# 🚀 LLM-Powered URL Health Checker Generator

[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://python.org)
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[![Jupyter](https://img.shields.io/badge/Jupyter-Notebook-orange.svg)](https://jupyter.org)

🤖 **AI-Powered Code Generation** | 🌐 **URL Health Monitoring** | 📊 **DevOps Tooling** | ⚡ **Automated CLI Tools**

> **Transform ideas into code with ANY LLM API!** This project showcases how to harness various Large Language Model APIs (OpenAI, Anthropic Claude, Google Gemini, Azure, local models, and more) to automatically generate production-ready Python CLI tools. Watch as AI creates a sophisticated URL health monitoring system that rivals hand-coded solutions.

## 🎯 What This Project Does

This project demonstrates cutting-edge AI-assisted development by:
- 🧠 **Intelligently generating** complete Python applications using any LLM API
- 🔍 **Creating a professional URL health checker** with advanced monitoring capabilities
- 📈 **Showcasing practical AI applications** in DevOps and system administration
- 🛠️ **Providing a universal framework** for AI-powered code generation across multiple providers

## 🌟 Supported LLM Providers

| Provider | Models | Configuration |
|----------|---------|--------------|
| **OpenAI** | GPT-4, GPT-3.5-Turbo | Standard OpenAI API |
| **Anthropic** | Claude 3 Opus, Sonnet, Haiku | Claude API |
| **Google** | Gemini Pro, Gemini Ultra | Google AI Studio |
| **Azure OpenAI** | GPT-4, GPT-3.5 | Azure OpenAI Service |
| **Cohere** | Command, Generate | Cohere API |
| **Hugging Face** | Various open models | Inference API |
| **Local Models** | LLaMA, Mistral, etc. | Ollama, LM Studio |
| **Custom APIs** | Any OpenAI-compatible | Custom endpoints |

## 🚀 Features

### Core Framework
- **Universal LLM Support**: Works with any OpenAI-compatible API endpoint
- **Provider Flexibility**: Easy switching between different LLM providers
- **Smart Configuration Management**: Secure environment variable handling with validation
- **AI-Powered Code Generation**: Uses your choice of LLM to generate complete, functional Python scripts
- **Modular Design**: Clean, reusable configuration class for API integration
- **Error Handling**: Robust validation and error management across providers

### Generated URL Health Checker
- **Batch URL Testing**: Process multiple URLs from input files
- **Performance Monitoring**: Measure and report response times
- **Status Code Tracking**: Monitor HTTP response codes
- **Configurable Parameters**: Customizable timeouts and retry mechanisms
- **CSV Export**: Structured data output for analysis
- **Summary Statistics**: Real-time reporting of test results
- **CLI Interface**: Professional command-line tool with argument parsing

## ⚡ Quick Start

```bash
# 1. Clone and setup
git clone https://github.com/Mahmoud-Zaafan/LLM-URL-Health-Checker-Generator.git
cd LLM-URL-Health-Checker-Generator

# 2. Install dependencies
pip install openai anthropic google-generativeai python-dotenv requests

# 3. Configure environment (see provider examples below)
cp .env.example .env
# Edit .env with your preferred provider

# 4. Run the notebook to generate the tool
jupyter notebook Final.ipynb

# 5. Use the generated URL checker
echo "https://github.com" > urls.txt
python url_checker.py --input urls.txt --output results.csv
```

## 📋 Prerequisites

- Python 3.10+
- Access to at least one LLM API (see supported providers)
- Required Python packages (see installation)

## 🔧 Detailed Installation

### 1. **Clone the Repository**
   ```bash
   git clone https://github.com/Mahmoud-Zaafan/LLM-URL-Health-Checker-Generator.git
   cd LLM-URL-Health-Checker-Generator
   ```

### 2. **Set Up Python Environment** (Recommended)
   ```bash
   # Create virtual environment
   python -m venv venv
   
   # Activate it (Windows)
   venv\Scripts\activate
   # Or on Linux/Mac
   source venv/bin/activate
   ```

### 3. **Install Dependencies**
   ```bash
   # Core dependencies
   pip install python-dotenv requests jupyter
   
   # Install based on your provider:
   pip install openai           # For OpenAI
   pip install anthropic        # For Claude
   pip install google-generativeai  # For Gemini
   # Or install all:
   pip install -r requirements.txt
   ```

### 4. **Configure Environment Variables**

Create a `.env` file based on your chosen provider:

#### OpenAI Configuration
```env
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-your_api_key_here
BASE_URL=https://api.openai.com/v1
MODEL_NAME=gpt-4
```

#### Anthropic Claude Configuration
```env
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-your_api_key_here
BASE_URL=https://api.anthropic.com
MODEL_NAME=claude-3-opus-20240229
```

#### Google Gemini Configuration
```env
LLM_PROVIDER=google
GOOGLE_API_KEY=your_api_key_here
MODEL_NAME=gemini-pro
```

#### Azure OpenAI Configuration
```env
LLM_PROVIDER=azure
AZURE_API_KEY=your_api_key_here
BASE_URL=https://your-resource.openai.azure.com
AZURE_DEPLOYMENT_NAME=your-deployment
MODEL_NAME=gpt-4
```

#### Local Models (Ollama) Configuration
```env
LLM_PROVIDER=ollama
BASE_URL=http://localhost:11434/v1
MODEL_NAME=llama2:13b
# No API key needed for local models
```

#### Custom OpenAI-Compatible API
```env
LLM_PROVIDER=custom
API_KEY=your_api_key_here
BASE_URL=https://your-custom-endpoint.com/v1
MODEL_NAME=your-model-name
```

## 🎯 Usage Guide

### Step 1: Generate the URL Checker
1. **Open the Jupyter Notebook:**
   ```bash
   jupyter notebook Final.ipynb
   ```

2. **Select your LLM provider** in the configuration cell

3. **Execute all cells** to:
   - Configure your chosen LLM API connection
   - Generate the URL health checker script
   - Save it as `url_checker.py`

### Step 2: Prepare Your URL List
Create a `urls.txt` file with websites to monitor:
```text
https://www.google.com
https://github.com
https://stackoverflow.com
https://httpbin.org/status/200
https://httpbin.org/delay/3
# Comments are ignored
https://nonexistent-website-12345.com
```

### Step 3: Run the Health Checker
```bash
# Basic usage
python url_checker.py

# Custom parameters
python url_checker.py --input my_sites.txt --output health_report.csv --timeout 10 --retries 5
```

## 📊 Example Output

### Console Output
```
🔍 URL Health Checker Starting...
✅ https://www.google.com - 200 OK (156ms)
✅ https://github.com - 200 OK (245ms)
❌ https://nonexistent-site.com - Connection failed
📊 Summary: 2/3 successful (66.7%), Average: 200ms
```

### CSV Report (`results.csv`)
```csv
url,status_code,response_time_ms,success
https://www.google.com,200,156.3,True
https://github.com,200,245.7,True
https://nonexistent-site.com,0,0.0,False
```

## 🛠️ Command Line Options

| Option | Default | Description | Example |
|--------|---------|-------------|---------|
| `--input` | `urls.txt` | Input file containing URLs | `--input websites.txt` |
| `--output` | `results.csv` | Output CSV file for results | `--output report.csv` |
| `--timeout` | `5` | Request timeout in seconds | `--timeout 10` |
| `--retries` | `3` | Number of retry attempts | `--retries 5` |

## 🏗️ Project Architecture

```
LLM-URL-Health-Checker-Generator/
├── 📓 Final.ipynb              # Main LLM integration notebook
├── 📄 README.md               # This documentation
├── 🐍 url_checker.py          # Generated URL health checker
├── ⚙️  .env                   # Environment variables (create this)
├── ⚙️  .env.example           # Example environment configuration
├── 📝 urls.txt               # Input URLs list
├── 📊 results.csv            # Output results
├── 📦 requirements.txt       # Python dependencies
└── 📜 .gitignore             # Git ignore file
```

## 🔐 Security & Best Practices

### Environment Security
- ✅ **Never commit `.env` files** - Add to `.gitignore`
- ✅ **Use environment variables** for all API keys
- ✅ **Rotate API keys regularly**
- ✅ **Monitor API usage** and set billing alerts
- ✅ **Use provider-specific security features** (API key restrictions, rate limits)

### Code Safety
- ✅ **Review generated code** before execution
- ✅ **Test with safe URLs** first
- ✅ **Use virtual environments** to isolate dependencies
- ✅ **Validate input files** before processing



## 🤖 LLM Integration Details

### Model Configuration Examples

#### OpenAI GPT-4
```python
model="gpt-4"
temperature=0.2        # Low for consistent code
max_tokens=8000        # Balanced for complete scripts
```

#### Claude 3 Opus
```python
model="claude-3-opus-20240229"
temperature=0.2
max_tokens=4000
```

#### Gemini Pro
```python
model="gemini-pro"
temperature=0.2
max_tokens=8192
```

#### Local Models (Ollama)
```python
model="codellama:13b"
temperature=0.1        # Very low for code consistency
max_tokens=4096
```

### Prompt Engineering
The project uses carefully crafted prompts optimized for each provider to ensure:
- 🎯 **Focused output** - Only relevant code generation
- 🔧 **Practical functionality** - Real-world utility
- 📚 **Documentation** - Self-explaining code
- ⚡ **Efficiency** - Optimized performance
- 🔄 **Provider compatibility** - Works across different LLMs

## 📈 Use Cases

### DevOps & Monitoring
- **Website uptime monitoring**
- **API endpoint health checks**
- **Load balancer status verification**
- **CDN performance testing**
- **Multi-region availability checking**

### Development & Testing
- **Integration test automation**
- **Environment validation**
- **Dependency verification**
- **Service discovery**
- **CI/CD pipeline validation**


## 🤝 Contributing

We welcome contributions! Here's how to get started:

### 1. Fork & Clone
```bash
# Fork the repository on GitHub first, then:
git clone https://github.com/Mahmoud-Zaafan/LLM-URL-Health-Checker-Generator.git
```

### 2. Create Feature Branch
```bash
git checkout -b feature/amazing-new-feature
```
### 3. Make Changes
- Add support for new LLM providers
- Improve prompt engineering
- Add new tool generation capabilities
- Update documentation
- Add tests if applicable

### 4. Submit Pull Request
```bash
git add .
git commit -m "✨ Add amazing new feature"
git push origin feature/amazing-new-feature
```


## 🙏 Acknowledgments

- **LLM Providers** - OpenAI, Anthropic, Google, and the open-source community
- **Python Community** - For excellent libraries (`requests`, `python-dotenv`)
- **Jupyter Project** - For the interactive development environment
- **Open Source Models** - LLaMA, Mistral, and other contributors
- **Contributors** - Everyone who helps improve this project

## 📞 Support & Community

### Getting Help
- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/Mahmoud-Zaafan/LLM-URL-Health-Checker-Generator/issues)
- 💡 **Feature Requests**: [GitHub Discussions](https://github.com/Mahmoud-Zaafan/LLM-URL-Health-Checker-Generator/discussions)
- 📖 **Documentation**: This README and notebook comments
- 💬 **Community**: Join our discussions for tips and tricks

### Troubleshooting
| Problem | Solution |
|---------|----------|
| `ModuleNotFoundError` | Run `pip install -r requirements.txt` |
| `API Key Error` | Check your `.env` file and provider configuration |
| `Timeout Issues` | Increase `--timeout` parameter or switch providers |
| `Generation Fails` | Verify API credits, permissions, and model availability |
| `Provider Not Working` | Check API endpoint, model name, and authentication |

---

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**Made with ❤️ and AI** | *Supporting ALL Major LLM Providers*

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