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# π Quick Deployment Instructions
## What You've Got
A complete Ubuntu sandbox environment that AI models can use to:
- β
Build applications in any language
- β
Ship containers and deployments
- β
Create innovative projects
- β
Experiment with technologies
- β
Access via web interface and REST API
## Files Created
- `app.py` - Main Gradio web application with terminal interface
- `Dockerfile` - Complete Ubuntu environment with dev tools
- `requirements.txt` - Python dependencies
- `config.yaml` - Environment configuration
- `README.md` - Comprehensive documentation
- `USAGE_GUIDE.md` - Deployment and usage guide
- `test_environment.py` - Environment validation script
## Deploy to HuggingFace Spaces
### Step 1: Create New Space
1. Go to [huggingface.co/spaces](https://huggingface.co/spaces)
2. Click "Create new Space"
3. Choose **Docker** as the SDK (important!)
4. Name: `ubuntu-sandbox` (or your choice)
5. License: MIT (recommended)
6. Hardware: CPU (minimum), GPU (optional)
### Step 2: Upload Files
Upload all the files to your space repository:
```
your-space/
βββ app.py
βββ requirements.txt
βββ Dockerfile
βββ config.yaml
βββ README.md
βββ USAGE_GUIDE.md
βββ test_environment.py
```
### Step 3: Build & Deploy
- HuggingFace will automatically build your space
- Takes 5-10 minutes for first build
- Your space will be available at: `https://username-ubuntu-sandbox.hf.space`
## Key Features Enabled
### π€ For AI Models
- **REST API** for programmatic control
- **Terminal interface** via web UI
- **File management** (create, read, write files)
- **System monitoring** and resource info
- **Command execution** with proper sandboxing
### π οΈ Pre-installed Tools
- **Python 3.x** + scientific libraries
- **Node.js & npm** for JavaScript
- **Docker & Docker Compose**
- **Git** for version control
- **Cloud CLIs** (AWS, GCP, Azure)
- **DevOps tools** (Kubernetes, Terraform)
- **Development languages** (Go, Rust, C/C++)
### π Security & Safety
- **Isolated container** environment
- **Resource limits** prevent abuse
- **No root access** for security
- **30-second command timeouts**
- **Session management**
## API Usage Examples
### Python
```python
import requests
BASE_URL = "https://your-space.hf.space"
# Execute command
response = requests.post(f"{BASE_URL}/api/execute",
json={"command": "python3 --version"})
print(response.json())
# Create file
requests.post(f"{BASE_URL}/api/create",
json={"filename": "test.py", "content": "print('Hello!')"})
# Run file
requests.post(f"{BASE_URL}/api/execute",
json={"command": "python3 test.py"})
```
### JavaScript
```javascript
// Execute command
fetch('/api/execute', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({command: 'ls -la'})
}).then(r => r.json()).then(console.log);
```
## Perfect for AI Agents
This environment enables AI models to:
1. **Build complete applications** from scratch
2. **Test and debug** code in real-time
3. **Deploy containers** and cloud services
4. **Create documentation** and reports
5. **Experiment safely** without risk
6. **Collaborate** on projects
## What Makes This Special
β
**AI-First Design** - Built specifically for AI model interaction
β
**Zero Configuration** - Works out of the box
β
**Comprehensive Tools** - Everything pre-installed
β
**Web + API** - Both human and machine accessible
β
**Secure Sandbox** - Safe for experimentation
β
**Full Ubuntu** - Real Linux environment, not simulated
## Next Steps
1. **Deploy** to HuggingFace Spaces
2. **Test** the environment using the web interface
3. **Integrate** with your AI models via API
4. **Start building** amazing projects!
## Support
- **Documentation**: See README.md for full details
- **Usage Guide**: See USAGE_GUIDE.md for deployment help
- **Testing**: Run `test_environment.py` to validate setup
---
**Ready to give your AI models the power to build, ship, and create anything! π―** |