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https://github.com/exios66/ai-interaction-scripts
Interaction Scripts for the Literary Vault focused Research.
https://github.com/exios66/ai-interaction-scripts
Last synced: about 2 months ago
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Interaction Scripts for the Literary Vault focused Research.
- Host: GitHub
- URL: https://github.com/exios66/ai-interaction-scripts
- Owner: Exios66
- License: mit
- Created: 2024-11-10T07:28:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-11T23:43:18.000Z (2 months ago)
- Last Synced: 2024-11-12T00:18:14.126Z (2 months ago)
- Language: Python
- Homepage: https://exios66.github.io/Ai-Interaction-Scripts/
- Size: 204 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI Interaction Scripts Repository
## 📋 Overview
A comprehensive collection of AI interaction and analysis tools, focusing on psychological clustering and open-ended response analysis. This repository provides tools for analyzing survey responses, performing psychological clustering, and processing qualitative data.
## 🚀 Key Features
### Open-Ended Response Analysis
- Process and analyze survey responses
- Automated coding of qualitative data
- Theme identification and analysis
- Verification step quantification
- Statistical summaries and reporting
### Psychological Clustering
- Advanced clustering algorithms
- Pattern recognition
- Data visualization
- Statistical analysis
### Core Capabilities
- CSV data import/export
- Natural Language Processing
- Automated theme detection
- Statistical analysis
- Comprehensive logging and debugging
- GUI interfaces for data selection
## 📁 Repository Structure
```bash
AI-Interaction-Scripts/
├── scripts/
│ ├── README.md # Detailed script documentation
│ ├── __init__.py # Package initialization
│ ├── polyPsych/ # Psychological analysis modules
│ │ ├── clustering.py # Clustering algorithms
│ │ └── open_end.py # Open-ended response analysis
│ └── utils/ # Utility modules
│ ├── __init__.py # Utils initialization
│ ├── debug_utils.py # Debugging utilities
│ └── logging_config.py # Logging configuration
├── logs/ # Log file directory
│ ├── analysis_*.log # Analysis logs
│ └── debug_*.log # Debug logs
├── debug_logs/ # Detailed debug information
├── requirements.txt # Project dependencies
├── setup.py # NLTK setup script
├── run.py # Main execution script
└── README.md # This file
```
## 🔧 Installation
### Prerequisites
- Python 3.7 or higher
- pip package manager
- Virtual environment (recommended)
### Setup Steps
1. Clone the repository:
```bash
git clone https://github.com/yourusername/AI-Interaction-Scripts.git
cd AI-Interaction-Scripts
```
2. Create and activate virtual environment:
```bash
python -m venv venv
# Windows
.\venv\Scripts\activate
# macOS/Linux
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run NLTK setup:
```bash
python setup.py
```
## 💻 Usage
### Running the Analysis Tool
1. Start the analysis tool:
```bash
python run.py
```
2. Choose from available options:
- Load and analyze CSV responses
- Run example analysis
- View debug logs
- Exit
### Data Requirements
#### CSV Format for Response Analysis
```csv
ID,Definition,VerificationSteps
1,"Response text...","Verification steps..."
```
### Analysis Features
1. **Response Coding**
- Automated categorization
- Theme identification
- Frequency analysis
2. **Verification Analysis**
- Step counting
- Statistical summaries
- Pattern identification
3. **Theme Analysis**
- Keyword extraction
- Frequency analysis
- Pattern recognition
## 🔍 Debugging and Logging
### Log Files
- Analysis logs: `logs/analysis_[timestamp].log`
- Debug logs: `debug_logs/debug_[timestamp].log`
- Error tracking: Comprehensive stack traces
### Debug Levels
- DEBUG: Detailed execution information
- INFO: General operational messages
- WARNING: Potential issues
- ERROR: Operation failures
- CRITICAL: System-critical issues
## 🛠 Advanced Features
### Custom Tokenization
- NLTK-based processing
- Fallback mechanisms
- Custom sentence splitting
### Statistical Analysis
- Descriptive statistics
- Frequency analysis
- Pattern recognition
- Clustering analysis
## 📊 Output Formats
### Analysis Results
- Coded responses
- Theme frequencies
- Statistical summaries
- Verification patterns
### Export Options
- CSV format
- JSON data
- Statistical reports
- Debug logs
## 🐛 Troubleshooting
### Common Issues
1. NLTK Data
- Run `setup.py`
- Check internet connection
- Verify data directory
2. Data Loading
- Check CSV format
- Verify encoding (UTF-8)
- Column name matching
3. System Resources
- Memory management
- Process optimization
- Resource allocation
## 🤝 Contributing
1. Fork the repository
2. Create feature branch
3. Implement changes
4. Submit pull request
### Development Guidelines
- Follow PEP 8 style guide
- Add comprehensive documentation
- Include unit tests
- Update README as needed
## 📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 📫 Support
- Review documentation
- Check debug logs
- Submit issues
- Contact maintainers
## 🙏 Acknowledgments
- NLTK Project
- Python Data Science Community
- Open Source Contributors
---
Made with ❤️ by [Your Name]
For detailed script-specific documentation, see [scripts/README.md](scripts/README.md)