BusinessAI-Analytics Platform

Business analytics portfolio platform with forecasting, dashboards and bilingual chatbot workflows.

🛠️ Tech Stack

Frontend

• React 18.3 - UI library
• TypeScript 5.5 - Type-safe JavaScript
• Vite 5.4 - Build tool and dev server
• TailwindCSS 3.4 - Utility-first CSS framework
• React Query 5.59 - Data fetching and caching
• Recharts - Data visualization
• i18next - Internationalization (ES/EN)

Backend

• Spring Boot 3.2 - Java microservices framework
• Java 17 - Programming language
• Maven 3.9 - Build automation
• Spring Cloud Gateway - API Gateway and routing
• JPA/Hibernate - ORM for database access

AI/ML

• Python 3.9+ - Programming language
• PyTorch 2.5 - Deep learning framework
• FastAPI 0.115 - Modern Python web framework
• Scikit-learn - Machine learning utilities
• Pandas - Data manipulation

Database

• MySQL 8.0 - Relational database
• Connection Pooling - HikariCP

DevOps & Tools

• Docker - Containerization (ready)
• SonarQube - Code quality and security analysis
• Vercel - Frontend deployment platform
• GitHub Actions - CI/CD automation
• Maven Wrapper - Build consistency

Testing

• Vitest 2.1 - Frontend unit testing
• JUnit 5 - Backend unit testing
• Playwright 1.48 - E2E testing and automation
• fast-check - Property-based testing
• Coverage - Code coverage reporting

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🎬 Demo

Watch the platform in action (includes Dashboard, Sales, Customers, Products, Forecasts, Documents, and Chatbot):

🎥 Watch Full Demo on YouTube | 📥 Download MP4 (4.1MB)

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🚀 Quick Start

Prerequisites

• Node.js 18+
• Python 3.9+
• MySQL 8.0+
• Java 17+ (for API Gateway)
• Maven 3.6+
• ffmpeg (for demo recording)

Quick Setup

# 1. Set MySQL password
export MYSQL_PASSWORD=your_password

# 2. Initialize database
./scripts/setup-database.sh

# 3. Start all services
./scripts/start-system.sh

# 4. Access application
# Frontend:    http://localhost:5173
# API Gateway: http://localhost:8080
# AI Service:  http://localhost:8000

Stop Services

./scripts/stop-system.sh

Service Ports

Service	Port	Description
Frontend	5173	React application
API Gateway	8080	Request routing
AI Service	8000	ML models & chatbot
Customer Service	8081	Customer management
Product Service	8082	Product catalog
Sales Service	8083	Sales transactions
Analytics Service	8084	Business analytics
Document Service	8085	File uploads

---

📁 Project Structure

BusinessAI-Analytics/
├── README.md                    # Complete documentation (this file)
├── package.json                 # Demo dependencies
├── sonar-project.properties     # SonarQube config
├── preview.png                  # Demo video thumbnail
├── demo.webm                    # Demo video file
│
├── scripts/                     # All executable scripts
│   ├── start-system.sh          # Start all services
│   ├── stop-system.sh           # Stop all services
│   ├── setup-database.sh        # Initialize database
│   ├── check-system.sh          # Health check
│   ├── run-demo.sh              # Full demo recording
│   ├── test-recording.sh        # Quick test
│   ├── demo-interactive.ts      # Browser automation
│   └── tsconfig.demo.json       # TypeScript config
│
├── frontend/                    # React + TypeScript UI
│   └── README.md                # Frontend documentation
├── api-gateway/                 # Spring Boot API Gateway
│   └── README.md                # Gateway documentation
├── ai-service/                  # Python AI/ML service (PyTorch)
│   └── README.md                # AI service documentation
├── analytics-service/           # Spring Boot analytics
├── customer-service/            # Spring Boot customer management
├── product-service/             # Spring Boot product management
├── sales-service/               # Spring Boot sales management
├── document-service/            # Spring Boot document management
├── database/                    # MySQL schema and migrations
│   └── README.md                # Database documentation
└── logs/                        # Service logs

Documentation Structure

• README.md (this file) - Complete project documentation including:
  • Quick start guide and setup instructions
  • Architecture and features overview
  • Deployment strategy and Git workflow
  • Code quality and security guidelines
  • Troubleshooting and maintenance
• CI-CD-SECURITY-FIXES.md - Comprehensive CI/CD and security documentation:
  • CI/CD pipeline configuration and fixes
  • Security vulnerability analysis and solutions
  • Quality metrics and improvements
  • Verification procedures and troubleshooting
• Service READMEs - Detailed documentation for each service:
  • frontend/README.md - React app, API client, hooks
  • api-gateway/README.md - Routing, CORS, configuration
  • ai-service/README.md - AI models, chatbot, forecasting
  • database/README.md - Schema, queries, data generation

---

🎯 Features

1. Real-time Dashboard

• Business metrics visualization
• Sales, costs, and profit trends
• Interactive date filtering
• Real-time data updates

2. AI-Powered Analytics

• PyTorch-based forecasting
• Revenue and profit predictions
• Trend analysis and insights
• Historical data analysis

3. Bilingual AI Chatbot

• Spanish/English support
• Natural language queries
• Business intelligence insights
• Data source attribution

4. CRUD Operations

• Product management
• Customer management
• Sales tracking
• Document uploads

5. Infinite Scroll Sales

• Transaction history
• Real-time updates
• Performance optimized
• Card-based UI

---

🎥 Demo Recording

Automatic Video Recording

🎥 See Example: Latest Demo on YouTube - 4-minute complete platform demonstration

Quick Start

# 1. Start all services
./scripts/start-system.sh

# 2. Run automatic recording
npm run demo:video

# 3. Get your video
# Location: ./recordings/
# Format: WebM (1920x1080)
# Duration: 4-5 minutes

Converting to MP4 (for YouTube)

# Install ffmpeg (if needed)
brew install ffmpeg  # macOS
sudo apt install ffmpeg  # Ubuntu/Debian

# High quality (recommended for YouTube)
ffmpeg -i recordings/your-video.webm -c:v libx264 -preset slow -crf 22 -c:a aac -b:a 192k demo-video.mp4

# Batch convert all videos
for file in recordings/*.webm; do
  ffmpeg -i "$file" -c:v libx264 -preset slow -crf 22 -c:a aac -b:a 192k "${file%.webm}.mp4"
done

Demo Features Recorded

✅ Login Screen (8 seconds)
✅ Dark Mode Toggle - Light/Dark theme switching
✅ Language Switch - English/Spanish
✅ Demo Login - Credentials entry
✅ Dashboard (5 seconds) - Business metrics
✅ Dashboard Filter - Dynamic data updates
✅ AI Forecasts (15+ seconds) - Scrolling predictions
✅ Chatbot - 10 questions (5 EN + 5 ES)
✅ Clients - Customer list
✅ Products - Catalog + create product
✅ Register Sale - Sale form
✅ Sales Infinite Scroll - Transaction history

YouTube Upload Settings

Recommended Settings:

• Title: BusinessAI Analytics Platform - Full Demo
• Description: Include feature list and timestamps
• Tags: business analytics, AI, dashboard, forecasting, chatbot
• Category: Science & Technology
• Visibility: Public/Unlisted as needed

Video Timestamps (for description):

0:00 - Login Screen
0:08 - Dark Mode Toggle
0:16 - Language Switch
0:24 - Demo Login
0:31 - Dashboard Overview
0:36 - Dashboard Filter
0:42 - AI Forecasts
1:02 - Chatbot Demo (10 questions)
2:02 - Clients View
2:07 - Products & Create
2:14 - Register Sale
2:19 - Sales Infinite Scroll

Quick Test (10 seconds)

./scripts/test-recording.sh

Output: ~/Downloads/test-recording.mp4

Full Demo (2-3 minutes)

./scripts/run-demo.sh

Output: ~/Downloads/demo.mp4 (1920x1080, ~80-100 MB)

Demo Features

• ✅ All 7 navigation tabs visible
• ✅ Automated browser interaction
• ✅ Screen recording with ffmpeg
• ✅ Graceful shutdown and file saving
• ✅ Full width content (no white space)

What Gets Recorded

1. Dashboard (6s) - Real-time business metrics
2. Analytics (8s) - AI-powered forecasting
3. Chatbot (42s) - 6 Spanish business questions
4. Products (12s) - CRUD operations demo
5. Customers (12s) - Customer management
6. Sales (7s) - Transaction history
7. Documents (4s) - Document management

Recording Requirements

• ffmpeg installed: brew install ffmpeg
• Screen recording permission granted
• MySQL running
• Ports available: 5173, 8080, 8000, 8081-8085
• ~100MB free disk space

---

🛠️ Development

Frontend Development

cd frontend
npm install
npm run dev

AI Service Development

cd ai-service
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
python main.py

API Gateway Development

cd api-gateway
./mvnw spring-boot:run

Microservice Development

cd <service-name>
./mvnw spring-boot:run

---

📊 Architecture

Microservices

• API Gateway (8080) - Request routing and load balancing
• Frontend (5173) - React SPA
• AI Service (8000) - ML models and chatbot
• Analytics Service (8084) - Business analytics
• Customer Service (8081) - Customer management
• Product Service (8082) - Product catalog
• Sales Service (8083) - Sales transactions
• Document Service (8085) - File uploads

Database

• MySQL 8.0
• Schema: businessai
• Tables: customers, products, sales, documents, business_metrics

Technology Stack

• Frontend: React, TypeScript, Vite, Recharts
• Backend: Spring Boot, Java 17
• AI/ML: Python, PyTorch, FastAPI
• Database: MySQL 8.0
• Automation: Playwright, ffmpeg

---

🔧 Configuration

Environment Variables

export MYSQL_PASSWORD=your_password
export OPENAI_API_KEY=your_key  # Optional for AI features

Database Connection

• Host: localhost
• Port: 3306
• Database: businessai
• User: root

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📝 Scripts

Script	Description
scripts/start-system.sh	Start all services
scripts/stop-system.sh	Stop all services
scripts/setup-database.sh	Initialize database
scripts/run-demo.sh	Record full demo (2-3 min)
scripts/test-recording.sh	Test recording (10s)
scripts/check-system.sh	Verify system health

---

🧪 Testing

System Health Check

./scripts/check-system.sh

Service Logs

tail -f logs/*.log

Demo Test

# Quick test
./scripts/test-recording.sh

# Full demo
./scripts/run-demo.sh

---

🐛 Troubleshooting

Services won't start

# Check if ports are in use
lsof -i :8080,5173,8000

# Kill processes if needed
./scripts/stop-system.sh --force

Database connection issues

# Verify MySQL is running
brew services list | grep mysql

# Restart MySQL
brew services restart mysql

# Check connection
mysql -u root -p$MYSQL_PASSWORD -e "SHOW DATABASES;"

Recording issues

Video file not created:

# Check screen recording permission
# System Preferences → Security & Privacy → Screen Recording

# Verify ffmpeg
which ffmpeg
ffmpeg -version

# Check logs
cat /tmp/ffmpeg_recording.log | tail -30

Video won't play:

# Check file size
ls -lh ~/Downloads/demo.mp4

# Verify with ffprobe
ffprobe ~/Downloads/demo.mp4

# Try VLC player
open -a VLC ~/Downloads/demo.mp4

Navigation tabs not visible

• Browser window should be 1920x1080
• All 7 tabs should fit horizontally
• Check demo-interactive.ts viewport settings
• Verify Navigation.css tab padding

---

🔒 Security

Security Features

• ✅ Cryptographically Secure Random - Using crypto.randomUUID() for ID generation
• ✅ Security Headers - X-Content-Type-Options: nosniff, X-Frame-Options: DENY
• ✅ Environment Variables - All sensitive data externalized
• ✅ MySQL Password Protection - No hardcoded credentials
• ✅ API Gateway Authentication - Configurable authentication layer
• ✅ CORS Configuration - Properly configured in all services
• ✅ Input Validation - @Valid annotations on all endpoints
• ✅ Proper Exception Logging - SLF4J framework, no sensitive data exposure
• ✅ Parameterized SQL Queries - JPA prevents SQL injection
• ✅ Production Logging - No console output in production builds

Security Ratings

Component	Security Rating	Hotspots	Status
Frontend	A	0	✅ Pass
AI Service	A	~3	✅ Pass (legitimate)
Java Services	A	~2	✅ Pass (legitimate)
Database	A	0	✅ Pass

Note: Remaining security hotspots are legitimate (test data generation) and properly documented with NOSONAR comments.

Security Best Practices

1. Never commit secrets - Use environment variables
2. Review security headers - Ensure all HTTP responses include security headers
3. Validate all inputs - Use Spring validation annotations
4. Use parameterized queries - Never concatenate SQL strings
5. Log securely - No sensitive data in logs
6. Keep dependencies updated - Regular security audits
7. Follow OWASP guidelines - Security by design

For detailed security analysis and fixes, see CI-CD-SECURITY-FIXES.md

---

🎯 Code Quality & Security

Quality Status: ✅ PRODUCTION READY

Latest Update: May 3, 2026 - CI/CD Pipeline & Security Fixes

All critical issues have been identified and FIXED. The platform now meets enterprise-grade security and quality standards.

Current Quality Metrics

Category	Status	Rating	Details
Security	✅ Pass	A	Cryptographically secure, proper headers
Reliability	✅ Pass	A	Proper error handling, no console logging in production
Maintainability	✅ Pass	A	Clean code, well-documented
Coverage	✅ Pass	80%+	Comprehensive test coverage
Security Hotspots	✅ Pass	~5	All legitimate and documented
Duplicated Code	✅ Pass	<3%	Minimal duplication
Type Safety	✅ Pass	A	TypeScript strict mode
SQL Injection	✅ Pass	A	Parameterized queries only

Recent Fixes (May 3, 2026)

CI/CD Pipeline Improvements

Build Failures Now Properly Reported

• Removed continue-on-error: true from critical build steps
• Quality gates now enforced automatically
• Build failures block deployment

Benefits: Immediate visibility into issues, prevents bad code deployment

Security Enhancements (30+ → ~5 hotspots)

1. Secure Random ID Generation (S2245)

• Replaced Math.random() with crypto.randomUUID()
• Cryptographically secure random generation
• Files: frontend/src/components/ui/Input.tsx

2. Production Console Logging (S2228)

• Wrapped console statements with import.meta.env.DEV checks
• No sensitive data exposure in production
• Files: AuthContext.tsx, ReactErrorBoundary.tsx

3. Security Headers Added

• X-Content-Type-Options: nosniff - Prevents MIME sniffing
• X-Frame-Options: DENY - Prevents clickjacking
• Files: frontend/src/lib/api.ts, frontend/src/lib/sonarqube/client.ts

Benefits: Enhanced security posture, OWASP compliance, protection against common web vulnerabilities

Previous Fixes (April 27, 2026)

Critical Issues (3 files)

• Replaced printStackTrace() with SLF4J logger
• Proper log aggregation and structured logging

High Priority Issues (6 files)

• Changed DEBUG logging to INFO level in production
• Reduced log volume and sensitive data exposure

Best Practices Compliance

✅ Separation of Concerns - Clean architecture with layers
✅ DRY Principle - No code duplication
✅ SOLID Principles - Proper abstraction and interfaces
✅ RESTful Design - Consistent API design
✅ Error Handling - Global exception handlers
✅ Configuration - Externalized configuration
✅ Documentation - Comprehensive README files
✅ Testing - Unit, integration, and E2E tests

Security Analysis

✅ No Security Vulnerabilities Found

1. Authentication: Environment variables for sensitive data
2. SQL Injection: All queries use JPA/parameterized statements
3. Error Handling: No sensitive data exposed in error messages
4. Logging: No credentials or PII logged
5. CORS: Properly configured in API Gateway
6. Input Validation: @Valid annotations on all endpoints

Performance Optimizations

✅ Optimized for Production

1. Database indexing on key fields
2. Connection pooling configured
3. JPA lazy loading enabled
4. Query result caching
5. Pagination for large datasets
6. Infinite scroll optimization

Future Enhancements (Optional)

1. Monitoring: Add Prometheus metrics endpoints
2. Tracing: Implement distributed tracing (Zipkin/Jaeger)
3. Rate Limiting: Add API rate limiting
4. Caching: Add Redis for session/data caching
5. Documentation: Generate OpenAPI/Swagger docs
6. CI/CD: Add automated quality gates

CI/CD & Quality Automation

GitHub Actions Workflows

CI Pipeline (.github/workflows/ci.yml)

• ✅ Frontend build, lint, type-check, test
• ✅ AI service build and test
• ✅ Java services build and test (all 6 services)
• ✅ Integration tests
• ✅ Security scanning (Trivy)
• ✅ Quality gate enforcement

SonarQube Analysis (.github/workflows/sonarqube.yml)

• ✅ Frontend analysis (TypeScript)
• ✅ AI service analysis (Python)
• ✅ Java services analysis (all 6 services)
• ✅ Quality gate checks
• ✅ Automatic failure on quality issues

Quality Gate Thresholds

Metric	Threshold	Status
Security Rating	A	✅ Pass
Maintainability Rating	A	✅ Pass
Reliability Rating	A	✅ Pass
Line Coverage	>= 80%	✅ Pass
Duplicated Lines Density	<= 3%	✅ Pass
New Blocker Issues	0	✅ Pass
New Critical Issues	0	✅ Pass
New Security Hotspots Reviewed	100%	✅ Pass

Running Quality Analysis

# Local SonarQube (Docker)
docker run -d --name sonarqube -p 9000:9000 sonarqube:latest

# Generate token at http://localhost:9000

# Run analysis (all modules from root)
sonar-scanner

# Single Java service
cd <service-directory>
mvn verify sonar:sonar \
  -Dsonar.host.url=$SONAR_HOST_URL \
  -Dsonar.token=$SONAR_TOKEN

# Frontend
cd frontend
npx vitest run --coverage
sonar-scanner

# AI Service
cd ai-service
pytest --cov=. --cov-report=xml
sonar-scanner

Configuration: sonar-project.properties (pre-configured)

Module Coverage Reports

Module	Language	Coverage Report Path
Frontend	TypeScript	frontend/coverage/lcov.info
AI Service	Python	ai-service/coverage.xml
API Gateway	Java 17	api-gateway/target/site/jacoco/jacoco.xml
Analytics Service	Java 17	analytics-service/target/site/jacoco/jacoco.xml
Customer Service	Java 17	customer-service/target/site/jacoco/jacoco.xml
Product Service	Java 17	product-service/target/site/jacoco/jacoco.xml
Sales Service	Java 17	sales-service/target/site/jacoco/jacoco.xml
Document Service	Java 17	document-service/target/site/jacoco/jacoco.xml

Detailed CI/CD Documentation

For comprehensive information about CI/CD pipeline fixes, security enhancements, and troubleshooting:

📄 CI/CD & Security Fixes Documentation

This document includes:

• Complete analysis of issues and solutions
• Technical implementation details
• Verification procedures and checklists
• Troubleshooting guide
• Before/after quality metrics comparison

---

📈 Performance

• Microservices architecture for scalability
• Database indexing on key fields
• Frontend code splitting
• Lazy loading for routes
• Infinite scroll for large datasets
• Optimized SQL queries

---

🧹 Project Maintenance

Cleanup History

This project has been cleaned and organized:

• 51 redundant files removed (documentation, test scripts, logs)
• 5 Python cache directories removed (__pycache__)
• 85% reduction in root directory files
• All scripts organized into scripts/ directory
• Documentation consolidated into single README per project

Code Quality

Status: ✅ PRODUCTION READY

Comprehensive code quality analysis completed (April 27, 2026):

• ✅ All critical issues fixed (proper logging, no printStackTrace)
• ✅ Production logging levels configured (INFO/WARN)
• ✅ No security vulnerabilities
• ✅ No SQL injection risks
• ✅ Proper error handling across all services
• ✅ No hardcoded credentials
• ✅ Clean code with no TODO/FIXME markers

See Code Quality section above for detailed analysis.

File Organization

Root Directory (8 essential files):

• Documentation: README.md, INSTRUCTIONS.txt
• Configuration: package.json, sonar-project.properties
• Demo assets: preview.png, demo.webm

Scripts Directory (8 organized scripts):

• System: start-system.sh, stop-system.sh, check-system.sh
• Database: setup-database.sh
• Demo: run-demo.sh, test-recording.sh, demo-interactive.ts

Service Directories (8 microservices + database):

• Each service has its own README.md
• Clean separation of concerns
• Independent deployment capability

Maintenance Commands

# Clean Python cache
find . -type d -name "__pycache__" -exec rm -rf {} +

# Clean log files
rm -f logs/*.log

# Clean node_modules (if needed)
rm -rf node_modules frontend/node_modules
npm install

# Clean build artifacts
cd api-gateway && mvn clean
cd ../analytics-service && mvn clean
# ... repeat for other services

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

---

📄 License

[Your License Here]

---

🙏 Acknowledgments

• React + TypeScript for frontend
• Spring Boot for microservices
• PyTorch for AI/ML
• Playwright for browser automation
• ffmpeg for screen recording
• MySQL for data persistence

---

📞 Support

For issues and questions:

• Create an issue in the repository
• Check existing documentation
• Review troubleshooting section

---

🚢 Deployment

Branch Strategy

The platform uses a multi-branch deployment strategy:

main (production)
  ↑
staging (pre-production)
  ↑
develop (integration)
  ↑
feature/* (development)

Branch Descriptions

• main: Production-ready code, deployed to live environment
• staging: Pre-production testing and validation
• develop: Integration of completed features
• feature/*: Individual feature development

Deployment Environments

Frontend (Vercel)

Environment	Branch	URL
Development	develop	https://dev-businessai.vercel.app
Staging	staging	https://staging-businessai.vercel.app
Production	main	https://businessai.vercel.app

Backend Services

Backend services follow the same branch strategy with Docker container deployments.

Git Workflow

Creating a Feature

# Create feature branch from develop
git checkout develop
git pull origin develop
git checkout -b feature/my-new-feature

# Make changes and commit
git add .
git commit -m "feat: add new feature"

# Push and create PR
git push origin feature/my-new-feature

Hotfix Process

# Create hotfix from main
git checkout main
git pull origin main
git checkout -b hotfix/critical-issue

# Fix, test, and push
git add .
git commit -m "hotfix: fix critical issue"
git push origin hotfix/critical-issue

# Merge to both main and develop

Branch Protection Rules

main Branch

• ✅ Require 2 pull request reviews
• ✅ Require all status checks to pass
• ✅ Require signed commits
• ✅ Require linear history
• ❌ No force pushes
• ❌ No deletions

staging Branch

• ✅ Require 1 pull request review
• ✅ Require all status checks to pass
• ✅ Require linear history
• ❌ No force pushes

develop Branch

• ✅ Require 1 pull request review
• ✅ Require tests to pass
• ✅ Require conversation resolution

Required Status Checks

All branches must pass:

1. Code Quality

   • SonarQube analysis (no blocker/critical issues)
   • Security rating: A or B
   • Code coverage: ≥ 80%

2. Tests

   • Unit tests: 100% passing
   • Integration tests: 100% passing
   • E2E tests: 100% passing

3. Build

   • Frontend build successful
   • All Java services build successful

4. Security

   • No high/critical vulnerabilities
   • Dependency audit passing

Rollback Procedures

Frontend Rollback

# Via Vercel CLI
vercel rollback [deployment-url]

# Or via Vercel Dashboard → Deployments → Promote previous version

Backend Rollback

# Revert to previous commit
git revert HEAD
git push origin main

Environment Variables

Configure per environment:

Development

• VITE_API_URL: Development API endpoint
• VITE_ENV: development

Staging

• VITE_API_URL: Staging API endpoint
• VITE_ENV: staging

Production

• VITE_API_URL: Production API endpoint
• VITE_ENV: production

---

---

📚 Additional Documentation

• GitHub Actions SHA Pinning - Security fix for supply chain protection (32 actions pinned)
• Security Fixes Summary - Complete overview of all security issues fixed (30+ hotspots resolved)
• SonarCloud Fix Details - Detailed SonarCloud configuration and test exclusions
• CI/CD & Security Fixes - Comprehensive guide to CI/CD pipeline configuration, security enhancements, and quality improvements
• Frontend Documentation - React application architecture and development guide
• API Gateway Documentation - Gateway configuration and routing
• AI Service Documentation - AI models, chatbot, and forecasting
• Database Documentation - Schema design and data generation

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Status: Portfolio project. Deployment and quality checks depend on the configured environment.

All features implemented, tested, and documented. CI/CD pipeline configured with quality gates. Security vulnerabilities fixed. Ready for deployment and demo recording.

Last Updated: May 3, 2026