A streamlit custom component for visualizing and interacting with graphs
Project description
st-link-analysis
A custom Streamlit component for link analysis, built with Cytoscape.js and Streamlit.
Overview
This project provides a Streamlit custom component for visualizing and interacting with graph data using Cytoscape.js. It supports customizable edge and node styles, labels, colors, captions, and icons.
Demo
A demo deployed with Render can be accessed here.
Features
- Customizable Node and Edge Styles: Easily define the appearance of nodes and edges using a variety of style options.
- Material Icons Support: Supports a subset of Material icons for styling nodes which can be passed by name (e.g.,
icon='person'
). Arbitrary icons can still be used by passing a url (icon='url(...)'
). - Customizable Layouts: Choose from different layout algorithms to arrange the graph elements. Currently only Cytoscape JS base layouts are supported.
- Interactive Features:
- Fullscreen button.
- JSON export button.
- Layout refresh button.
- Highlights neighboring nodes or edges when an element is selected.
- View all properties of the selected elements in a sidebar.
Installation
To install the package, use pip:
pip install st-link-analysis
Usage
Here is a basic example of how to use the component in your Streamlit app:
import streamlit as st
from st_link_analysis import st_link_analysis, NodeStyle, EdgeStyle
st.set_page_config(layout="wide")
# Sample Data
elements = {
"nodes": [
{"data": {"id": 1, "label": "PERSON", "name": "Streamlit"}},
{"data": {"id": 2, "label": "PERSON", "name": "Hello"}},
{"data": {"id": 3, "label": "PERSON", "name": "World"}},
{"data": {"id": 4, "label": "POST", "content": "x"}},
{"data": {"id": 5, "label": "POST", "content": "y"}},
],
"edges": [
{"data": {"id": 6, "label": "FOLLOWS", "source": 1, "target": 2}},
{"data": {"id": 7, "label": "FOLLOWS", "source": 2, "target": 3}},
{"data": {"id": 8, "label": "POSTED", "source": 3, "target": 4}},
{"data": {"id": 9, "label": "POSTED", "source": 1, "target": 5}},
{"data": {"id": 10, "label": "QUOTES", "source": 5, "target": 4}},
],
}
# Style node & edge groups
node_styles = [
NodeStyle("PERSON", "#FF7F3E", "name", "person"),
NodeStyle("POST", "#2A629A", "content", "description"),
]
edge_styles = [
EdgeStyle("FOLLOWS", labeled=True, directed=True),
EdgeStyle("POSTED", labeled=True, directed=True),
EdgeStyle("QUOTES", labeled=True, directed=True),
]
# Render the component
st.markdown("### st-link-analysis: Example")
st_link_analysis(elements, "cose", node_styles, edge_styles)
API Reference
Element | Description |
---|---|
NodeStyle |
Defines styles for nodes, including labels, colors, captions, and icons. Refer to docstring. |
EdgeStyle |
Defines styles for edges, including curve styles, labels, colors, and directionality. Refer to docstring. |
st_link_analysis |
Main component for creating and displaying the graph, including layout and height settings. Refer to docstring. |
Development
- Ensure you have Python 3.9+, Node.js, and npm installed.
- Clone this repository
- Navigate to root directory
Python Setup
Create and activate a virtual environment, then install the package in editable mode:
python3 -m venv .venv
source .venv/bin/activate # On Windows use `.venv\Scripts\activate`
pip install -e .
Node Setup
Navigate to the frontend directory and install the necessary npm packages:
cd st_link_analysis/frontend
npm install
Running the App
Change RELEASE
flag in st_link_analysis/component/component.py
to False
.
In one terminal start the frontend dev server
cd st_link_analysis/frontend
npm run start
In another terminal run the streamlit server
cd examples
streamlit run app.py
Testing
Install the testing requirements and run linting and tests:
pip install tests/requirements.txt
ruff check
pytest
Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments
Project details
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