README.md

metadata

title: 'Sheikh-Kitty: Secure Autonomous Coding AI'
sdk: gradio
sdk_version: 3.35.0
app_port: 7860
tags:
  - coding-ai
  - code-generation
  - security
  - sandbox
  - python
  - javascript
  - typescript
  - solidity
  - gradio
  - machine-learning
  - ai
  - secure-coding

🚀 Sheikh-Kitty: Secure Autonomous Coding AI

Live Demo: https://huggingface.co/spaces/likhonsheikh/sheikh-kitty

Sheikh-Kitty is a production-ready, secure autonomous coding AI that generates, analyzes, and executes code across multiple programming languages with built-in security verification and sandboxed execution.

🌟 Features

Core Capabilities

• Multi-Language Support: Python, JavaScript, TypeScript, Solidity
• Secure Code Generation: AI-powered code generation with security-first approach
• Real-time Security Analysis: Static analysis, threat detection, vulnerability scanning
• Sandboxed Execution: Safe code execution in isolated containers
• Offline Operation: No external API dependencies, fully self-contained

Security Features

• Threat Detection: Identifies malicious patterns, unsafe operations
• Static Code Analysis: Comprehensive security scanning before execution
• Sandboxed Runtime: Isolated execution environment with resource limits
• Input Validation: Strict input sanitization and validation
• Security Scoring: Automated security assessment with detailed reports

Performance

• Sub-2s Response: Fast code generation and analysis
• Memory Efficient: Optimized for resource-constrained environments
• Offline Ready: No internet connectivity required for operation
• Scalable Architecture: Modular design for easy deployment

🚀 Quick Start

Space Interface

Visit the live space: https://huggingface.co/spaces/likhonsheikh/sheikh-kitty

Local Development

# Clone the space repository
git clone https://huggingface.co/spaces/likhonsheikh/sheikh-kitty
cd sheikh-kitty

# Install dependencies
pip install -r requirements.txt

# Run locally
python app.py

📖 Usage Guide

1. Generate Code

1. Enter your code requirements in the prompt box
2. Select the target programming language
3. Adjust max tokens if needed (128-1024)
4. Click "Generate Code" 🚀
5. Review generated code, security analysis, and execution results

2. Debug Existing Code

1. Paste your code in the "Debug Code" tab
2. Select the programming language
3. Click "Analyze Code" 🔍
4. Review security analysis, static analysis, and execution results

3. Monitor System Performance

1. Check the "System Metrics" tab
2. View real-time performance data
3. Monitor API usage and execution statistics

🛠️ Technical Architecture

Core Components

Model Layer

• Tokenizer: Fixed tokenizer with corruption-resistant decode
• Model: Production-ready generation model with security awareness
• Verifier: Multi-layer security analysis and threat detection

Execution Layer

• Sandbox: Docker-based isolated execution environment
• API: RESTful endpoints for code generation and analysis
• Monitoring: Real-time metrics and performance tracking

Interface Layer

• Gradio UI: Intuitive web interface for code generation
• API Endpoints: RESTful API for programmatic access
• Dashboard: System monitoring and analytics

Security Architecture

User Input → Security Validation → Code Generation → Security Analysis → Sandboxed Execution → Results
     ↓              ↓                    ↓               ↓                  ↓              ↓
  Sanitization → Threat Detection → Pattern Analysis → Vulnerability Scan → Container Isolation → Output Filtering

🔧 API Reference

Endpoints

/generate - Code Generation

POST /generate
Content-Type: application/json

{
  "prompt": "Create a fibonacci function",
  "language": "python",
  "max_tokens": 512,
  "temperature": 0.7,
  "security_level": "strict"
}

/debug - Code Analysis

POST /debug
Content-Type: application/json

{
  "code": "def hello(): print('world')",
  "language": "python"
}

/health - System Health

GET /health

/metrics - Performance Metrics

GET /metrics

Response Format

{
  "success": true,
  "code": "generated code here",
  "language": "python",
  "security_score": 0.95,
  "execution_time": 1.23,
  "metadata": {
    "security_analysis": {...},
    "execution_result": {...}
  }
}

📊 Supported Languages

Language	Generation	Security Analysis	Sandbox Execution	Best For
Python	✅	✅	✅	Backend, Data Science, Automation
JavaScript	✅	✅	✅	Frontend, Node.js, Web Apps
TypeScript	✅	✅	✅	Large-scale Frontend, Type Safety
Solidity	✅	✅	✅	Smart Contracts, Blockchain

🛡️ Security Details

Security Measures

• Input Sanitization: All inputs are validated and sanitized
• Pattern Matching: Identifies known malicious patterns
• Static Analysis: Deep code analysis for security issues
• Threat Detection: Real-time identification of security threats
• Sandboxed Execution: Isolated runtime environment
• Resource Limits: CPU, memory, and time constraints

Security Scoring

• 0.9-1.0: Excellent security, ready for production
• 0.7-0.9: Good security, minor issues detected
• 0.5-0.7: Moderate security, review recommended
• 0.0-0.5: Poor security, execution blocked

Blocked Operations

• File system operations (unless whitelisted)
• Network requests (internet access disabled)
• System command execution
• Memory allocation over limits
• CPU time over limits

📈 Performance Metrics

Target Performance

• Response Time: < 2 seconds average
• Memory Usage: < 1GB per request
• CPU Usage: < 50% per execution
• Success Rate: > 90% for standard prompts
• Security Detection: > 95% accuracy

Monitoring

• Real-time API request tracking
• Security scan statistics
• Sandbox execution metrics
• Performance benchmarking
• Error rate monitoring

🔧 Development

Project Structure

sheikh-kitty/
├── app.py                  # Main Gradio application
├── model/                  # AI model components
│   ├── model_interfaces.py # Core model interfaces
│   └── checkpoints/        # Model weights
├── api/                    # REST API endpoints
├── sandbox/                # Docker sandbox execution
├── monitoring/             # Metrics and monitoring
├── datasets/               # Training and test data
└── logs/                   # Application logs

Testing

# Run integration tests
python integration_verification.py

# Run unit tests
pytest tests/

# Performance benchmarking
python benchmarks/

Deployment

# Build and deploy to Hugging Face Spaces
git add .
git commit -m "Deploy Sheikh-Kitty Space v1.0"
git push origin main

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

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

📄 License

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

🙏 Acknowledgments

• MiniMax Agent: Core AI development
• Hugging Face: Spaces platform and infrastructure
• Open Source Community: Various libraries and frameworks

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@likhonsheikh.com

🗺️ Roadmap

Version 1.1 (Coming Soon)

• Additional language support (Go, Rust, Java)
• Advanced debugging features
• Code optimization suggestions
• Performance improvements

Version 1.2 (Planned)

• Collaborative coding features
• Git integration
• Advanced security policies
• Custom model training

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Made with ❤️ by MiniMax Agent

Sheikh-Kitty - Where Security Meets Intelligence