Update app.py

app.py

@@ -104,12 +104,13 @@ class Visualizer:
             if d <= thresh and (neighbor == entity2 or neighbor == entity1) and len(neighbors) < num_neighbors + 1:
                 neighbors.add(entity1)
                 neighbors.add(entity2)
-                spring_tension = (thresh - d) * 1000  # for vis purposes
+                spring_tension = ((thresh + 0.1) - d) * 100  # for vis purposes
                 G.add_edge(entity1, entity2, weight=spring_tension)
         neighbors.remove(neighbor)
+        thresh_for_neighbors = max([x for _, _, x in normalized_distances])
         for entity1, entity2, d in tqdm(normalized_distances):
             if entity2 in neighbors and entity1 in neighbors:
-                spring_tension = (thresh - d) * 10
+                spring_tension = (thresh_for_neighbors + 0.1) - d
                 G.add_edge(entity1, entity2, weight=spring_tension)
 
         pos = nx.spring_layout(G, weight="weight", iterations=200, threshold=1e-6)  # Positions for all nodes
@@ -117,11 +118,12 @@ class Visualizer:
         nx.draw_networkx_nodes(G, pos, node_size=1, alpha=0.01)
         edges_connected_to_specific_node = [(u, v) for u, v in G.edges() if u == neighbor or v == neighbor]
         nx.draw_networkx_edges(G, pos, edgelist=edges_connected_to_specific_node, edge_color='orange', alpha=0.4, width=3)
-        # edges_not_connected_to_specific_node = [(u, v) for u, v in G.edges() if u != neighbor and v != neighbor]
-        # nx.draw_networkx_edges(G, pos, edgelist=edges_not_connected_to_specific_node, edge_color='gray', alpha=0.1, width=1)
+        if num_neighbors < 6:
+            edges_not_connected_to_specific_node = [(u, v) for u, v in G.edges() if u != neighbor and v != neighbor]
+            nx.draw_networkx_edges(G, pos, edgelist=edges_not_connected_to_specific_node, edge_color='gray', alpha=0.05, width=1)
         for u, v, d in edges:
             if u == neighbor or v == neighbor:
-                nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): round((thresh - (d['weight'] / 1000)) * 100, 2)}, font_color="red", alpha=0.4)  # reverse modifications
+                nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): round(((thresh + 0.1) - (d['weight'] / 100)) * 100, 2)}, font_color="red", alpha=0.4)  # reverse modifications
         nx.draw_networkx_labels(G, pos, font_size=14, font_family='sans-serif', font_color='green')
         nx.draw_networkx_labels(G, pos, labels={neighbor: neighbor}, font_size=14, font_family='sans-serif', font_color='red')
         plt.title(f'Graph of {distance_type}')
@@ -152,6 +154,5 @@ if __name__ == '__main__':
                          description="<br><br> This demo allows you to find the nearest neighbors of a language from the ISO 639-3 list according to several distance measurement functions. "
                                      "For more information, check out our paper: https://arxiv.org/abs/2406.06403 and our text-to-speech tool, in which we make use of "
                                      "this technique: https://github.com/DigitalPhonetics/IMS-Toucan <br><br>",
-                         fill_width=True,
                          allow_flagging="never")
     iface.launch()