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AI Text Humanizer Documentation

Overview 🤖→🧠

AI Text Humanizer is a Streamlit-based web application that transforms AI-generated text into more natural, human-like writing using the phi3 language model and advanced text processing techniques.

Installation 🚀

# Install required dependencies
pip install -r requirements.txt

# Start Ollama with phi3 model
ollama run phi3

# Launch the application
streamlit run ai_humanizer.py

Screenshots 📸

AI Humanizing Process

AI Humanizing Process

AI Detection Process

AI Detection Process

Features ✨

Text Humanization

  • Advanced text transformation using phi3 model
  • Adjustable temperature control (0.1-1.0)
  • Multiple humanization techniques:
    • Smart typo insertion
    • Natural punctuation variation
    • Organic repetition
    • Dynamic text formatting

AI Detection Analysis

  • Real-time detection risk assessment
  • Human-likeness scoring
  • Comprehensive text metrics
  • Actionable improvement suggestions

Usage Guide 📖

  1. Text Humanization

    • Open the Text Humanizer tab
    • Input your AI-generated text
    • Adjust settings in sidebar
    • Click "Humanize Text"
  2. Detection Check

    • Navigate to AI Detection Check tab
    • Paste text for analysis
    • Review detailed metrics and scores
    • Follow improvement suggestions

Components 🔧

Core Engine

  • phi3 model integration via Ollama API
  • Advanced post-processing pipeline
  • Real-time text analysis
  • Metric calculation system

User Interface

  • Three-tab layout:
    • Text Humanizer
    • AI Detection Check
    • About
  • Interactive controls
  • Real-time progress tracking
  • Detailed statistics display

Technical Requirements 💻

  • Python 3.7+
  • Ollama with phi3 model
  • NLTK library
  • Streamlit
  • Internet connection for initial setup

Privacy & Security 🔒

  • Local processing only
  • No external API calls
  • Data remains on your machine
  • Open-source codebase

Best Practices 💡

  1. Start with default settings
  2. Adjust temperature for desired variation
  3. Enable techniques selectively
  4. Use detection check for validation
  5. Iterate based on feedback

Limitations ⚠️

  • Requires local Ollama setup
  • Performance depends on hardware
  • Results vary with input length
  • Heuristic-based detection

Legal Note ⚖️

This tool is intended for legitimate use cases only. Not for unethical purposes.

Contributing 🤝

Pull requests are welcome. For major changes, please open an issue first to discuss proposed changes.


Made with ❤️ by an AI enthusiast

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