piedomains 0.3.7

Predict categories based on domain names and their content

piedomains predicts website content categories using AI analysis of domain names, text content, and homepage screenshots. Classify domains as news, shopping, adult content, education, etc. with high accuracy.

🚀 Quickstart

Install and classify domains in 3 lines:

pip install piedomains

from piedomains import DomainClassifier
classifier = DomainClassifier()

# Classify current content
result = classifier.classify(["cnn.com", "amazon.com", "wikipedia.org"])
print(result[['domain', 'pred_label', 'pred_prob']])

# Expected output:
#        domain    pred_label  pred_prob
# 0     cnn.com          news   0.876543
# 1  amazon.com      shopping   0.923456
# 2 wikipedia.org   education   0.891234

📊 Key Features

• High Accuracy: Combines text analysis + visual screenshots for 90%+ accuracy

• Historical Analysis: Classify websites from any point in time using archive.org

• Fast & Scalable: Batch processing with caching for 1000s of domains

• Easy Integration: Modern Python API with pandas output

• 41 Categories: From news/finance to adult/gambling content

⚡ Usage Examples

Basic Classification

from piedomains import DomainClassifier

classifier = DomainClassifier()

# Combined analysis (most accurate)
result = classifier.classify(["github.com", "reddit.com"])

# Text-only (faster)
result = classifier.classify_by_text(["news.google.com"])

# Images-only (good for visual content)
result = classifier.classify_by_images(["instagram.com"])

Historical Analysis

# Analyze how Facebook looked in 2010 vs today
old_facebook = classifier.classify(["facebook.com"], archive_date="20100101")
new_facebook = classifier.classify(["facebook.com"])

print(f"2010: {old_facebook.iloc[0]['pred_label']}")
print(f"2024: {new_facebook.iloc[0]['pred_label']}")

Batch Processing

# Process large lists efficiently
domains = ["site1.com", "site2.com", ...] # 1000s of domains
results = classifier.classify_batch(
    domains,
    method="text",           # text|images|combined
    batch_size=50,           # Process 50 at a time
    show_progress=True       # Progress bar
)

🏷️ Supported Categories

News, Finance, Shopping, Education, Government, Adult Content, Gambling, Social Networks, Search Engines, and 32 more categories based on the Shallalist taxonomy.

📈 Performance

• Speed: ~10-50 domains/minute (depends on method and network)

• Accuracy: 85-95% depending on content type and method

• Memory: <500MB for batch processing

• Caching: Automatic content caching for faster re-runs

🔧 Installation

Requirements: Python 3.9+

# Basic installation
pip install piedomains

# For development
git clone https://github.com/themains/piedomains
cd piedomains
pip install -e .

🔄 Migration from v0.2.x

Old API (still supported):

from piedomains import domain
result = domain.pred_shalla_cat_with_text(["example.com"])

New API (recommended):

from piedomains import DomainClassifier
classifier = DomainClassifier()
result = classifier.classify_by_text(["example.com"])

📖 Documentation

• API Reference: https://piedomains.readthedocs.io

• Examples: /examples directory

• Notebooks: /piedomains/notebooks (training & analysis)

🤝 Contributing

# Setup development environment
git clone https://github.com/themains/piedomains
cd piedomains
pip install -e ".[dev]"

# Run tests
pytest piedomains/tests/ -v

# Run linting
flake8 piedomains/

📄 License

MIT License - see LICENSE file.

📚 Citation

If you use piedomains in research, please cite:

@software{piedomains,
  title={piedomains: AI-powered domain content classification},
  author={Chintalapati, Rajashekar and Sood, Gaurav},
  year={2024},
  url={https://github.com/themains/piedomains}
}

—

Legacy Documentation

For legacy API documentation, see LEGACY_API.rst