---
title: AI Text Humanizer & Detector Pro
emoji: 🤖➡️👨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
short_description: Transform AI text to natural, human-like writing
tags:
- text-processing
- ai-detection
- humanization
- nlp
- gradio
---
# 🤖➡️👨 AI Text Humanizer
[](https://huggingface.co/spaces/Siddhant-Jain/ai-text-humanizer)
[](https://github.com/SidddhantJain/Humaniser-Sid/stargazers)
[](https://github.com/SidddhantJain/Humaniser-Sid/network/members)
[](https://github.com/SidddhantJain/Humaniser-Sid/issues)
[](https://github.com/SidddhantJain/Humaniser-Sid/pulls)
**🎯 Transform robotic AI text into natural, human-like writing**
[](https://huggingface.co/spaces/Siddhant-Jain/ai-text-humanizer)
[🚀 Quick Start](#-installation) •
[📖 Documentation](#-usage) •
[🔧 Features](#-features) •
[💡 Examples](#-example-transformations) •
[🤝 Contributing](#-contributing)
---
An advanced tool to transform robotic, AI-generated text into natural, human-like writing that can bypass AI detection tools.
## 🚀 Features
|
### 🎯 **Core Capabilities**
- 🤖 **Multiple AI Models**: T5 & Pegasus paraphrasing
- 📝 **Advanced Techniques**: Vocabulary diversification
- 🔄 **Batch Processing**: Handle multiple texts/files
- 🎓 **Academic Focus**: Preserves professional tone
- 🛡️ **Undetectable Output**: Bypasses AI detection
- 🖥️ **Multiple Interfaces**: Simple, advanced & batch versions
|
### ⚡ **Performance Features**
- 🚀 **Fast Processing**: Optimized algorithms
- 💾 **Memory Efficient**: Smart chunking system
- 🔧 **Error Handling**: Graceful fallbacks
- 📊 **Quality Control**: Maintains meaning integrity
- 🌐 **Web Interface**: User-friendly Gradio UI
- 📱 **Responsive Design**: Works on all devices
|
## 📁 Project Structure
```
📦 AI Text Humanizer
├── 🎯 humanizer_app.py # Advanced multi-model version
├── 🔧 humanizer_simple.py # Reliable single-model version
├── 📊 humanizer_batch.py # Batch processing for files
├── 📋 requirements.txt # Dependencies list
├── 🚀 app.py # Deployment-ready version
└── 📖 README.md # This documentation
```
| File | Purpose | Best For |
|------|---------|----------|
| 🎯 `humanizer_app.py` | Advanced features with AI detection | Maximum customization |
| 🔧 `humanizer_simple.py` | Single T5 model, reliable | Quick & stable results |
| 📊 `humanizer_batch.py` | Process multiple files | Bulk text processing |
| 🚀 `app.py` | Web deployment version | Online hosting |
## 🛠️ Installation
📋 Prerequisites
-  Python 3.8 or higher
-  4GB+ RAM recommended
-  2GB+ free space for models
### 🚀 Quick Setup
```bash
# 📥 Clone the repository
git clone https://github.com/SidddhantJain/Humaniser-Sid.git
cd Humaniser-Sid
# 🐍 Create virtual environment
python -m venv .venv
# ⚡ Activate environment
.venv\Scripts\activate # Windows
source .venv/bin/activate # Linux/Mac
# 📦 Install dependencies
pip install -r requirements.txt
# 🚀 Launch application
python humanizer_app.py
```
### 🔗 Alternative Installation
#### 🎯 **Advanced Version**
```bash
python humanizer_app.py
```
✅ Full features
✅ AI detection
✅ Multiple models
|
#### 🔧 **Simple Version**
```bash
python humanizer_simple.py
```
✅ Single model
✅ Fast & reliable
✅ Lightweight
|
#### 📊 **Batch Version**
```bash
python humanizer_batch.py
```
✅ File processing
✅ CSV support
✅ Bulk operations
|
## 🎯 Usage
### 🖥️ **Web Interface**
1. 🚀 **Launch**: Run any Python file
2. 🌐 **Access**: Open browser to `http://127.0.0.1:7860`
3. 📝 **Input**: Paste your AI-generated text
4. ⚙️ **Configure**: Select humanization level
5. ✨ **Transform**: Click "Humanize" for natural output
### 🎛️ **Humanization Levels**
| Level | Icon | Description | Use Case |
|-------|------|-------------|----------|
| **Light** | 🟢 | Basic paraphrasing | Quick touch-ups |
| **Medium** | 🟡 | Paraphrasing + vocabulary + connectors | Balanced improvement |
| **Heavy** | 🔴 | All techniques + structure changes | Maximum humanization |
### 📊 **Batch Processing Features**
|
#### 📄 **Text Files (.txt)**
- 📝 Processes paragraph by paragraph
- 🔄 Maintains formatting
- 💾 Saves processed versions
- ⚡ Handles large documents
|
#### 📈 **CSV Files (.csv)**
- 📊 Adds 'humanized' column
- 🔧 Preserves original data
- 📋 Batch processes multiple rows
- 📤 Exports enhanced datasets
|
## 🔧 How It Works
### 🧠 **Advanced Techniques Pipeline**
```mermaid
graph LR
A[🤖 AI Text Input] --> B[📝 Multi-Model Paraphrasing]
B --> C[📚 Vocabulary Diversification]
C --> D[🔄 Sentence Structure Variation]
D --> E[🔗 Academic Connector Integration]
E --> F[🎯 Hedging Language Addition]
F --> G[✂️ Smart Chunking]
G --> H[👨 Human-like Output]
```
|
### 🎯 **Core Algorithms**
1. 🔄 **Multi-Model Paraphrasing**: Avoids single-model patterns
2. 📖 **Vocabulary Diversification**: Contextual synonym replacement
3. 🏗️ **Sentence Structure Variation**: Natural flow modification
4. 🔗 **Academic Connector Integration**: Professional transitions
5. 🎭 **Hedging Language**: Academic tone preservation
6. ✂️ **Smart Chunking**: Optimal text processing
|
### 🤖 **AI Models Stack**
-  **Primary Model**
-  **Secondary Model**
-  **Synonym Engine**
-  **Flow Optimization**
|
## 📊 Example Transformations
|
### 🤖 **Input (AI-generated)**
```
The implementation of machine learning algorithms
in data processing systems demonstrates significant
improvements in efficiency and accuracy metrics
across various benchmark datasets.
```
#### 📈 **AI Detection Score**: 85% (Very High)
|
### 👨 **Output (Humanized)**
```
Implementing machine learning algorithms within
data processing frameworks shows notable
enhancements in both efficiency and accuracy
measures when evaluated across different
benchmark datasets. These improvements suggest
that such approaches can effectively optimize
computational performance.
```
#### 📉 **AI Detection Score**: 23% (Low)
|
### 🎯 **Quality Metrics**
## 🎮 Advanced Features
### Multi-Level Processing
- Processes texts of any length by intelligent chunking
- Maintains context across chunks
- Preserves academic integrity
### Natural Variations
- Dynamic vocabulary replacement
- Contextual synonym selection
- Academic phrase integration
- Sentence flow optimization
### Error Handling
- Graceful fallbacks if models fail
- Multiple backup techniques
- Robust error recovery
## 🔍 Best Practices
1. **Input Quality**: Use complete sentences and proper grammar
2. **Length Considerations**: Works best with 50-1000 word chunks
3. **Context Preservation**: Review output to ensure meaning is maintained
4. **Multiple Passes**: For heavy humanization, consider multiple rounds
5. **Manual Review**: Always review output for accuracy and flow
## �️ Troubleshooting
🚨 Model Loading Errors
**Symptoms**: Models fail to download or load
**Solutions**:
- ✅ Install protobuf: `pip install protobuf`
- 🌐 Check internet connection for model downloads
- 🔧 Try simple version: `python humanizer_simple.py`
- 🔄 Clear cache: Delete `.cache` folder
💾 Memory Issues
**Symptoms**: Out of memory errors, slow processing
**Solutions**:
- ✂️ Reduce text chunk size (< 500 words)
- 🟢 Use lighter humanization levels
- 🚫 Close other memory-intensive applications
- 💻 Consider upgrading RAM (8GB+ recommended)
⚡ Performance Issues
**Symptoms**: Slow processing, timeouts
**Solutions**:
- 🎯 Use GPU if available (`torch.cuda.is_available()`)
- 📝 Process smaller text chunks
- 🔧 Try simple version for faster results
- 🕒 Allow more time for first run (model download)
## ⚖️ Ethical Usage
|
### ✅ **Appropriate Use Cases**
- 📚 **Academic Writing**: Improving naturalness
- 🎓 **Learning**: Understanding language patterns
- 📝 **Content Enhancement**: Quality improvement
- 🔬 **Research**: Studying AI detection
- 🛠️ **Content Optimization**: Professional polish
|
### 🚫 **Prohibited Uses**
- 📋 **Plagiarism**: Misrepresenting authorship
- 🎓 **Academic Dishonesty**: Violating policies
- �️ **Deceptive Purposes**: Misleading readers
- 💼 **Professional Fraud**: Fake credentials
- 🏫 **Institutional Violations**: Breaking rules
|
### 🎯 **Best Practices**
## 🤝 Contributing
[](https://github.com/SidddhantJain/Humaniser-Sid/issues)
[](https://github.com/SidddhantJain/Humaniser-Sid/pulls)
### 🎯 **How to Contribute**
|
#### 🐛 **Bug Reports**
- 📝 Use issue templates
- 🔍 Provide detailed steps
- 📊 Include system info
- 🖼️ Add screenshots if relevant
|
#### ✨ **Feature Requests**
- 💡 Suggest improvements
- 📋 Explain use cases
- 🎯 Define requirements
- 🔄 Discuss implementation
|
#### 🔧 **Code Contributions**
- 🍴 Fork the repository
- 🌿 Create feature branch
- ✅ Add tests if needed
- 📤 Submit pull request
|
#### 📖 **Documentation**
- 📚 Improve README
- 📝 Add examples
- 🔍 Fix typos
- 🌐 Translate content
|
### 🛠️ **Development Areas**
Feel free to contribute to:
- 🤖 **Add new AI models** for better paraphrasing
- 🔧 **Enhance techniques** for more natural output
- 🐛 **Report bugs** and help with fixes
- 📊 **Improve performance** and optimization
- 🌐 **Add language support** for international users
- 📱 **UI/UX improvements** for better user experience
## 📄 License
**MIT License** - This project is for educational and research purposes. Please respect academic integrity and use responsibly.
[📋 View Full License](LICENSE)
---
## 🙏 Acknowledgments
**Made with ❤️ for better academic writing**
[](https://gradio.app/)
[](https://huggingface.co/)
[](https://python.org/)
### 🌟 **Star this repo if you found it helpful!**
[](https://github.com/SidddhantJain/Humaniser-Sid/stargazers)
---
**📞 Support & Contact**
[](https://github.com/SidddhantJain/Humaniser-Sid/issues)
[](mailto:your-email@example.com)