AI Project Template

A comprehensive, production-ready template for AI-based projects with modern development practices, organized structure, and deployment-ready configurations.

⭐ Star this repository if you find it useful!
🔗 Use this template to create your new AI project

🌟 Features

• 🏗️ Organized Structure: Clean separation of concerns with dedicated directories for issues, docs, deployment, and testing
• 🤖 AI/ML Ready: Built-in support for AI/ML workflows and model integration
• 🧪 Comprehensive Testing: Unit, integration, and E2E testing with coverage reporting
• � Deployment Ready: Multi-platform deployment configurations (Docker, Cloud platforms)
• 📚 Rich Documentation: Detailed documentation structure with automated generation
• 🔄 CI/CD Pipelines: GitHub Actions workflows for automated testing and deployment
• 🔒 Security First: Security best practices and automated vulnerability scanning
• 📋 Issue Management: Structured issue tracking with completion documentation
• ⚡ Performance: Optimized for development speed and production performance
• 🛠️ Developer Experience: Modern tooling with TypeScript, ESLint, Prettier

🚀 Quick Start

Option 1: Use as GitHub Template (Recommended)

1. Click "Use this template" on GitHub to create your repository
2. Clone your new repository:

   git clone https://github.com/your-username/your-project-name.git
   cd your-project-name

3. Run the interactive setup:

   ./scripts/setup-template.sh

4. Start developing:

   npm install
   npm run dev

Option 2: Manual Clone

# Clone the template
git clone https://github.com/your-username/ai-project-template.git my-ai-project
cd my-ai-project

# Run setup script
./scripts/setup-template.sh

# Install dependencies  
npm install

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Start development
npm run dev

🛠️ Technology Stack

This template is technology-agnostic and supports multiple stacks:

🎨 Frontend Options

• React + TypeScript + Vite
• Vue.js + TypeScript + Vite
• Angular + TypeScript
• Static Sites (HTML/CSS/JS)

⚡ Backend Options

• Node.js + Express + TypeScript
• Python + FastAPI + Pydantic
• Python + Flask + SQLAlchemy
• Go + Gin + GORM
• Any other backend framework

🤖 AI/ML Integration

• PyTorch - Deep learning framework
• TensorFlow - Machine learning platform
• Hugging Face - Pre-trained models and transformers
• OpenAI API - GPT models and embeddings
• Custom Models - Your own trained models
• MLflow - Experiment tracking and model registry

🚀 Deployment & Infrastructure

• Containers: Docker + Docker Compose
• AWS: ECS, Lambda, EC2, S3
• Azure: Container Apps, Functions, Blob Storage
• GCP: Cloud Run, Functions, Storage
• Edge: Vercel, Netlify, Cloudflare

🗄️ Database Support

• PostgreSQL - Primary database choice
• MongoDB - Document database
• Redis - Caching and sessions
• SQLite - Development and testing

📁 Project Structure

your-ai-project/
├── .github/                    # GitHub configuration
│   ├── workflows/             # CI/CD pipelines
│   ├── ISSUE_TEMPLATE/        # Issue templates
│   └── instructions/          # AI coding instructions
├── issues/                     # Issue tracking and completion docs
│   ├── issue1/               # Individual issue directories
│   │   ├── README.md         # Issue overview
│   │   ├── completion.md     # Implementation docs
│   │   └── handoff.md        # Handoff notes
│   └── issue[N]/             # Additional issues
├── docs/                       # Project documentation
│   ├── ROADMAP.md            # Development roadmap
│   ├── API.md                # API documentation
│   └── ARCHITECTURE.md       # System architecture
├── deployment/                 # Deployment configurations
│   ├── container/            # Docker configurations
│   ├── cloud/                # Cloud platform configs
│   └── local/                # Local development
├── testing/                    # Test files and results
│   ├── unit/                 # Unit tests
│   ├── integration/          # Integration tests
│   ├── e2e/                  # End-to-end tests
│   └── test-results/         # Test outputs and coverage
├── scripts/                    # Development scripts
│   └── setup-template.sh     # Template customization script
├── src/                        # Source code
│   ├── components/           # Reusable components
│   ├── services/             # Business logic
│   ├── utils/                # Utility functions
│   └── types/                # Type definitions
├── assets/                     # Static assets
├── archive/                    # Historical files
├── data/                       # Data files and datasets
├── models/                     # AI/ML models
├── config/                     # Configuration files
└── frontend/                   # Frontend application (if applicable)

🎯 Getting Started

Prerequisites

• Node.js 18+ (for JavaScript/TypeScript projects)
• Python 3.11+ (for Python projects)
• Git for version control
• Docker (optional, for containerized development)

Step 1: Create Your Project

Option A: Use GitHub Template (Recommended)

1. Click "Use this template" button above
2. Create your new repository
3. Clone your repository locally

Option B: Manual Clone

git clone https://github.com/hartou/ai-project-template.git my-ai-project
cd my-ai-project
rm -rf .git && git init

Step 2: Customize Your Project

Run the interactive setup script:

chmod +x scripts/setup-template.sh
./scripts/setup-template.sh

This will prompt you for:

• Project name and description
• Technology stack choice
• Author information
• Deployment preferences

Step 3: Install Dependencies

# Install Node.js dependencies
npm install

# For Python projects, also install Python dependencies
pip install -r requirements.txt  # if using Python

Step 4: Configure Environment

# Copy environment template
cp .env.example .env

# Edit with your configuration
nano .env  # or use your preferred editor

Step 5: Start Development

# Start development server
npm run dev

# Run tests
npm test

# Build for production
npm run build

🧪 Development Workflow

Issue Management

1. Create issue directory: issues/issue[N]/
2. Document requirements in README.md
3. Implement solution
4. Document completion in completion.md
5. Update tests and results

Testing Strategy

• Unit Tests: Test individual components
• Integration Tests: Test component interactions
• E2E Tests: Test complete user workflows
• AI/ML Tests: Test model performance and accuracy

Deployment Pipeline

1. Development: Local development with hot reload
2. Testing: Automated testing on every commit
3. Staging: Deploy to staging environment for review
4. Production: Deploy to production with approval

🤖 AI/ML Integration Examples

Model Integration

// Example: OpenAI API integration
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

export async function generateResponse(prompt) {
  const response = await openai.chat.completions.create({
    model: 'gpt-3.5-turbo',
    messages: [{ role: 'user', content: prompt }],
  });
  return response.choices[0].message.content;
}

# Example: Hugging Face model integration
from transformers import pipeline

class TextClassifier:
    def __init__(self):
        self.classifier = pipeline(
            "text-classification",
            model="distilbert-base-uncased-finetuned-sst-2-english"
        )
    
    def classify(self, text):
        result = self.classifier(text)
        return result[0]

Data Processing Pipeline

# Example: Data processing workflow
import pandas as pd
from sklearn.preprocessing import StandardScaler

class DataProcessor:
    def __init__(self):
        self.scaler = StandardScaler()
    
    def preprocess_data(self, df):
        # Clean and transform data
        cleaned_df = self.clean_data(df)
        scaled_features = self.scaler.fit_transform(cleaned_df)
        return scaled_features

🚀 Deployment Options

Docker Deployment

# Build container
docker build -f deployment/container/Dockerfile -t my-ai-project .

# Run locally
docker run -p 3000:3000 my-ai-project

# Deploy with Docker Compose
docker-compose -f deployment/local/docker-compose.yml up

Cloud Deployment

AWS (ECS)

# Build and push to ECR
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin
docker tag my-ai-project:latest your-account.dkr.ecr.us-east-1.amazonaws.com/my-ai-project:latest
docker push your-account.dkr.ecr.us-east-1.amazonaws.com/my-ai-project:latest

Azure (Container Apps)

# Deploy to Azure Container Apps
az containerapp up --name my-ai-project --source .

Vercel (Frontend)

# Deploy to Vercel
vercel --prod

📚 Documentation

Available Guides

• Development Guide - Setup and development workflow
• API Documentation - REST API reference
• Architecture Guide - System architecture and decisions
• Deployment Guide - Production deployment instructions
• Contributing Guide - How to contribute to the project

Auto-Generated Documentation

# Generate API documentation
npm run docs:generate

# Serve documentation locally
npm run docs:serve

# Check documentation health
npm run docs:lint
npm run docs:link-check

🔒 Security Features

• Environment Variables: Secure configuration management
• Input Validation: Data validation with Joi/Pydantic
• Rate Limiting: API rate limiting protection
• CORS Configuration: Cross-origin resource sharing setup
• Security Headers: HTTP security headers with Helmet
• Dependency Scanning: Automated vulnerability scanning
• Container Security: Security-focused Docker configurations

🤝 Contributing

We welcome contributions to make this template even better! Please see our Contributing Guide for details.

How to Contribute

1. Fork the repository
2. Create a feature branch
3. Make your improvements
4. Add tests for new functionality
5. Submit a pull request

Areas for Contribution

• New integrations: Add support for more AI/ML frameworks
• Deployment options: Add new cloud platform configurations
• Testing improvements: Enhance testing frameworks and coverage
• Documentation: Improve guides and examples
• Bug fixes: Fix issues and improve stability

📈 Roadmap

Current Features ✅

• Organized project structure
• Multi-stack support (Node.js, Python)
• Comprehensive testing framework
• Docker containerization
• CI/CD with GitHub Actions
• Security best practices
• Documentation structure

Planned Features 🚧

• Template Marketplace: Specialized templates for different AI use cases
• CLI Tool: Command-line tool for easier project setup
• VS Code Extension: Integrated development experience
• More Integrations: Support for more AI/ML platforms
• Performance Monitoring: Built-in monitoring and alerting
• Multi-language Support: Java, Go, Rust, C# templates

Future Enhancements 🔮

• Visual Project Builder: GUI for customizing templates
• Cloud IDE Integration: GitHub Codespaces, GitPod templates
• Enterprise Features: Team collaboration and governance tools

🏆 Showcase

Projects built with this template:

• [Your Project Here] - Submit a PR to showcase your project!

Want to feature your project? Open an issue or submit a pull request!

Related Templates

• AI Dev Toolkit Template - Add AI assistance to existing projects
• AI Project Template - Start new AI projects (this repo)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Microsoft AI - For inspiring modern AI development practices
• Vercel - For excellent deployment platform and developer experience
• GitHub - For comprehensive development platform and Actions
• Open Source Community - For amazing tools and frameworks that make this possible

📞 Support

• 📖 Documentation: Check our comprehensive docs first
• 💬 Discussions: Join our GitHub Discussions
• 🐛 Issues: Report bugs or request features via GitHub Issues
• ⭐ Star: Star this repo if you find it useful!

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Happy Building! 🚀 Build amazing AI projects with confidence using this template.