Building AI-Powered Web Apps with Browser Native APIs in 2025

async function checkAIAvailability() {
  // First, check if the API exists
  if (!('ai' in window)) {
    console.log('Browser AI not supported. Are you using Chrome Canary with flags enabled?');
    return { available: false, reason: 'API not found' };
  }
  
  try {
    // Check capabilities
    const capabilities = await window.ai.languageModel.capabilities();
    
    console.log('AI Capabilities:', capabilities);
    
    // Status can be: 'readily', 'after-download', 'no'
    if (capabilities.available === 'no') {
      return { 
        available: false, 
        reason: 'Model not available on this device' 
      };
    }
    
    if (capabilities.available === 'after-download') {
      console.log('Model needs to be downloaded first');
      // Trigger download by creating a session
      const session = await window.ai.languageModel.create();
      session.destroy();
      return { 
        available: true, 
        reason: 'Model downloading...',
        needsDownload: true 
      };
    }
    
    return { 
      available: true, 
      reason: 'Ready to use' 
    };
  } catch (error) {
    console.error('Error checking AI availability:', error);
    return { 
      available: false, 
      reason: error.message 
    };
  }
}

// Usage with user feedback
async function initializeAI() {
  const status = await checkAIAvailability();
  
  if (!status.available) {
    alert(`AI not available: ${status.reason}\n\nPlease use Chrome Canary with flags enabled.`);
    return null;
  }
  
  if (status.needsDownload) {
    alert('AI model is downloading in the background. This may take a few minutes.');
  }
  
  return status;
}

async function generateText(prompt) {
  // Check availability first
  const status = await checkAIAvailability();
  if (!status.available) {
    throw new Error(`AI not available: ${status.reason}`);
  }
  
  // Create a language model session
  // Note: This will fail if the API isn't enabled in Chrome flags
  try {
    const session = await window.ai.languageModel.create({
      temperature: 0.7,
      topK: 40,
    });
    
    try {
      // Generate text
      const result = await session.prompt(prompt);
      console.log('Generated:', result);
      return result;
    } finally {
      // Always clean up
      session.destroy();
    }
  } catch (error) {
    console.error('Error creating AI session:', error);
    throw new Error('Failed to create AI session. Make sure you have enabled the required Chrome flags.');
  }
}

// Usage with error handling
try {
  const response = await generateText('Explain quantum computing in simple terms');
  console.log(response);
} catch (error) {
  console.error('AI generation failed:', error.message);
  // Fallback to alternative implementation or show error to user
}

async function streamText(prompt, onChunk) {
  const session = await window.ai.languageModel.create();
  
  try {
    const stream = session.promptStreaming(prompt);
    
    let fullResponse = '';
    for await (const chunk of stream) {
      fullResponse = chunk;
      onChunk(chunk);
    }
    
    return fullResponse;
  } finally {
    session.destroy();
  }
}

// Usage with real-time UI updates
const outputElement = document.getElementById('output');
await streamText('Write a short story about a robot', (chunk) => {
  outputElement.textContent = chunk;
});

class BrowserAIChat {
  constructor() {
    this.session = null;
    this.conversationHistory = [];
  }
  
  async initialize() {
    const available = await checkAIAvailability();
    if (!available) {
      throw new Error('Browser AI not available');
    }
    
    this.session = await window.ai.languageModel.create({
      temperature: 0.8,
      systemPrompt: 'You are a helpful assistant running locally in the browser.'
    });
  }
  
  async chat(userMessage) {
    if (!this.session) {
      await this.initialize();
    }
    
    // Add to history
    this.conversationHistory.push({
      role: 'user',
      content: userMessage
    });
    
    // Create context-aware prompt
    const prompt = this.buildPrompt();
    
    // Get response
    const response = await this.session.prompt(prompt);
    
    // Add to history
    this.conversationHistory.push({
      role: 'assistant',
      content: response
    });
    
    return response;
  }
  
  buildPrompt() {
    // Format conversation history for the model
    return this.conversationHistory
      .map(msg => `${msg.role}: ${msg.content}`)
      .join('\n');
  }
  
  reset() {
    this.conversationHistory = [];
  }
  
  destroy() {
    if (this.session) {
      this.session.destroy();
      this.session = null;
    }
  }
}

// Usage
const chat = new BrowserAIChat();
await chat.initialize();

const response1 = await chat.chat('What is JavaScript?');
const response2 = await chat.chat('How is it different from Python?');
console.log(response2); // Context-aware response

async function summarizeText(text, maxLength = 100) {
  const session = await window.ai.languageModel.create({
    temperature: 0.3, // Lower temperature for more focused summaries
  });
  
  try {
    const prompt = `Summarize the following text in ${maxLength} words or less:\n\n${text}`;
    return await session.prompt(prompt);
  } finally {
    session.destroy();
  }
}

// Usage
const article = `[Long article text here...]`;
const summary = await summarizeText(article, 50);

async function analyzeSentiment(text) {
  const session = await window.ai.languageModel.create({
    temperature: 0.1, // Very low for consistent classification
  });
  
  try {
    const prompt = `Analyze the sentiment of the following text. 
Respond with only one word: positive, negative, or neutral.

Text: ${text}`;
    
    const sentiment = await session.prompt(prompt);
    return sentiment.trim().toLowerCase();
  } finally {
    session.destroy();
  }
}

// Usage
const sentiment = await analyzeSentiment('This product is amazing!');
console.log(sentiment); // 'positive'

async function initWebGPU() {
  // Check for WebGPU support
  if (!navigator.gpu) {
    throw new Error('WebGPU not supported');
  }
  
  // Request GPU adapter
  const adapter = await navigator.gpu.requestAdapter();
  if (!adapter) {
    throw new Error('No GPU adapter found');
  }
  
  // Get device
  const device = await adapter.requestDevice();
  
  return { adapter, device };
}

import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-backend-webgpu';

async function setupTensorFlowWebGPU() {
  // Set WebGPU as backend
  await tf.setBackend('webgpu');
  await tf.ready();
  
  console.log('TensorFlow.js backend:', tf.getBackend());
}

// Load and run a model
async function runImageClassification(imageElement) {
  await setupTensorFlowWebGPU();
  
  // Load MobileNet model
  const model = await tf.loadLayersModel(
    'https://tfhub.dev/google/tfjs-model/imagenet/mobilenet_v2_100_224/classification/3/default/1',
    { fromTFHub: true }
  );
  
  // Preprocess image
  const tensor = tf.browser.fromPixels(imageElement)
    .resizeBilinear([224, 224])
    .expandDims(0)
    .toFloat()
    .div(127.5)
    .sub(1);
  
  // Run inference
  const predictions = await model.predict(tensor);
  const topK = await predictions.topk(5);
  
  // Get results
  const classes = await topK.indices.data();
  const scores = await topK.values.data();
  
  return { classes, scores };
}

async function matrixMultiplyWebGPU(matrixA, matrixB) {
  const { device } = await initWebGPU();
  
  const shaderCode = `
    @group(0) @binding(0) var<storage, read> matrixA: array<f32>;
    @group(0) @binding(1) var<storage, read> matrixB: array<f32>;
    @group(0) @binding(2) var<storage, read_write> result: array<f32>;
    @group(0) @binding(3) var<uniform> dimensions: vec3<u32>;
    
    @compute @workgroup_size(8, 8)
    fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
      let row = global_id.x;
      let col = global_id.y;
      let M = dimensions.x;
      let N = dimensions.y;
      let K = dimensions.z;
      
      if (row < M && col < K) {
        var sum = 0.0;
        for (var i = 0u; i < N; i = i + 1u) {
          sum = sum + matrixA[row * N + i] * matrixB[i * K + col];
        }
        result[row * K + col] = sum;
      }
    }
  `;
  
  // Create shader module
  const shaderModule = device.createShaderModule({
    code: shaderCode
  });
  
  // Create compute pipeline
  const pipeline = device.createComputePipeline({
    layout: 'auto',
    compute: {
      module: shaderModule,
      entryPoint: 'main'
    }
  });
  
  // Create buffers and bind group
  // [Buffer creation and execution code here...]
  
  return result;
}

class WebGPUObjectDetector {
  constructor() {
    this.model = null;
    this.device = null;
  }
  
  async initialize() {
    // Setup WebGPU
    const { device } = await initWebGPU();
    this.device = device;
    
    // Setup TensorFlow with WebGPU
    await tf.setBackend('webgpu');
    await tf.ready();
    
    // Load COCO-SSD model
    this.model = await cocoSsd.load({
      base: 'mobilenet_v2'
    });
  }
  
  async detectObjects(videoElement) {
    if (!this.model) {
      await this.initialize();
    }
    
    // Run detection
    const predictions = await this.model.detect(videoElement);
    
    return predictions.map(pred => ({
      class: pred.class,
      score: pred.score,
      bbox: pred.bbox
    }));
  }
  
  async startRealtimeDetection(videoElement, onDetect) {
    const detect = async () => {
      const predictions = await this.detectObjects(videoElement);
      onDetect(predictions);
      requestAnimationFrame(detect);
    };
    
    detect();
  }
}

// Usage
const detector = new WebGPUObjectDetector();
const video = document.getElementById('webcam');

detector.startRealtimeDetection(video, (predictions) => {
  console.log('Detected objects:', predictions);
  // Draw bounding boxes on canvas
});

async function checkWebNNSupport() {
  if (!('ml' in navigator)) {
    console.log('WebNN not supported');
    return false;
  }
  
  const context = await navigator.ml.createContext();
  console.log('WebNN context created');
  return true;
}

async function createSimpleNetwork() {
  const context = await navigator.ml.createContext();
  const builder = new MLGraphBuilder(context);
  
  // Define input
  const input = builder.input('input', {
    type: 'float32',
    dimensions: [1, 784] // MNIST input
  });
  
  // First dense layer
  const weights1 = builder.constant({
    type: 'float32',
    dimensions: [784, 128]
  }, new Float32Array(784 * 128).fill(0.01));
  
  const bias1 = builder.constant({
    type: 'float32',
    dimensions: [128]
  }, new Float32Array(128).fill(0));
  
  const dense1 = builder.add(
    builder.matmul(input, weights1),
    bias1
  );
  const relu1 = builder.relu(dense1);
  
  // Output layer
  const weights2 = builder.constant({
    type: 'float32',
    dimensions: [128, 10]
  }, new Float32Array(128 * 10).fill(0.01));
  
  const bias2 = builder.constant({
    type: 'float32',
    dimensions: [10]
  }, new Float32Array(10).fill(0));
  
  const output = builder.add(
    builder.matmul(relu1, weights2),
    bias2
  );
  const softmax = builder.softmax(output);
  
  // Build graph
  const graph = await builder.build({ output: softmax });
  
  return { graph, context };
}

// Run inference
async function runInference(graph, context, inputData) {
  const inputs = {
    'input': new Float32Array(inputData)
  };
  
  const outputs = await context.compute(graph, inputs);
  return outputs.output;
}

class WebNNImageClassifier {
  constructor(modelPath) {
    this.modelPath = modelPath;
    this.graph = null;
    this.context = null;
  }
  
  async load() {
    this.context = await navigator.ml.createContext();
    
    // Load pre-trained model
    const response = await fetch(this.modelPath);
    const modelData = await response.arrayBuffer();
    
    // Build graph from model
    this.graph = await this.context.loadModel(modelData);
  }
  
  preprocessImage(imageElement) {
    const canvas = document.createElement('canvas');
    canvas.width = 224;
    canvas.height = 224;
    
    const ctx = canvas.getContext('2d');
    ctx.drawImage(imageElement, 0, 0, 224, 224);
    
    const imageData = ctx.getImageData(0, 0, 224, 224);
    const pixels = imageData.data;
    
    // Normalize to [-1, 1]
    const normalized = new Float32Array(224 * 224 * 3);
    for (let i = 0; i < pixels.length; i += 4) {
      const idx = i / 4;
      normalized[idx * 3] = (pixels[i] / 255.0 - 0.5) * 2;
      normalized[idx * 3 + 1] = (pixels[i + 1] / 255.0 - 0.5) * 2;
      normalized[idx * 3 + 2] = (pixels[i + 2] / 255.0 - 0.5) * 2;
    }
    
    return normalized;
  }
  
  async classify(imageElement) {
    if (!this.graph) {
      await this.load();
    }
    
    const inputTensor = this.preprocessImage(imageElement);
    
    const inputs = { 'input': inputTensor };
    const outputs = await this.context.compute(this.graph, inputs);
    
    // Get top prediction
    const predictions = Array.from(outputs.output);
    const topIndex = predictions.indexOf(Math.max(...predictions));
    
    return {
      class: topIndex,
      confidence: predictions[topIndex]
    };
  }
}

class ContentAssistant {
  constructor() {
    this.session = null;
  }
  
  async initialize() {
    if (!('ai' in window)) {
      throw new Error('Browser AI not available');
    }
    
    this.session = await window.ai.languageModel.create({
      temperature: 0.7,
      systemPrompt: 'You are a writing assistant. Help improve text quality.'
    });
  }
  
  async improveText(text) {
    const prompt = `Improve the following text while maintaining its meaning:

${text}

Improved version:`;
    
    return await this.session.prompt(prompt);
  }
  
  async generateHeadlines(topic, count = 5) {
    const prompt = `Generate ${count} catchy headlines about: ${topic}`;
    const response = await this.session.prompt(prompt);
    return response.split('\n').filter(line => line.trim());
  }
  
  async expandBulletPoints(bullets) {
    const prompt = `Expand these bullet points into full paragraphs:

${bullets}`;
    
    return await this.session.prompt(prompt);
  }
  
  async checkGrammar(text) {
    const prompt = `Check this text for grammar errors and suggest corrections:

${text}

List any errors found:`;
    
    return await this.session.prompt(prompt);
  }
}

// Usage in a text editor
const assistant = new ContentAssistant();
await assistant.initialize();

document.getElementById('improveBtn').addEventListener('click', async () => {
  const text = document.getElementById('editor').value;
  const improved = await assistant.improveText(text);
  document.getElementById('suggestions').textContent = improved;
});

class BrowserTranslator {
  constructor() {
    this.session = null;
  }
  
  async initialize() {
    this.session = await window.ai.languageModel.create({
      temperature: 0.3, // Low temp for consistent translations
    });
  }
  
  async translate(text, targetLang) {
    const prompt = `Translate the following text to ${targetLang}. 
Provide only the translation, no explanations:

${text}`;
    
    return await this.session.prompt(prompt);
  }
  
  async detectLanguage(text) {
    const prompt = `What language is this text? Answer with only the language name:

${text}`;
    
    return await this.session.prompt(prompt);
  }
  
  async translateWithContext(text, targetLang, context) {
    const prompt = `Translate this ${context} text to ${targetLang}:

${text}`;
    
    return await this.session.prompt(prompt);
  }
}

// Real-time translation UI
const translator = new BrowserTranslator();
await translator.initialize();

const inputText = document.getElementById('inputText');
const outputText = document.getElementById('outputText');
const targetLang = document.getElementById('targetLang');

let debounceTimer;
inputText.addEventListener('input', () => {
  clearTimeout(debounceTimer);
  debounceTimer = setTimeout(async () => {
    const translated = await translator.translate(
      inputText.value,
      targetLang.value
    );
    outputText.value = translated;
  }, 500);
});

class ImageAnalyzer {
  constructor() {
    this.detector = null;
    this.classifier = null;
  }
  
  async initialize() {
    // Setup WebGPU backend
    await tf.setBackend('webgpu');
    await tf.ready();
    
    // Load models
    this.detector = await cocoSsd.load();
    this.classifier = await mobilenet.load();
  }
  
  async analyzeImage(imageElement) {
    if (!this.detector || !this.classifier) {
      await this.initialize();
    }
    
    // Object detection
    const objects = await this.detector.detect(imageElement);
    
    // Image classification
    const classifications = await this.classifier.classify(imageElement);
    
    // Generate description using browser AI
    const description = await this.generateDescription(
      objects,
      classifications
    );
    
    return {
      objects,
      classifications,
      description
    };
  }
  
  async generateDescription(objects, classifications) {
    const session = await window.ai.languageModel.create();
    
    const objectList = objects.map(o => o.class).join(', ');
    const topClass = classifications[0]?.className || 'unknown';
    
    const prompt = `Describe an image that contains: ${objectList}. 
The main subject is: ${topClass}.
Write a brief, natural description:`;
    
    const description = await session.prompt(prompt);
    session.destroy();
    
    return description;
  }
  
  async batch ProcessImages(images) {
    const results = await Promise.all(
      images.map(img => this.analyzeImage(img))
    );
    return results;
  }
}

// Usage
const analyzer = new ImageAnalyzer();
const uploadInput = document.getElementById('imageUpload');

uploadInput.addEventListener('change', async (e) => {
  const file = e.target.files[0];
  const img = new Image();
  img.src = URL.createObjectURL(file);
  
  img.onload = async () => {
    const analysis = await analyzer.analyzeImage(img);
    displayResults(analysis);
  };
});

class SemanticSearch {
  constructor() {
    this.session = null;
    this.documents = [];
    this.embeddings = [];
  }
  
  async initialize() {
    this.session = await window.ai.languageModel.create();
  }
  
  async addDocuments(docs) {
    this.documents = docs;
    
    // Generate embeddings for each document
    for (const doc of docs) {
      const embedding = await this.generateEmbedding(doc.content);
      this.embeddings.push(embedding);
    }
  }
  
  async generateEmbedding(text) {
    // Use the AI model to generate a semantic representation
    const prompt = `Generate a semantic summary of this text in 5 key concepts:

${text}

Key concepts:`;
    
    const summary = await this.session.prompt(prompt);
    return summary.toLowerCase().split(',').map(s => s.trim());
  }
  
  cosineSimilarity(a, b) {
    const intersection = a.filter(x => b.includes(x)).length;
    return intersection / Math.sqrt(a.length * b.length);
  }
  
  async search(query, topK = 5) {
    const queryEmbedding = await this.generateEmbedding(query);
    
    // Calculate similarity scores
    const scores = this.embeddings.map((embedding, idx) => ({
      index: idx,
      score: this.cosineSimilarity(queryEmbedding, embedding),
      document: this.documents[idx]
    }));
    
    // Sort and return top results
    return scores
      .sort((a, b) => b.score - a.score)
      .slice(0, topK);
  }
  
  async answerQuestion(query) {
    // Find relevant documents
    const results = await this.search(query, 3);
    
    // Build context from top results
    const context = results
      .map(r => r.document.content)
      .join('\n\n');
    
    // Generate answer using context
    const prompt = `Based on the following information, answer this question: ${query}

Information:
${context}

Answer:`;
    
    return await this.session.prompt(prompt);
  }
}

// Usage
const search = new SemanticSearch();
await search.initialize();

await search.addDocuments([
  { id: 1, title: 'AI Basics', content: 'Artificial intelligence is...' },
  { id: 2, title: 'Web Development', content: 'Building websites requires...' },
  // More documents...
]);

const results = await search.search('How do I build AI apps?');
const answer = await search.answerQuestion('What is machine learning?');

class SmartFormFiller {
  constructor() {
    this.session = null;
    this.userProfile = null;
  }
  
  async initialize(userProfile) {
    this.userProfile = userProfile; // Stored locally
    this.session = await window.ai.languageModel.create({
      temperature: 0.1,
      systemPrompt: 'You help fill out forms based on user profile data.'
    });
  }
  
  async analyzeForm(formElement) {
    const fields = Array.from(formElement.querySelectorAll('input, textarea, select'));
    
    return fields.map(field => ({
      element: field,
      type: field.type,
      name: field.name,
      label: this.findLabel(field),
      placeholder: field.placeholder
    }));
  }
  
  findLabel(field) {
    const label = field.closest('label') || 
                  document.querySelector(`label[for="${field.id}"]`);
    return label?.textContent.trim() || '';
  }
  
  async suggestValue(fieldInfo) {
    const prompt = `Given this user profile:
${JSON.stringify(this.userProfile, null, 2)}

What value should be used for a form field with:
- Label: ${fieldInfo.label}
- Name: ${fieldInfo.name}
- Type: ${fieldInfo.type}
- Placeholder: ${fieldInfo.placeholder}

Provide only the value, no explanation:`;
    
    return await this.session.prompt(prompt);
  }
  
  async fillForm(formElement, preview = true) {
    const fields = await this.analyzeForm(formElement);
    const suggestions = new Map();
    
    for (const field of fields) {
      if (field.type !== 'submit' && field.type !== 'button') {
        const value = await this.suggestValue(field);
        suggestions.set(field.element, value);
      }
    }
    
    if (preview) {
      return this.showPreview(suggestions);
    } else {
      this.applyValues(suggestions);
    }
  }
  
  showPreview(suggestions) {
    // Show confirmation dialog with suggested values
    return suggestions;
  }
  
  applyValues(suggestions) {
    suggestions.forEach((value, element) => {
      element.value = value;
      element.dispatchEvent(new Event('input', { bubbles: true }));
    });
  }
}

class ModelCache {
  constructor() {
    this.cache = new Map();
  }
  
  async getOrLoad(modelName, loader) {
    if (this.cache.has(modelName)) {
      return this.cache.get(modelName);
    }
    
    const model = await loader();
    this.cache.set(modelName, model);
    return model;
  }
  
  async preload(models) {
    await Promise.all(
      models.map(({ name, loader }) => this.getOrLoad(name, loader))
    );
  }
  
  clear() {
    this.cache.forEach(model => {
      if (model.dispose) model.dispose();
    });
    this.cache.clear();
  }
}

// Usage
const cache = new ModelCache();

// Preload models during idle time
if ('requestIdleCallback' in window) {
  requestIdleCallback(async () => {
    await cache.preload([
      { name: 'mobilenet', loader: () => mobilenet.load() },
      { name: 'cocoSsd', loader: () => cocoSsd.load() }
    ]);
  });
}

// ai-worker.js
self.importScripts('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs');

let model = null;

self.addEventListener('message', async (e) => {
  const { type, data } = e.data;
  
  switch (type) {
    case 'load-model':
      model = await tf.loadLayersModel(data.modelPath);
      self.postMessage({ type: 'model-loaded' });
      break;
      
    case 'predict':
      if (!model) {
        self.postMessage({ type: 'error', error: 'Model not loaded' });
        return;
      }
      
      const tensor = tf.tensor(data.input);
      const prediction = model.predict(tensor);
      const result = await prediction.data();
      
      self.postMessage({ type: 'prediction', result: Array.from(result) });
      
      tensor.dispose();
      prediction.dispose();
      break;
  }
});

// main.js
class AIWorkerManager {
  constructor() {
    this.worker = new Worker('ai-worker.js');
    this.callbacks = new Map();
    this.messageId = 0;
    
    this.worker.addEventListener('message', (e) => {
      const { type, messageId, result, error } = e.data;
      
      if (this.callbacks.has(messageId)) {
        const { resolve, reject } = this.callbacks.get(messageId);
        
        if (error) {
          reject(error);
        } else {
          resolve(result);
        }
        
        this.callbacks.delete(messageId);
      }
    });
  }
  
  async loadModel(modelPath) {
    const messageId = this.messageId++;
    
    return new Promise((resolve, reject) => {
      this.callbacks.set(messageId, { resolve, reject });
      this.worker.postMessage({
        type: 'load-model',
        messageId,
        data: { modelPath }
      });
    });
  }
  
  async predict(input) {
    const messageId = this.messageId++;
    
    return new Promise((resolve, reject) => {
      this.callbacks.set(messageId, { resolve, reject });
      this.worker.postMessage({
        type: 'predict',
        messageId,
        data: { input }
      });
    });
  }
}

class BatchProcessor {
  constructor(processFn, { batchSize = 10, maxWait = 100 } = {}) {
    this.processFn = processFn;
    this.batchSize = batchSize;
    this.maxWait = maxWait;
    this.queue = [];
    this.timer = null;
  }
  
  async add(item) {
    return new Promise((resolve, reject) => {
      this.queue.push({ item, resolve, reject });
      
      if (this.queue.length >= this.batchSize) {
        this.flush();
      } else if (!this.timer) {
        this.timer = setTimeout(() => this.flush(), this.maxWait);
      }
    });
  }
  
  async flush() {
    if (this.timer) {
      clearTimeout(this.timer);
      this.timer = null;
    }
    
    if (this.queue.length === 0) return;
    
    const batch = this.queue.splice(0, this.batchSize);
    const items = batch.map(b => b.item);
    
    try {
      const results = await this.processFn(items);
      
      batch.forEach((b, i) => {
        b.resolve(results[i]);
      });
    } catch (error) {
      batch.forEach(b => b.reject(error));
    }
  }
}

// Usage with image classification
const classifier = new BatchProcessor(
  async (images) => {
    const model = await mobilenet.load();
    return Promise.all(images.map(img => model.classify(img)));
  },
  { batchSize: 5, maxWait: 200 }
);

// Individual calls are automatically batched
const result1 = classifier.add(image1);
const result2 = classifier.add(image2);
const result3 = classifier.add(image3);

class ProgressiveModelLoader {
  constructor() {
    this.models = {
      tiny: null,
      small: null,
      full: null
    };
  }
  
  async loadTiny() {
    if (!this.models.tiny) {
      this.models.tiny = await this.loadModel('tiny-model.json');
    }
    return this.models.tiny;
  }
  
  async loadSmall() {
    if (!this.models.small) {
      this.models.small = await this.loadModel('small-model.json');
    }
    return this.models.small;
  }
  
  async loadFull() {
    if (!this.models.full) {
      this.models.full = await this.loadModel('full-model.json');
    }
    return this.models.full;
  }
  
  async predict(input, quality = 'auto') {
    // Start with tiny model for instant feedback
    const tinyModel = await this.loadTiny();
    const tinyResult = await tinyModel.predict(input);
    
    if (quality === 'fast') {
      return tinyResult;
    }
    
    // Upgrade to small model in background
    const smallModel = await this.loadSmall();
    const smallResult = await smallModel.predict(input);
    
    if (quality === 'balanced') {
      return smallResult;
    }
    
    // Use full model for best quality
    const fullModel = await this.loadFull();
    return await fullModel.predict(input);
  }
}

class PrivacyFirstAI {
  constructor() {
    this.session = null;
    this.localOnly = true;
  }
  
  async initialize() {
    // Verify browser AI is available (local processing)
    if (!('ai' in window)) {
      throw new Error('Local AI not available. Cannot process data.');
    }
    
    this.session = await window.ai.languageModel.create();
  }
  
  async processData(sensitiveData) {
    // Ensure data never leaves the device
    if (!this.localOnly) {
      throw new Error('Remote processing not allowed');
    }
    
    // Process locally
    const result = await this.session.prompt(
      `Anonymize this data: ${sensitiveData}`
    );
    
    return result;
  }
  
  async clearData() {
    // Explicitly clear any cached data
    if (this.session) {
      this.session.destroy();
      this.session = null;
    }
    
    // Clear browser cache if needed
    if ('caches' in window) {
      const cacheNames = await caches.keys();
      await Promise.all(
        cacheNames.map(name => caches.delete(name))
      );
    }
  }
}

class ConsentManager {
  constructor() {
    this.permissions = {
      aiProcessing: false,
      dataStorage: false,
      analytics: false
    };
  }
  
  async requestPermission(type) {
    const consent = await this.showConsentDialog(type);
    this.permissions[type] = consent;
    this.savePermissions();
    return consent;
  }
  
  showConsentDialog(type) {
    return new Promise((resolve) => {
      const dialog = document.createElement('dialog');
      dialog.innerHTML = `
        <h3>Permission Required</h3>
        <p>${this.getPermissionDescription(type)}</p>
        <button id="allow">Allow</button>
        <button id="deny">Deny</button>
      `;
      
      document.body.appendChild(dialog);
      dialog.showModal();
      
      dialog.querySelector('#allow').onclick = () => {
        dialog.close();
        dialog.remove();
        resolve(true);
      };
      
      dialog.querySelector('#deny').onclick = () => {
        dialog.close();
        dialog.remove();
        resolve(false);
      };
    });
  }
  
  getPermissionDescription(type) {
    const descriptions = {
      aiProcessing: 'Allow AI processing of your data locally on your device. Your data never leaves your browser.',
      dataStorage: 'Store processed results locally for faster access.',
      analytics: 'Collect anonymous usage statistics to improve the app.'
    };
    return descriptions[type] || '';
  }
  
  savePermissions() {
    localStorage.setItem('ai-permissions', JSON.stringify(this.permissions));
  }
  
  loadPermissions() {
    const saved = localStorage.getItem('ai-permissions');
    if (saved) {
      this.permissions = JSON.parse(saved);
    }
  }
}

class AIFeatureDetector {
  static async detect() {
    const features = {
      browserAI: false,
      webGPU: false,
      webNN: false,
      webGL: false,
      webAssembly: false
    };
    
    // Check Chrome Built-in AI
    if ('ai' in window) {
      try {
        const capabilities = await window.ai.capabilities();
        features.browserAI = capabilities.available === 'readily';
      } catch (e) {
        features.browserAI = false;
      }
    }
    
    // Check WebGPU
    if ('gpu' in navigator) {
      try {
        const adapter = await navigator.gpu.requestAdapter();
        features.webGPU = adapter !== null;
      } catch (e) {
        features.webGPU = false;
      }
    }
    
    // Check WebNN
    features.webNN = 'ml' in navigator;
    
    // Check WebGL
    const canvas = document.createElement('canvas');
    features.webGL = !!(
      canvas.getContext('webgl') || 
      canvas.getContext('experimental-webgl')
    );
    
    // Check WebAssembly
    features.webAssembly = typeof WebAssembly !== 'undefined';
    
    return features;
  }
  
  static async getBestBackend() {
    const features = await this.detect();
    
    if (features.webGPU) return 'webgpu';
    if (features.webNN) return 'webnn';
    if (features.webGL) return 'webgl';
    if (features.webAssembly) return 'wasm';
    return 'cpu';
  }
}

// Usage
const features = await AIFeatureDetector.detect();
console.log('Available features:', features);

const backend = await AIFeatureDetector.getBestBackend();
console.log('Optimal backend:', backend);

class AdaptiveAIApp {
  constructor() {
    this.backend = null;
    this.capabilities = null;
  }
  
  async initialize() {
    this.capabilities = await AIFeatureDetector.detect();
    
    if (this.capabilities.browserAI) {
      return this.initializeBrowserAI();
    } else if (this.capabilities.webGPU) {
      return this.initializeWebGPU();
    } else if (this.capabilities.webGL) {
      return this.initializeWebGL();
    } else {
      return this.initializeFallback();
    }
  }
  
  async initializeBrowserAI() {
    this.backend = 'browser-ai';
    this.session = await window.ai.languageModel.create();
    console.log('Using Browser AI');
  }
  
  async initializeWebGPU() {
    this.backend = 'webgpu';
    await tf.setBackend('webgpu');
    console.log('Using WebGPU');
  }
  
  async initializeWebGL() {
    this.backend = 'webgl';
    await tf.setBackend('webgl');
    console.log('Using WebGL');
  }
  
  async initializeFallback() {
    this.backend = 'fallback';
    console.log('Using CPU fallback');
    
    // Optionally, use a lightweight model or cloud API
    this.showWarning('Limited AI capabilities. Consider upgrading your browser.');
  }
  
  showWarning(message) {
    const banner = document.createElement('div');
    banner.className = 'warning-banner';
    banner.textContent = message;
    document.body.prepend(banner);
  }
  
  async process(input) {
    switch (this.backend) {
      case 'browser-ai':
        return await this.session.prompt(input);
      
      case 'webgpu':
      case 'webgl':
        // Use TensorFlow.js
        return await this.processWithTF(input);
      
      case 'fallback':
        return await this.processFallback(input);
    }
  }
}

// Future-proof your code
class FutureAIApp {
  async initialize() {
    // Check for future APIs
    if (window.ai?.imageGeneration) {
      this.imageGen = await window.ai.imageGeneration.create();
    }
    
    if (window.ai?.speechRecognition) {
      this.speech = await window.ai.speechRecognition.create();
    }
    
    // Fallback to current APIs
    this.language = await window.ai.languageModel.create();
  }
  
  async generateImage(prompt) {
    if (this.imageGen) {
      return await this.imageGen.generate(prompt);
    }
    
    // Fallback: use text-to-image API or service
    return await this.fallbackImageGeneration(prompt);
  }
}

import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-backend-webgpu';

// This works in stable Chrome today!
await tf.setBackend('webgpu');
await tf.ready();

// Load and run any TensorFlow model
const model = await tf.loadLayersModel('path/to/model.json');
const result = await model.predict(inputTensor);