checktica 0.1.4

Open Source Python SDK for Checktica - AI-written text detector.

Checktica Python SDK

Open Source Python SDK for Checktica - detect AI-generated text with high accuracy.

Features

• 🎯 99%+ accuracy in detecting AI-generated content
• 🚀 Multiple detection methods from fastest to most accurate
• 📦 Simple API - just one function to call
• 🔄 Automatic retries with smart error handling
• 📝 No length limits on text analysis
• 🆓 Free API access

Installation

Install Checktica using uv:

uv add checktica

Quick Start

Step 1: Import the SDK

from checktica import detect

Step 2: Check Your Text

text = """
    AI detectors help distinguish between human and AI-generated writing.
    Checktica offers high accuracy, a free Python SDK, and no text length limits.
"""

result = detect(text)

Step 3: Interpret Results

if result.is_llm_generated:
    print(f"AI-generated (confidence: {result.confidence:.2%})")
    print(f"Remarks: {result.remarks}")
else:
    print(f"Human-written (confidence: {result.confidence:.2%})")

Detection Methods

Checktica provides five detection methods with different speed/accuracy tradeoffs:

Method	Speed	Accuracy	Best For
most_accurate	Slowest	Highest	Critical decisions, academic integrity
more_accurate	Slow	Very High	Important content verification
balanced	Medium	High	General purpose use
fast	Fast	Good	Real-time filtering
fastest	Fastest	Acceptable	Bulk processing

Choosing a Detection Method

# Use the most accurate method (default, recommended)
result = detect(text, detection_method="most_accurate")

# Use faster method for bulk processing
result = detect(text, detection_method="fastest")

# Balanced approach for most cases
result = detect(text, detection_method="balanced")

Understanding Results

The detect() function returns a DetectionResult object with three fields:

result = detect(text)

# Is the text AI-generated?
print(result.is_llm_generated)  # True or False

# Confidence score (0.0 to 1.0)
print(result.confidence)  # e.g., 0.95 means 95% confident

# Additional context about the detection
print(result.remarks)  # e.g., "None."

More Examples

Check out the examples/ directory for complete working examples:

• hello_world.py - Basic usage
• product_reviews.py - Analyzing product reviews
• student_essay_scanner.py - Educational use case

Development Setup

Want to contribute? Here's how to set up your development environment:

Step 1: Clone the Repository

git clone https://github.com/checktica/checktica-python.git
cd checktica-python

Step 2: Install Dependencies

This project uses uv for dependency management:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

Step 3: Run Tests

# Run all tests with coverage
uv run pytest

# View coverage report
open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux

Step 4: Code Quality

We use Ruff for linting and formatting:

# Format code
uv run ruff format .

# Check for issues
uv run ruff check .

# Fix auto-fixable issues
uv run ruff check --fix .

Step 5: Pre-commit Hooks

Set up pre-commit hooks to automatically check code quality:

uv run pre-commit install

Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/amazing-feature).
3. Make your changes.
4. Test your changes (uv run pytest tests/).
5. Commit your changes (git commit -m 'feat: Add amazing feature').
6. Push to the branch (git push origin feature/amazing-feature).
7. Open a Pull Request.

Please ensure:

• All tests pass (uv run pytest).
• Code is formatted (uv run ruff format .).
• Linting passes (uv run ruff check .).
• Coverage remains at 100%.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 📧 Email: [e-mail]
• 🌐 Website: https://checktica.com
• 📚 Documentation: https://api.checktica.com/v1/docs
• 🐛 Issues: https://github.com/checktica/checktica-python/issues

Acknowledgments

Built with ❤️ by the Checktica team.