Update pages/17_Graphs2.py

pages/17_Graphs2.py

@@ -78,39 +78,45 @@ def create_sample_graph():
 def main():
     st.title("Graph Neural Network Architecture Visualization")
 
-    # Create sample graph
-    nx_graph, graph_tensor, node_features, edge_features = create_sample_graph()
-
-    # Create and compile the model
-    model = model_fn(graph_tensor.spec)
-    model.compile(optimizer='adam', loss='binary_crossentropy')
-
-    # Display model summary
-    st.subheader("Model Summary")
-    model.summary(print_fn=lambda x: st.text(x))
-
-    # Visualize the graph
-    st.subheader("Sample Graph Visualization")
-    fig, ax = plt.subplots(figsize=(10, 8))
-    pos = nx.spring_layout(nx_graph)
-    labels = nx.get_node_attributes(nx_graph, 'title')
-    nx.draw(nx_graph, pos, labels=labels, with_labels=True, node_color='lightblue',
-            node_size=3000, arrowsize=20, ax=ax)  # Increased node_size to 3000
-    st.pyplot(fig)
-
-    # Display graph tensor info
-    st.subheader("Graph Tensor Information")
-    st.text(f"Number of nodes: {graph_tensor.node_sets['papers'].total_size}")
-    st.text(f"Number of edges: {graph_tensor.edge_sets['cites'].total_size}")
-    st.text(f"Node feature shape: {graph_tensor.node_sets['papers']['features'].shape}")
-    st.text(f"Edge feature shape: {graph_tensor.edge_sets['cites']['features'].shape}")
-
-    # Display sample node and edge features
-    st.subheader("Sample Node and Edge Features")
-    st.write("Node Features (Year Published, Number of Authors):")
-    st.write(node_features)
-    st.write("Edge Features (Citation Weight):")
-    st.write(edge_features)
+    if st.button("Recreate Graph"):
+        recreate_graph = True
+    else:
+        recreate_graph = False
+
+    if recreate_graph:
+        # Create sample graph
+        nx_graph, graph_tensor, node_features, edge_features = create_sample_graph()
+
+        # Create and compile the model
+        model = model_fn(graph_tensor.spec)
+        model.compile(optimizer='adam', loss='binary_crossentropy')
+
+        # Display model summary
+        st.subheader("Model Summary")
+        model.summary(print_fn=lambda x: st.text(x))
+
+        # Visualize the graph
+        st.subheader("Sample Graph Visualization")
+        fig, ax = plt.subplots(figsize=(10, 8))
+        pos = nx.spring_layout(nx_graph)
+        labels = nx.get_node_attributes(nx_graph, 'title')
+        nx.draw(nx_graph, pos, labels=labels, with_labels=True, node_color='lightblue',
+                node_size=3000, arrowsize=20, ax=ax)  # Increased node_size to 3000
+        st.pyplot(fig)
+
+        # Display graph tensor info
+        st.subheader("Graph Tensor Information")
+        st.text(f"Number of nodes: {graph_tensor.node_sets['papers'].total_size}")
+        st.text(f"Number of edges: {graph_tensor.edge_sets['cites'].total_size}")
+        st.text(f"Node feature shape: {graph_tensor.node_sets['papers']['features'].shape}")
+        st.text(f"Edge feature shape: {graph_tensor.edge_sets['cites']['features'].shape}")
+
+        # Display sample node and edge features
+        st.subheader("Sample Node and Edge Features")
+        st.write("Node Features (Year Published, Number of Authors):")
+        st.write(node_features)
+        st.write("Edge Features (Citation Weight):")
+        st.write(edge_features)
 
 if __name__ == "__main__":
     main()