README.md

metadata

title: Ubuntu Sandbox Environment
emoji: 🖥️
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860

🖥️ Ubuntu Sandbox Environment

A comprehensive, AI-accessible Ubuntu development environment hosted on HuggingFace Spaces. Perfect for AI models to build, ship, and create anything!

🎯 What This Space Provides

This environment enables AI models to:

• 🔨 Build applications and software in any language
• 🚀 Ship containers, deployments, and applications
• ✨ Create innovative solutions and projects
• 🧪 Experiment with new technologies safely
• 📚 Learn through hands-on development work

🌟 Key Features

For AI Models

• Web-based API for programmatic access
• Full Ubuntu command support via terminal interface
• Secure sandboxed environment with proper isolation
• Persistent file system for project work
• Development tools pre-installed (Docker, Git, Python, Node.js, etc.)

For Humans

• Intuitive web interface with real-time terminal
• File management system for creating and editing files
• System monitoring dashboard
• Command history and session management
• Quick action buttons for common operations

🛠️ Pre-installed Development Environment

Core Tools

• Python 3.x with scientific computing (numpy, pandas, matplotlib, etc.)
• Node.js & npm for JavaScript development
• Docker & Docker Compose for containerization
• Git for version control
• System utilities (curl, wget, jq, tree, htop, etc.)

Cloud & DevOps Tools

• Kubernetes & Helm for orchestration
• Terraform for infrastructure as code
• AWS CLI, Google Cloud SDK, Azure CLI for cloud services
• CI/CD tools and monitoring utilities

Programming Languages

• Python (with pip, setuptools, wheel)
• JavaScript/Node.js
• Go with common tools (goimports, golangci-lint)
• Rust with Cargo
• C/C++ with build tools (gcc, g++, make, cmake)

🤖 AI Model Integration

API Endpoints

The environment provides REST API endpoints for seamless AI model integration:

Execute Commands

POST /api/execute
Content-Type: application/json

{
  "command": "ls -la"
}

Create Files

POST /api/create
Content-Type: application/json

{
  "filename": "hello.py",
  "content": "print('Hello from AI!')"
}

Read Files

POST /api/read
Content-Type: application/json

{
  "filename": "hello.py"
}

List Directories

POST /api/list
Content-Type: application/json

{
  "path": "/home/user/workspace"
}

Python Integration Example

import requests

# Execute a command
response = requests.post("https://your-space.hf.space/api/execute", 
                        json={"command": "python3 --version"})
print(response.json())

# Create and run a Python script
requests.post("https://your-space.hf.space/api/create",
              json={
                  "filename": "ai_test.py", 
                  "content": "print('AI created this!')"
              })

result = requests.post("https://your-space.hf.space/api/execute",
                      json={"command": "python3 ai_test.py"})
print(result.json())

JavaScript Integration Example

// Execute command
fetch('/api/execute', {
    method: 'POST',
    headers: {'Content-Type': 'application/json'},
    body: JSON.stringify({command: 'node --version'})
})
.then(response => response.json())
.then(data => console.log(data));

// Create a Node.js app
fetch('/api/create', {
    method: 'POST',
    headers: {'Content-Type': 'application/json'},
    body: JSON.stringify({
        filename: 'app.js',
        content: 'console.log("Hello from AI!");'
    })
});

🎮 How to Use

1. Web Interface

• Terminal Tab: Type commands and see real-time output
• File Manager: Create, edit, and read files through the UI
• System Info: Monitor system resources and environment
• Quick Commands: One-click access to common operations

2. Programmatic Access

• Use the REST API endpoints for automated interaction
• Perfect for AI models to control the environment
• Supports all Ubuntu commands and operations

3. Development Workflow

# Check what's available
system_info
ls -la

# Create a new project
mkdir my_ai_project
cd my_ai_project
echo "# My AI Project" > README.md

# Install dependencies and start coding
pip3 install requests flask
vim main.py

🏗️ Common Use Cases

1. AI Software Development

• Create applications in any programming language
• Run tests and debugging
• Build containers and deploy applications
• Manage version control with Git

2. Data Science & ML

• Analyze datasets and create visualizations
• Train machine learning models
• Generate reports and documentation
• Experiment with new algorithms

3. Web Development

• Build full-stack applications
• Test APIs and web services
• Deploy to cloud platforms
• Monitor application performance

4. DevOps & Infrastructure

• Create infrastructure as code
• Set up container orchestration
• Build CI/CD pipelines
• Configure monitoring and logging

5. Research & Education

• Academic research projects
• Algorithm development and testing
• Documentation and tutorial creation
• Learning new technologies

🔒 Security & Safety

Built-in Protections

• Isolated container environment
• Resource limits to prevent abuse
• No root access for security
• Session management and monitoring
• Command timeouts (30 seconds)

Usage Guidelines

• Perfect for experimentation and learning
• Safe environment for AI model testing
• No risk to the underlying system
• Files persist during session

📁 Workspace Structure

/home/user/workspace/
├── projects/          # Your development projects
├── data/             # Data files and datasets  
├── temp/             # Temporary files
├── logs/             # Application logs
├── tools/            # Additional tools and utilities
└── scripts/          # Custom scripts and automation

🚀 Advanced Features

Pre-configured Development Environment

• Auto-installed tools on first run
• Optimized for AI interaction
• Built-in help and documentation
• Command history and session management

System Monitoring

• Real-time resource usage
• Process monitoring
• Network status
• Storage information

Integration Ready

• RESTful API for programmatic access
• WebSocket support for real-time communication
• JSON-based request/response format
• Error handling and logging

💡 Tips for AI Models

Best Practices

1. Start with system checks: Use system_info to understand the environment
2. Use the API: Programmatic access is more reliable than web scraping
3. Monitor resources: Keep track of memory and CPU usage
4. Clean up: Remove temporary files when done
5. Document work: Use file system for persistent project state

Example Workflow

# 1. Check environment
requests.post("/api/execute", json={"command": "system_info"})

# 2. Create project structure  
requests.post("/api/create", json={"filename": "README.md", "content": "# My AI Project"})

# 3. Install dependencies
requests.post("/api/execute", json={"command": "pip3 install numpy pandas"})

# 4. Write code
requests.post("/api/create", json={
    "filename": "main.py", 
    "content": "import pandas as pd\\nprint('AI is working!')"
})

# 5. Execute and test
requests.post("/api/execute", json={"command": "python3 main.py"})

🆘 Getting Help

• Built-in help: Use the help command in the terminal
• System info: Run system_info to see environment details
• Command history: Use history to see previous commands
• File exploration: Use ls, tree, or the file manager UI

🎯 Perfect For

✅ AI Research and Development
✅ Automated Testing and Validation
✅ Educational Demonstrations
✅ Proof of Concept Development
✅ Rapid Prototyping
✅ Learning and Experimentation
✅ CI/CD Pipeline Integration
✅ Multi-language Development

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Ready to enable your AI agents to build, ship, and create anything! 🚀

This environment is specifically designed to be the perfect development playground for AI models, providing all the tools and capabilities needed for comprehensive software development, deployment, and innovation.