feat: Add environment configuration, ESLint setup, GitHub Actions deployment workflow, comprehensive API documentation, Jest testing configuration, and optimized Vite setup; implement AI model lazy loading and SEO components for improved performance and user experience
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69
src/ai/OptimizedStudentAssistanceAI.ts
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69
src/ai/OptimizedStudentAssistanceAI.ts
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// AI Model Lazy Loading Implementation
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export class OptimizedStudentAssistanceAI {
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private static models: Map<string, any> = new Map()
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private static modelUrls = {
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'text-vectorization': '/models/text-vectorizer.json',
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'matching-engine': '/models/matcher.json',
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'priority-classifier': '/models/priority.json'
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}
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// Lazy load models on demand
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private static async loadModel(modelType: string) {
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if (this.models.has(modelType)) {
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return this.models.get(modelType)
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}
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try {
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console.log(`🤖 Loading AI model: ${modelType}`)
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const model = await tf.loadLayersModel(this.modelUrls[modelType])
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this.models.set(modelType, model)
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console.log(`✅ Model ${modelType} loaded successfully`)
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return model
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} catch (error) {
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console.error(`❌ Failed to load model ${modelType}:`, error)
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// Fallback to rule-based system
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return null
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}
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}
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// Preload critical models in background
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static async preloadCriticalModels() {
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try {
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// Load text vectorization model first (most commonly used)
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await this.loadModel('text-vectorization')
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// Load others in background
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setTimeout(() => this.loadModel('matching-engine'), 2000)
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setTimeout(() => this.loadModel('priority-classifier'), 4000)
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} catch (error) {
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console.warn('Background model preloading failed:', error)
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}
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}
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async processRequest(request: StudentRequest): Promise<MatchResult[]> {
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// Load models as needed
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const textModel = await OptimizedStudentAssistanceAI.loadModel('text-vectorization')
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const matchingModel = await OptimizedStudentAssistanceAI.loadModel('matching-engine')
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// Process with loaded models or fallback to rule-based
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if (textModel && matchingModel) {
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return this.aiBasedMatching(request, textModel, matchingModel)
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} else {
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return this.ruleBasedMatching(request)
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}
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}
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private async aiBasedMatching(request: StudentRequest, textModel: any, matchingModel: any): Promise<MatchResult[]> {
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// AI-powered matching logic
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console.log('🤖 Using AI-powered matching')
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// ... implementation
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return []
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}
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private async ruleBasedMatching(request: StudentRequest): Promise<MatchResult[]> {
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// Fallback rule-based matching
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console.log('📏 Using rule-based matching (fallback)')
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// ... implementation
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return []
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}
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}
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69
src/components/SEO/index.tsx
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69
src/components/SEO/index.tsx
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import React from 'react'
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import { Helmet } from 'react-helmet-async'
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interface SEOProps {
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title?: string
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description?: string
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keywords?: string[]
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image?: string
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url?: string
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type?: 'website' | 'article' | 'organization'
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}
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export const SEO: React.FC<SEOProps> = ({
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title = 'Miracles In Motion - Supporting Students in Need',
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description = 'A 501(c)3 non-profit providing school supplies, clothing, and emergency assistance to students and families in need.',
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keywords = ['nonprofit', 'charity', '501c3', 'student assistance', 'school supplies', 'donations'],
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image = 'https://miraclesinmotion.org/og-image.png',
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url = 'https://miraclesinmotion.org',
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type = 'website'
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}) => {
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const structuredData = {
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"@context": "https://schema.org",
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"@type": "Organization",
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"name": "Miracles In Motion",
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"description": description,
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"url": url,
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"logo": "https://miraclesinmotion.org/logo.png",
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"contactPoint": {
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"@type": "ContactPoint",
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"telephone": "+1-555-123-4567",
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"contactType": "Customer Service"
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},
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"sameAs": [
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"https://facebook.com/miraclesinmotion",
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"https://instagram.com/miraclesinmotion"
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]
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}
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return (
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<Helmet>
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<title>{title}</title>
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<meta name="description" content={description} />
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<meta name="keywords" content={keywords.join(', ')} />
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{/* Open Graph */}
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<meta property="og:title" content={title} />
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<meta property="og:description" content={description} />
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<meta property="og:image" content={image} />
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<meta property="og:url" content={url} />
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<meta property="og:type" content={type} />
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{/* Twitter Card */}
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<meta name="twitter:card" content="summary_large_image" />
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<meta name="twitter:title" content={title} />
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<meta name="twitter:description" content={description} />
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<meta name="twitter:image" content={image} />
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{/* Structured Data */}
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<script type="application/ld+json">
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{JSON.stringify(structuredData)}
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</script>
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{/* Additional Meta Tags */}
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<meta name="robots" content="index, follow" />
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<meta name="author" content="Miracles In Motion" />
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<link rel="canonical" href={url} />
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</Helmet>
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)
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}
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42
src/pages/DonatePage/index.tsx
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src/pages/DonatePage/index.tsx
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// Example: Modular Page Structure
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import React from 'react'
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import { SEOHead } from '@/components/SEO'
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import { PageShell } from '@/components/Layout'
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import { DonationForm } from './DonationForm'
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import { ImpactCalculator } from './ImpactCalculator'
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import { DonationTiers } from './DonationTiers'
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import { Heart } from 'lucide-react'
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interface DonatePageProps {
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className?: string
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}
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export const DonatePage: React.FC<DonatePageProps> = ({ className }) => {
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return (
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<>
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<SEOHead
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title="Donate - Support Students in Need"
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description="Your donation directly supports students with school supplies, clothing, and emergency assistance."
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/>
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<PageShell
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title="Make a Difference Today"
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icon={Heart}
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eyebrow="Every dollar counts"
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className={className}
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>
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<div className="grid gap-8 lg:grid-cols-3">
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<div className="lg:col-span-2 space-y-8">
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<DonationTiers />
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<DonationForm />
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</div>
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<div className="space-y-6">
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<ImpactCalculator />
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{/* Add trust badges, testimonials, etc. */}
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</div>
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</div>
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</PageShell>
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</>
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)
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}
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