https://github.com/jingew/ai-polished-text-assessment
https://github.com/jingew/ai-polished-text-assessment
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/jingew/ai-polished-text-assessment
- Owner: JingeW
- License: mit
- Created: 2024-12-16T16:36:28.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2025-01-15T14:43:07.000Z (4 months ago)
- Last Synced: 2025-01-15T17:09:00.869Z (4 months ago)
- Language: Python
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Article AI Detection and Readability Assessment
[](LICENSE)
This project analyzes scientific articles to detect AI-generated content and assess readability improvements after AI polishing. It provides workflows for:
1. **Text Polishing**: Enhancing text clarity and readability using AI.
2. **AI Detection**: Identifying the probability of AI-generated text.
3. **Readability Assessment**: Calculating traditional readability metrics and scientific clarity metrics.
---
## **Project Structure**
```
ARTICLE_AI_DETECTION/
│-- data/ # Original article text files (input)
│-- outputs/
│ └── polished_articles/ # AI-polished articles for rep1, rep2, rep3
│-- plots/ # Generated plots
│-- results/ # Analysis results (JSON, CSV)
│-- service/ # Core services for analysis and preprocessing
│ │-- analysis_service.py # Readability and clarity metrics
│ │-- data_prep_service.py # Text preprocessing (clean-up)
│ │-- polish_service.py # AI polishing service
│ │-- prompt_service.py # AI prompt management
│-- main_ai_detection.py # AI detection workflow
│-- main_article_polish.py # Text polishing workflow
│-- main_readability_assessment.py # Readability assessment workflow
│-- plot_results.py # Plotting results
│-- utils.py # Utility functions (file I/O, plotting)
│-- .env # Environment variables (API keys)
│-- README.md # Project documentation
```
---
## **Requirements**
Ensure you have the following tools and libraries installed:
- Python 3.8+
- Required libraries (installed via `requirements.txt`)
### **Dependencies**
Install the required Python libraries:
```bash
pip install -r requirements.txt
```
---
## **How to Use**
### **1. Prepare the Data**
- Place the original Excel file with articles into the `data/` folder.
- Run the preprocessing and extraction script:
```bash
python main_article_polish.py
```
This script will:
- Extract articles from the Excel file.
- Preprocess and save the original text in `data/`.
---
### **2. AI Text Polishing**
Run the polishing workflow to generate AI-enhanced versions of the text:
```bash
python main_article_polish.py --repetitions 3
```
- Polished text files will be saved in `outputs/polished_articles/`.
---
### **3. Readability Assessment**
Analyze readability and clarity of both original and polished texts:
```bash
python main_readability_assessment.py
```
- Results will be saved in `results/readability_results.csv`
---
### **4. AI Detection**
Run the AI detection workflow to evaluate articles:
```bash
python main_ai_detection.py
```
- Results will be saved in `results/ai_detection_results.csv`.
---
### **5. Plot Results**
Visualize results using the plotting script:
```bash
python plot_results.py
```
Plots will be saved in the `plots/` folder.
---
## **Environment Variables**
Add your API keys to the `.env` file in the project root:
```plaintext
GPTZERO_API_KEY=your_gptzero_api_key
API_KEY=your_openai_api_key
```
---
## **Outputs**
- **Results**: Stored in `results/` (CSV files).
- **Polished Text**: Stored in `outputs/polished_articles/`.
- **GPTZERO Responses**: Stored in `outputs/gptzero_responses/`.
- **Plots**: Visualizations saved in `plots/`.
---
## **License**
This project is licensed under the MIT License.
---