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

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:

git clone https://github.com/yourusername/AI-Interaction-Scripts.git
cd AI-Interaction-Scripts

2. Create and activate virtual environment:

python -m venv venv

# Windows
.\venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Run NLTK setup:

python setup.py

💻 Usage

Running the Analysis Tool

1. Start the analysis tool:

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

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

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For detailed script-specific documentation, see scripts/README.md