Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+import networkx as nx
+
+# Create lollipop graph
+G = nx.lollipop_graph(4, 6)
+
+# Initialize a list for path lengths
+pathlengths = []
+
+# Display the source-target shortest path lengths
+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])
+
+# Calculate and display the average shortest path length
+avg_path_length = sum(pathlengths) / len(pathlengths)
+st.write(f"### Average shortest path length: {avg_path_length}")
+
+# Calculate and display the distribution of path lengths
+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")
+
+# Display the graph metrics
+st.write(f"### Graph Metrics")
+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)}")
+
+# Visualize the graph
+st.write("### Graph Visualization")
+pos = nx.spring_layout(G, seed=3068)  # Seed layout for reproducibility
+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)