Metadata-Version: 2.4
Name: sv-smartpdf
Version: 0.1.2
Summary: High-level PDF structure extraction library for developers.
Author: SV SmartPDF Team
License: MIT
License-File: LICENSE
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.9
Requires-Dist: pandas>=2.0.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: pymupdf>=1.23.0
Provides-Extra: dev
Requires-Dist: black>=23.0.0; extra == 'dev'
Requires-Dist: mypy>=1.0.0; extra == 'dev'
Requires-Dist: pytest>=7.0.0; extra == 'dev'
Requires-Dist: ruff>=0.1.0; extra == 'dev'
Provides-Extra: ml
Requires-Dist: pdfplumber>=0.10.0; extra == 'ml'
Requires-Dist: unstructured>=0.11.0; extra == 'ml'
Provides-Extra: ocr
Requires-Dist: pillow>=10.0.0; extra == 'ocr'
Requires-Dist: pytesseract>=0.3.10; extra == 'ocr'
Description-Content-Type: text/markdown

# sv-smartpdf

The best high-level PDF structure extraction library in Python that solves the painful manual work developers currently face.

Existing tools are powerful but low-level. `sv-smartpdf` provides a beautiful, high-level interface for extracting hierarchical sections, tables, figures, and metadata from complex PDFs.

## Installation

```bash
pip install sv-smartpdf
```

For OCR support:
```bash
pip install sv-smartpdf[ocr]
```

## Quick Start

```python
from sv_smartpdf import parse

# Parse a document
doc = parse("paper.pdf")

# Beautiful high-level interface
print(doc.title)
print(doc.metadata)

# Hierarchical sections
for section in doc.sections:
    print(f"{'  ' * section.level}- {section.title}")
    
# Rich table objects
for table in doc.tables:
    print(table.df) # pandas DataFrame
```
