Documentation

app.py

@@ -4,8 +4,15 @@ import networkx as nx
 import numpy as np
 dataset = load_dataset("roneneldan/TinyStories")
 
+st.markdown('# Short Stories, networks and connections')
+st.markdown('In this example we consider the semantic similarity between short stories generatited by GenAI.')
+st.markdown('We study the relationshis between the stories using a network. The laplacian connectivity provides inights about the closeness of the graph')
+
+st.markdown('# Short Stories')
+st.markdown('We are using a sample fo the TinyStories dataset from roneneldan work')
 st.write(dataset['train'][10]['text'])
 
+st.markdown('The threshold changes the level of connectivity in the network. The reange is from 0 (less similar) to 1 (completely similar)')
 threshhold = st.slider('Threshhold',0.0,1.0,step=0.1)
 
 #-------------------------------------------------------------
@@ -39,6 +46,8 @@ for i in range(len(sentences)):
 #G = nx.from_numpy_array(A)
 G = nx.from_numpy_array(cosine_scores.numpy()>threshhold)
 
+st.markdown('We can visualize the similarity between the shorts stories as a network. It the similarity is greater than the threshold, the two nodes are conencted')
+
 
 #-------------------------------------------------------------
 #-------------------------------------------------------------
@@ -98,4 +107,5 @@ return_value = agraph(nodes=nodes,
                       edges=edges, 
                       config=config)
 
+st.markdown('The Laplacian centrality is a measure of closeness')
 st.write(str(nx.laplacian_centrality(G)))