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

{
  "command": "ls -la"
}
```

#### Create Files
```http
POST /api/create
Content-Type: application/json

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

#### Read Files
```http
POST /api/read
Content-Type: application/json

{
  "filename": "hello.py"
}
```

#### List Directories
```http
POST /api/list
Content-Type: application/json

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

### Python Integration Example
```python
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
```javascript
// 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
```bash
# 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
```python
# 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**

---

**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.