---
title: Structured Docling
emoji: 📄
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: gpl-3.0
---

# Docling Structured Extraction Demo

A Gradio-based demo application for extracting structured data from documents using Docling's beta structured extraction feature.

## Features

- 📄 Support for PDF and image files (PNG, JPG, JPEG, TIFF, BMP)
- 🌐 URL input for remote documents
- 🎯 Customizable JSON templates for extraction
- 🚀 Optimized for Hugging Face Spaces with Zero GPU support
- 📊 Clean JSON output with extracted data

## Files

- `app.py` - Standard Gradio application
- `app_hf_spaces.py` - Version optimized for Hugging Face Spaces with Zero GPU decorator
- `requirements.txt` - Python dependencies

## Installation

```bash
pip install -r requirements.txt
```

## Usage

### Local Development

Run the standard version:
```bash
python app.py
```

### Hugging Face Spaces

The `app_hf_spaces.py` file is specifically designed for deployment on Hugging Face Spaces with Zero GPU support.

To deploy:
1. Create a new Space on Hugging Face
2. Upload `app_hf_spaces.py` (rename to `app.py`)
3. Upload `requirements.txt`
4. Enable Zero GPU in Space settings

## How to Use the Demo

1. **Input Source**: Either upload a document file or provide a URL to a document
2. **Define Template**: Create a JSON template specifying the fields you want to extract
   - Use `"string"` for text fields
   - Use `"float"` for decimal numbers
   - Use `"int"` for whole numbers
3. **Extract**: Click the "Extract" button to process the document
4. **View Results**: The extracted data will appear in JSON format in the output box

## Template Examples

### Simple Invoice Extraction
```json
{
  "bill_no": "string",
  "total": "float",
  "date": "string"
}
```

### Detailed Invoice Extraction
```json
{
  "bill_no": "string",
  "total": "float",
  "sender_name": "string",
  "receiver_name": "string",
  "postal_code": "string",
  "city": "string"
}
```

## Notes

- The structured extraction API is currently in **beta** and may change
- Only PDF and image formats are supported
- The extraction uses Vision Language Models (VLM) for understanding document content
- Processing time depends on document complexity and size

## Requirements

- Python 3.9+
- gradio >= 4.0.0
- docling[vlm] >= 2.0.0
- spaces >= 0.19.0 (for Hugging Face Spaces deployment)

## License

This demo is provided as-is for demonstration purposes.