From 47d913cf34cf961152a2765ff83dc64fb32c2010 Mon Sep 17 00:00:00 2001 From: defiQUG Date: Sun, 5 Oct 2025 05:14:58 -0700 Subject: [PATCH] feat: Implement AI Assistance Portal with types and components for student requests and insights --- PHASE3_AI_IMPLEMENTATION.md | 683 ++++++++ PHASE3_ARCHITECTURE.md | 668 ++++++++ package-lock.json | 2174 ++++++++++++++++++++++++- package.json | 19 +- src/App.tsx | 1185 +++++++++++++- src/ai/ProcessingPipeline.ts | 418 +++++ src/ai/StudentAssistanceAI.ts | 803 +++++++++ src/ai/types.ts | 191 +++ src/components/AIAssistancePortal.tsx | 721 ++++++++ 9 files changed, 6761 insertions(+), 101 deletions(-) create mode 100644 PHASE3_AI_IMPLEMENTATION.md create mode 100644 PHASE3_ARCHITECTURE.md create mode 100644 src/ai/ProcessingPipeline.ts create mode 100644 src/ai/StudentAssistanceAI.ts create mode 100644 src/ai/types.ts create mode 100644 src/components/AIAssistancePortal.tsx diff --git a/PHASE3_AI_IMPLEMENTATION.md b/PHASE3_AI_IMPLEMENTATION.md new file mode 100644 index 0000000..b318dac --- /dev/null +++ b/PHASE3_AI_IMPLEMENTATION.md @@ -0,0 +1,683 @@ +# Phase 3 Implementation Plan: Enterprise AI Integration + +## 🤖 Priority 1: AI-Powered Student Assistance Matching + +### Implementation Strategy +This document outlines the immediate next steps to begin Phase 3 implementation with the AI-powered student assistance matching engine - the highest impact feature for immediate organizational transformation. + +### Technical Architecture + +#### 1. AI Model Infrastructure +```typescript +// src/ai/StudentMatchingEngine.ts +interface StudentRequest { + id: string + studentId: string + description: string + category: AssistanceCategory + urgency: UrgencyLevel + location: GeographicLocation + constraints: RequestConstraints + deadline?: Date +} + +interface MatchResult { + resourceId: string + confidenceScore: number + estimatedImpact: number + logisticalComplexity: number + volunteerMatch?: VolunteerAssignment + estimatedCost: number + fulfillmentTimeline: Timeline +} + +class StudentAssistanceAI { + private vectorizer: TextVectorizer + private matchingModel: tf.LayersModel + private impactPredictor: tf.LayersModel + + constructor() { + this.initializeModels() + } + + private async initializeModels() { + // Load pre-trained TensorFlow.js models + this.matchingModel = await tf.loadLayersModel('/models/student-matching.json') + this.impactPredictor = await tf.loadLayersModel('/models/impact-prediction.json') + this.vectorizer = new TextVectorizer() + } + + async processRequest(request: StudentRequest): Promise { + // 1. Analyze and vectorize request + const analysis = await this.analyzeRequest(request) + + // 2. Find optimal resource matches + const candidates = await this.findCandidateResources(analysis) + + // 3. Score and rank matches + const scoredMatches = await this.scoreMatches(candidates, analysis) + + // 4. Predict impact and logistics + const enrichedMatches = await this.enrichWithPredictions(scoredMatches) + + return enrichedMatches.sort((a, b) => b.confidenceScore - a.confidenceScore) + } + + private async analyzeRequest(request: StudentRequest): Promise { + // NLP analysis of request description + const textVector = await this.vectorizer.encode(request.description) + + // Extract key features + const features = { + categoryVector: this.encodeCategoryVector(request.category), + urgencyScore: this.encodeUrgency(request.urgency), + locationVector: this.encodeLocation(request.location), + temporalFeatures: this.encodeTemporalConstraints(request.constraints), + semanticFeatures: textVector + } + + return { + primaryNeeds: await this.extractNeedCategories(textVector), + urgencyScore: features.urgencyScore, + complexityEstimate: await this.estimateComplexity(features), + resourceRequirements: await this.estimateResources(features) + } + } + + private async findCandidateResources(analysis: RequestAnalysis): Promise { + // Query available resources based on analysis + const availableResources = await ResourceManager.getAvailableResources({ + categories: analysis.primaryNeeds, + location: analysis.locationConstraints, + availability: analysis.timeConstraints + }) + + // Add volunteer availability + const volunteerCandidates = await VolunteerManager.getAvailableVolunteers({ + skills: analysis.requiredSkills, + location: analysis.locationConstraints, + availability: analysis.timeConstraints + }) + + return this.combineResourcesAndVolunteers(availableResources, volunteerCandidates) + } + + private async scoreMatches(candidates: ResourceCandidate[], analysis: RequestAnalysis): Promise { + const scoredMatches: ScoredMatch[] = [] + + for (const candidate of candidates) { + // Prepare input tensor for ML model + const inputFeatures = this.prepareFeaturesForML(candidate, analysis) + + // Get confidence score from trained model + const prediction = this.matchingModel.predict(inputFeatures) as tf.Tensor + const confidenceScore = await prediction.data() + + scoredMatches.push({ + ...candidate, + confidenceScore: confidenceScore[0], + reasoningFactors: this.explainScore(candidate, analysis) + }) + + prediction.dispose() // Clean up memory + } + + return scoredMatches + } + + async predictImpact(match: ScoredMatch): Promise { + // Use impact prediction model + const impactFeatures = this.prepareImpactFeatures(match) + const impactPrediction = this.impactPredictor.predict(impactFeatures) as tf.Tensor + const impactScore = await impactPrediction.data() + + impactPrediction.dispose() + + return { + estimatedBeneficiaries: Math.round(impactScore[0]), + successProbability: impactScore[1], + timeToImpact: impactScore[2], + sustainabilityScore: impactScore[3], + rippleEffects: await this.predictRippleEffects(match) + } + } +} +``` + +#### 2. Real-time Processing Pipeline +```typescript +// src/ai/ProcessingPipeline.ts +class RealTimeProcessingPipeline { + private queue: Queue + private aiEngine: StudentAssistanceAI + private notificationService: NotificationService + + constructor() { + this.queue = new Queue('assistance-requests') + this.aiEngine = new StudentAssistanceAI() + this.setupQueueProcessors() + } + + private setupQueueProcessors() { + // Process requests as they come in + this.queue.process('analyze-request', 5, async (job) => { + const request = job.data as StudentRequest + + try { + // AI analysis and matching + const matches = await this.aiEngine.processRequest(request) + + // Auto-approval for high-confidence matches + if (matches[0]?.confidenceScore > 0.9) { + await this.autoApproveRequest(request, matches[0]) + } else { + await this.routeForHumanReview(request, matches) + } + + // Update real-time dashboard + await this.updateDashboard(request.id, matches) + + } catch (error) { + await this.handleProcessingError(request, error) + } + }) + } + + async submitRequest(request: StudentRequest): Promise { + // Add to processing queue + const job = await this.queue.add('analyze-request', request, { + priority: this.calculatePriority(request.urgency), + attempts: 3, + backoff: 'exponential' + }) + + // Immediate acknowledgment + await this.sendAcknowledgment(request) + + return job.id + } + + private async autoApproveRequest(request: StudentRequest, match: MatchResult): Promise { + // Create assistance assignment + const assignment = await AssignmentManager.createAssignment({ + requestId: request.id, + resourceId: match.resourceId, + volunteerId: match.volunteerMatch?.id, + scheduledDate: match.fulfillmentTimeline.startDate, + estimatedCost: match.estimatedCost, + approvalStatus: 'auto-approved', + confidence: match.confidenceScore + }) + + // Notify all stakeholders + await Promise.all([ + this.notificationService.notifyStudent(request.studentId, assignment), + this.notificationService.notifyVolunteer(assignment.volunteerId, assignment), + this.notificationService.notifyCoordinators(assignment), + this.notificationService.updateDonors(assignment.estimatedCost) + ]) + + // Track for learning + await this.trackDecision(request, match, 'auto-approved') + } + + private async routeForHumanReview(request: StudentRequest, matches: MatchResult[]): Promise { + // Determine best reviewer based on request type and matches + const reviewer = await this.selectOptimalReviewer(request, matches) + + // Create review assignment + const reviewTask = await ReviewManager.createReviewTask({ + requestId: request.id, + assignedTo: reviewer.id, + aiRecommendations: matches, + priority: this.calculateReviewPriority(request, matches), + deadline: this.calculateReviewDeadline(request.urgency) + }) + + // Notify reviewer with AI insights + await this.notificationService.notifyReviewer(reviewer, reviewTask, { + aiConfidence: matches[0]?.confidenceScore, + recommendedAction: this.generateRecommendation(matches), + riskFactors: this.identifyRiskFactors(request, matches) + }) + } +} +``` + +#### 3. Learning and Improvement System +```typescript +// src/ai/LearningSystem.ts +class ContinuousLearningSystem { + private feedbackCollector: FeedbackCollector + private modelTrainer: ModelTrainer + + async collectOutcome(assignmentId: string, outcome: AssignmentOutcome): Promise { + // Collect real-world outcomes for model improvement + const assignment = await AssignmentManager.getById(assignmentId) + const originalRequest = await RequestManager.getById(assignment.requestId) + const aiDecision = await this.getOriginalAIDecision(assignmentId) + + const trainingExample = { + features: aiDecision.inputFeatures, + prediction: aiDecision.prediction, + actualOutcome: { + success: outcome.successful, + impactAchieved: outcome.measuredImpact, + costActual: outcome.actualCost, + timeToComplete: outcome.completionTime, + satisfactionScore: outcome.satisfactionRatings + } + } + + // Add to training dataset + await this.feedbackCollector.addTrainingExample(trainingExample) + + // Trigger model retraining if sufficient new data + if (await this.shouldRetrain()) { + await this.scheduleRetraining() + } + } + + async identifyImprovementOpportunities(): Promise { + const insights: ImprovementInsight[] = [] + + // Analyze prediction accuracy trends + const accuracyTrends = await this.analyzeAccuracyTrends() + if (accuracyTrends.declining) { + insights.push({ + type: 'accuracy-decline', + severity: accuracyTrends.severity, + recommendation: 'Model retraining recommended', + estimatedImpact: 'High' + }) + } + + // Identify bias in predictions + const biasAnalysis = await this.analyzeBias() + if (biasAnalysis.significantBias) { + insights.push({ + type: 'prediction-bias', + biasFactors: biasAnalysis.factors, + recommendation: 'Implement bias correction', + estimatedImpact: 'Critical' + }) + } + + // Find optimization opportunities + const optimizations = await this.findOptimizations() + insights.push(...optimizations) + + return insights + } + + private async scheduleRetraining(): Promise { + // Schedule model retraining job + const retrainingJob = await this.queue.add('retrain-models', { + modelTypes: ['matching', 'impact-prediction'], + trainingDataVersion: await this.getLatestDataVersion(), + validationSplit: 0.2, + hyperparameterTuning: true + }, { + priority: 1, + delay: 60000 // Start in 1 minute + }) + + await this.notifyAdministrators({ + message: 'AI model retraining initiated', + jobId: retrainingJob.id, + estimatedDuration: '45-60 minutes' + }) + } +} +``` + +#### 4. Frontend Integration Components +```typescript +// src/components/AIAssistancePortal.tsx +import React, { useState, useEffect } from 'react' +import { motion, AnimatePresence } from 'framer-motion' + +interface AIAssistancePortalProps { + userRole: 'student' | 'coordinator' | 'admin' +} + +export function AIAssistancePortal({ userRole }: AIAssistancePortalProps) { + const [requests, setRequests] = useState([]) + const [aiInsights, setAIInsights] = useState([]) + const [processing, setProcessing] = useState(false) + + useEffect(() => { + // Real-time updates via WebSocket + const ws = new WebSocket(`wss://api.miraclesinmotion.org/ai-updates`) + + ws.onmessage = (event) => { + const update = JSON.parse(event.data) + handleRealTimeUpdate(update) + } + + return () => ws.close() + }, []) + + const handleRealTimeUpdate = (update: AIUpdate) => { + switch (update.type) { + case 'request-processed': + setRequests(prev => prev.map(r => + r.id === update.requestId + ? { ...r, status: update.status, aiRecommendations: update.recommendations } + : r + )) + break + + case 'new-insight': + setAIInsights(prev => [update.insight, ...prev.slice(0, 9)]) + break + + case 'auto-approval': + // Show success notification + showNotification({ + type: 'success', + title: 'Request Auto-Approved', + message: `High-confidence match found for ${update.studentName}`, + action: { + label: 'View Details', + onClick: () => navigateToRequest(update.requestId) + } + }) + break + } + } + + return ( +
+ {/* AI Insights Panel */} + +

+ + AI Insights +

+ + + {aiInsights.map((insight) => ( + +
+
+
+

{insight.title}

+

+ {insight.description} +

+ {insight.confidence && ( +
+
+
+
+ + {Math.round(insight.confidence * 100)}% confidence + +
+ )} +
+
+ + ))} + + + + {/* Request Processing Interface */} +
+

Smart Request Processing

+ + {requests.map((request) => ( + + ))} +
+ + {/* Performance Metrics */} + +
+ ) +} + +function RequestCard({ request, onApprove, onModify, showAIRecommendations }: RequestCardProps) { + return ( + +
+
+

{request.description}

+

+ Student: {request.studentName} • {formatDistanceToNow(request.submittedAt)} ago +

+
+ +
+ + {showAIRecommendations && request.aiRecommendations && ( + +
+ + + AI Recommendation + + +
+ +
+ {request.aiRecommendations.slice(0, 2).map((rec, index) => ( +
+ {rec.resourceName} +
+ ${rec.estimatedCost} + {rec.fulfillmentTimeline} +
+
+ ))} +
+ +
+ onApprove(request.id, request.aiRecommendations[0])} + className="btn-primary text-xs px-3 py-1" + whileHover={{ scale: 1.05 }} + whileTap={{ scale: 0.95 }} + > + Approve AI Recommendation + + +
+ + )} + +
+
+ + +
+ + +
+ + ) +} + +function AIPerformanceMetrics() { + const [metrics, setMetrics] = useState() + + useEffect(() => { + // Fetch AI performance metrics + fetchAIMetrics().then(setMetrics) + }, []) + + if (!metrics) return null + + return ( +
+

AI Performance

+ +
+ + + + +
+
+ ) +} +``` + +## 🚀 Implementation Timeline (Weeks 1-2) + +### Week 1: Foundation Setup +**Days 1-2: Infrastructure** +- Set up TensorFlow.js environment +- Create AI model loading infrastructure +- Implement basic text vectorization system +- Set up Redis for caching ML predictions + +**Days 3-4: Core AI Engine** +- Build `StudentAssistanceAI` class structure +- Implement request analysis pipeline +- Create resource matching algorithms +- Add confidence scoring system + +**Days 5-7: Integration Layer** +- Create processing pipeline with queue system +- Implement WebSocket for real-time updates +- Build AI portal React components +- Add notification integration + +### Week 2: Enhancement & Testing +**Days 8-10: Learning System** +- Implement feedback collection +- Create model retraining pipeline +- Add performance monitoring +- Build improvement insights system + +**Days 11-12: Frontend Polish** +- Complete AI portal interface +- Add visualizations for AI confidence +- Implement real-time updates +- Create admin controls for AI parameters + +**Days 13-14: Testing & Optimization** +- Comprehensive testing with sample data +- Performance optimization +- Security review +- Documentation completion + +## 📊 Expected Impact + +### Immediate Benefits (Week 2) +- **50% faster** request processing +- **30% improvement** in match accuracy +- **Real-time insights** for coordinators +- **Automated low-risk approvals** + +### Short-term Benefits (Month 1) +- **75% reduction** in manual review time +- **90% accuracy** in resource matching +- **Predictive analytics** for resource planning +- **Continuous learning** from outcomes + +### Long-term Benefits (3-6 months) +- **AI-driven optimization** of entire operation +- **Predictive demand forecasting** +- **Automated workflow recommendations** +- **Data-driven program improvements** + +## 💻 Technical Requirements + +### Dependencies to Add +```bash +npm install @tensorflow/tfjs @tensorflow/tfjs-node +npm install bull redis ioredis +npm install ws socket.io-client +npm install natural compromise +npm install ml-matrix +``` + +### Environment Setup +```bash +# Redis for caching and queues +docker run -d -p 6379:6379 redis:alpine + +# GPU support for faster ML (optional) +npm install @tensorflow/tfjs-node-gpu +``` + +### Model Files Structure +``` +/public/models/ + ├── student-matching.json # Core matching model + ├── student-matching.bin # Model weights + ├── impact-prediction.json # Impact prediction model + ├── impact-prediction.bin # Impact weights + └── text-vectorizer.json # Text processing config +``` + +## 🎯 Success Metrics for Phase 3A + +### Technical Metrics +- **Model Accuracy**: >85% initial, >90% after learning +- **Processing Speed**: <2 seconds per request +- **System Uptime**: >99.5% +- **Auto-Approval Rate**: 60-70% of requests + +### Business Metrics +- **Coordinator Efficiency**: 50% time savings +- **Student Satisfaction**: >4.5/5 rating +- **Resource Utilization**: 25% improvement +- **Response Time**: <2 hours for urgent requests + +Ready to begin Phase 3 AI implementation! This foundation will revolutionize how Miracles in Motion matches students with resources, creating unprecedented efficiency and impact measurement capabilities. \ No newline at end of file diff --git a/PHASE3_ARCHITECTURE.md b/PHASE3_ARCHITECTURE.md new file mode 100644 index 0000000..9d583af --- /dev/null +++ b/PHASE3_ARCHITECTURE.md @@ -0,0 +1,668 @@ +# Phase 3: Enterprise Nonprofit Platform Architecture + +## 🏗️ System Architecture Overview + +### Core Enterprise Components + +#### 1. Microservices Backend Architecture +``` +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ API Gateway │ │ Load Balancer │ │ CDN Network │ +│ (Kong/Nginx) │────│ (HAProxy) │────│ (CloudFlare) │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ + │ │ │ +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ Authentication │ │ Donation │ │ Volunteer │ +│ Service │ │ Service │ │ Service │ +│ (Auth0/JWT) │ │ (Stripe API) │ │ (Scheduling) │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ + │ │ │ +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ CRM Service │ │ Analytics Svc │ │ Notification │ +│ (Salesforce) │ │ (Real-time) │ │ Service │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +#### 2. Data Architecture +``` +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ PostgreSQL │ │ Redis │ │ Elasticsearch │ +│ (Primary DB) │────│ (Cache) │────│ (Search) │ +│ Multi-tenant │ │ Sessions │ │ Analytics │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ + │ │ │ +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ Data Lake │ │ ML Pipeline │ │ Reporting │ +│ (AWS S3) │ │ (TensorFlow) │ │ (Tableau) │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +## 🤖 AI & Machine Learning Layer + +### Smart Assistance Matching Engine +```typescript +interface AssistanceAI { + matchStudent(request: StudentRequest): Promise + predictNeeds(studentProfile: StudentProfile): Promise + optimizeResources(availableResources: Resource[]): Promise +} + +class StudentAssistanceAI { + private mlModel: TensorFlow.LayersModel + private vectorizer: TextVectorizer + + async matchStudent(request: StudentRequest): Promise { + // 1. Vectorize request text and categorize needs + const requestVector = await this.vectorizer.encode(request.description) + const category = await this.classifyNeed(requestVector) + + // 2. Find similar past successful matches + const historicalMatches = await this.findSimilarMatches(requestVector) + + // 3. Score available resources + const scoredResources = await this.scoreResources(category, historicalMatches) + + // 4. Consider logistics (location, timing, volunteer availability) + return this.optimizeMatches(scoredResources, request.constraints) + } + + async predictImpact(intervention: Intervention): Promise { + // ML model trained on historical data to predict intervention success + const features = this.extractFeatures(intervention) + const prediction = await this.mlModel.predict(features) + + return { + successProbability: prediction.dataSync()[0], + estimatedBeneficiaries: Math.round(prediction.dataSync()[1]), + timeToImpact: prediction.dataSync()[2], + confidenceInterval: [ + prediction.dataSync()[3], + prediction.dataSync()[4] + ] + } + } +} +``` + +### Donor Engagement Intelligence +```typescript +class DonorEngagementAI { + async predictDonationTiming(donor: DonorProfile): Promise { + // Analyze donor history, external events, seasonal patterns + const features = { + pastDonations: donor.donationHistory, + emailEngagement: donor.emailMetrics, + seasonality: this.getSeasonalFactors(), + externalEvents: await this.getRelevantEvents(donor.interests) + } + + return { + nextOptimalAsk: new Date(prediction.nextAskDate), + suggestedAmount: prediction.suggestedAmount, + preferredChannel: prediction.channel, + confidence: prediction.confidence + } + } + + async generatePersonalizedContent(donor: DonorProfile): Promise { + // Use GPT-style model fine-tuned on successful donor communications + const context = { + donorValues: donor.motivations, + pastSupport: donor.supportedPrograms, + communicationStyle: donor.preferences + } + + return { + emailSubject: await this.generateSubject(context), + bodyContent: await this.generateBody(context), + callToAction: await this.generateCTA(context), + imageRecommendations: await this.selectImages(context) + } + } +} +``` + +## 🔄 Advanced Workflow Automation + +### Intelligent Request Processing +```typescript +class AutomatedRequestProcessor { + private aiMatcher: StudentAssistanceAI + private workflowEngine: WorkflowEngine + + async processRequest(request: AssistanceRequest): Promise { + // 1. Auto-categorization and urgency scoring + const analysis = await this.analyzeRequest(request) + + // 2. Fraud/spam detection + const securityCheck = await this.performSecurityCheck(request) + if (!securityCheck.isValid) { + return this.handleSuspiciousRequest(request, securityCheck) + } + + // 3. Auto-approval for routine requests + if (analysis.confidence > 0.95 && analysis.urgency < 0.3) { + return await this.autoApprove(request, analysis) + } + + // 4. Route to appropriate human reviewer + return await this.routeForReview(request, analysis) + } + + private async autoApprove(request: AssistanceRequest, analysis: RequestAnalysis) { + // Find optimal resource match + const matches = await this.aiMatcher.matchStudent(request) + const bestMatch = matches[0] + + // Auto-assign volunteer and schedule delivery + const assignment = await this.assignVolunteer(bestMatch) + await this.scheduleDelivery(assignment) + + // Generate communications + await this.notifyStudent(request, assignment) + await this.notifyVolunteer(assignment) + await this.notifyDonors(request, assignment.estimatedCost) + + return { + status: 'auto-approved', + assignment, + estimatedFulfillment: assignment.scheduledDate + } + } +} +``` + +### Smart Donation Workflows +```typescript +class SmartDonationWorkflow { + async processDonation(donation: Donation): Promise { + // 1. Real-time fraud detection + const fraudScore = await this.assessFraudRisk(donation) + + // 2. Tax optimization suggestions + const taxAdvice = await this.generateTaxAdvice(donation) + + // 3. Impact prediction and allocation + const impactForecast = await this.predictImpact(donation.amount) + + // 4. Auto-generate personalized thank you + const thankYou = await this.generateThankYou(donation, impactForecast) + + // 5. Schedule follow-up engagement + await this.scheduleFollowUps(donation, impactForecast) + + return { + transactionId: donation.id, + impactForecast, + taxAdvice, + thankYou, + nextEngagement: await this.getNextEngagement(donation.donor) + } + } + + async optimizeRecurringGifts(donor: DonorProfile): Promise { + // Analyze optimal frequency and amounts based on donor behavior + const analysis = await this.analyzeDonorCapacity(donor) + + return { + recommendedFrequency: analysis.optimalFrequency, + suggestedAmount: analysis.optimalAmount, + projectedAnnualIncrease: analysis.growthPotential, + retentionProbability: analysis.retentionRisk + } + } +} +``` + +## 🏢 Enterprise Integration Hub + +### CRM Integration Layer +```typescript +interface CRMConnector { + // Salesforce Nonprofit Cloud Integration + salesforce: { + contacts: ContactManager + opportunities: OpportunityManager + campaigns: CampaignManager + grants: GrantManager + } + + // HubSpot Nonprofit Integration + hubspot: { + contacts: HubSpotContactAPI + deals: HubSpotDealsAPI + workflows: HubSpotWorkflowAPI + } +} + +class SalesforceIntegration implements CRMConnector['salesforce'] { + async syncDonor(donor: DonorProfile): Promise { + // Bi-directional sync with Salesforce NPSP + const contact = await this.salesforceAPI.createOrUpdateContact({ + firstName: donor.firstName, + lastName: donor.lastName, + email: donor.email, + phone: donor.phone, + donorLevel: this.calculateDonorLevel(donor.totalGiving), + lastGift: donor.lastDonation, + lifetimeGiving: donor.totalGiving, + customFields: { + preferredCommunication: donor.communicationPreference, + volunteerInterest: donor.volunteerInterest, + programInterests: donor.programInterests + } + }) + + // Sync donation history + await this.syncDonationHistory(donor.id, contact.id) + + return contact + } + + async createOpportunity(donation: PendingDonation): Promise { + return await this.salesforceAPI.createOpportunity({ + accountId: donation.donor.salesforceId, + amount: donation.amount, + stageName: 'Pledged', + closeDate: donation.expectedDate, + recordType: 'Donation', + campaign: donation.campaign?.salesforceId, + customFields: { + donationSource: donation.source, + paymentMethod: donation.paymentMethod, + isRecurring: donation.recurring + } + }) + } +} +``` + +### Financial System Integration +```typescript +class QuickBooksIntegration { + async recordDonation(donation: CompletedDonation): Promise { + // Auto-categorize donation for proper bookkeeping + const account = await this.categorizeRevenue(donation) + + const transaction = await this.qbAPI.createTransaction({ + type: 'Income', + account: account.id, + amount: donation.netAmount, + description: `Online donation - ${donation.donor.name}`, + class: donation.program?.qbClass, + customer: await this.getOrCreateDonor(donation.donor), + customFields: { + campaignId: donation.campaign?.id, + processingFee: donation.processingFee, + grossAmount: donation.amount + } + }) + + // Auto-generate receipt + await this.generateReceipt(donation, transaction.id) + + return transaction + } + + async reconcilePayments(startDate: Date, endDate: Date): Promise { + // Auto-match bank deposits with recorded donations + const bankDeposits = await this.getBankDeposits(startDate, endDate) + const recordedDonations = await this.getRecordedDonations(startDate, endDate) + + return this.performReconciliation(bankDeposits, recordedDonations) + } +} +``` + +## 📈 Advanced Analytics & Intelligence + +### Real-time Intelligence Dashboard +```typescript +class AdvancedAnalyticsDashboard { + async getRealTimeMetrics(): Promise { + return { + // Live donation tracking + donations: { + todayTotal: await this.getTodayDonations(), + hourlyTrend: await this.getHourlyTrend(), + conversionRate: await this.getLiveConversionRate(), + averageGift: await this.getAverageGift(), + recurringSignups: await this.getRecurringSignups() + }, + + // Volunteer engagement + volunteers: { + activeToday: await this.getActiveVolunteers(), + pendingAssignments: await this.getPendingAssignments(), + completionRate: await this.getCompletionRate(), + responseTime: await this.getAverageResponseTime() + }, + + // Student assistance + students: { + requestsToday: await this.getTodayRequests(), + fulfillmentRate: await this.getFulfillmentRate(), + averageResponseTime: await this.getAverageProcessingTime(), + impactDelivered: await this.getTodayImpact() + }, + + // Predictive insights + predictions: { + monthEndProjection: await this.projectMonthEnd(), + seasonalForecast: await this.getSeasonalForecast(), + churnRisk: await this.getChurnRisk(), + growthOpportunities: await this.getGrowthOpportunities() + } + } + } + + async generateInsights(): Promise { + const insights: AIInsight[] = [] + + // Anomaly detection + const anomalies = await this.detectAnomalies() + insights.push(...anomalies.map(a => ({ + type: 'anomaly', + title: a.title, + description: a.description, + severity: a.severity, + actionItems: a.suggestedActions + }))) + + // Optimization opportunities + const optimizations = await this.findOptimizations() + insights.push(...optimizations.map(o => ({ + type: 'optimization', + title: o.title, + description: o.description, + potentialImpact: o.estimatedBenefit, + actionItems: o.recommendedActions + }))) + + // Trend analysis + const trends = await this.analyzeTrends() + insights.push(...trends.map(t => ({ + type: 'trend', + title: t.title, + description: t.description, + trajectory: t.direction, + confidence: t.confidence + }))) + + return insights + } +} +``` + +### Predictive Analytics Engine +```typescript +class PredictiveAnalytics { + async forecastDonations(timeframe: DateRange): Promise { + // Multi-model ensemble for accurate predictions + const models = [ + await this.seasonalModel.predict(timeframe), + await this.trendModel.predict(timeframe), + await this.eventBasedModel.predict(timeframe), + await this.economicModel.predict(timeframe) + ] + + const ensemble = this.combineModels(models) + + return { + expectedTotal: ensemble.amount, + confidenceInterval: ensemble.interval, + breakdown: { + new: ensemble.newDonors, + recurring: ensemble.recurringDonors, + major: ensemble.majorGifts + }, + riskFactors: await this.identifyRisks(timeframe), + opportunities: await this.identifyOpportunities(timeframe) + } + } + + async predictVolunteerNeeds(): Promise { + // Predict volunteer capacity needs based on: + // - Student request patterns + // - Seasonal variations + // - Volunteer availability trends + // - Special events and campaigns + + const demandForecast = await this.forecastStudentDemand() + const supplyForecast = await this.forecastVolunteerSupply() + + return { + projectedGap: demandForecast.total - supplyForecast.available, + criticalPeriods: this.identifyCriticalPeriods(demandForecast, supplyForecast), + recruitmentNeeds: this.calculateRecruitmentNeeds(), + skillGaps: await this.identifySkillGaps() + } + } +} +``` + +## 🌐 Multi-Tenant Architecture + +### Organization Management System +```typescript +class MultiTenantManager { + async createOrganization(config: OrganizationConfig): Promise { + // Create isolated tenant environment + const org = await this.createTenant({ + name: config.name, + subdomain: config.subdomain, + plan: config.subscriptionPlan, + features: this.getFeaturesByPlan(config.subscriptionPlan) + }) + + // Setup isolated database schema + await this.setupTenantSchema(org.id) + + // Configure branding and customization + await this.setupBranding(org.id, config.branding) + + // Initialize default workflows and settings + await this.initializeDefaults(org.id, config.organizationType) + + return org + } + + async scaleResources(orgId: string, metrics: UsageMetrics): Promise { + // Auto-scale resources based on usage + const currentUsage = await this.getUsageMetrics(orgId) + const prediction = await this.predictGrowth(orgId, currentUsage) + + if (prediction.needsScaling) { + return await this.implementScaling(orgId, prediction.requirements) + } + + return { status: 'no-action-needed', currentCapacity: currentUsage } + } +} +``` + +### Data Isolation & Security +```typescript +class SecureDataManager { + async accessData(request: DataRequest): Promise { + // Tenant isolation validation + await this.validateTenantAccess(request.userId, request.tenantId) + + // Row-level security enforcement + const securityContext = await this.buildSecurityContext(request.userId) + + // Encrypted data access + const encryptedData = await this.queryWithSecurity( + request.query, + securityContext + ) + + // Decrypt for authorized user + return this.decryptForUser(encryptedData, request.userId) + } + + async auditAccess(request: DataRequest, response: DataResponse): Promise { + await this.logAccess({ + userId: request.userId, + tenantId: request.tenantId, + dataAccessed: response.dataTypes, + timestamp: new Date(), + ipAddress: request.ipAddress, + userAgent: request.userAgent + }) + } +} +``` + +## 📱 Native Mobile Applications + +### React Native Cross-Platform Apps +```typescript +// Mobile App Architecture +interface MobileApp { + authentication: OfflineAuthManager + synchronization: OfflineSyncManager + notifications: PushNotificationManager + geolocation: LocationServicesManager + camera: DocumentScanManager +} + +class MiraclesMobileApp { + async initializeApp(): Promise { + // Setup offline-first architecture + await this.setupOfflineStorage() + await this.initializeSyncEngine() + await this.setupPushNotifications() + + // Initialize secure authentication + await this.setupBiometricAuth() + await this.configureSecureStorage() + } + + async syncData(): Promise { + // Intelligent sync based on connection quality + const connectionType = await this.detectConnectionType() + const syncStrategy = this.selectSyncStrategy(connectionType) + + return await this.performSync(syncStrategy) + } +} + +// Volunteer Mobile Features +class VolunteerMobileApp extends MiraclesMobileApp { + async acceptAssignment(assignmentId: string): Promise { + // Offline-capable assignment acceptance + await this.queueAction('accept_assignment', { assignmentId }) + await this.updateLocalState(assignmentId, 'accepted') + await this.notifyCoordinator(assignmentId) + } + + async scanDeliveryReceipt(imageUri: string): Promise { + // AI-powered receipt processing + const ocrResult = await this.processReceiptOCR(imageUri) + const extracted = await this.extractReceiptData(ocrResult) + + return { + vendor: extracted.vendor, + amount: extracted.amount, + items: extracted.items, + date: extracted.date, + confidence: extracted.confidence + } + } + + async trackDelivery(studentId: string): Promise { + // Real-time delivery tracking with geofencing + const location = await this.getCurrentLocation() + await this.updateDeliveryProgress(studentId, location) + + // Auto-complete when near student location + const distance = this.calculateDistance(location, student.location) + if (distance < 50) { // 50 meters + await this.promptDeliveryCompletion(studentId) + } + } +} +``` + +## 🔧 Implementation Roadmap + +### Week 1-2: Foundation Infrastructure +- Microservices architecture setup +- Database partitioning and multi-tenancy +- API Gateway and load balancing +- Redis caching layer implementation + +### Week 3-4: AI/ML Integration +- TensorFlow.js model deployment +- Student assistance matching engine +- Donor prediction models +- Natural language processing setup + +### Week 5-6: Enterprise Integrations +- Salesforce NPSP connector +- QuickBooks API integration +- Email marketing platform sync +- Payment processor enhancements + +### Week 7-8: Advanced Features +- Mobile app development +- Real-time collaboration tools +- Advanced reporting suite +- Workflow automation engine + +### Week 9-10: Security & Compliance +- SOC 2 Type II implementation +- GDPR compliance framework +- Security audit and penetration testing +- Compliance reporting automation + +## 💰 Investment & ROI Analysis + +### Development Investment +- **Infrastructure**: $15K-25K (cloud setup, security) +- **Development**: $40K-60K (full-stack team for 10 weeks) +- **AI/ML Models**: $10K-15K (training data, compute) +- **Integration Costs**: $8K-12K (third-party APIs, licenses) +- **Total Investment**: $73K-112K + +### Projected ROI (Year 1) +- **Operational Efficiency**: 75% reduction in manual tasks +- **Donation Increase**: 40% improvement in conversion rates +- **Cost Savings**: $45K annually in reduced overhead +- **Revenue Growth**: $150K+ additional donations +- **Net ROI**: 180-250% in first year + +### Scalability Benefits +- **Multi-organization Platform**: $50K-100K annual revenue potential +- **Licensing Opportunities**: Additional revenue streams +- **Consulting Services**: Expert implementation support +- **Partnership Revenue**: Integration and referral income + +## 🎯 Success Metrics + +### Operational KPIs +- **Request Processing Time**: <2 hours average +- **Volunteer Response Rate**: >85% +- **Donor Retention Rate**: >75% +- **System Uptime**: 99.9% +- **Mobile App Rating**: >4.5 stars + +### Business Impact KPIs +- **Students Served Growth**: 300% increase capacity +- **Volunteer Engagement**: 60% improvement +- **Donation Efficiency**: 45% better conversion +- **Administrative Overhead**: 70% reduction +- **Compliance Score**: 100% automated compliance + +## 🚀 Next Phase Execution + +Ready to begin Phase 3 implementation! The recommended starting approach: + +1. **Begin with AI Foundation** - Implement the student assistance matching engine +2. **Parallel Infrastructure Setup** - Microservices and database architecture +3. **CRM Integration Priority** - Salesforce connector for immediate impact +4. **Mobile App Development** - Native apps for volunteers and staff +5. **Advanced Analytics** - Real-time intelligence dashboard + +This Phase 3 architecture will position Miracles in Motion as the premier nonprofit technology platform, capable of serving as a model for the entire sector while dramatically increasing impact and efficiency. \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 54ac889..dda3ceb 100644 --- a/package-lock.json +++ b/package-lock.json @@ -9,10 +9,21 @@ "version": "1.0.0", "license": "MIT", "dependencies": { + "@tensorflow/tfjs": "^4.22.0", + "bull": "^4.16.5", + "compromise": "^14.14.4", + "date-fns": "^4.1.0", "framer-motion": "^10.16.16", + 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"tr46": "^5.1.0", + "webidl-conversions": "^7.0.0" + }, + "engines": { + "node": ">=18" + } + }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", @@ -4376,6 +6411,15 @@ "node": ">=0.10.0" } }, + "node_modules/wordnet-db": { + "version": "3.1.14", + "resolved": "https://registry.npmjs.org/wordnet-db/-/wordnet-db-3.1.14.tgz", + "integrity": "sha512-zVyFsvE+mq9MCmwXUWHIcpfbrHHClZWZiVOzKSxNJruIcFn2RbY55zkhiAMMxM8zCVSmtNiViq8FsAZSFpMYag==", + "license": "MIT", + "engines": { + "node": ">=0.6.0" + } + }, "node_modules/wrap-ansi": { "version": "8.1.0", "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz", @@ -4484,6 +6528,53 @@ "dev": true, "license": "ISC" }, + "node_modules/ws": { + "version": "8.18.3", + "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz", + "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==", + "license": "MIT", + "engines": { 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"sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==", + "license": "ISC", + "engines": { + "node": ">=10" + } + }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", @@ -4491,6 +6582,53 @@ "dev": true, "license": "ISC" }, + "node_modules/yargs": { + "version": "16.2.0", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz", + "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==", + "license": "MIT", + "dependencies": { + "cliui": "^7.0.2", + "escalade": "^3.1.1", + "get-caller-file": "^2.0.5", + "require-directory": "^2.1.1", + "string-width": "^4.2.0", + "y18n": "^5.0.5", + "yargs-parser": "^20.2.2" + }, + "engines": { + "node": ">=10" + } + }, + "node_modules/yargs-parser": { + "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", + "license": "ISC", + "engines": { + "node": ">=10" + } + }, + "node_modules/yargs/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", + "license": "MIT" + }, + "node_modules/yargs/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "license": "MIT", + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", diff --git a/package.json b/package.json index 598b198..484cde8 100644 --- a/package.json +++ b/package.json @@ -31,12 +31,24 @@ }, "homepage": "https://miraclesinmotion.org", "dependencies": { + "@tensorflow/tfjs": "^4.22.0", + "bull": "^4.16.5", + "compromise": "^14.14.4", + "date-fns": "^4.1.0", + "framer-motion": "^10.16.16", + "ioredis": "^5.8.0", + "lucide-react": "^0.290.0", + "ml-matrix": "^6.12.1", + "natural": "^8.1.0", "react": "^18.2.0", "react-dom": "^18.2.0", - "framer-motion": "^10.16.16", - "lucide-react": "^0.290.0" + "redis": "^5.8.3", + "socket.io-client": "^4.8.1", + "uuid": "^13.0.0", + "ws": "^8.18.3" }, "devDependencies": { + "@tailwindcss/typography": "^0.5.10", "@types/react": "^18.2.37", "@types/react-dom": "^18.2.15", "@typescript-eslint/eslint-plugin": "^6.10.0", @@ -49,8 +61,7 @@ "gh-pages": "^6.0.0", "postcss": "^8.4.31", "tailwindcss": "^3.3.5", - "@tailwindcss/typography": "^0.5.10", "typescript": "^5.2.2", "vite": "^4.5.0" } -} \ No newline at end of file +} diff --git a/src/App.tsx b/src/App.tsx index 965861a..42ec96c 100644 --- a/src/App.tsx +++ b/src/App.tsx @@ -1,5 +1,5 @@ import React, { useState, useEffect, useRef, useContext, createContext } from 'react' -import { motion, useMotionValue, useSpring, useTransform, useScroll } from 'framer-motion' +import { motion, useMotionValue, useSpring, useTransform, useScroll, AnimatePresence } from 'framer-motion' import { ArrowRight, Backpack, @@ -43,14 +43,146 @@ import { Database, Check, Clock, + Bell, + BellRing, + BarChart3, + TrendingUp, + Languages, + Brain, + Cpu, + Download, + WifiOff, + ChevronDown, + Eye, + Zap, + Target, + Activity, } from 'lucide-react' +// Phase 3: AI Components +import AIAssistancePortal from './components/AIAssistancePortal' + /** * Miracles in Motion — Complete Non-Profit Website * A comprehensive 501(c)3 organization website with modern design, * donation processing, volunteer management, and impact tracking. */ +/* ===================== Phase 2: Enhanced Context Systems ===================== */ + +// Notification System Context +const NotificationContext = createContext(null) + +function NotificationProvider({ children }: { children: React.ReactNode }) { + const [notifications, setNotifications] = useState([]) + + const addNotification = (notif: Omit) => { + const newNotification: Notification = { + ...notif, + id: Math.random().toString(36), + timestamp: new Date(), + read: false + } + setNotifications(prev => [newNotification, ...prev]) + + // Auto-remove success notifications after 5 seconds + if (notif.type === 'success') { + setTimeout(() => { + setNotifications(prev => prev.filter(n => n.id !== newNotification.id)) + }, 5000) + } + } + + const markAsRead = (id: string) => { + setNotifications(prev => + prev.map(n => n.id === id ? { ...n, read: true } : n) + ) + } + + const clearAll = () => setNotifications([]) + const unreadCount = notifications.filter(n => !n.read).length + + return ( + + {children} + + ) +} + +function useNotifications() { + const context = useContext(NotificationContext) + if (!context) throw new Error('useNotifications must be used within NotificationProvider') + return context +} + +// Language/Internationalization Context +const LanguageContext = createContext<{ + currentLanguage: Language + languages: Language[] + changeLanguage: (code: string) => void + t: (key: string) => string +} | null>(null) + +const languages: Language[] = [ + { code: 'en', name: 'English', nativeName: 'English', flag: '🇺🇸' }, + { code: 'es', name: 'Spanish', nativeName: 'Español', flag: '🇪🇸' }, + { code: 'fr', name: 'French', nativeName: 'Français', flag: '🇫🇷' } +] + +const translations: Translations = { + 'nav.home': { en: 'Home', es: 'Inicio', fr: 'Accueil' }, + 'nav.donate': { en: 'Donate', es: 'Donar', fr: 'Faire un don' }, + 'nav.volunteer': { en: 'Volunteer', es: 'Voluntario', fr: 'Bénévole' }, + 'hero.title': { + en: 'Equipping kids for success—school supplies, clothing, & more', + es: 'Equipando a los niños para el éxito: útiles escolares, ropa y más', + fr: 'Équiper les enfants pour réussir — fournitures scolaires, vêtements et plus' + }, + 'donate.title': { en: 'Donate Now', es: 'Donar Ahora', fr: 'Faire un don maintenant' }, + 'impact.students': { en: 'Students Supported', es: 'Estudiantes Apoyados', fr: 'Étudiants Soutenus' } +} + +function LanguageProvider({ children }: { children: React.ReactNode }) { + const [currentLanguage, setCurrentLanguage] = useState(() => { + const saved = localStorage.getItem('mim_language') + if (saved) { + return languages.find(l => l.code === saved) || languages[0] + } + return languages[0] + }) + + const changeLanguage = (code: string) => { + const language = languages.find(l => l.code === code) + if (language) { + setCurrentLanguage(language) + localStorage.setItem('mim_language', code) + trackEvent('language_changed', { from: currentLanguage.code, to: code }) + } + } + + const t = (key: string): string => { + return translations[key]?.[currentLanguage.code] || key + } + + return ( + + {children} + + ) +} + +function useLanguage() { + const context = useContext(LanguageContext) + if (!context) throw new Error('useLanguage must be used within LanguageProvider') + return context +} + /* ===================== Authentication Context ===================== */ const AuthContext = createContext(null) @@ -155,6 +287,175 @@ function trackEvent(eventName: string, properties: Record = {}) { console.log(`Analytics: ${eventName}`, properties) } +/* ===================== Phase 2: Payment Integration System ===================== */ +const paymentMethods: PaymentMethod[] = [ + { + id: 'stripe', + type: 'card', + name: 'Credit/Debit Card', + icon: '💳', + description: 'Secure payment via Stripe', + processingFee: 2.9 + }, + { + id: 'paypal', + type: 'paypal', + name: 'PayPal', + icon: '🌐', + description: 'Pay with PayPal account', + processingFee: 2.9 + }, + { + id: 'venmo', + type: 'venmo', + name: 'Venmo', + icon: '📱', + description: 'Quick mobile payment', + processingFee: 1.9 + }, + { + id: 'bank', + type: 'bank', + name: 'Bank Transfer', + icon: '🏦', + description: 'Direct bank transfer (ACH)', + processingFee: 0.8 + } +] + +class PaymentProcessor { + static async processPayment(amount: number, method: PaymentMethod, _donorInfo: any): Promise<{ + success: boolean + transactionId?: string + error?: string + }> { + // Simulate payment processing + await new Promise(resolve => setTimeout(resolve, 2000)) + + // Simulate different success rates by payment method + const successRate = method.type === 'bank' ? 0.95 : method.type === 'card' ? 0.92 : 0.98 + const success = Math.random() < successRate + + if (success) { + const transactionId = `txn_${Date.now()}_${Math.random().toString(36).substr(2, 9)}` + + // Track successful payment + trackEvent('donation_completed', { + amount, + method: method.id, + transactionId, + processingFee: amount * (method.processingFee / 100) + }) + + return { success: true, transactionId } + } else { + const errors = [ + 'Payment declined by bank', + 'Insufficient funds', + 'Invalid card information', + 'Network timeout - please try again' + ] + return { success: false, error: errors[Math.floor(Math.random() * errors.length)] } + } + } + + static calculateFees(amount: number, method: PaymentMethod) { + const fee = amount * (method.processingFee / 100) + return { + fee: Math.round(fee * 100) / 100, + net: Math.round((amount - fee) * 100) / 100, + feePercentage: method.processingFee + } + } +} + +/* ===================== Advanced Analytics System ===================== */ +function useAnalytics() { + const [analyticsData, setAnalyticsData] = useState(() => ({ + pageViews: [ + { page: 'Home', views: 2847, trend: 12.5 }, + { page: 'Donate', views: 1203, trend: 8.3 }, + { page: 'Volunteer', views: 856, trend: -2.1 }, + { page: 'Stories', views: 645, trend: 15.8 }, + { page: 'About', views: 432, trend: 5.2 } + ], + donationMetrics: { amount: 45280, count: 186, recurring: 67 }, + userEngagement: { sessions: 3241, avgDuration: 185, bounceRate: 0.34 }, + conversionRates: { donation: 0.078, volunteer: 0.032, contact: 0.156 } + })) + + const refreshAnalytics = () => { + // Simulate real-time data updates + setAnalyticsData(prev => ({ + ...prev, + pageViews: prev.pageViews.map(pv => ({ + ...pv, + views: pv.views + Math.floor(Math.random() * 10), + trend: (Math.random() - 0.5) * 20 + })), + donationMetrics: { + ...prev.donationMetrics, + amount: prev.donationMetrics.amount + Math.floor(Math.random() * 500), + count: prev.donationMetrics.count + Math.floor(Math.random() * 3) + } + })) + } + + useEffect(() => { + const interval = setInterval(refreshAnalytics, 30000) // Update every 30 seconds + return () => clearInterval(interval) + }, []) + + return { analyticsData, refreshAnalytics } +} + +/* ===================== PWA Features ===================== */ +function usePWA() { + const [isOnline, setIsOnline] = useState(navigator.onLine) + const [installPrompt, setInstallPrompt] = useState(null) + const [isInstallable, setIsInstallable] = useState(false) + + useEffect(() => { + const handleOnline = () => setIsOnline(true) + const handleOffline = () => setIsOnline(false) + + window.addEventListener('online', handleOnline) + window.addEventListener('offline', handleOffline) + + // PWA Install Prompt + const handleBeforeInstallPrompt = (e: any) => { + e.preventDefault() + setInstallPrompt(e) + setIsInstallable(true) + } + + window.addEventListener('beforeinstallprompt', handleBeforeInstallPrompt) + + return () => { + window.removeEventListener('online', handleOnline) + window.removeEventListener('offline', handleOffline) + window.removeEventListener('beforeinstallprompt', handleBeforeInstallPrompt) + } + }, []) + + const installApp = async () => { + if (!installPrompt) return false + + installPrompt.prompt() + const { outcome } = await installPrompt.userChoice + + if (outcome === 'accepted') { + trackEvent('pwa_installed') + setInstallPrompt(null) + setIsInstallable(false) + return true + } + return false + } + + return { isOnline, isInstallable, installApp } +} + /* ===================== SEO Meta Tags Component ===================== */ function SEOHead({ title, description, image }: { title?: string, description?: string, image?: string }) { useEffect(() => { @@ -223,6 +524,54 @@ interface ImpactCalculation { } } +// Phase 2 Interfaces +interface Notification { + id: string + type: 'success' | 'info' | 'warning' | 'error' + title: string + message: string + timestamp: Date + read: boolean + actions?: { label: string; action: () => void }[] +} + +interface NotificationContextType { + notifications: Notification[] + addNotification: (notification: Omit) => void + markAsRead: (id: string) => void + clearAll: () => void + unreadCount: number +} + +interface AnalyticsData { + pageViews: { page: string; views: number; trend: number }[] + donationMetrics: { amount: number; count: number; recurring: number } + userEngagement: { sessions: number; avgDuration: number; bounceRate: number } + conversionRates: { donation: number; volunteer: number; contact: number } +} + +interface PaymentMethod { + id: string + type: 'card' | 'paypal' | 'venmo' | 'bank' + name: string + icon: string + description: string + processingFee: number +} + +interface Language { + code: string + name: string + nativeName: string + flag: string +} + +interface Translations { + [key: string]: { + [langCode: string]: string + } +} + interface TiltCardProps { icon: React.ComponentType title: string @@ -396,7 +745,7 @@ function Floating3DShapes() { } /* ===================== Router ===================== */ -function useHashRoute() { +export function useHashRoute() { const parse = () => (window.location.hash?.slice(1) || "/") const [route, setRoute] = useState(parse()) @@ -409,74 +758,8 @@ function useHashRoute() { return route } -/* ===================== Main App ===================== */ -export default function MiraclesInMotionSite() { - const [darkMode, setDarkMode] = useState(false) - const [mobileMenuOpen, setMobileMenuOpen] = useState(false) - const route = useHashRoute() +/* ===================== Legacy Router Component (Repurposed) ===================== */ - useEffect(() => { - document.title = - route === "/" - ? "Miracles in Motion — Essentials for Every Student" - : `Miracles in Motion — ${route.replace("/", "").replace(/-/g, " ").replace(/\b\w/g, (m) => m.toUpperCase())}` - }, [route]) - - useEffect(() => { - if (darkMode) { - document.documentElement.classList.add('dark') - } else { - document.documentElement.classList.remove('dark') - } - }, [darkMode]) - - return ( -
- - - -
-
-
- -
-
- -
- ) -} - -function Router({ route }: { route: string }) { - switch (route) { - case "/": - return - case "/donate": - return - case "/volunteers": - return - case "/sponsors": - return - case "/stories": - return - case "/testimonies": - return - case "/legal": - return - case "/request-assistance": - return - case "/portals": - return - case "/admin-portal": - return - case "/volunteer-portal": - return - case "/resource-portal": - return - default: - return - } -} /* ===================== Shared UI ===================== */ function SkipToContent() { @@ -1499,6 +1782,10 @@ function DonatePage() { const [customAmount, setCustomAmount] = useState('') const [isRecurring, setIsRecurring] = useState(false) const [donorInfo, setDonorInfo] = useState({ email: '', name: '', anonymous: false }) + const [selectedPaymentMethod, setSelectedPaymentMethod] = useState(paymentMethods[0]) + const [isProcessing, setIsProcessing] = useState(false) + const { addNotification } = useNotifications() + const { t } = useLanguage() const suggestedAmounts = [ { amount: 25, impact: "School supplies for 1 student", popular: false }, @@ -1528,14 +1815,63 @@ function DonatePage() { trackEvent('donation_page_view', { amount: finalAmount, recurring: isRecurring }) }, []) - const handleDonationSubmit = () => { + const handleDonationSubmit = async () => { + if (finalAmount <= 0) return + + setIsProcessing(true) trackEvent('donation_initiated', { amount: finalAmount, recurring: isRecurring, - anonymous: donorInfo.anonymous + anonymous: donorInfo.anonymous, + method: selectedPaymentMethod.id }) - // This would integrate with Stripe or another payment processor - alert(`Processing ${isRecurring ? 'monthly ' : ''}donation of $${finalAmount}`) + + addNotification({ + type: 'info', + title: 'Processing Payment', + message: `Processing your ${isRecurring ? 'monthly ' : ''}donation of $${finalAmount}...` + }) + + try { + const result = await PaymentProcessor.processPayment(finalAmount, selectedPaymentMethod, donorInfo) + + if (result.success) { + addNotification({ + type: 'success', + title: 'Donation Successful!', + message: `Thank you for your ${isRecurring ? 'monthly ' : ''}donation of $${finalAmount}. Transaction ID: ${result.transactionId}`, + actions: [ + { + label: 'View Receipt', + action: () => { + // In production, would show receipt modal or download PDF + trackEvent('receipt_viewed', { transactionId: result.transactionId }) + alert('Receipt functionality would open here in production') + } + } + ] + }) + + // Reset form + setSelectedAmount(50) + setCustomAmount('') + setDonorInfo({ email: '', name: '', anonymous: false }) + } else { + addNotification({ + type: 'error', + title: 'Payment Failed', + message: result.error || 'Unable to process payment. Please try again.' + }) + } + } catch (error) { + addNotification({ + type: 'error', + title: 'Payment Error', + message: 'An unexpected error occurred. Please try again.' + }) + } finally { + setIsProcessing(false) + } } return ( @@ -1732,19 +2068,61 @@ function DonatePage() {
+ {/* Payment Method Selection */} +
+

Payment Method

+
+ {paymentMethods.map((method) => { + const fees = PaymentProcessor.calculateFees(finalAmount, method) + return ( + setSelectedPaymentMethod(method)} + className={`p-4 rounded-xl border-2 transition-all focus:outline-none focus:ring-2 focus:ring-primary-500 focus:ring-offset-2 text-left ${ + selectedPaymentMethod.id === method.id + ? 'border-primary-500 bg-primary-50 dark:bg-primary-900/20' + : 'border-neutral-200 dark:border-neutral-700 hover:border-primary-300' + }`} + whileHover={{ scale: 1.02 }} + whileTap={{ scale: 0.98 }} + > +
+ {method.icon} +
+
{method.name}
+
{method.description}
+
+ {selectedPaymentMethod.id === method.id && ( + + )} +
+ {finalAmount > 0 && ( +
+ Fee: ${fees.fee} ({method.processingFee}%) • You give: ${fees.net} +
+ )} + + ) + })} +
+
+ {/* Enhanced Donation Buttons */}
0 ? { scale: 1.02 } : {}} - whileTap={finalAmount > 0 ? { scale: 0.98 } : {}} + whileHover={finalAmount > 0 && !isProcessing ? { scale: 1.02 } : {}} + whileTap={finalAmount > 0 && !isProcessing ? { scale: 0.98 } : {}} onClick={handleDonationSubmit} - aria-label={`Donate $${finalAmount} ${isRecurring ? 'monthly' : 'one-time'} via credit card`} + aria-label={`Donate $${finalAmount} ${isRecurring ? 'monthly' : 'one-time'} via ${selectedPaymentMethod.name}`} > - - Donate ${finalAmount} {isRecurring ? 'Monthly' : 'Securely'} + {isProcessing ? ( + <> Processing... + ) : ( + <> {t('donate.title')} ${finalAmount} via {selectedPaymentMethod.icon} + )}
@@ -2671,6 +3049,251 @@ function PortalsPage() { ) } +/* ===================== Phase 2: UI Components ===================== */ + +// Real-time Notification System +export function NotificationCenter() { + const { notifications, markAsRead, clearAll, unreadCount } = useNotifications() + const [isOpen, setIsOpen] = useState(false) + + // Close dropdown when clicking outside + useEffect(() => { + const handleClickOutside = (event: MouseEvent) => { + const target = event.target as Element + if (isOpen && !target.closest('[data-notification-center]')) { + setIsOpen(false) + } + } + + document.addEventListener('mousedown', handleClickOutside) + return () => document.removeEventListener('mousedown', handleClickOutside) + }, [isOpen]) + + return ( +
+ setIsOpen(!isOpen)} + className="relative p-2 text-neutral-700 dark:text-neutral-200 hover:bg-neutral-100 dark:hover:bg-neutral-800 rounded-lg transition-colors" + whileHover={{ scale: 1.05 }} + whileTap={{ scale: 0.95 }} + > + {unreadCount > 0 ? : } + {unreadCount > 0 && ( + + {unreadCount > 9 ? '9+' : unreadCount} + + )} + + + + {isOpen && ( + +
+
+

Notifications

+ {notifications.length > 0 && ( + + )} +
+
+ +
+ {notifications.length === 0 ? ( +
+ +

No notifications

+
+ ) : ( + notifications.map((notification) => ( + markAsRead(notification.id)} + whileHover={{ x: 4 }} + > +
+
+ {notification.type === 'success' ? : + notification.type === 'error' ? : + notification.type === 'warning' ? : + } +
+
+

{notification.title}

+

{notification.message}

+

+ {new Date(notification.timestamp).toLocaleString()} +

+
+ {!notification.read && ( +
+ )} +
+ + )) + )} +
+ + )} + +
+ ) +} + +// Language Selector Component +export function LanguageSelector() { + const { currentLanguage, languages, changeLanguage } = useLanguage() + const [isOpen, setIsOpen] = useState(false) + + // Close dropdown when clicking outside + useEffect(() => { + const handleClickOutside = (event: MouseEvent) => { + const target = event.target as Element + if (isOpen && !target.closest('[data-language-selector]')) { + setIsOpen(false) + } + } + + document.addEventListener('mousedown', handleClickOutside) + return () => document.removeEventListener('mousedown', handleClickOutside) + }, [isOpen]) + + return ( +
+ setIsOpen(!isOpen)} + className="flex items-center gap-2 p-2 text-neutral-700 dark:text-neutral-200 hover:bg-neutral-100 dark:hover:bg-neutral-800 rounded-lg transition-colors" + whileHover={{ scale: 1.05 }} + whileTap={{ scale: 0.95 }} + > + + {currentLanguage.flag} + + + + + {isOpen && ( + + {languages.map((language) => ( + { + changeLanguage(language.code) + setIsOpen(false) + }} + className={`w-full flex items-center gap-3 p-3 hover:bg-neutral-100 dark:hover:bg-neutral-800 transition-colors first:rounded-t-xl last:rounded-b-xl ${ + currentLanguage.code === language.code ? 'bg-primary-50 dark:bg-primary-900/20' : '' + }`} + whileHover={{ x: 4 }} + > + {language.flag} +
+
{language.name}
+
{language.nativeName}
+
+ {currentLanguage.code === language.code && ( + + )} + + ))} + + )} + +
+ ) +} + +// PWA Install Prompt +function PWAInstallPrompt() { + const { isInstallable, installApp } = usePWA() + const [showPrompt, setShowPrompt] = useState(false) + + useEffect(() => { + if (isInstallable) { + const timer = setTimeout(() => setShowPrompt(true), 3000) + return () => clearTimeout(timer) + } + }, [isInstallable]) + + if (!showPrompt) return null + + return ( + + +
+
+
+ +
+
+

Install Miracles in Motion

+

+ Get faster access and offline features by installing our app +

+
+ +
+
+ + +
+
+ + + ) +} + /* ===================== Authentication Components ===================== */ function LoginForm({ requiredRole }: { requiredRole?: 'admin' | 'volunteer' | 'resource' }) { const { login, isLoading } = useAuth() @@ -3234,6 +3857,262 @@ function ResourcePortalPage() { ) } +// Advanced Analytics Dashboard +function AnalyticsDashboard() { + const { user, logout } = useAuth() + const { analyticsData, refreshAnalytics } = useAnalytics() + const { addNotification } = useNotifications() + + useEffect(() => { + trackEvent('analytics_dashboard_view', { user_id: user?.id, user_role: user?.role }) + }, []) + + const handleRefresh = () => { + refreshAnalytics() + addNotification({ + type: 'success', + title: 'Data Refreshed', + message: 'Analytics data has been updated with the latest information' + }) + } + + return ( + + + + + Refresh + + +
+ } + > +
+ {/* Key Metrics */} +
+ +
+
+

Total Donations

+

+ ${analyticsData.donationMetrics.amount.toLocaleString()} +

+
+ +
+
+ +12.5% + vs last month +
+ + + +
+
+

Active Volunteers

+

247

+
+ +
+
+ +8.3% + vs last month +
+ + + +
+
+

Students Helped

+

1,847

+
+ +
+
+ +15.2% + vs last month +
+ + + +
+
+

Conversion Rate

+

+ {(analyticsData.conversionRates.donation * 100).toFixed(1)}% +

+
+ +
+
+ +3.1% + vs last month +
+ +
+ + {/* Page Views Chart */} +
+
+

Page Performance

+
+ + Last 30 days +
+
+
+ {analyticsData.pageViews.map((page, index) => ( + +
+
0 ? 'bg-green-500' : page.trend < 0 ? 'bg-red-500' : 'bg-gray-400' + }`} /> +
+
{page.page}
+
{page.views.toLocaleString()} views
+
+
+
0 ? 'text-green-600' : page.trend < 0 ? 'text-red-600' : 'text-gray-600' + }`}> + + {page.trend > 0 ? '+' : ''}{page.trend.toFixed(1)}% +
+ + ))} +
+
+ + {/* Real-time Activity */} +
+
+

Recent Activity

+
+ {[ + { action: 'New donation', details: '$125 from Sarah M.', time: '2 minutes ago', icon: Heart }, + { action: 'Volunteer signup', details: 'John D. registered', time: '8 minutes ago', icon: Users }, + { action: 'Assistance request', details: 'Roosevelt Elementary', time: '15 minutes ago', icon: School }, + { action: 'Story shared', details: 'Maria\'s success story', time: '1 hour ago', icon: BookOpenText } + ].map((activity, index) => ( + +
+ +
+
+
{activity.action}
+
{activity.details}
+
+
{activity.time}
+ + ))} +
+
+ +
+

Impact Summary

+
+
+
+ + Backpacks Distributed +
+ 342 +
+
+
+ + Clothing Items +
+ 789 +
+
+
+ + Emergency Responses +
+ 156 +
+
+
+
+
+ + + ) +} + +// Phase 3: AI Portal Page +function AIPortalPage() { + const { user, logout } = useAuth() + + useEffect(() => { + trackEvent('ai_portal_view', { user_id: user?.id, user_role: user?.role }) + }, []) + + return ( + + + + + Model Status + + +
+ } + > + + + + ) +} + function NotFoundPage() { return (
@@ -3364,4 +4243,152 @@ function Magnetic({ children }: { children: React.ReactNode }) { {children}
) +} + +/* ===================== Main App Component ===================== */ +export default function App() { + return ( + + + + + + + + + ) +} + +function AppContent() { + const [currentPath, setCurrentPath] = useState(window.location.hash.slice(1) || '/') + const [darkMode, setDarkMode] = useState(() => { + if (typeof window !== 'undefined') { + return localStorage.getItem('darkMode') === 'true' || + (!localStorage.getItem('darkMode') && window.matchMedia('(prefers-color-scheme: dark)').matches) + } + return false + }) + const [isMobileMenuOpen, setIsMobileMenuOpen] = useState(false) + const { isOnline } = usePWA() + const { addNotification } = useNotifications() + + useEffect(() => { + const handleHashChange = () => setCurrentPath(window.location.hash.slice(1) || '/') + window.addEventListener('hashchange', handleHashChange) + return () => window.removeEventListener('hashchange', handleHashChange) + }, []) + + useEffect(() => { + if (darkMode) { + document.documentElement.classList.add('dark') + } else { + document.documentElement.classList.remove('dark') + } + localStorage.setItem('darkMode', darkMode.toString()) + }, [darkMode]) + + // Online/Offline notifications + useEffect(() => { + const handleOnline = () => { + addNotification({ + type: 'success', + title: 'Back Online', + message: 'Your internet connection has been restored' + }) + } + + const handleOffline = () => { + addNotification({ + type: 'warning', + title: 'Connection Lost', + message: "You're currently offline. Some features may be limited." + }) + } + + if (!isOnline) { + handleOffline() + } + + window.addEventListener('online', handleOnline) + window.addEventListener('offline', handleOffline) + + return () => { + window.removeEventListener('online', handleOnline) + window.removeEventListener('offline', handleOffline) + } + }, [isOnline, addNotification]) + + const renderPage = () => { + // Update document title based on current route + useEffect(() => { + document.title = currentPath === "/" + ? "Miracles in Motion — Essentials for Every Student" + : `Miracles in Motion — ${currentPath.replace("/", "").replace(/-/g, " ").replace(/\b\w/g, (m) => m.toUpperCase())}` + }, [currentPath]) + + switch (currentPath) { + case '/': + return + case '/donate': + return + case '/volunteers': + return + case '/sponsors': + return + case '/stories': + return + case '/testimonies': + return + case '/legal': + return + case '/request-assistance': + return + case '/portals': + return + case '/admin-portal': + return + case '/volunteer-portal': + return + case '/resource-portal': + return + case '/analytics': + return + case '/ai-portal': + return + default: + return + } + } + + return ( +
+ + + + {/* Offline Indicator */} + {!isOnline && ( +
+ + You're currently offline +
+ )} + +
+
+ +
+ {renderPage()} +
+ +
+ + +
+ ) } \ No newline at end of file diff --git a/src/ai/ProcessingPipeline.ts b/src/ai/ProcessingPipeline.ts new file mode 100644 index 0000000..d36e4cc --- /dev/null +++ b/src/ai/ProcessingPipeline.ts @@ -0,0 +1,418 @@ +// Phase 3: Real-time Processing Pipeline for AI Assistance Matching +import type { + StudentRequest, + MatchResult, + AIUpdate, + AIInsight, + ProcessingPipelineConfig +} from './types' +import { aiEngine } from './StudentAssistanceAI' + +// Mock Queue Implementation (in production, use Redis + Bull) +class MockQueue { + private jobs: Array<{ id: string; data: T; priority: number }> = [] + private processors: Map Promise> = new Map() + + async add(jobType: string, data: T, options: { priority: number; attempts?: number; backoff?: string } = { priority: 0 }): Promise<{ id: string }> { + const job = { + id: `job-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, + data, + priority: options.priority + } + + this.jobs.push(job) + this.jobs.sort((a, b) => b.priority - a.priority) // Higher priority first + + // Process job immediately for demo + setTimeout(() => this.processNextJob(jobType), 100) + + return job + } + + process(jobType: string, concurrency: number, processor: (job: { id: string; data: T }) => Promise): void { + this.processors.set(jobType, processor) + } + + private async processNextJob(jobType: string): Promise { + const processor = this.processors.get(jobType) + if (!processor || this.jobs.length === 0) return + + const job = this.jobs.shift()! + + try { + await processor(job) + } catch (error) { + console.error(`Error processing job ${job.id}:`, error) + } + } +} + +// Notification Service for real-time updates +class NotificationService { + private subscribers: Set<(update: AIUpdate) => void> = new Set() + + subscribe(callback: (update: AIUpdate) => void): () => void { + this.subscribers.add(callback) + return () => this.subscribers.delete(callback) + } + + notify(update: AIUpdate): void { + this.subscribers.forEach(callback => { + try { + callback(update) + } catch (error) { + console.error('Error in notification callback:', error) + } + }) + } + + async notifyStudent(studentId: string, assignment: any): Promise { + console.log(`📧 Notifying student ${studentId} about assignment`) + // In production: send email, SMS, or push notification + } + + async notifyVolunteer(volunteerId: string, assignment: any): Promise { + console.log(`📧 Notifying volunteer ${volunteerId} about new assignment`) + // In production: send volunteer notification + } + + async notifyCoordinators(assignment: any): Promise { + console.log(`📧 Notifying coordinators about new assignment`) + // In production: alert coordination team + } + + async updateDonors(estimatedCost: number): Promise { + console.log(`💰 Updating donors about $${estimatedCost} impact opportunity`) + // In production: trigger donor engagement campaign + } + + async notifyReviewer(reviewer: any, reviewTask: any, aiInsights: any): Promise { + console.log(`👥 Notifying reviewer ${reviewer.id} about review task with AI confidence: ${aiInsights.aiConfidence}`) + // In production: send detailed review notification with AI recommendations + } +} + +// Assignment and Review Management +class AssignmentManager { + private static assignments: Map = new Map() + + static async createAssignment(assignmentData: any): Promise { + const assignment = { + id: `assign-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, + ...assignmentData, + createdAt: new Date(), + status: 'pending' + } + + this.assignments.set(assignment.id, assignment) + console.log(`✅ Created assignment ${assignment.id}`) + return assignment + } + + static async getById(id: string): Promise { + return this.assignments.get(id) || null + } +} + +class ReviewManager { + private static reviewTasks: Map = new Map() + + static async createReviewTask(taskData: any): Promise { + const task = { + id: `review-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`, + ...taskData, + createdAt: new Date(), + status: 'pending' + } + + this.reviewTasks.set(task.id, task) + console.log(`📋 Created review task ${task.id}`) + return task + } +} + +// Main Processing Pipeline +export class RealTimeProcessingPipeline { + private queue: MockQueue + private notificationService: NotificationService + private config: ProcessingPipelineConfig + + constructor(config: Partial = {}) { + this.queue = new MockQueue() + this.notificationService = new NotificationService() + this.config = { + autoApprovalThreshold: 0.85, + urgencyWeights: { + 'emergency': 1.0, + 'high': 0.8, + 'medium': 0.5, + 'low': 0.2 + }, + categoryWeights: { + 'emergency-housing': 1.0, + 'food-assistance': 0.9, + 'medical-needs': 0.85, + 'clothing': 0.7, + 'school-supplies': 0.6, + 'transportation': 0.5, + 'technology': 0.4, + 'extracurricular': 0.3, + 'other': 0.4 + }, + maxProcessingTime: 5000, // 5 seconds + retryAttempts: 3, + notificationEnabled: true, + ...config + } + + this.setupQueueProcessors() + } + + private setupQueueProcessors(): void { + this.queue.process('analyze-request', 5, async (job) => { + const request = job.data + + try { + console.log(`🔄 Processing request ${request.id} for ${request.studentName}`) + + // AI analysis and matching + const matches = await aiEngine.processRequest(request) + + // Auto-approval for high-confidence matches + if (matches.length > 0 && matches[0].confidenceScore >= this.config.autoApprovalThreshold) { + await this.autoApproveRequest(request, matches[0]) + } else { + await this.routeForHumanReview(request, matches) + } + + // Update real-time dashboard + await this.updateDashboard(request.id, matches) + + // Notify subscribers of processing completion + this.notificationService.notify({ + type: 'request-processed', + requestId: request.id, + studentName: request.studentName, + status: matches.length > 0 && matches[0].confidenceScore >= this.config.autoApprovalThreshold ? 'auto-approved' : 'under-review', + recommendations: matches, + timestamp: new Date() + }) + + } catch (error) { + await this.handleProcessingError(request, error as Error) + } + }) + } + + async submitRequest(request: StudentRequest): Promise { + console.log(`📥 Submitting request from ${request.studentName}: ${request.category}`) + + // Add to processing queue with priority based on urgency + const priority = this.calculatePriority(request.urgency) + const job = await this.queue.add('analyze-request', request, { + priority, + attempts: this.config.retryAttempts, + backoff: 'exponential' + }) + + // Immediate acknowledgment + await this.sendAcknowledgment(request) + + return job.id + } + + private calculatePriority(urgency: string): number { + return this.config.urgencyWeights[urgency as keyof typeof this.config.urgencyWeights] || 0.5 + } + + private async sendAcknowledgment(request: StudentRequest): Promise { + console.log(`✉️ Sending acknowledgment to ${request.studentName}`) + // In production: send immediate confirmation email/SMS + } + + private async autoApproveRequest(request: StudentRequest, match: MatchResult): Promise { + console.log(`🤖 Auto-approving request ${request.id} with ${(match.confidenceScore * 100).toFixed(1)}% confidence`) + + // Create assistance assignment + const assignment = await AssignmentManager.createAssignment({ + requestId: request.id, + studentId: request.studentId, + studentName: request.studentName, + resourceId: match.resourceId, + resourceName: match.resourceName, + volunteerId: match.volunteerMatch?.id, + volunteerName: match.volunteerMatch?.volunteerName, + scheduledDate: new Date(Date.now() + 24 * 60 * 60 * 1000), // Tomorrow + estimatedCost: match.estimatedCost, + approvalStatus: 'auto-approved', + confidence: match.confidenceScore, + aiRecommendation: true, + urgency: request.urgency, + category: request.category + }) + + // Notify all stakeholders + if (this.config.notificationEnabled) { + await Promise.all([ + this.notificationService.notifyStudent(request.studentId, assignment), + match.volunteerMatch ? this.notificationService.notifyVolunteer(assignment.volunteerId, assignment) : Promise.resolve(), + this.notificationService.notifyCoordinators(assignment), + this.notificationService.updateDonors(assignment.estimatedCost) + ]) + } + + // Notify real-time subscribers + this.notificationService.notify({ + type: 'auto-approval', + requestId: request.id, + studentName: request.studentName, + message: `Request automatically approved with ${(match.confidenceScore * 100).toFixed(1)}% confidence`, + timestamp: new Date() + }) + + // Track decision for learning + await this.trackDecision(request, match, 'auto-approved') + } + + private async routeForHumanReview(request: StudentRequest, matches: MatchResult[]): Promise { + console.log(`👤 Routing request ${request.id} for human review`) + + // Determine best reviewer based on request type and matches + const reviewer = await this.selectOptimalReviewer(request, matches) + + // Create review assignment + const reviewTask = await ReviewManager.createReviewTask({ + requestId: request.id, + assignedTo: reviewer.id, + assignedToName: reviewer.name, + aiRecommendations: matches, + priority: this.calculateReviewPriority(request, matches), + deadline: this.calculateReviewDeadline(request.urgency), + studentName: request.studentName, + category: request.category, + urgency: request.urgency + }) + + // Notify reviewer with AI insights + if (this.config.notificationEnabled) { + await this.notificationService.notifyReviewer(reviewer, reviewTask, { + aiConfidence: matches[0]?.confidenceScore || 0, + recommendedAction: this.generateRecommendation(matches), + riskFactors: matches[0]?.riskFactors || [] + }) + } + } + + private async selectOptimalReviewer(request: StudentRequest, matches: MatchResult[]) { + // Mock reviewer selection - in production, this would use actual staff data + const reviewers = [ + { id: 'rev1', name: 'Sarah Martinez', specialties: ['clothing', 'emergency-housing'], workload: 5 }, + { id: 'rev2', name: 'John Davis', specialties: ['food-assistance', 'transportation'], workload: 3 }, + { id: 'rev3', name: 'Lisa Chen', specialties: ['school-supplies', 'technology'], workload: 7 }, + { id: 'rev4', name: 'Mike Johnson', specialties: ['medical-needs', 'other'], workload: 4 } + ] + + // Select reviewer based on specialty and workload + const categoryReviewers = reviewers.filter(r => + r.specialties.includes(request.category) || r.specialties.includes('other') + ) + + // Return reviewer with lowest workload + return categoryReviewers.sort((a, b) => a.workload - b.workload)[0] || reviewers[0] + } + + private calculateReviewPriority(request: StudentRequest, matches: MatchResult[]): number { + let priority = this.config.urgencyWeights[request.urgency as keyof typeof this.config.urgencyWeights] || 0.5 + + // Boost priority for high AI confidence but below threshold + if (matches.length > 0) { + const topMatch = matches[0] + if (topMatch.confidenceScore > 0.7 && topMatch.confidenceScore < this.config.autoApprovalThreshold) { + priority += 0.2 + } + } + + // Boost priority for critical categories + priority += this.config.categoryWeights[request.category as keyof typeof this.config.categoryWeights] || 0 + + return Math.min(priority, 1.0) + } + + private calculateReviewDeadline(urgency: string): Date { + const now = new Date() + switch (urgency) { + case 'emergency': + return new Date(now.getTime() + 30 * 60 * 1000) // 30 minutes + case 'high': + return new Date(now.getTime() + 2 * 60 * 60 * 1000) // 2 hours + case 'medium': + return new Date(now.getTime() + 8 * 60 * 60 * 1000) // 8 hours + case 'low': + default: + return new Date(now.getTime() + 24 * 60 * 60 * 1000) // 24 hours + } + } + + private generateRecommendation(matches: MatchResult[]): string { + if (matches.length === 0) return 'No suitable matches found - manual resource allocation needed' + + const topMatch = matches[0] + if (topMatch.confidenceScore > 0.8) { + return `Strong AI recommendation: ${topMatch.resourceName} (${(topMatch.confidenceScore * 100).toFixed(1)}% confidence)` + } else if (topMatch.confidenceScore > 0.6) { + return `Moderate AI recommendation: ${topMatch.resourceName} - review for accuracy` + } else { + return `Low confidence match: manual evaluation recommended` + } + } + + private async updateDashboard(requestId: string, matches: MatchResult[]): Promise { + console.log(`📊 Updating dashboard for request ${requestId}`) + // In production: update real-time analytics dashboard + } + + private async handleProcessingError(request: StudentRequest, error: Error): Promise { + console.error(`❌ Error processing request ${request.id}:`, error.message) + + // Notify administrators of processing error + this.notificationService.notify({ + type: 'alert', + requestId: request.id, + studentName: request.studentName, + message: `Processing error: ${error.message}`, + timestamp: new Date() + }) + + // Route to manual processing + await this.routeForHumanReview(request, []) + } + + private async trackDecision(request: StudentRequest, match: MatchResult, decision: string): Promise { + console.log(`📈 Tracking decision: ${decision} for request ${request.id}`) + // In production: log decision for ML model training + } + + // Public methods for integration + subscribe(callback: (update: AIUpdate) => void): () => void { + return this.notificationService.subscribe(callback) + } + + async generateInsights(requests: StudentRequest[]): Promise { + return await aiEngine.generateInsights(requests) + } + + getConfig(): ProcessingPipelineConfig { + return { ...this.config } + } + + updateConfig(newConfig: Partial): void { + this.config = { ...this.config, ...newConfig } + console.log('🔧 Pipeline configuration updated') + } +} + +// Export singleton instance +export const processingPipeline = new RealTimeProcessingPipeline() + +// Export classes for testing and advanced usage +export { NotificationService, AssignmentManager, ReviewManager } \ No newline at end of file diff --git a/src/ai/StudentAssistanceAI.ts b/src/ai/StudentAssistanceAI.ts new file mode 100644 index 0000000..6c28c3d --- /dev/null +++ b/src/ai/StudentAssistanceAI.ts @@ -0,0 +1,803 @@ +// Phase 3: AI-Powered Student Assistance Matching Engine +import * as tf from '@tensorflow/tfjs' +import type { + StudentRequest, + MatchResult, + RequestAnalysis, + AIInsight, + LearningFeedback, + NeedCategory, + ResourceRequirement +} from './types' + +// Text Vectorization for NLP Analysis +class TextVectorizer { + private vocabulary: Map = new Map() + private vectorSize: number = 100 + + constructor() { + this.initializeVocabulary() + } + + private initializeVocabulary() { + // Common assistance-related vocabulary + const assistanceVocabulary = [ + // Clothing + 'clothes', 'clothing', 'shirt', 'pants', 'shoes', 'coat', 'jacket', 'uniform', 'dress', + 'socks', 'underwear', 'boots', 'sneakers', 'winter', 'warm', 'size', 'outgrown', + + // School supplies + 'supplies', 'backpack', 'notebook', 'pencil', 'pen', 'binder', 'calculator', 'books', + 'textbook', 'paper', 'folders', 'highlighter', 'ruler', 'glue', 'scissors', + + // Food assistance + 'food', 'hungry', 'lunch', 'breakfast', 'dinner', 'meal', 'snack', 'groceries', + 'nutrition', 'eat', 'starving', 'appetite', 'diet', 'allergic', + + // Transportation + 'transport', 'bus', 'ride', 'car', 'walk', 'distance', 'far', 'pickup', 'drop-off', + 'gas', 'vehicle', 'bicycle', 'train', 'subway', + + // Emergency/housing + 'emergency', 'urgent', 'homeless', 'shelter', 'roof', 'home', 'housing', 'evicted', + 'temporary', 'crisis', 'help', 'desperate', + + // Medical + 'medical', 'doctor', 'medicine', 'sick', 'health', 'prescription', 'hospital', + 'glasses', 'dental', 'therapy', 'insurance', + + // Technology + 'computer', 'laptop', 'tablet', 'phone', 'internet', 'wifi', 'online', 'digital', + 'device', 'charger', 'software', 'access', + + // Emotional/contextual + 'need', 'help', 'family', 'parent', 'mother', 'father', 'guardian', 'sibling', + 'student', 'school', 'grade', 'class', 'teacher', 'principal', 'counselor', + 'poor', 'afford', 'money', 'financial', 'struggle', 'difficult', 'hardship' + ] + + assistanceVocabulary.forEach((word, index) => { + this.vocabulary.set(word.toLowerCase(), index) + }) + } + + async encode(text: string): Promise { + const words = this.preprocessText(text) + const vector = new Array(this.vectorSize).fill(0) + + // Simple bag-of-words with TF-IDF weighting + const wordCounts = new Map() + words.forEach(word => { + wordCounts.set(word, (wordCounts.get(word) || 0) + 1) + }) + + // Create weighted vector + wordCounts.forEach((count, word) => { + const vocabIndex = this.vocabulary.get(word) + if (vocabIndex !== undefined && vocabIndex < this.vectorSize) { + // Simple TF-IDF approximation + const tf = count / words.length + const idf = Math.log(this.vocabulary.size / (count + 1)) + vector[vocabIndex] = tf * idf + } + }) + + // Simple semantic analysis without external NLP library + // Extract entities and sentiment patterns + + // Boost urgency indicators + const urgencyWords = ['urgent', 'emergency', 'immediate', 'asap', 'desperate', 'critical'] + const urgencyScore = urgencyWords.reduce((score, word) => { + return score + (text.toLowerCase().includes(word) ? 1 : 0) + }, 0) + + // Add urgency as a feature + if (vector.length > this.vectorSize - 10) { + vector[this.vectorSize - 1] = urgencyScore / urgencyWords.length + } + + return vector + } + + private preprocessText(text: string): string[] { + return text + .toLowerCase() + .replace(/[^\w\s]/g, ' ') + .split(/\s+/) + .filter(word => word.length > 2) + } +} + +// Core AI Matching Engine +export class StudentAssistanceAI { + private vectorizer: TextVectorizer + private matchingModel: tf.LayersModel | null = null + private impactModel: tf.LayersModel | null = null + private isInitialized = false + + // Mock data for demonstration - in production, this would come from databases + private mockResources = [ + { id: 'r1', name: 'Emergency Clothing Fund', type: 'clothing', capacity: 100, avgCost: 75 }, + { id: 'r2', name: 'School Supply Backpack Program', type: 'supplies', capacity: 50, avgCost: 45 }, + { id: 'r3', name: 'Weekend Food Program', type: 'food', capacity: 200, avgCost: 25 }, + { id: 'r4', name: 'Transportation Assistance', type: 'transport', capacity: 30, avgCost: 40 }, + { id: 'r5', name: 'Technology Access Fund', type: 'technology', capacity: 25, avgCost: 200 } + ] + + private mockVolunteers = [ + { id: 'v1', name: 'Sarah Johnson', skills: ['clothing', 'shopping'], rating: 4.8, location: { city: 'Austin', state: 'TX' } }, + { id: 'v2', name: 'Mike Chen', skills: ['supplies', 'delivery'], rating: 4.9, location: { city: 'Austin', state: 'TX' } }, + { id: 'v3', name: 'Emily Rodriguez', skills: ['food', 'cooking'], rating: 4.7, location: { city: 'Round Rock', state: 'TX' } }, + { id: 'v4', name: 'David Thompson', skills: ['transport', 'technology'], rating: 4.6, location: { city: 'Cedar Park', state: 'TX' } } + ] + + constructor() { + this.vectorizer = new TextVectorizer() + this.initializeModels() + } + + private async initializeModels() { + try { + // Create simple neural network models for demonstration + // In production, these would be pre-trained models loaded from files + + // Matching Model: Input features -> Match probability + this.matchingModel = tf.sequential({ + layers: [ + tf.layers.dense({ inputShape: [110], units: 64, activation: 'relu' }), + tf.layers.dropout({ rate: 0.2 }), + tf.layers.dense({ units: 32, activation: 'relu' }), + tf.layers.dense({ units: 16, activation: 'relu' }), + tf.layers.dense({ units: 1, activation: 'sigmoid' }) + ] + }) + + // Impact Prediction Model: Match features -> Impact score + this.impactModel = tf.sequential({ + layers: [ + tf.layers.dense({ inputShape: [20], units: 32, activation: 'relu' }), + tf.layers.dense({ units: 16, activation: 'relu' }), + tf.layers.dense({ units: 4, activation: 'linear' }) // [beneficiaries, success_prob, time, sustainability] + ] + }) + + // Compile models + this.matchingModel.compile({ + optimizer: tf.train.adam(0.001), + loss: 'binaryCrossentropy', + metrics: ['accuracy'] + }) + + this.impactModel.compile({ + optimizer: tf.train.adam(0.001), + loss: 'meanSquaredError', + metrics: ['mae'] + }) + + this.isInitialized = true + console.log('🤖 AI Models initialized successfully') + + } catch (error) { + console.error('Failed to initialize AI models:', error) + // Fallback to rule-based system + this.isInitialized = false + } + } + + async processRequest(request: StudentRequest): Promise { + console.log(`🧠 Processing request for ${request.studentName}: ${request.description}`) + + try { + // 1. Analyze request with NLP + const analysis = await this.analyzeRequest(request) + + // 2. Find candidate resources + const candidates = await this.findCandidateResources(analysis) + + // 3. Score matches using AI or rule-based fallback + const scoredMatches = this.isInitialized + ? await this.aiScoreMatches(candidates, analysis, request) + : await this.ruleBasedScoring(candidates, analysis, request) + + // 4. Predict impact for top matches + const enrichedMatches = await this.enrichWithPredictions(scoredMatches) + + // 5. Sort by confidence and return top 3 + return enrichedMatches + .sort((a, b) => b.confidenceScore - a.confidenceScore) + .slice(0, 3) + + } catch (error) { + console.error('Error processing request:', error) + return this.generateFallbackMatches() + } + } + + private async analyzeRequest(request: StudentRequest): Promise { + // NLP analysis of request description + await this.vectorizer.encode(request.description) // Process text for any side effects + + // Extract needs using rule-based analysis + NLP + const primaryNeeds = this.extractNeedCategories(request) + const urgencyScore = this.calculateUrgencyScore(request) + const complexityEstimate = this.estimateComplexity(request) + + return { + primaryNeeds, + urgencyScore, + complexityEstimate, + resourceRequirements: this.estimateResourceRequirements(primaryNeeds), + estimatedBudget: this.estimateBudget(primaryNeeds, request.description) + } + } + + private extractNeedCategories(request: StudentRequest): NeedCategory[] { + const categories: NeedCategory[] = [] + const desc = request.description.toLowerCase() + + // Clothing detection + if (desc.includes('clothes') || desc.includes('clothing') || desc.includes('shirt') || + desc.includes('pants') || desc.includes('shoes') || desc.includes('coat')) { + categories.push({ + category: 'clothing', + priority: desc.includes('winter') || desc.includes('cold') ? 0.9 : 0.7, + specifications: { season: desc.includes('winter') ? 'winter' : 'general' } + }) + } + + // School supplies detection + if (desc.includes('supplies') || desc.includes('backpack') || desc.includes('notebook') || + desc.includes('pencil') || desc.includes('books')) { + categories.push({ + category: 'school-supplies', + priority: 0.8, + specifications: { type: 'general' } + }) + } + + // Food assistance detection + if (desc.includes('food') || desc.includes('hungry') || desc.includes('lunch') || + desc.includes('meal') || desc.includes('eat')) { + categories.push({ + category: 'food-assistance', + priority: desc.includes('hungry') || desc.includes('starving') ? 0.95 : 0.75, + specifications: { urgency: desc.includes('hungry') ? 'high' : 'medium' } + }) + } + + // Transportation detection + if (desc.includes('transport') || desc.includes('bus') || desc.includes('ride') || + desc.includes('car') || desc.includes('far')) { + categories.push({ + category: 'transportation', + priority: 0.6, + specifications: { type: 'general' } + }) + } + + // Emergency/housing detection + if (desc.includes('emergency') || desc.includes('urgent') || desc.includes('homeless') || + desc.includes('shelter') || desc.includes('crisis')) { + categories.push({ + category: 'emergency-housing', + priority: 0.98, + specifications: { urgency: 'critical' } + }) + } + + // Technology detection + if (desc.includes('computer') || desc.includes('laptop') || desc.includes('internet') || + desc.includes('online') || desc.includes('device')) { + categories.push({ + category: 'technology', + priority: 0.65, + specifications: { type: 'educational' } + }) + } + + // Default to the request category if nothing else matches + if (categories.length === 0) { + categories.push({ + category: request.category, + priority: 0.5, + specifications: {} + }) + } + + return categories.sort((a, b) => b.priority - a.priority) + } + + private calculateUrgencyScore(request: StudentRequest): number { + let score = 0 + + // Base score from urgency level + switch (request.urgency) { + case 'emergency': score += 1.0; break + case 'high': score += 0.8; break + case 'medium': score += 0.5; break + case 'low': score += 0.2; break + } + + // Boost for urgency keywords in description + const desc = request.description.toLowerCase() + const urgencyKeywords = ['urgent', 'emergency', 'immediate', 'asap', 'desperate', 'critical', 'help', 'need now'] + urgencyKeywords.forEach(keyword => { + if (desc.includes(keyword)) score += 0.1 + }) + + // Boost for deadline + if (request.deadline) { + const daysUntilDeadline = (request.deadline.getTime() - Date.now()) / (1000 * 60 * 60 * 24) + if (daysUntilDeadline < 1) score += 0.3 + else if (daysUntilDeadline < 3) score += 0.2 + else if (daysUntilDeadline < 7) score += 0.1 + } + + return Math.min(score, 1.0) // Cap at 1.0 + } + + private estimateComplexity(request: StudentRequest): number { + let complexity = 0.3 // Base complexity + + // Multiple needs increase complexity + const needCount = this.extractNeedCategories(request).length + complexity += needCount * 0.15 + + // Special constraints increase complexity + if (request.constraints.deliveryMethod === 'delivery') complexity += 0.2 + if (request.constraints.privacyLevel === 'anonymous') complexity += 0.1 + if (request.constraints.specialRequirements?.length) complexity += 0.2 + + // Geographic complexity + if (!request.location.city || !request.location.zipCode) complexity += 0.15 + + return Math.min(complexity, 1.0) + } + + private estimateResourceRequirements(needs: NeedCategory[]): ResourceRequirement[] { + return needs.map(need => ({ + type: need.category, + quantity: 1, + specifications: need.specifications || {}, + estimatedCost: this.estimateCategoryBudget(need.category) + })) + } + + private estimateBudget(needs: NeedCategory[], description: string): number { + let totalBudget = 0 + + needs.forEach(need => { + totalBudget += this.estimateCategoryBudget(need.category) * need.priority + }) + + // Adjust based on description indicators + const desc = description.toLowerCase() + if (desc.includes('multiple') || desc.includes('several')) totalBudget *= 1.5 + if (desc.includes('family') || desc.includes('siblings')) totalBudget *= 2 + if (desc.includes('brand new') || desc.includes('quality')) totalBudget *= 1.3 + + return Math.round(totalBudget) + } + + private estimateCategoryBudget(category: string): number { + const budgets: Record = { + 'clothing': 75, + 'school-supplies': 45, + 'food-assistance': 25, + 'transportation': 40, + 'emergency-housing': 200, + 'medical-needs': 150, + 'technology': 200, + 'extracurricular': 60, + 'other': 50 + } + return budgets[category] || 50 + } + + private async findCandidateResources(analysis: RequestAnalysis) { + // Find resources that match the primary needs + const candidates = this.mockResources.filter(resource => + analysis.primaryNeeds.some(need => + resource.type === need.category || + this.isCompatibleResourceType(resource.type, need.category) + ) + ) + + // Add volunteer matches + const volunteerCandidates = this.mockVolunteers.filter(volunteer => + volunteer.skills.some(skill => + analysis.primaryNeeds.some(need => + skill.includes(need.category) || need.category.includes(skill) + ) + ) + ) + + return candidates.map(resource => ({ + ...resource, + volunteers: volunteerCandidates.filter(v => + v.skills.some(skill => skill.includes(resource.type)) + ) + })) + } + + private isCompatibleResourceType(resourceType: string, needCategory: string): boolean { + const compatibility: Record = { + 'clothing': ['clothing'], + 'supplies': ['school-supplies'], + 'food': ['food-assistance'], + 'transport': ['transportation'], + 'technology': ['technology'], + 'emergency': ['emergency-housing', 'medical-needs'] + } + + return compatibility[resourceType]?.includes(needCategory) || false + } + + private async aiScoreMatches(candidates: any[], analysis: RequestAnalysis, request: StudentRequest): Promise { + if (!this.matchingModel) return this.ruleBasedScoring(candidates, analysis, request) + + const matches: MatchResult[] = [] + + for (const candidate of candidates) { + try { + // Prepare features for ML model + const features = await this.prepareFeaturesForML(candidate, analysis, request) + const featureTensor = tf.tensor2d([features]) + + // Get confidence score from AI model + const prediction = this.matchingModel.predict(featureTensor) as tf.Tensor + const confidenceData = await prediction.data() + const confidenceScore = confidenceData[0] + + // Clean up tensors + featureTensor.dispose() + prediction.dispose() + + // Select best volunteer + const bestVolunteer = candidate.volunteers.length > 0 + ? candidate.volunteers.sort((a: any, b: any) => b.rating - a.rating)[0] + : null + + matches.push({ + resourceId: candidate.id, + resourceName: candidate.name, + resourceType: candidate.type, + confidenceScore, + estimatedImpact: this.estimateImpact(analysis, candidate), + logisticalComplexity: analysis.complexityEstimate, + volunteerMatch: bestVolunteer ? { + id: bestVolunteer.id, + volunteerId: bestVolunteer.id, + volunteerName: bestVolunteer.name, + skills: bestVolunteer.skills, + availability: [], // Would be populated from real data + location: bestVolunteer.location, + rating: bestVolunteer.rating, + completedAssignments: Math.floor(Math.random() * 50) + 10 + } : undefined, + estimatedCost: candidate.avgCost, + fulfillmentTimeline: this.estimateTimeline(analysis.urgencyScore, analysis.complexityEstimate), + reasoningFactors: this.generateReasoningFactors(candidate, analysis), + riskFactors: this.identifyRiskFactors(candidate, analysis) + }) + + } catch (error) { + console.error('Error in AI scoring:', error) + // Fallback to rule-based for this candidate + const ruleBasedScore = this.calculateRuleBasedScore(candidate, analysis, request) + matches.push(ruleBasedScore) + } + } + + return matches + } + + private async prepareFeaturesForML(candidate: any, analysis: RequestAnalysis, request: StudentRequest): Promise { + // Create feature vector for ML model (110 features to match model input) + const features = new Array(110).fill(0) + + // Request analysis features (first 100 from text vectorization) + const textVector = await this.vectorizer.encode(request.description) + for (let i = 0; i < Math.min(textVector.length, 100); i++) { + features[i] = textVector[i] + } + + // Resource matching features + features[100] = analysis.urgencyScore + features[101] = analysis.complexityEstimate + features[102] = analysis.estimatedBudget / 1000 // Normalize + features[103] = candidate.capacity / 100 // Normalize + features[104] = candidate.avgCost / 200 // Normalize + features[105] = candidate.volunteers.length / 10 // Normalize + features[106] = candidate.volunteers.length > 0 ? Math.max(...candidate.volunteers.map((v: any) => v.rating)) / 5 : 0 + features[107] = analysis.primaryNeeds.length / 5 // Normalize + features[108] = request.constraints.maxBudget ? (candidate.avgCost / request.constraints.maxBudget) : 0.5 + features[109] = this.getTimeframeScore(request.constraints.timeframe) + + return features + } + + private getTimeframeScore(timeframe: string): number { + switch (timeframe) { + case 'immediate': return 1.0 + case 'within-week': return 0.7 + case 'within-month': return 0.4 + case 'flexible': return 0.2 + default: return 0.5 + } + } + + private async ruleBasedScoring(candidates: any[], analysis: RequestAnalysis, request: StudentRequest): Promise { + return candidates.map(candidate => this.calculateRuleBasedScore(candidate, analysis, request)) + } + + private calculateRuleBasedScore(candidate: any, analysis: RequestAnalysis, request: StudentRequest): MatchResult { + let score = 0.5 // Base score + + // Category match bonus + const categoryMatch = analysis.primaryNeeds.some(need => + candidate.type === need.category || this.isCompatibleResourceType(candidate.type, need.category) + ) + if (categoryMatch) score += 0.3 + + // Capacity and cost considerations + if (candidate.capacity > 10) score += 0.1 + if (candidate.avgCost <= analysis.estimatedBudget) score += 0.2 + + // Volunteer availability bonus + if (candidate.volunteers?.length > 0) { + score += 0.15 + const avgRating = candidate.volunteers.reduce((sum: number, v: any) => sum + v.rating, 0) / candidate.volunteers.length + score += (avgRating / 5) * 0.1 + } + + // Urgency adjustment + score += analysis.urgencyScore * 0.1 + + // Complexity penalty + score -= analysis.complexityEstimate * 0.05 + + const bestVolunteer = candidate.volunteers?.length > 0 + ? candidate.volunteers.sort((a: any, b: any) => b.rating - a.rating)[0] + : null + + return { + resourceId: candidate.id, + resourceName: candidate.name, + resourceType: candidate.type, + confidenceScore: Math.min(Math.max(score, 0), 1), + estimatedImpact: this.estimateImpact(analysis, candidate), + logisticalComplexity: analysis.complexityEstimate, + volunteerMatch: bestVolunteer ? { + id: bestVolunteer.id, + volunteerId: bestVolunteer.id, + volunteerName: bestVolunteer.name, + skills: bestVolunteer.skills, + availability: [], + location: bestVolunteer.location, + rating: bestVolunteer.rating, + completedAssignments: Math.floor(Math.random() * 50) + 10 + } : undefined, + estimatedCost: candidate.avgCost, + fulfillmentTimeline: this.estimateTimeline(analysis.urgencyScore, analysis.complexityEstimate), + reasoningFactors: this.generateReasoningFactors(candidate, analysis), + riskFactors: this.identifyRiskFactors(candidate, analysis, request) + } + } + + private estimateImpact(analysis: RequestAnalysis, candidate: any): number { + let impact = 0.6 // Base impact + + // Higher impact for urgent needs + impact += analysis.urgencyScore * 0.2 + + // Higher impact for critical categories + const criticalCategories = ['emergency-housing', 'food-assistance', 'medical-needs'] + if (analysis.primaryNeeds.some(need => criticalCategories.includes(need.category))) { + impact += 0.15 + } + + // Volunteer quality impact + if (candidate.volunteers?.length > 0) { + const avgRating = candidate.volunteers.reduce((sum: number, v: any) => sum + v.rating, 0) / candidate.volunteers.length + impact += (avgRating / 5) * 0.1 + } + + return Math.min(impact, 1.0) + } + + private estimateTimeline(urgencyScore: number, complexityScore: number): string { + const baseHours = 24 + (complexityScore * 48) - (urgencyScore * 12) + + if (baseHours <= 2) return 'Within 2 hours' + if (baseHours <= 24) return 'Same day' + if (baseHours <= 48) return '1-2 days' + if (baseHours <= 168) return '3-7 days' + return '1-2 weeks' + } + + private generateReasoningFactors(candidate: any, analysis: RequestAnalysis): string[] { + const factors: string[] = [] + + factors.push(`Resource specializes in ${candidate.type} assistance`) + + if (candidate.capacity > 50) factors.push('High capacity resource available') + if (candidate.avgCost <= analysis.estimatedBudget) factors.push('Cost within estimated budget') + + if (candidate.volunteers?.length > 0) { + const avgRating = candidate.volunteers.reduce((sum: number, v: any) => sum + v.rating, 0) / candidate.volunteers.length + factors.push(`${candidate.volunteers.length} volunteers available (avg rating: ${avgRating.toFixed(1)})`) + } + + if (analysis.urgencyScore > 0.7) factors.push('High urgency request prioritized') + + return factors + } + + private identifyRiskFactors(candidate: any, analysis: RequestAnalysis): string[] { + const risks: string[] = [] + + if (candidate.capacity < 5) risks.push('Limited resource capacity') + if (candidate.avgCost > analysis.estimatedBudget * 1.2) risks.push('Cost exceeds estimated budget') + if (candidate.volunteers?.length === 0) risks.push('No volunteers currently available') + if (analysis.complexityEstimate > 0.7) risks.push('High complexity request') + + return risks + } + + private async enrichWithPredictions(matches: MatchResult[]): Promise { + // For each match, predict impact using AI model if available + for (const match of matches) { + try { + if (this.impactModel && this.isInitialized) { + const impactFeatures = this.prepareImpactFeatures(match) + const impactTensor = tf.tensor2d([impactFeatures]) + + const impactPrediction = this.impactModel.predict(impactTensor) as tf.Tensor + const impactData = await impactPrediction.data() + + // Update match with AI predictions + match.estimatedImpact = Math.min(Math.max(impactData[1], 0), 1) // success probability + + // Clean up tensors + impactTensor.dispose() + impactPrediction.dispose() + } + } catch (error) { + console.error('Error in impact prediction:', error) + // Keep existing impact estimate + } + } + + return matches + } + + private prepareImpactFeatures(match: MatchResult): number[] { + // Create 20-feature vector for impact prediction + return [ + match.confidenceScore, + match.estimatedCost / 200, // Normalize + match.logisticalComplexity, + match.volunteerMatch ? match.volunteerMatch.rating / 5 : 0, + match.volunteerMatch ? match.volunteerMatch.completedAssignments / 50 : 0, + match.resourceType === 'emergency' ? 1 : 0, + match.resourceType === 'food' ? 1 : 0, + match.resourceType === 'clothing' ? 1 : 0, + match.resourceType === 'supplies' ? 1 : 0, + match.resourceType === 'transport' ? 1 : 0, + match.reasoningFactors.length / 5, + match.riskFactors.length / 5, + Math.random(), // Random noise for variation + Math.random(), + Math.random(), + Math.random(), + Math.random(), + Math.random(), + Math.random(), + Math.random() + ] + } + + private generateFallbackMatches(): MatchResult[] { + // Emergency fallback when AI fails + return [{ + resourceId: 'fallback-1', + resourceName: 'General Assistance Fund', + resourceType: 'other', + confidenceScore: 0.5, + estimatedImpact: 0.6, + logisticalComplexity: 0.5, + estimatedCost: 75, + fulfillmentTimeline: '1-2 days', + reasoningFactors: ['Fallback resource for emergency situations'], + riskFactors: ['System processing temporarily unavailable'] + }] + } + + async generateInsights(requests: StudentRequest[]): Promise { + const insights: AIInsight[] = [] + + // Pattern analysis + const categoryDistribution = this.analyzeCategoryDistribution(requests) + const urgencyTrends = this.analyzeUrgencyTrends(requests) + + // Generate insights based on patterns + if (categoryDistribution.clothing > 0.4) { + insights.push({ + id: `insight-${Date.now()}-1`, + type: 'trend', + title: 'High Clothing Demand Detected', + description: `${Math.round(categoryDistribution.clothing * 100)}% of recent requests are for clothing assistance. Consider expanding clothing inventory.`, + confidence: 0.85, + severity: 'medium', + timestamp: new Date(), + actionItems: ['Increase clothing fund allocation', 'Recruit clothing-focused volunteers', 'Partner with local clothing stores'] + }) + } + + if (urgencyTrends.emergencyRate > 0.3) { + insights.push({ + id: `insight-${Date.now()}-2`, + type: 'anomaly', + title: 'Increased Emergency Requests', + description: `${Math.round(urgencyTrends.emergencyRate * 100)}% of requests are marked as emergency. This is above normal baseline.`, + confidence: 0.9, + severity: 'high', + timestamp: new Date(), + actionItems: ['Review emergency response protocols', 'Ensure emergency fund availability', 'Alert senior coordinators'] + }) + } + + return insights + } + + private analyzeCategoryDistribution(requests: StudentRequest[]) { + const total = requests.length + if (total === 0) return {} + + const counts: Record = {} + requests.forEach(req => { + counts[req.category] = (counts[req.category] || 0) + 1 + }) + + const distribution: Record = {} + Object.entries(counts).forEach(([category, count]) => { + distribution[category] = count / total + }) + + return distribution + } + + private analyzeUrgencyTrends(requests: StudentRequest[]) { + const total = requests.length + if (total === 0) return { emergencyRate: 0 } + + const emergencyCount = requests.filter(req => req.urgency === 'emergency').length + return { + emergencyRate: emergencyCount / total + } + } + + async learnFromFeedback(feedback: LearningFeedback): Promise { + console.log(`📚 Learning from feedback for request ${feedback.requestId}`) + + // In production, this would update training data and trigger model retraining + // For now, we'll log the feedback for analysis + + const learningData = { + requestId: feedback.requestId, + outcome: feedback.outcome, + accuracyScore: feedback.outcome === 'successful' ? 1 : 0, + costAccuracy: Math.abs(feedback.actualCost - (feedback.actualCost)) / feedback.actualCost, + timeAccuracy: feedback.actualTimeToComplete, + satisfaction: feedback.satisfactionScore, + timestamp: new Date() + } + + // Store for batch processing and model improvement + console.log('Learning data recorded:', learningData) + } +} + +// Export the AI engine +export const aiEngine = new StudentAssistanceAI() \ No newline at end of file diff --git a/src/ai/types.ts b/src/ai/types.ts new file mode 100644 index 0000000..35aaaaa --- /dev/null +++ b/src/ai/types.ts @@ -0,0 +1,191 @@ +// Phase 3: AI Types and Interfaces + +export interface StudentRequest { + id: string + studentId: string + studentName: string + description: string + category: AssistanceCategory + urgency: UrgencyLevel + location: GeographicLocation + constraints: RequestConstraints + deadline?: Date + submittedAt: Date + estimatedCost?: number + requiredSkills?: string[] +} + +export interface MatchResult { + resourceId: string + resourceName: string + resourceType: 'clothing' | 'supplies' | 'food' | 'transport' | 'emergency' | 'other' + confidenceScore: number + estimatedImpact: number + logisticalComplexity: number + volunteerMatch?: VolunteerAssignment + estimatedCost: number + fulfillmentTimeline: string + reasoningFactors: string[] + riskFactors: string[] +} + +export interface RequestAnalysis { + primaryNeeds: NeedCategory[] + urgencyScore: number + complexityEstimate: number + resourceRequirements: ResourceRequirement[] + locationConstraints?: GeographicConstraint[] + timeConstraints?: TemporalConstraint[] + requiredSkills?: string[] + estimatedBudget: number +} + +export interface AIInsight { + id: string + type: 'anomaly' | 'optimization' | 'trend' | 'prediction' | 'recommendation' + title: string + description: string + confidence: number + severity?: 'low' | 'medium' | 'high' | 'critical' + timestamp: Date + actionItems?: string[] + estimatedImpact?: string + data?: Record +} + +export interface AIMetrics { + accuracyRate: number + accuracyTrend: number + avgProcessingTime: number + speedTrend: number + autoApprovalRate: number + automationTrend: number + impactPredictionAccuracy: number + impactTrend: number + totalRequestsProcessed: number + successfulMatches: number +} + +export interface AIUpdate { + type: 'request-processed' | 'new-insight' | 'auto-approval' | 'model-updated' | 'alert' + requestId?: string + studentName?: string + status?: string + recommendations?: MatchResult[] + insight?: AIInsight + message?: string + timestamp: Date +} + +export type AssistanceCategory = + | 'clothing' + | 'school-supplies' + | 'food-assistance' + | 'transportation' + | 'emergency-housing' + | 'medical-needs' + | 'technology' + | 'extracurricular' + | 'other' + +export type UrgencyLevel = 'low' | 'medium' | 'high' | 'emergency' + +export interface GeographicLocation { + latitude?: number + longitude?: number + address?: string + city: string + state: string + zipCode: string + schoolDistrict?: string +} + +export interface RequestConstraints { + maxBudget?: number + timeframe: 'immediate' | 'within-week' | 'within-month' | 'flexible' + deliveryMethod: 'pickup' | 'delivery' | 'mail' | 'school-delivery' | 'any' + privacyLevel: 'anonymous' | 'semi-anonymous' | 'open' + specialRequirements?: string[] +} + +export interface VolunteerAssignment { + id: string + volunteerId: string + volunteerName: string + skills: string[] + availability: Date[] + location: GeographicLocation + rating: number + completedAssignments: number +} + +export interface NeedCategory { + category: AssistanceCategory + subcategory?: string + priority: number + quantity?: number + specifications?: Record +} + +export interface ResourceRequirement { + type: string + quantity: number + specifications: Record + alternatives?: string[] + estimatedCost: number +} + +export interface GeographicConstraint { + maxDistance: number + preferredAreas?: string[] + excludedAreas?: string[] +} + +export interface TemporalConstraint { + earliestStart: Date + latestCompletion: Date + preferredTimes?: string[] + blackoutPeriods?: DateRange[] +} + +export interface DateRange { + start: Date + end: Date +} + +export interface ImpactPrediction { + estimatedBeneficiaries: number + successProbability: number + timeToImpact: number + sustainabilityScore: number + rippleEffects: RippleEffect[] + measurableOutcomes: string[] +} + +export interface RippleEffect { + type: 'family' | 'community' | 'academic' | 'social' | 'economic' + description: string + estimatedBeneficiaries: number + confidenceLevel: number +} + +export interface LearningFeedback { + requestId: string + matchId: string + outcome: 'successful' | 'partial' | 'failed' + actualCost: number + actualTimeToComplete: number + satisfactionScore: number + issues?: string[] + improvements?: string[] + measuredImpact?: Record +} + +export interface ProcessingPipelineConfig { + autoApprovalThreshold: number + urgencyWeights: Record + categoryWeights: Record + maxProcessingTime: number + retryAttempts: number + notificationEnabled: boolean +} \ No newline at end of file diff --git a/src/components/AIAssistancePortal.tsx b/src/components/AIAssistancePortal.tsx new file mode 100644 index 0000000..57a07da --- /dev/null +++ b/src/components/AIAssistancePortal.tsx @@ -0,0 +1,721 @@ +// Phase 3: AI Assistance Portal React Components +import React, { useState, useEffect } from 'react' +import { motion, AnimatePresence } from 'framer-motion' +import { formatDistanceToNow } from 'date-fns' +import type { + StudentRequest, + MatchResult, + AIInsight, + AIMetrics, + AIUpdate, + UrgencyLevel, + AssistanceCategory +} from '../ai/types' +import { processingPipeline } from '../ai/ProcessingPipeline' + +// Icons (using the existing icon system) +const Brain = ({ className = "w-5 h-5" }: { className?: string }) => ( + + + +) + +const Cpu = ({ className = "w-4 h-4" }: { className?: string }) => ( + + + + + +) + +const Activity = ({ className = "w-4 h-4" }: { className?: string }) => ( + + + +) + +const TrendingUp = ({ className = "w-8 h-8" }: { className?: string }) => ( + + + + +) + +const Users = ({ className = "w-8 h-8" }: { className?: string }) => ( + + + + + +) + +// Mock data for demonstration +const mockRequests: StudentRequest[] = [ + { + id: 'req-1', + studentId: 'std-1', + studentName: 'Maria Rodriguez', + description: 'Need winter coat and boots for my daughter. Size 8 shoes and medium coat. Getting cold and she only has summer clothes.', + category: 'clothing', + urgency: 'high', + location: { city: 'Austin', state: 'TX', zipCode: '78701' }, + constraints: { timeframe: 'within-week', deliveryMethod: 'school-delivery', privacyLevel: 'semi-anonymous' }, + submittedAt: new Date(Date.now() - 2 * 60 * 60 * 1000), // 2 hours ago + }, + { + id: 'req-2', + studentId: 'std-2', + studentName: 'James Thompson', + description: 'My son needs school supplies - notebooks, pencils, calculator for math class. Starting new semester next week.', + category: 'school-supplies', + urgency: 'medium', + location: { city: 'Round Rock', state: 'TX', zipCode: '78664' }, + constraints: { timeframe: 'within-week', deliveryMethod: 'pickup', privacyLevel: 'open' }, + submittedAt: new Date(Date.now() - 45 * 60 * 1000), // 45 minutes ago + }, + { + id: 'req-3', + studentId: 'std-3', + studentName: 'Sarah Kim', + description: 'Emergency - no food at home for kids this weekend. Need groceries or meal assistance ASAP.', + category: 'food-assistance', + urgency: 'emergency', + location: { city: 'Cedar Park', state: 'TX', zipCode: '78613' }, + constraints: { timeframe: 'immediate', deliveryMethod: 'delivery', privacyLevel: 'anonymous' }, + submittedAt: new Date(Date.now() - 20 * 60 * 1000), // 20 minutes ago + } +] + +interface AIAssistancePortalProps { + userRole: 'student' | 'coordinator' | 'admin' +} + +export function AIAssistancePortal({ userRole }: AIAssistancePortalProps) { + const [requests, setRequests] = useState(mockRequests) + const [aiInsights, setAIInsights] = useState([]) + const [processing, setProcessing] = useState(false) + const [selectedRequest, setSelectedRequest] = useState(null) + + useEffect(() => { + // Subscribe to real-time AI updates + const unsubscribe = processingPipeline.subscribe(handleRealTimeUpdate) + + // Generate initial insights + generateInsights() + + return unsubscribe + }, []) + + const handleRealTimeUpdate = (update: AIUpdate) => { + console.log('🔄 Real-time update received:', update) + + switch (update.type) { + case 'request-processed': + setRequests(prev => prev.map(r => + r.id === update.requestId + ? { ...r, status: update.status, aiRecommendations: update.recommendations } + : r + )) + break + + case 'new-insight': + if (update.insight) { + setAIInsights(prev => [update.insight!, ...prev.slice(0, 4)]) + } + break + + case 'auto-approval': + // Show success notification + console.log('✅ Auto-approval notification:', update.message) + break + } + } + + const generateInsights = async () => { + try { + const insights = await processingPipeline.generateInsights(requests) + setAIInsights(insights) + } catch (error) { + console.error('Error generating insights:', error) + } + } + + const handleApproval = async (requestId: string, recommendation: MatchResult) => { + setProcessing(true) + try { + console.log(`✅ Approving request ${requestId} with recommendation:`, recommendation) + // In production: make API call to approve + + setRequests(prev => prev.map(r => + r.id === requestId ? { ...r, status: 'approved' } : r + )) + + } catch (error) { + console.error('Error approving request:', error) + } finally { + setProcessing(false) + } + } + + const handleModification = async (requestId: string) => { + console.log(`✏️ Modifying request ${requestId}`) + setSelectedRequest(requestId) + } + + const submitNewRequest = async (request: Omit) => { + setProcessing(true) + try { + const newRequest: StudentRequest = { + ...request, + id: `req-${Date.now()}`, + submittedAt: new Date() + } + + // Submit to AI processing pipeline + await processingPipeline.submitRequest(newRequest) + + setRequests(prev => [newRequest, ...prev]) + + } catch (error) { + console.error('Error submitting request:', error) + } finally { + setProcessing(false) + } + } + + return ( +
+ {/* AI Insights Panel */} + +

+ + AI Insights +

+ + + {aiInsights.map((insight) => ( + +
+
+
+

{insight.title}

+

+ {insight.description} +

+ {insight.confidence && ( +
+
+
+
+ + {Math.round(insight.confidence * 100)}% + +
+ )} +
+
+ + ))} + + + {aiInsights.length === 0 && ( +
+ +

No insights available yet

+
+ )} + + + {/* Request Processing Interface */} +
+
+

+ Smart Request Processing +

+ + + {processing ? 'Processing...' : 'Refresh'} + +
+ +
+ {requests.map((request) => ( + + ))} +
+ + {userRole === 'student' && ( + + )} +
+ + {/* Performance Metrics */} +
+ +
+
+ ) +} + +interface RequestCardProps { + request: StudentRequest & { status?: string; aiRecommendations?: MatchResult[] } + onApprove: (requestId: string, recommendation: MatchResult) => void + onModify: (requestId: string) => void + showAIRecommendations: boolean + isSelected: boolean +} + +function RequestCard({ request, onApprove, onModify, showAIRecommendations, isSelected }: RequestCardProps) { + return ( + +
+
+

+ {request.studentName} +

+

+ {request.description} +

+

+ Submitted {formatDistanceToNow(request.submittedAt)} ago +

+
+
+ + + {request.status && } +
+
+ + {showAIRecommendations && request.aiRecommendations && ( + +
+ + + AI Recommendation + + +
+ +
+ {request.aiRecommendations.slice(0, 2).map((rec, index) => ( +
+
+

+ {rec.resourceName} +

+

+ {rec.reasoningFactors[0]} +

+
+
+ + ${rec.estimatedCost} + + + {rec.fulfillmentTimeline} + +
+
+ ))} +
+ +
+ onApprove(request.id, request.aiRecommendations![0])} + className="btn-primary text-xs px-4 py-2 flex items-center gap-2" + whileHover={{ scale: 1.05 }} + whileTap={{ scale: 0.95 }} + > + ✓ Approve AI Recommendation + + +
+ + )} + +
+
+ +
+ +
+ {!showAIRecommendations && ( + + Processing... + + )} +
+
+ + ) +} + +function UrgencyBadge({ urgency }: { urgency: UrgencyLevel }) { + const colors = { + emergency: 'bg-red-100 text-red-800 dark:bg-red-900/30 dark:text-red-300', + high: 'bg-orange-100 text-orange-800 dark:bg-orange-900/30 dark:text-orange-300', + medium: 'bg-yellow-100 text-yellow-800 dark:bg-yellow-900/30 dark:text-yellow-300', + low: 'bg-green-100 text-green-800 dark:bg-green-900/30 dark:text-green-300' + } + + return ( + + {urgency.toUpperCase()} + + ) +} + +function CategoryBadge({ category }: { category: AssistanceCategory }) { + return ( + + {category.replace('-', ' ').toUpperCase()} + + ) +} + +function StatusBadge({ status }: { status: string }) { + const colors = { + 'approved': 'bg-green-100 text-green-800 dark:bg-green-900/30 dark:text-green-300', + 'auto-approved': 'bg-blue-100 text-blue-800 dark:bg-blue-900/30 dark:text-blue-300', + 'under-review': 'bg-yellow-100 text-yellow-800 dark:bg-yellow-900/30 dark:text-yellow-300', + 'pending': 'bg-gray-100 text-gray-800 dark:bg-gray-700 dark:text-gray-300' + } + + return ( + + {status.replace('-', ' ').toUpperCase()} + + ) +} + +function LocationBadge({ location }: { location: string }) { + return ( + + 📍 {location} + + ) +} + +function ConfidenceIndicator({ confidence }: { confidence: number }) { + const getColor = (conf: number) => { + if (conf >= 0.8) return 'text-green-500' + if (conf >= 0.6) return 'text-yellow-500' + return 'text-red-500' + } + + return ( +
+
+
+
+ + {Math.round(confidence * 100)}% + +
+ ) +} + +function AIPerformanceMetrics() { + const [metrics, setMetrics] = useState({ + accuracyRate: 0.87, + accuracyTrend: 2.3, + avgProcessingTime: 1.8, + speedTrend: -0.5, + autoApprovalRate: 0.68, + automationTrend: 5.2, + impactPredictionAccuracy: 0.82, + impactTrend: 1.8, + totalRequestsProcessed: 247, + successfulMatches: 213 + }) + + return ( +
+

+ + AI Performance Dashboard +

+ +
+ } + /> + } + /> + } + /> + } + /> +
+ +
+
+ Total Requests Processed + {metrics.totalRequestsProcessed} +
+
+ Successful Matches + {metrics.successfulMatches} +
+
+
+ ) +} + +interface MetricCardProps { + title: string + value: string + trend: number + color: 'green' | 'blue' | 'purple' | 'orange' + icon: React.ReactNode +} + +function MetricCard({ title, value, trend, color, icon }: MetricCardProps) { + const colorClasses = { + green: 'bg-green-50 border-green-200 dark:bg-green-900/20 dark:border-green-800', + blue: 'bg-blue-50 border-blue-200 dark:bg-blue-900/20 dark:border-blue-800', + purple: 'bg-purple-50 border-purple-200 dark:bg-purple-900/20 dark:border-purple-800', + orange: 'bg-orange-50 border-orange-200 dark:bg-orange-900/20 dark:border-orange-800' + } + + const iconColors = { + green: 'text-green-500', + blue: 'text-blue-500', + purple: 'text-purple-500', + orange: 'text-orange-500' + } + + return ( + +
+
{icon}
+
0 + ? 'bg-green-100 text-green-700 dark:bg-green-900/30 dark:text-green-300' + : 'bg-red-100 text-red-700 dark:bg-red-900/30 dark:text-red-300' + }`}> + {trend > 0 ? '↗' : '↘'} {Math.abs(trend)}% +
+
+
+

{value}

+

{title}

+
+ + ) +} + +function NewRequestForm({ onSubmit }: { onSubmit: (request: Omit) => void }) { + const [formData, setFormData] = useState({ + studentName: '', + description: '', + category: 'clothing' as AssistanceCategory, + urgency: 'medium' as UrgencyLevel, + city: '', + state: 'TX', + zipCode: '' + }) + + const handleSubmit = (e: React.FormEvent) => { + e.preventDefault() + + onSubmit({ + studentId: `std-${Date.now()}`, + studentName: formData.studentName, + description: formData.description, + category: formData.category, + urgency: formData.urgency, + location: { + city: formData.city, + state: formData.state, + zipCode: formData.zipCode + }, + constraints: { + timeframe: 'within-week', + deliveryMethod: 'any', + privacyLevel: 'semi-anonymous' + } + }) + + // Reset form + setFormData({ + studentName: '', + description: '', + category: 'clothing', + urgency: 'medium', + city: '', + state: 'TX', + zipCode: '' + }) + } + + return ( + +

Submit New Request

+ +
+
+ setFormData(prev => ({ ...prev, studentName: e.target.value }))} + className="input-field" + required + /> + + +
+ +