test size of network and adding labels to nodes

app.py

@@ -15,8 +15,8 @@ model = SentenceTransformer('all-MiniLM-L6-v2')
 # Sentences from the data set
 #sentences = [item['text'] for item in dataset['train'][:10]]
 
-sentences = [dataset['train'][0],dataset['train'][1],dataset['train'][2]]
-#sentences = [dataset['train'][ii] for ii in range(10)]
+#sentences = [dataset['train'][0],dataset['train'][1],dataset['train'][2]]
+sentences = [dataset['train'][ii] for ii in range(10)]
 
 #Compute embedding 
 embeddings = model.encode(sentences, convert_to_tensor=True)
@@ -77,7 +77,7 @@ degree = G.degree(most_connected_node)
 hub_ego = nx.ego_graph(G, most_connected_node[0])
 
 # Now create the equivalent Node and Edge lists
-nodes = [Node(id=i, label=str(i), size=20) for i in hub_ego.nodes]
+nodes = [Node(id=i, label='node_'+str(i), size=20) for i in hub_ego.nodes]
 edges = [Edge(source=i, target=j, type="CURVE_SMOOTH") for (i,j) in G.edges
         if i in hub_ego.nodes and j in hub_ego.nodes]