Bug fixes (duplicate edges, preserving start node)

app.py

@@ -58,21 +58,19 @@ def dynamic_visualize_graph(graph, start_node, layers=3, top_k=5):
     var options = {
       "physics": {
         "barnesHut": {
-          "gravitationalConstant": -20000,
-          "centralGravity": 0.6
-        } 
+          "gravitationalConstant": -15000,
+          "centralGravity": 0.8
+        }
       }
-      }
-      """)
-
-
+    }
+    """)
 
     visited_nodes = set()
+    added_edges = set()  # Track edges to avoid duplicates
     current_nodes = [int(start_node)]  # Convert start_node to int
+
     # Add the starting node, color it red, and include a tooltip
     start_title = graph.nodes[int(start_node)].get('title', 'No title available')  # Get the title attribute
-    if isinstance(start_title, float) and math.isnan(start_title):  # Check if the title is NaN
-        start_title = "No title available"
     net.add_node(int(start_node), label=str(start_node), color="red", title=start_title)
     visited_nodes.add(int(start_node))
 
@@ -87,14 +85,12 @@ def dynamic_visualize_graph(graph, start_node, layers=3, top_k=5):
 
             for neighbor, weight in neighbors:
                 if neighbor not in visited_nodes:
-                    neighbor_title = graph.nodes[neighbor].get('title', 'No title available')
-                    if isinstance(neighbor_title, float) and math.isnan(neighbor_title):  # Check if the title is NaN
-                        neighbor_title = "No title available"
-                    if not isinstance(neighbor_title, str):
-                        neighbor_title = str(neighbor_title)  # Ensure it's converted to a string
-                    net.add_node(neighbor, label=str(neighbor), title=neighbor_title)
-
-                net.add_edge(node, neighbor, label=f"w:{weight}")  # Add the edge regardless of visited status
+                    neighbor_title = graph.nodes[neighbor].get('title', 'No title available')  # Get tooltip
+                    net.add_node(neighbor, label=str(neighbor), title=neighbor_title)  # Add node with tooltip
+                edge = (node, neighbor)  # Represent the edge as a tuple
+                if edge not in added_edges:
+                    net.add_edge(node, neighbor, label=f"w:{weight}")  # Add the edge only if not already added
+                    added_edges.add(edge)  # Track this edge as added
                 visited_nodes.add(neighbor)
                 next_nodes.append(neighbor)  # Always add to next_nodes for further expansion
 
@@ -104,6 +100,7 @@ def dynamic_visualize_graph(graph, start_node, layers=3, top_k=5):
     html_content = net.generate_html()
     st.components.v1.html(html_content, height=600, scrolling=False)
 
+
 def display_node_info(graph, node_id):
     """
     Display all attributes of a node and its edges in the graph.
@@ -141,21 +138,17 @@ st.title("Interactive Graph Expansion with Tooltips")
 # Brand Selection
 selected_brand = st.selectbox("Select a brand:", options=list(BRAND_GRAPHS.keys()))
 
-# Load the graph for the selected brand
-G = load_graph(selected_brand)
-
-# Filter the graph for popular nodes and edges
-node_degree_threshold = 1  # Minimum degree for nodes
-edge_weight_threshold = 1   # Minimum weight for edges
-G_filtered = filter_graph(G, node_threshold=node_degree_threshold, edge_threshold=edge_weight_threshold)
-#print('spot check degree', G.degree(389062))
-#print('spot check degree', G_filtered.degree(389062))
-#display_node_info(G_filtered, 389062)
 import random
-# Randomly sample a starting node
-# Initialize session state for the starting node
-#if "start_node" not in st.session_state:
-st.session_state.start_node = random.choice(list(G_filtered.nodes))  # Randomly select a node once
+
+# Check if the brand has changed
+if "selected_brand" not in st.session_state or st.session_state.selected_brand != selected_brand:
+    # Load the new graph and reset the start node
+    st.session_state.selected_brand = selected_brand
+    G = load_graph(selected_brand)
+    st.session_state.start_node = random.choice(list(G.nodes))
+else:
+    # Use the existing graph
+    G = load_graph(selected_brand)
 
 # Input: Starting node
 start_node = st.number_input(
@@ -163,6 +156,12 @@ start_node = st.number_input(
     value=st.session_state.start_node,
     step=1
 )
+
+# Filter the graph for popular nodes and edges
+node_degree_threshold = 1  # Minimum degree for nodes
+edge_weight_threshold = 1   # Minimum weight for edges
+G_filtered = filter_graph(G, node_threshold=node_degree_threshold, edge_threshold=edge_weight_threshold)
+
 layers = st.slider("Depth to explore:", 1, 6, value=3)
 top_k = st.slider("Branching factor (per node):", 1, 6, value=3)