https://github.com/ettorecaputo/streamlit-annotator

st-annotator is a Streamlit component usefull to annotate text, expecially for NLP and Argument Mining purposes.
https://github.com/ettorecaputo/streamlit-annotator

annotator argument mining nlp streamlit streamlit-components

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st-annotator is a Streamlit component usefull to annotate text, expecially for NLP and Argument Mining purposes.

• Host: GitHub
• URL: https://github.com/ettorecaputo/streamlit-annotator
• Owner: EttoreCaputo
• License: mit
• Created: 2025-06-26T15:01:22.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-06-26T22:06:53.000Z (11 months ago)
• Last Synced: 2025-06-26T23:17:29.725Z (11 months ago)
• Topics: annotator, argument, mining, nlp, streamlit, streamlit-components
• Language: TypeScript
• Homepage: https://pypi.org/project/st-annotator/
• Size: 4 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE.txt

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README

          

# Streamlit Annotator

**Download the package from PyPI:**

[![PyPI version](https://badge.fury.io/py/st-annotator.svg)](https://badge.fury.io/py/st-annotator)

**Try the demo on Streamlit Cloud:**

[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://st-annotator.streamlit.app)

# Install with pip

```

pip install st-annotator

```

st-annotator is a Streamlit component usefull to annotate text, expecially for NLP and Argument Mining purposes.

Based on the original project [Streamlit Annotation Tools](https://github.com/streamlit/annotation-tools) of [rmarquet21](https://github.com/rmarquet21).

![](docs/example.png)

### Features:

- 📍 Smart positioning that stays within screen bounds

- 📊 Shows text content, label category, position, and all custom metadata

- 🎨 Matches your custom color scheme

- ⚡ Instant display on hover, disappears on mouse leave

- 🔧 Supports strings, numbers, booleans, lists, and objects

### New Features

- Key parameter to text_annotator function

- A special button to show all the annotations together

- **🆕 Hover Popup with Metadata**: Hover over annotations to see detailed information including custom metadata

### Metadata Support

You can now add custom metadata to each annotation that will appear in a hover popup:

```python

labels = {

    "Sentiment": [

        {

            "start": 0,

            "end": 20,

            "label": "This is amazing!",

            "metadata": {

                "confidence": 0.95,

                "emotion": "Joy",

                "intensity": "High",

                "source": "Customer feedback"

            }

        }

    ]

}

```

# Quick Start

Run the example.py file:

```

streamlit run examples/example.py

```