style-transfer-ai 1.0.0

Advanced stylometry analysis system with modular architecture

Style Transfer AI - Enhanced Deep Stylometry Analyzer v1.0.0

🎯 Advanced stylometry analysis system with personalized linguistic fingerprinting and modular architecture

🚀 Quick Start

Getting installation errors? → See QUICK_FIX_INSTALLATION.md

Need detailed setup? → See INSTALLATION_GUIDE.md

# Simple Method (Works for everyone)
git clone https://github.com/alwynrejicser/style-transfer-ai.git
cd style-transfer-ai
pip install requests
python run.py

# Advanced Method (May need troubleshooting)
pip install -e .
style-transfer-ai

Features

✅ 🏗️ Modular Architecture:

• Clean separation: Feature-based modules for maintainability
• Scalable design: Easy to extend with new models or features
• Professional structure: Industry-standard Python package organization

✅ Personalized Stylometric Fingerprints:

• Name-based file organization: Files saved as {name}_stylometric_profile_{timestamp}
• Personal identity integration: Your name prominently displayed in analysis
• Stylometric fingerprint concept: Treats writing analysis as unique personal identifiers
• Safe filename handling: Automatic sanitization for filesystem compatibility

✅ Performance Optimization:

• Multi-model support: Local Ollama + Cloud APIs (OpenAI, Gemini)
• Intelligent processing: Statistical-only or full deep analysis modes
• Resource-aware processing: Optimized for different analysis depths

✅ Hierarchical Model Selection:

• Local Processing: Ollama models (privacy-first, free)
• Cloud Processing: OpenAI GPT-3.5-turbo, Google Gemini-1.5-flash
• Automatic fallback: Graceful degradation when models unavailable
• Intuitive navigation: Main menu → Sub-menus with back navigation
• Professional interface: Clean, emoji-free design for serious analysis

✅ Enhanced Deep Analysis:

• 25-point stylometric framework (upgraded from 15-point)
• Dual output formats: JSON (machine-readable) + TXT (human-readable)
• Advanced metrics: Readability scores, lexical diversity, psychological profiling
• Statistical analysis: Word frequencies, punctuation patterns, complexity indices
• Individual file analysis + consolidated profiling

✅ Cloud Storage Integration:

• Firebase Firestore: Optional cloud database storage for profiles
• Automatic backup: Profiles saved locally AND in cloud (when configured)
• Cross-device access: Access your stylometric profiles from anywhere
• Search and retrieval: Query profiles by user name and creation date
• Privacy-first: Cloud storage is optional, local-only mode still available

✅ Privacy & Flexibility:

• Local processing for confidential content
• Multiple cloud API options (OpenAI, Gemini)
• Flexible API key management
• No data sharing with local models
• Production-ready architecture

Quick Start

1. Setup Requirements

For Local Models (Recommended)

# Install Ollama
# Visit: https://ollama.ai/download

# Pull the models
ollama pull gpt-oss:20b      # Advanced model
ollama pull gemma3:1b        # Fast model

# Start Ollama server
ollama serve

For Cloud APIs (Optional)

OpenAI API:

1. Get your API key from OpenAI Platform
2. Replace placeholder in the code:

   OPENAI_API_KEY = "your-openai-api-key-here"  # Replace with your actual key
   

Google Gemini API:

1. Get your API key from Google AI Studio
2. Replace placeholder in the code:

   GEMINI_API_KEY = "your-gemini-api-key-here"  # Replace with your actual key
   

Firebase Firestore (Optional - Cloud Storage):

1. Create a Firebase project at Firebase Console
2. Enable Firestore Database
3. Download service account JSON key
4. Place the key file as firebase-credentials.json in project root
5. Update project ID in the code (replace 'styler-24736' with your project ID)
6. See FIREBASE_SETUP.md for detailed instructions

2. Install Dependencies

pip install -r requirements.txt
# Or manually:
pip install requests openai google-generativeai firebase-admin

3. Prepare Your Text Samples

Place your writing samples in the project directory:

• about_my_pet.txt
• about_my _pet_1.txt
• Or modify file paths in the code

4. Run Analysis

python style_analyzer_enhanced.py

CLI Installation & Usage

Automated Installation (Windows)

For Windows users, use the provided batch files for easy setup:

# Full installation with dependency checks and Ollama detection
install\install_cli.bat

# Quick installation (minimal output)
install\quick_install.bat

The installation script will:

• ✅ Check Python and pip installation
• ✅ Create isolated virtual environment
• ✅ Install ALL AI dependencies (OpenAI, Gemini, Firebase)
• ✅ Set up global CLI access (adds to user PATH)
• ✅ Test the installation
• ✅ Check for Ollama and AI models
• ✅ Provide next steps and usage examples

Post-Installation:

• Restart your command prompt for global access
• Use style-transfer-ai from any directory
• All AI models and APIs ready to use

Manual Installation

Install the package to use the style-transfer-ai command globally:

# From project root directory
pip install -e .

For Global Access (Recommended): The installation automatically adds the CLI to your PATH. If you encounter issues, the virtual environment path is added automatically. You can now use the CLI from anywhere:

# Works from any directory
style-transfer-ai

# Run from anywhere on your system
cd C:\
style-transfer-ai --help

Note: The CLI automatically changes to the correct project directory when run, so it works regardless of your current working directory.

CLI Usage Examples

Interactive Mode (Default)

# Run interactive menu (same as python style_analyzer_enhanced.py)
style-transfer-ai
style-transfer-ai --interactive

Batch Analysis

# Analyze single file
style-transfer-ai --analyze sample.txt

# Analyze multiple files
style-transfer-ai --analyze file1.txt file2.txt file3.txt

# Analyze with specific model
style-transfer-ai --analyze sample.txt --model gpt-oss:20b

# Force local processing
style-transfer-ai --analyze sample.txt --local

# Force cloud processing
style-transfer-ai --analyze sample.txt --cloud

Custom Output

# Custom output filename base
style-transfer-ai --analyze sample.txt --output "my_analysis"

# Disable cloud storage for this run
style-transfer-ai --analyze sample.txt --no-cloud-storage

Data Management

# Open Firestore data retention management
style-transfer-ai --data-retention

CLI Options Reference

Option	Description	Example
--interactive	Run in interactive menu mode (default)	style-transfer-ai --interactive
--analyze FILE [FILE ...]	Analyze one or more text files	style-transfer-ai --analyze text1.txt text2.txt
--model MODEL	Specify model (gpt-oss:20b, gemma3:1b, openai, gemini)	style-transfer-ai --analyze file.txt --model gemma3:1b
--local	Force use of local Ollama models	style-transfer-ai --analyze file.txt --local
--cloud	Force use of cloud models (OpenAI/Gemini)	style-transfer-ai --analyze file.txt --cloud
--output NAME	Base name for output files (no extension)	style-transfer-ai --analyze file.txt --output my_profile
--no-cloud-storage	Disable Firestore cloud storage for this run	style-transfer-ai --analyze file.txt --no-cloud-storage
--data-retention	Open Firestore data retention management	style-transfer-ai --data-retention
--help	Show help message and exit	style-transfer-ai --help

Advanced CLI Workflows

Research Pipeline

# Analyze academic papers with cloud processing
style-transfer-ai --analyze paper1.txt paper2.txt --cloud --output "academic_style"

# Quick analysis with local fast model
style-transfer-ai --analyze draft.txt --local --model gemma3:1b

Batch Processing

# Analyze all text files in current directory (Windows PowerShell)
Get-ChildItem *.txt | ForEach-Object { style-transfer-ai --analyze $_.Name --output $_.BaseName }

# Analyze with privacy mode (no cloud storage)
style-transfer-ai --analyze sensitive.txt --local --no-cloud-storage

Development & Testing

# Test different models on same content
style-transfer-ai --analyze test.txt --model gpt-oss:20b --output "test_advanced"
style-transfer-ai --analyze test.txt --model gemma3:1b --output "test_fast"

CLI vs Interactive Mode

Feature	CLI Mode	Interactive Mode
Speed	Fast, direct analysis	Menu navigation required
Automation	Perfect for scripts/batch	Manual operation
Customization	Command-line arguments	Interactive prompts
User Experience	Technical users	User-friendly menus
Integration	CI/CD, automation tools	Stand-alone usage

Troubleshooting CLI

Command not found after installation:

# Verify installation
pip list | grep style-transfer-ai

# Reinstall in development mode
pip uninstall style-transfer-ai
pip install -e .

Permission errors:

# Use user installation
pip install --user -e .

# Or with elevated permissions (Windows)
# Run PowerShell as Administrator, then install

Path issues:

# Check if Python Scripts directory is in PATH
python -m site --user-base

# Add to PATH if needed (Windows PowerShell)
$env:PATH += ";$(python -m site --user-base)\Scripts"

Key Workflow

Personal Profile Setup

1. Name Collection: Your name is collected first as stylometric profiles are personal fingerprints
2. Cultural Context: Language background, nationality, education details
3. Writing Experience: Frequency and background information
4. Streamlined Process: Only essential information collected for meaningful analysis

Processing Modes (GPT-OSS)

When using GPT-OSS models, choose your processing mode:

• Turbo Mode: Faster analysis (2000 tokens, 120s timeout) for quick insights
• Normal Mode: Thorough analysis (3000 tokens, 180s timeout) for comprehensive profiling

Usage

Model Selection

The analyzer features a hierarchical menu system for intuitive model selection:

Main Menu:

1. Local Processing (Privacy-focused)
2. Online Processing (Cloud-based)

Local Models Sub-menu:

• GPT-OSS 20B - Advanced comprehensive analysis
• Gemma 3:1B - Fast efficient processing

Online Models Sub-menu:

• OpenAI GPT-3.5-turbo - Cloud-based analysis
• Google Gemini-1.5-flash - Google's latest language model

Navigation: Use '0' to go back to the previous menu or exit the application.

Enhanced Output

The analyzer generates personalized stylometric fingerprints:

• Individual analyses for each text file with 25-point deep analysis
• Consolidated style profile combining all samples
• Personalized file naming: {your_name}_stylometric_profile_{timestamp}
  • Example: John_Doe_stylometric_profile_20250915_123456.json
• Dual format outputs:
  • JSON file - Machine-readable data for further processing
  • TXT file - Human-readable report with your name prominently displayed
• Writer Identity Section: Your name featured prominently in analysis header
• Advanced metrics: Readability scores, lexical diversity, statistical analysis
• Comprehensive metadata with timestamps and detailed file information
• Automatic cleanup: Option to remove old reports with both naming patterns

API Key Configuration

Method 1: Direct Code Modification

Replace the placeholders in style_analyzer_enhanced.py:

OPENAI_API_KEY = "your-actual-openai-api-key-here"
GEMINI_API_KEY = "your-actual-gemini-api-key-here"

Method 2: Interactive Setup (Recommended)

The analyzer will automatically:

1. Detect existing API keys for both OpenAI and Gemini
2. Prompt for new keys if needed based on your model choice
3. Validate key format and provide helpful URLs
4. Handle setup cancellation gracefully
5. Support switching between different API providers

Project Structure

style-transfer-ai/
├── style_analyzer_enhanced.py           # Enhanced deep analyzer v4.5 (25-point framework)
├── .github/
│   └── copilot-instructions.md         # Development guidelines for GitHub Copilot
├── IMPLEMENTATION.md                    # Detailed technical documentation
├── README.md                           # This file
├── futureimprov.md                     # Future improvement plans
├── FIREBASE_SETUP.md                    # Firebase Firestore setup guide
├── requirements.txt                    # Python dependencies
├── .env.example                        # Environment configuration template
├── .gitignore                          # Git ignore for security
├── about_my_pet.txt                    # Sample text file 1
├── about_my_pet_1.txt                  # Sample text file 2
└── {name}_stylometric_profile_*.json   # Your personalized analysis (JSON)
└── {name}_stylometric_profile_*.txt    # Your personalized analysis (TXT)

What's New

🆕 Personalized Stylometric Fingerprints:

• Your name is now collected and used for file naming
• Files saved as {Name}_stylometric_profile_{timestamp}
• Writer identity prominently displayed in analysis
• Automatic filename sanitization for safe storage

⚡ GPT-OSS Performance Modes:

• Turbo Mode: Quick analysis with optimized parameters
• Normal Mode: Comprehensive deep analysis
• User choice for processing speed vs thoroughness

☁️ Firebase Firestore Integration:

• Cloud Database Storage: Optional backup to Firebase Firestore
• Cross-Device Access: Access profiles from any device
• Search & Retrieval: Query profiles by user and date
• Privacy Maintained: Local storage still default, cloud is optional

🏗️ Enhanced Architecture:

• Streamlined user profile collection (8 essential fields)
• Professional interface without emoji clutter
• Improved error handling and connection validation
• Updated cleanup functionality for all naming patterns

Enhanced Stylometric Analysis Framework

The analyzer evaluates 25 comprehensive dimensions across 7 categories:

Part 1: Linguistic Architecture

1. Sentence Structure Mastery: Exact averages, complexity ratios with percentages
2. Clause Choreography: Subordinate clause frequency, coordination patterns
3. Punctuation Symphony: Complete punctuation analysis with frequencies
4. Syntactic Sophistication: Sentence variety index, grammatical complexity scoring

Part 2: Lexical Intelligence

5. Vocabulary Sophistication: Word complexity levels, formal/informal ratios
6. Semantic Field Preferences: Domain categorization (abstract/concrete, emotional/logical)
7. Lexical Diversity Metrics: Type-token ratio, vocabulary richness index
8. Register Flexibility: Formality spectrum analysis, colloquialism detection

Part 3: Stylistic DNA

9. Tone Architecture: Confidence indicators, emotional markers with examples
10. Voice Consistency: Person preference analysis, active/passive voice ratios
11. Rhetorical Weaponry: Metaphor counting, parallel structures, repetition patterns
12. Narrative Technique: Point of view consistency, storytelling vs explanatory modes

Part 4: Cognitive Patterns

13. Logical Flow Design: Argument structure, cause-effect pattern analysis
14. Transition Mastery: Transition word categorization, coherence mechanisms
15. Emphasis Engineering: Key point highlighting strategies, linguistic intensity
16. Information Density: Concept-to-word ratios, information packaging efficiency

Part 5: Psychological Markers

17. Cognitive Processing Style: Linear vs circular thinking, analytical patterns
18. Emotional Intelligence: Empathy markers, emotional vocabulary richness
19. Authority Positioning: Hedging language, assertiveness markers, expertise indicators
20. Risk Tolerance: Certainty language analysis, qualification usage patterns

Part 6: Structural Genius

21. Paragraph Architecture: Length variance, topic development patterns
22. Coherence Engineering: Text cohesion measurement, referential chains
23. Temporal Dynamics: Tense usage patterns, time reference preferences
24. Modal Expression: Modal verb counting, probability vs obligation language

Part 7: Unique Fingerprint

25. Personal Signature Elements: Unique phrases, idiosyncratic expressions, personal habits

Stylistic Markers

7. Tone Indicators: Confidence, emotional markers, certainty
8. Narrative Voice: Person preference, active/passive ratio
9. Rhetorical Devices: Metaphors, repetition, parallel structure

Structural Preferences

10. Paragraph Organization: Length, transition methods
11. Flow Patterns: Idea connection, progression style
12. Emphasis Techniques: Highlighting methods

Personal Markers

13. Idiomatic Expressions: Unique phrases, expressions
14. Cultural References: Reference types and patterns
15. Formality Range: Casual to formal adaptability

Security & Best Practices

🔐 API Key Security:

• Never commit real API keys to version control
• Use environment variables for production
• Rotate keys regularly
• Monitor usage and costs

🛡️ Privacy:

• Use local models for sensitive content
• Local processing keeps data on your machine
• No internet connection required for Ollama models

Troubleshooting

Common Issues

Ollama Connection Failed:

# Make sure Ollama is running
ollama serve

# Check if models are installed
ollama list

# Pull missing models
ollama pull gpt-oss:20b
ollama pull gemma3:1b

GPT-OSS Performance Issues:

• Try Turbo Mode for faster processing
• Switch to Normal Mode for detailed analysis
• Check system resources during processing
• Verify model is fully loaded in Ollama

OpenAI API Errors:

• Check API key validity
• Verify account has sufficient credits
• Ensure proper key format (starts with 'sk-')

File Naming Issues:

• Special characters in names are automatically sanitized
• Long names are truncated to 50 characters
• Empty names default to "Anonymous_User"
• Files are timestamped for unique identification

File Not Found:

• Verify text files exist in project directory
• Check file names match exactly
• Ensure proper file encoding (UTF-8)

Contributing

Contributions welcome! Please ensure:

• No real API keys in commits
• Update documentation for new features
• Test with all model types and processing modes
• Follow existing code style and naming conventions
• Include personalization features in new developments

Version History

• v4.5 (Current): Personalized stylometric fingerprints, GPT-OSS performance modes
• v4.0: Enhanced deep analysis framework (25-point system)
• v3.0: Hierarchical model selection with local/online options
• v2.0: Dual output formats (JSON + TXT)
• v1.0: Basic stylometry analysis

License

MIT License