# 🎯 Interview Question Generator
A powerful CLI tool that analyzes Python codebases and automatically generates contextual technical interview questions using AI. Perfect for hiring managers, technical interviewers, and educators who want to create relevant, code-specific interview questions.
## ✨ Features
- **🔍 Intelligent Code Analysis**: Deep analysis of Python code structure, patterns, and complexity
- **🤖 AI-Powered Question Generation**: Uses OpenAI's GPT models to generate contextual questions
- **📊 Multiple Question Categories**: Comprehension, debugging, optimization, design, edge cases, and more
- **🎚️ Difficulty Levels**: Beginner, intermediate, advanced, and expert questions
- **📄 Multiple Output Formats**: JSON, Markdown, structured reports
- **⚡ Fast Processing**: Efficient analysis with progress tracking
- **🛠️ Configurable**: Flexible configuration options and CLI parameters
## 🚀 Installation
### Install from GitHub
```bash
pip install git+https://github.com/your-username/interview-generator.git
```
### Install for Development
```bash
git clone https://github.com/your-username/interview-generator.git
cd interview-generator
pip install -e .
```
### Install with Development Dependencies
```bash
pip install -e ".[dev,test]"
```
## 📋 Prerequisites
- Python 3.8 or higher
- OpenAI API key (get one at [OpenAI](https://platform.openai.com/api-keys))
## 🔧 Quick Start
### 1. Set up your API key
```bash
export OPENAI_API_KEY="your-api-key-here"
```
Or create a configuration file:
```bash
interview-generator config create --interactive
```
### 2. Analyze your code
```bash
# Basic analysis
interview-generator analyze /path/to/your/project
# Save as Markdown
interview-generator analyze /path/to/your/project --format markdown --output questions.md
# Generate specific question types
interview-generator analyze /path/to/your/project \
--categories comprehension debugging optimization \
--difficulty intermediate advanced \
--max-questions 15
```
## 📖 Usage Examples
### Basic Analysis
```bash
# Analyze a directory with default settings
interview-generator analyze ./src
# Analyze and save to a specific file
interview-generator analyze ./src --output interview_questions.json
```
### Advanced Filtering
```bash
# Generate only comprehension and debugging questions
interview-generator analyze ./src -c comprehension -c debugging
# Filter by difficulty level
interview-generator analyze ./src -d intermediate -d advanced
# Limit the number of questions
interview-generator analyze ./src --max-questions 20
```
### Different Output Formats
```bash
# Export as Markdown
interview-generator analyze ./src --format markdown --output questions.md
# Create structured output with reports
interview-generator analyze ./src --format structured --output ./results
# Export both JSON and Markdown
interview-generator analyze ./src --format both --output questions
```
### Configuration Management
```bash
# Create interactive configuration
interview-generator config create --interactive
# Validate your setup
interview-generator validate setup
# Test API connectivity
interview-generator validate api
# Show current configuration
interview-generator config show
```
## 🎯 Question Categories
The tool generates questions in several categories:
- **🧠 Comprehension**: Understanding code purpose and functionality
- **🐛 Debugging**: Identifying and fixing issues
- **⚡ Optimization**: Performance improvements and efficiency
- **🏗️ Design**: Architecture and design patterns
- **🔍 Edge Cases**: Boundary conditions and error handling
- **🧪 Testing**: Test strategies and coverage
- **♻️ Refactoring**: Code improvement and maintainability
- **🔒 Security**: Security vulnerabilities and best practices
## 🎚️ Difficulty Levels
- **🟢 Beginner**: Basic concepts and simple implementations
- **🟡 Intermediate**: Moderate complexity and common patterns
- **🟠 Advanced**: Complex algorithms and advanced concepts
- **🔴 Expert**: Highly sophisticated and specialized knowledge
## ⚙️ Configuration
### Environment Variables
- `OPENAI_API_KEY`: Your OpenAI API key
- `INTERVIEW_GENERATOR_CONFIG`: Path to custom config file
### Configuration File
Create a configuration file for persistent settings:
```bash
interview-generator config create --interactive
```
Example configuration:
```json
{
"llm_api_key": "your-api-key",
"llm_model": "gpt-3.5-turbo",
"max_questions_per_category": 5,
"output_format": "json",
"include_hints": true,
"quality_threshold": 0.7
}
```
## 🔍 CLI Reference
### Main Commands
- `analyze`: Analyze code and generate questions
- `config`: Manage configuration settings
- `validate`: Validate setup and test components
### Global Options
- `--verbose, -v`: Enable verbose output
- `--help`: Show help information
- `--version`: Show version information
### Analyze Command Options
```bash
interview-generator analyze [OPTIONS] DIRECTORY
Options:
-o, --output PATH Output file or directory path
-f, --format [json|markdown|both|structured]
Output format (default: json)
-c, --categories [comprehension|debugging|optimization|design|edge_cases|testing|refactoring|security]
Question categories (can be used multiple times)
-d, --difficulty [beginner|intermediate|advanced|expert]
Difficulty levels (can be used multiple times)
-n, --max-questions INTEGER RANGE
Maximum number of questions (1-50, default: 10)
--config PATH Path to configuration file
--dry-run Show what would be analyzed without API calls
-q, --quiet Suppress progress output
--help Show this message and exit
```
## 🧪 Development
### Running Tests
```bash
pytest
```
### Code Formatting
```bash
black src tests
isort src tests
```
### Type Checking
```bash
mypy src
```
### Pre-commit Hooks
```bash
pre-commit install
pre-commit run --all-files
```
## 📊 Example Output
### JSON Format
```json
{
"questions": [
{
"id": "q1",
"category": "comprehension",
"difficulty": "intermediate",
"question_text": "Explain the purpose of the UserManager class...",
"code_snippet": "class UserManager:\n def authenticate(self, username, password):\n ...",
"expected_answer": "The UserManager class handles user authentication...",
"hints": ["Focus on the authentication method", "Consider security implications"],
"context_references": ["Domain: web", "Pattern: authentication"]
}
],
"metadata": {
"total_questions": 10,
"processing_time": 15.2,
"files_analyzed": 5,
"model_used": "gpt-3.5-turbo"
}
}
```
### Markdown Format
```markdown
# Interview Questions
## Question 1: Code Comprehension (Intermediate)
**Question:** Explain the purpose and functionality of the UserManager class.
**Code:**
```python
class UserManager:
def authenticate(self, username, password):
# Implementation details...
```
**Expected Answer:** The UserManager class handles user authentication...
**Hints:**
- Focus on the authentication method
- Consider security implications
```
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests (`pytest`)
5. Commit your changes (`git commit -m 'Add amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- OpenAI for providing the GPT models
- The Python community for excellent tooling and libraries
- Contributors and users who help improve this tool
## 📞 Support
- 📖 [Documentation](https://github.com/your-username/interview-generator#readme)
- 🐛 [Issue Tracker](https://github.com/your-username/interview-generator/issues)
- 💬 [Discussions](https://github.com/your-username/interview-generator/discussions)
---
Made with ❤️ for the developer community
Raw data
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"description": "# \ud83c\udfaf Interview Question Generator\n\nA powerful CLI tool that analyzes Python codebases and automatically generates contextual technical interview questions using AI. Perfect for hiring managers, technical interviewers, and educators who want to create relevant, code-specific interview questions.\n\n## \u2728 Features\n\n- **\ud83d\udd0d Intelligent Code Analysis**: Deep analysis of Python code structure, patterns, and complexity\n- **\ud83e\udd16 AI-Powered Question Generation**: Uses OpenAI's GPT models to generate contextual questions\n- **\ud83d\udcca Multiple Question Categories**: Comprehension, debugging, optimization, design, edge cases, and more\n- **\ud83c\udf9a\ufe0f Difficulty Levels**: Beginner, intermediate, advanced, and expert questions\n- **\ud83d\udcc4 Multiple Output Formats**: JSON, Markdown, structured reports\n- **\u26a1 Fast Processing**: Efficient analysis with progress tracking\n- **\ud83d\udee0\ufe0f Configurable**: Flexible configuration options and CLI parameters\n\n## \ud83d\ude80 Installation\n\n### Install from GitHub\n\n```bash\npip install git+https://github.com/your-username/interview-generator.git\n```\n\n### Install for Development\n\n```bash\ngit clone https://github.com/your-username/interview-generator.git\ncd interview-generator\npip install -e .\n```\n\n### Install with Development Dependencies\n\n```bash\npip install -e \".[dev,test]\"\n```\n\n## \ud83d\udccb Prerequisites\n\n- Python 3.8 or higher\n- OpenAI API key (get one at [OpenAI](https://platform.openai.com/api-keys))\n\n## \ud83d\udd27 Quick Start\n\n### 1. Set up your API key\n\n```bash\nexport OPENAI_API_KEY=\"your-api-key-here\"\n```\n\nOr create a configuration file:\n\n```bash\ninterview-generator config create --interactive\n```\n\n### 2. Analyze your code\n\n```bash\n# Basic analysis\ninterview-generator analyze /path/to/your/project\n\n# Save as Markdown\ninterview-generator analyze /path/to/your/project --format markdown --output questions.md\n\n# Generate specific question types\ninterview-generator analyze /path/to/your/project \\\n --categories comprehension debugging optimization \\\n --difficulty intermediate advanced \\\n --max-questions 15\n```\n\n## \ud83d\udcd6 Usage Examples\n\n### Basic Analysis\n```bash\n# Analyze a directory with default settings\ninterview-generator analyze ./src\n\n# Analyze and save to a specific file\ninterview-generator analyze ./src --output interview_questions.json\n```\n\n### Advanced Filtering\n```bash\n# Generate only comprehension and debugging questions\ninterview-generator analyze ./src -c comprehension -c debugging\n\n# Filter by difficulty level\ninterview-generator analyze ./src -d intermediate -d advanced\n\n# Limit the number of questions\ninterview-generator analyze ./src --max-questions 20\n```\n\n### Different Output Formats\n```bash\n# Export as Markdown\ninterview-generator analyze ./src --format markdown --output questions.md\n\n# Create structured output with reports\ninterview-generator analyze ./src --format structured --output ./results\n\n# Export both JSON and Markdown\ninterview-generator analyze ./src --format both --output questions\n```\n\n### Configuration Management\n```bash\n# Create interactive configuration\ninterview-generator config create --interactive\n\n# Validate your setup\ninterview-generator validate setup\n\n# Test API connectivity\ninterview-generator validate api\n\n# Show current configuration\ninterview-generator config show\n```\n\n## \ud83c\udfaf Question Categories\n\nThe tool generates questions in several categories:\n\n- **\ud83e\udde0 Comprehension**: Understanding code purpose and functionality\n- **\ud83d\udc1b Debugging**: Identifying and fixing issues\n- **\u26a1 Optimization**: Performance improvements and efficiency\n- **\ud83c\udfd7\ufe0f Design**: Architecture and design patterns\n- **\ud83d\udd0d Edge Cases**: Boundary conditions and error handling\n- **\ud83e\uddea Testing**: Test strategies and coverage\n- **\u267b\ufe0f Refactoring**: Code improvement and maintainability\n- **\ud83d\udd12 Security**: Security vulnerabilities and best practices\n\n## \ud83c\udf9a\ufe0f Difficulty Levels\n\n- **\ud83d\udfe2 Beginner**: Basic concepts and simple implementations\n- **\ud83d\udfe1 Intermediate**: Moderate complexity and common patterns\n- **\ud83d\udfe0 Advanced**: Complex algorithms and advanced concepts\n- **\ud83d\udd34 Expert**: Highly sophisticated and specialized knowledge\n\n## \u2699\ufe0f Configuration\n\n### Environment Variables\n- `OPENAI_API_KEY`: Your OpenAI API key\n- `INTERVIEW_GENERATOR_CONFIG`: Path to custom config file\n\n### Configuration File\nCreate a configuration file for persistent settings:\n\n```bash\ninterview-generator config create --interactive\n```\n\nExample configuration:\n```json\n{\n \"llm_api_key\": \"your-api-key\",\n \"llm_model\": \"gpt-3.5-turbo\",\n \"max_questions_per_category\": 5,\n \"output_format\": \"json\",\n \"include_hints\": true,\n \"quality_threshold\": 0.7\n}\n```\n\n## \ud83d\udd0d CLI Reference\n\n### Main Commands\n\n- `analyze`: Analyze code and generate questions\n- `config`: Manage configuration settings\n- `validate`: Validate setup and test components\n\n### Global Options\n\n- `--verbose, -v`: Enable verbose output\n- `--help`: Show help information\n- `--version`: Show version information\n\n### Analyze Command Options\n\n```bash\ninterview-generator analyze [OPTIONS] DIRECTORY\n\nOptions:\n -o, --output PATH Output file or directory path\n -f, --format [json|markdown|both|structured]\n Output format (default: json)\n -c, --categories [comprehension|debugging|optimization|design|edge_cases|testing|refactoring|security]\n Question categories (can be used multiple times)\n -d, --difficulty [beginner|intermediate|advanced|expert]\n Difficulty levels (can be used multiple times)\n -n, --max-questions INTEGER RANGE\n Maximum number of questions (1-50, default: 10)\n --config PATH Path to configuration file\n --dry-run Show what would be analyzed without API calls\n -q, --quiet Suppress progress output\n --help Show this message and exit\n```\n\n## \ud83e\uddea Development\n\n### Running Tests\n```bash\npytest\n```\n\n### Code Formatting\n```bash\nblack src tests\nisort src tests\n```\n\n### Type Checking\n```bash\nmypy src\n```\n\n### Pre-commit Hooks\n```bash\npre-commit install\npre-commit run --all-files\n```\n\n## \ud83d\udcca Example Output\n\n### JSON Format\n```json\n{\n \"questions\": [\n {\n \"id\": \"q1\",\n \"category\": \"comprehension\",\n \"difficulty\": \"intermediate\",\n \"question_text\": \"Explain the purpose of the UserManager class...\",\n \"code_snippet\": \"class UserManager:\\n def authenticate(self, username, password):\\n ...\",\n \"expected_answer\": \"The UserManager class handles user authentication...\",\n \"hints\": [\"Focus on the authentication method\", \"Consider security implications\"],\n \"context_references\": [\"Domain: web\", \"Pattern: authentication\"]\n }\n ],\n \"metadata\": {\n \"total_questions\": 10,\n \"processing_time\": 15.2,\n \"files_analyzed\": 5,\n \"model_used\": \"gpt-3.5-turbo\"\n }\n}\n```\n\n### Markdown Format\n```markdown\n# Interview Questions\n\n## Question 1: Code Comprehension (Intermediate)\n\n**Question:** Explain the purpose and functionality of the UserManager class.\n\n**Code:**\n```python\nclass UserManager:\n def authenticate(self, username, password):\n # Implementation details...\n```\n\n**Expected Answer:** The UserManager class handles user authentication...\n\n**Hints:**\n- Focus on the authentication method\n- Consider security implications\n```\n\n## \ud83e\udd1d Contributing\n\n1. 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