from '@tensorflow-models/universal-sentence-encoder'
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import https from 'https'
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import { promisify } from 'util'
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const __filename = fileURLToPath(import.meta.url)
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const __dirname = path.dirname(__filename)
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const MODELS_DIR = path.join(__dirname, '..', 'models')
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const USE_MODEL_DIR = path.join(MODELS_DIR, 'universal-sentence-encoder')
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// Ensure directories exist
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if (!fs.existsSync(MODELS_DIR)) {
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fs.mkdirSync(MODELS_DIR, { recursive: true })
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}
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if (!fs.existsSync(USE_MODEL_DIR)) {
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fs.mkdirSync(USE_MODEL_DIR, { recursive: true })
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}
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console.log('🚀 Starting full model download for @soulcraft/brainy-models...')
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console.log('This will download the complete Universal Sentence Encoder model (~25MB)')
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console.log('for offline use and maximum reliability.\n')
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/**
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* Download a file from URL to local path
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*/
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async function downloadFile(url, filePath) {
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return new Promise((resolve, reject) => {
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const file = fs.createWriteStream(filePath)
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https.get(url, (response) => {
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if (response.statusCode !== 200) {
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reject(new Error(`Failed to download ${url}: ${response.statusCode}`))
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return
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}
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const totalSize = parseInt(response.headers['content-length'] || '0')
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let downloadedSize = 0
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response.on('data', (chunk) => {
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downloadedSize += chunk.length
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if (totalSize > 0) {
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const progress = ((downloadedSize / totalSize) * 100).toFixed(1)
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process.stdout.write(`\r📥 Downloading: ${progress}% (${downloadedSize}/${totalSize} bytes)`)
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}
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})
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response.pipe(file)
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file.on('finish', () => {
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file.close()
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console.log(`\n✅ Downloaded: ${path.basename(filePath)}`)
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resolve()
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})
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file.on('error', (err) => {
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fs.unlink(filePath, () => {}) // Delete partial file
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reject(err)
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})
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}).on('error', reject)
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})
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}
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/**
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* Download the complete Universal Sentence Encoder model
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*/
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async function downloadFullModel() {
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try {
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console.log('🔍 Loading model to get download URLs...')
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// Load the model to get access to its internal structure
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const model = await use.load()
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console.log('✅ Model loaded successfully')
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// Test the model to ensure it works
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console.log('🧪 Testing model functionality...')
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const testEmbedding = await model.embed(['Hello world'])
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const testArray = await testEmbedding.array()
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console.log(`✅ Model test passed - embedding dimensions: ${testArray[0].length}`)
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testEmbedding.dispose()
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// The Universal Sentence Encoder model URL
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const modelBaseUrl = 'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1'
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console.log('📦 Downloading model files...')
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// Download model.json
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const modelJsonUrl = `${modelBaseUrl}/model.json`
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const modelJsonPath = path.join(USE_MODEL_DIR, 'model.json')
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await downloadFile(modelJsonUrl, modelJsonPath)
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// Read the model.json to get the weights manifest
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const modelJson = JSON.parse(fs.readFileSync(modelJsonPath, 'utf8'))
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// Download all weight files
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if (modelJson.weightsManifest) {
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for (const manifest of modelJson.weightsManifest) {
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for (const weightFile of manifest.paths) {
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const weightUrl = `${modelBaseUrl}/${weightFile}`
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const weightPath = path.join(USE_MODEL_DIR, weightFile)
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await downloadFile(weightUrl, weightPath)
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}
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}
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}
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// Create metadata for the bundled model
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const metadata = {
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name: 'universal-sentence-encoder',
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version: '1.0.0',
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description: 'Complete Universal Sentence Encoder model bundled for offline use',
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dimensions: 512,
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downloadDate: new Date().toISOString(),
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source: 'tensorflow-models/universal-sentence-encoder',
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approach: 'full-bundle',
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modelUrl: modelBaseUrl,
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bundledLocally: true,
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reliability: 'maximum'
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}
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'metadata.json'),
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JSON.stringify(metadata, null, 2)
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)
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// Verify all files exist and calculate total size
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const modelFiles = fs.readdirSync(USE_MODEL_DIR)
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let totalSize = 0
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console.log('\n📋 Downloaded files:')
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for (const file of modelFiles) {
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const filePath = path.join(USE_MODEL_DIR, file)
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const stats = fs.statSync(filePath)
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totalSize += stats.size
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console.log(` ✅ ${file} (${(stats.size / 1024 / 1024).toFixed(2)} MB)`)
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}
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console.log(`\n🎉 Model download complete!`)
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console.log(`📊 Total size: ${(totalSize / 1024 / 1024).toFixed(2)} MB`)
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console.log(`📁 Location: ${USE_MODEL_DIR}`)
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console.log(`🔒 Reliability: Maximum (fully offline)`)
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// Test loading the downloaded model
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console.log('\n🧪 Testing downloaded model...')
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const offlineModel = await tf.loadGraphModel(`file://${path.join(USE_MODEL_DIR, 'model.json')}`)
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console.log('✅ Offline model loads successfully')
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// Clean up
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model.dispose()
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offlineModel.dispose()
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console.log('\n✨ Full model bundling completed successfully!')
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console.log('The model is now available for offline use with maximum reliability.')
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} catch (error) {
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console.error('❌ Error downloading full model:', error)
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process.exit(1)
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}
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}
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// Run the download
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downloadFullModel().catch(console.error)
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