app.py

import streamlit as st
from datasets import load_dataset
import networkx as nx
import numpy as np
import pandas as pd
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](roneneldan/TinyStories) dataset from roneneldan work')
text_text = dataset['train'][10]['text']
st.markdown("<span style='color:red'>" + text_text.replace('\n',' ') + "</span>",unsafe_allow_html=True)

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)

#-------------------------------------------------------------
#-------------------------------------------------------------

from sentence_transformers import SentenceTransformer, util
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)]

#Compute embedding 
embeddings = model.encode(sentences, convert_to_tensor=True)

#Compute cosine-similarities
cosine_scores = util.cos_sim(embeddings, embeddings)

# creating adjacency matrix
A = np.zeros((len(sentences),len(sentences)))

#Output the pairs with their score
for i in range(len(sentences)):
    for j in range(i):
        #st.write("{} \t\t {} \t\t Score: {:.4f}".format(sentences[i], sentences[j], cosine_scores[i][j]))
        A[i][j] = cosine_scores[i][j]
        A[j][i] = cosine_scores[i][j]

#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')


#-------------------------------------------------------------
#-------------------------------------------------------------
# ego_graph.py
# An example of how to plot a node's ego network 
# (egonet). This indirectly showcases slightly more involved 
# interoperability between streamlit-agraph and networkx.

# An egonet can be # created from (almost) any network (graph),
# and exemplifies the # concept of a subnetwork (subgraph):
# A node's egonet is the (sub)network comprised of the focal node 
# and all the nodes to whom it is adjacent. The edges included
# in the egonet are those nodes are both included in the aforementioned
# nodes. 

# Use the following command to launch the app
# streamlit run <path-to-script>.py

# standard library dependencies
from operator import itemgetter

# external dependencies
import networkx as nx
from streamlit_agraph import agraph, Node, Edge, Config

# First create a graph using the Barabasi-Albert model
n = 2000
m = 2
#G = nx.generators.barabasi_albert_graph(n, m, seed=2023)

# Then find the node with the largest degree; 
# This node's egonet will be the focus of this example.
node_and_degree = G.degree()
most_connected_node = sorted(G.degree, key=lambda x: x[1], reverse=True)[0]
degree = G.degree(most_connected_node)

# Create egonet for the focal node
hub_ego = nx.ego_graph(G, most_connected_node[0])

# Now create the equivalent Node and Edge lists
nodes = [Node(title=str(sentences[i]['text']), 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]


config = Config(width=500, 
                height=500, 
                directed=True,
                nodeHighlightBehavior=False, 
                highlightColor="#F7A7A6", # or "blue"
                collapsible=False,
                node={'labelProperty':'label'},
                # **kwargs e.g. node_size=1000 or node_color="blue"
                ) 

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)))
d_lc = nx.laplacian_centrality(G)
#st.write(d_lc[0])
#df_lc = pd.DataFrame.from_dict(nx.laplacian_centrality(G))
df_lc = pd.DataFrame(np.transpose([list(d_lc.keys()),list(d_lc.values())]),columns=['node','laplacian_centrality'])
st.bar_chart(df_lc,x='node',y='laplacian_centrality')