app.py

import streamlit as st
import networkx as nx
import matplotlib.pyplot as plt

# Sidebar for selecting an option
sidebar_option = st.sidebar.radio("Select an option", 
                                 ["Select an option", "Basic: Properties", 
                                  "Basic: Read and write graphs", "Basic: Simple graph", 
                                  "Basic: Simple graph Directed", "Drawing: Custom Node Position", 
                                  "Drawing: Chess Masters", "Drawing: Cluster Layout"])

# Helper function to draw and display graph
def draw_graph(G, pos=None, title="Graph Visualization", edgewidth=None, nodesize=None):
    if edgewidth is None:
        edgewidth = [1] * len(G.edges())  # Default edge width if not provided

    if nodesize is None:
        nodesize = [300] * len(G.nodes())  # Default node size if not provided

    plt.figure(figsize=(12, 12))
    nx.draw_networkx_edges(G, pos, alpha=0.3, width=edgewidth, edge_color="m")
    nx.draw_networkx_nodes(G, pos, node_size=nodesize, node_color="#210070", alpha=0.9)
    label_options = {"ec": "k", "fc": "white", "alpha": 0.7}
    nx.draw_networkx_labels(G, pos, font_size=14, bbox=label_options)

    # Title/legend
    font = {"fontname": "Helvetica", "color": "k", "fontweight": "bold", "fontsize": 14}
    ax = plt.gca()
    ax.set_title(title, font)
    ax.text(
        0.80,
        0.10,
        "edge width = # games played",
        horizontalalignment="center",
        transform=ax.transAxes,
        fontdict=font,
    )
    ax.text(
        0.80,
        0.06,
        "node size = # games won",
        horizontalalignment="center",
        transform=ax.transAxes,
        fontdict=font,
    )

    # Resize figure for label readability
    ax.margins(0.1, 0.05)
    plt.axis("off")
    st.pyplot(plt)

# Drawing: Cluster Layout
def display_cluster_layout():
    st.title("Drawing: Cluster Layout")

    option = st.radio("Choose a graph type:", ("Default Example", "Create your own"))

    if option == "Default Example":
        G = nx.davis_southern_women_graph()

        # Compute communities using greedy modularity community detection
        communities = nx.community.greedy_modularity_communities(G)

        # Compute positions for the node clusters as if they were themselves nodes in a supergraph
        supergraph = nx.cycle_graph(len(communities))
        superpos = nx.spring_layout(G, scale=50, seed=429)

        # Use the "supernode" positions as the center of each node cluster
        centers = list(superpos.values())
        pos = {}
        for center, comm in zip(centers, communities):
            pos.update(nx.spring_layout(nx.subgraph(G, comm), center=center, seed=1430))

        # Nodes colored by cluster
        for nodes, clr in zip(communities, ("tab:blue", "tab:orange", "tab:green")):
            nx.draw_networkx_nodes(G, pos=pos, nodelist=nodes, node_color=clr, node_size=100)
        nx.draw_networkx_edges(G, pos=pos)

        plt.tight_layout()
        st.pyplot(plt)

    elif option == "Create your own":
        uploaded_file = st.file_uploader("Upload your own graph file (in GML format)", type="gml")
        if uploaded_file is not None:
            G_custom = nx.read_gml(uploaded_file)
            # Compute communities using greedy modularity community detection
            communities = nx.community.greedy_modularity_communities(G_custom)

            # Compute positions for the node clusters as if they were themselves nodes in a supergraph
            supergraph = nx.cycle_graph(len(communities))
            superpos = nx.spring_layout(G_custom, scale=50, seed=429)

            # Use the "supernode" positions as the center of each node cluster
            centers = list(superpos.values())
            pos = {}
            for center, comm in zip(centers, communities):
                pos.update(nx.spring_layout(nx.subgraph(G_custom, comm), center=center, seed=1430))

            # Nodes colored by cluster
            for nodes, clr in zip(communities, ("tab:blue", "tab:orange", "tab:green")):
                nx.draw_networkx_nodes(G_custom, pos=pos, nodelist=nodes, node_color=clr, node_size=100)
            nx.draw_networkx_edges(G_custom, pos=pos)

            plt.tight_layout()
            st.pyplot(plt)

# Display other sections
def display_basic_properties():
    st.title("Basic: Properties")
    option = st.radio("Choose a graph type:", ("Default Example", "Create your own"))

    # Default example: 5x5 grid graph
    if option == "Default Example":
        G = nx.lollipop_graph(4, 6)
        display_graph_properties(G)

    elif option == "Create your own":
        num_nodes = st.number_input("Number of nodes:", min_value=2, max_value=50, value=5)
        num_edges = st.number_input("Number of edges per group (for lollipop graph):", min_value=1, max_value=10, value=3)

        if st.button("Generate"):
            if num_nodes >= 2 and num_edges >= 1:
                G_custom = nx.lollipop_graph(num_nodes, num_edges)
                display_graph_properties(G_custom)

def display_graph_properties(G):
    pathlengths = []
    st.write("### Source vertex {target:length, }")
    for v in G.nodes():
        spl = dict(nx.single_source_shortest_path_length(G, v))
        st.write(f"Vertex {v}: {spl}")
        for p in spl:
            pathlengths.append(spl[p])

    avg_path_length = sum(pathlengths) / len(pathlengths)
    st.write(f"### Average shortest path length: {avg_path_length}")

    dist = {}
    for p in pathlengths:
        if p in dist:
            dist[p] += 1
        else:
            dist[p] = 1

    st.write("### Length #paths")
    for d in sorted(dist.keys()):
        st.write(f"Length {d}: {dist[d]} paths")

    st.write("### Properties")
    st.write(f"Radius: {nx.radius(G)}")
    st.write(f"Diameter: {nx.diameter(G)}")
    st.write(f"Eccentricity: {nx.eccentricity(G)}")
    st.write(f"Center: {nx.center(G)}")
    st.write(f"Periphery: {nx.periphery(G)}")
    st.write(f"Density: {nx.density(G)}")

    st.write("### Graph Visualization")
    pos = nx.spring_layout(G, seed=3068)
    plt.figure(figsize=(8, 6))
    nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500, font_size=10, font_weight='bold')
    st.pyplot(plt)

# Display other sections
def display_read_write_graph():
    st.title("Basic: Read and write graphs")
    G = nx.karate_club_graph()

    # Write the graph
    nx.write_gml(G, "karate_club.gml")
    st.write("Graph written to 'karate_club.gml'.")

    # Read the graph back
    G_new = nx.read_gml("karate_club.gml")
    st.write("Graph read back from 'karate_club.gml'.")
    nx.draw(G_new, with_labels=True)
    st.pyplot(plt)

def display_simple_graph():
    st.title("Basic: Simple graph")
    G = nx.complete_graph(5)
    nx.draw(G, with_labels=True)
    st.pyplot(plt)

def display_simple_directed_graph():
    st.title("Basic: Simple graph Directed")
    G = nx.complete_graph(5, nx.DiGraph())
    nx.draw(G, with_labels=True)
    st.pyplot(plt)

def display_custom_node_position():
    st.title("Drawing: Custom Node Position")
    pos = {"A": (1, 2), "B": (2, 3), "C": (3, 1)}
    G = nx.Graph(pos)
    nx.draw(G, pos=pos, with_labels=True)
    st.pyplot(plt)


# Call the appropriate function based on sidebar selection
if sidebar_option == "Basic: Properties":
    display_basic_properties()
elif sidebar_option == "Basic: Read and write graphs":
    display_read_write_graph()
elif sidebar_option == "Basic: Simple graph":
    display_simple_graph()
elif sidebar_option == "Basic: Simple graph Directed":
    display_simple_directed_graph()
elif sidebar_option == "Drawing: Custom Node Position":
    display_custom_node_position()
elif sidebar_option == "Drawing: Chess Masters":
    display_chess_masters_graph()
elif sidebar_option == "Drawing: Cluster Layout":
    display_cluster_layout()
else:
    st.write("Please select a valid option from the sidebar.")