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

This commit is contained in:
defiQUG
2025-10-05 06:34:26 -07:00
parent 52e9acb329
commit e6aec40163
10 changed files with 583 additions and 0 deletions

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// AI Model Lazy Loading Implementation
export class OptimizedStudentAssistanceAI {
private static models: Map<string, any> = new Map()
private static modelUrls = {
'text-vectorization': '/models/text-vectorizer.json',
'matching-engine': '/models/matcher.json',
'priority-classifier': '/models/priority.json'
}
// Lazy load models on demand
private static async loadModel(modelType: string) {
if (this.models.has(modelType)) {
return this.models.get(modelType)
}
try {
console.log(`🤖 Loading AI model: ${modelType}`)
const model = await tf.loadLayersModel(this.modelUrls[modelType])
this.models.set(modelType, model)
console.log(`✅ Model ${modelType} loaded successfully`)
return model
} catch (error) {
console.error(`❌ Failed to load model ${modelType}:`, error)
// Fallback to rule-based system
return null
}
}
// Preload critical models in background
static async preloadCriticalModels() {
try {
// Load text vectorization model first (most commonly used)
await this.loadModel('text-vectorization')
// Load others in background
setTimeout(() => this.loadModel('matching-engine'), 2000)
setTimeout(() => this.loadModel('priority-classifier'), 4000)
} catch (error) {
console.warn('Background model preloading failed:', error)
}
}
async processRequest(request: StudentRequest): Promise<MatchResult[]> {
// Load models as needed
const textModel = await OptimizedStudentAssistanceAI.loadModel('text-vectorization')
const matchingModel = await OptimizedStudentAssistanceAI.loadModel('matching-engine')
// Process with loaded models or fallback to rule-based
if (textModel && matchingModel) {
return this.aiBasedMatching(request, textModel, matchingModel)
} else {
return this.ruleBasedMatching(request)
}
}
private async aiBasedMatching(request: StudentRequest, textModel: any, matchingModel: any): Promise<MatchResult[]> {
// AI-powered matching logic
console.log('🤖 Using AI-powered matching')
// ... implementation
return []
}
private async ruleBasedMatching(request: StudentRequest): Promise<MatchResult[]> {
// Fallback rule-based matching
console.log('📏 Using rule-based matching (fallback)')
// ... implementation
return []
}
}

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import React from 'react'
import { Helmet } from 'react-helmet-async'
interface SEOProps {
title?: string
description?: string
keywords?: string[]
image?: string
url?: string
type?: 'website' | 'article' | 'organization'
}
export const SEO: React.FC<SEOProps> = ({
title = 'Miracles In Motion - Supporting Students in Need',
description = 'A 501(c)3 non-profit providing school supplies, clothing, and emergency assistance to students and families in need.',
keywords = ['nonprofit', 'charity', '501c3', 'student assistance', 'school supplies', 'donations'],
image = 'https://miraclesinmotion.org/og-image.png',
url = 'https://miraclesinmotion.org',
type = 'website'
}) => {
const structuredData = {
"@context": "https://schema.org",
"@type": "Organization",
"name": "Miracles In Motion",
"description": description,
"url": url,
"logo": "https://miraclesinmotion.org/logo.png",
"contactPoint": {
"@type": "ContactPoint",
"telephone": "+1-555-123-4567",
"contactType": "Customer Service"
},
"sameAs": [
"https://facebook.com/miraclesinmotion",
"https://instagram.com/miraclesinmotion"
]
}
return (
<Helmet>
<title>{title}</title>
<meta name="description" content={description} />
<meta name="keywords" content={keywords.join(', ')} />
{/* Open Graph */}
<meta property="og:title" content={title} />
<meta property="og:description" content={description} />
<meta property="og:image" content={image} />
<meta property="og:url" content={url} />
<meta property="og:type" content={type} />
{/* Twitter Card */}
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content={title} />
<meta name="twitter:description" content={description} />
<meta name="twitter:image" content={image} />
{/* Structured Data */}
<script type="application/ld+json">
{JSON.stringify(structuredData)}
</script>
{/* Additional Meta Tags */}
<meta name="robots" content="index, follow" />
<meta name="author" content="Miracles In Motion" />
<link rel="canonical" href={url} />
</Helmet>
)
}

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// Example: Modular Page Structure
import React from 'react'
import { SEOHead } from '@/components/SEO'
import { PageShell } from '@/components/Layout'
import { DonationForm } from './DonationForm'
import { ImpactCalculator } from './ImpactCalculator'
import { DonationTiers } from './DonationTiers'
import { Heart } from 'lucide-react'
interface DonatePageProps {
className?: string
}
export const DonatePage: React.FC<DonatePageProps> = ({ className }) => {
return (
<>
<SEOHead
title="Donate - Support Students in Need"
description="Your donation directly supports students with school supplies, clothing, and emergency assistance."
/>
<PageShell
title="Make a Difference Today"
icon={Heart}
eyebrow="Every dollar counts"
className={className}
>
<div className="grid gap-8 lg:grid-cols-3">
<div className="lg:col-span-2 space-y-8">
<DonationTiers />
<DonationForm />
</div>
<div className="space-y-6">
<ImpactCalculator />
{/* Add trust badges, testimonials, etc. */}
</div>
</div>
</PageShell>
</>
)
}