streamlit-agraph 0.0.41

Interactive Graph Vis for Streamlit.

Based on react-graph-vis

Install

pip install streamlit-agraph

Use

import streamlit
from streamlit_agraph import agraph, Node, Edge, Config

nodes = []
edges = []
nodes.append( Node(id="Spiderman", 
                   label="Peter Parker", 
                   size=25, 
                   shape="circularImage",
                   image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_spiderman.png") 
            ) # includes **kwargs
nodes.append( Node(id="Captain_Marvel", 
                   size=25,
                   shape="circularImage",
                   image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_captainmarvel.png") 
            )
edges.append( Edge(source="Captain_Marvel", 
                   label="friend_of", 
                   target="Spiderman", 
                   # **kwargs
                   ) 
            ) 

config = Config(width=500, 
                height=500, 
                # **kwargs
                ) 

return_value = agraph(nodes=nodes, 
                      edges=edges, 
                      config=config)

You may also want to use the TripleStore (untested & incomplete - yet):

# Currently not workin since update to agraph 2.0 - work in progress
from rdflib import Graph
from streamlit_agraph import TripleStore, agraph

graph = Graph()
graph.parse("http://www.w3.org/People/Berners-Lee/card")
store = TripleStore()

for subj, pred, obj in graph:
    store.add_triple(subj, pred, obj, "")

agraph(list(store.getNodes()), list(store.getEdges()), config)

Also graph algos can dirctly supported via the networkx API (untested & incomplete - yet):

from streamlit_agraph import GraphAlgos

algos = GraphAlgos(store)
algos.shortest_path("Spiderman", "Captain_Marvel")
algos.density()

Formating the graph with hierachies is also possible, see examples/iris_decision_tree.py: