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import streamlit as st
from pyvis.network import Network
import plotly.express as px
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import numpy as np
import networkx as nx
import textwrap
import logging
from streamlit.report_thread import REPORT_CONTEXT_ATTR_NAME
from threading import current_thread
from contextlib import contextmanager
from io import StringIO
import sys
import time
logger = logging.getLogger('main')
def reset_default_topic_sliders(min_topic_size, n_gram_range):
st.session_state['min_topic_size'] = min_topic_size
st.session_state['n_gram_range'] = n_gram_range
def reset_default_threshold_slider(threshold):
st.session_state['threshold'] = threshold
@st.cache()
def load_data(uploaded_file):
data = pd.read_csv(uploaded_file)
return data
@st.cache()
def embedding_gen(data):
logger.info('Calculating Embeddings')
return SentenceTransformer('allenai-specter').encode(data['Text'])
@st.cache()
def load_bertopic_model(min_topic_size, n_gram_range):
logger.info('Loading BERTopic model')
return BERTopic(
vectorizer_model=CountVectorizer(
stop_words='english', ngram_range=n_gram_range
),
min_topic_size=min_topic_size,
verbose=True
)
@st.cache()
def topic_modeling(data, min_topic_size, n_gram_range):
"""Topic modeling using BERTopic
"""
logger.info('Calculating Topic Model')
topic_model = load_bertopic_model(min_topic_size, n_gram_range)
# Train the topic model
topic_data = data.copy()
topic_data["Topic"], topic_data["Probs"] = topic_model.fit_transform(
data['Text'], embeddings=embedding_gen(data))
# Merge topic results
topic_df = topic_model.get_topic_info()
topic_df.columns = ['Topic', 'Topic_Count', 'Topic_Name']
topic_df = topic_df.sort_values(by='Topic_Count', ascending=False)
topic_data = topic_data.merge(topic_df, on='Topic', how='left')
# Topics
# Optimization: Only take top 10 largest topics
topics = topic_df.head(10).set_index('Topic').to_dict(orient='index')
logger.info('Topic Modeling Complete')
return topic_data, topic_model, topics
@st.cache()
def cosine_sim(data):
logger.info('Cosine similarity')
cosine_sim_matrix = cosine_similarity(embedding_gen(data))
# Take only upper triangular matrix
cosine_sim_matrix = np.triu(cosine_sim_matrix, k=1)
return cosine_sim_matrix
@st.cache()
def calc_max_connections(num_papers, ratio):
n = ratio*num_papers
return n*(n-1)/2
@st.cache()
def calc_neighbors(cosine_sim_matrix, threshold):
neighbors = np.argwhere(cosine_sim_matrix >= threshold).tolist()
return neighbors, len(neighbors)
@st.cache()
def calc_optimal_threshold(cosine_sim_matrix, max_connections):
"""Calculates the optimal threshold for the cosine similarity matrix.
Allows a max of max_connections
"""
logger.info('Calculating optimal threshold')
thresh_sweep = np.arange(0.05, 1.05, 0.05)[::-1]
for idx, threshold in enumerate(thresh_sweep):
_, num_neighbors = calc_neighbors(cosine_sim_matrix, threshold)
if num_neighbors > max_connections:
break
return round(thresh_sweep[idx-1], 2).item(), round(thresh_sweep[idx], 2).item()
def nx_hash_func(nx_net):
"""Hash function for NetworkX graphs.
"""
return (list(nx_net.nodes()), list(nx_net.edges()))
def pyvis_hash_func(pyvis_net):
"""Hash function for pyvis graphs.
"""
return (pyvis_net.nodes, pyvis_net.edges)
@st.cache(hash_funcs={nx.Graph: nx_hash_func, Network: pyvis_hash_func})
def network_plot(topic_data, topics, neighbors):
"""Creates a network plot of connected papers. Colored by Topic Model topics.
"""
logger.info('Calculating Network Plot')
nx_net = nx.Graph()
pyvis_net = Network(height='750px', width='100%', bgcolor='#222222')
# Add Nodes
nodes = [
(
row.Index,
{
'group': row.Topic,
'label': row.Index,
'title': row.Text,
'size': 20, 'font': {'size': 20, 'color': 'white'}
}
)
for row in topic_data.itertuples()
]
nx_net.add_nodes_from(nodes)
assert(nx_net.number_of_nodes() == len(topic_data))
# Add Edges
nx_net.add_edges_from(neighbors)
assert(nx_net.number_of_edges() == len(neighbors))
# Optimization: Remove Isolated nodes
nx_net.remove_nodes_from(list(nx.isolates(nx_net)))
# Add Legend Nodes
step = 150
x = -2000
y = -500
legend_nodes = [
(
len(topic_data)+idx,
{
'group': key, 'label': ', '.join(value['Topic_Name'].split('_')[1:]),
'size': 30, 'physics': False, 'x': x, 'y': f'{y + idx*step}px',
# , 'fixed': True,
'shape': 'box', 'widthConstraint': 1000, 'font': {'size': 40, 'color': 'black'}
}
)
for idx, (key, value) in enumerate(topics.items())
]
nx_net.add_nodes_from(legend_nodes)
# Plot the Pyvis graph
pyvis_net.from_nx(nx_net)
return nx_net, pyvis_net
def text_processing(text):
text = text.split('[SEP]')
text = '<br><br>'.join(text)
text = '<br>'.join(textwrap.wrap(text, width=50))[:500]
text = text + '...'
return text
@st.cache()
def network_centrality(topic_data, centrality, centrality_option):
"""Calculates the centrality of the network
"""
logger.info('Calculating Network Centrality')
# Sort Top 10 Central nodes
central_nodes = sorted(
centrality.items(), key=lambda item: item[1], reverse=True)
central_nodes = pd.DataFrame(central_nodes, columns=[
'node', centrality_option]).set_index('node')
joined_data = topic_data.join(central_nodes)
top_central_nodes = joined_data.sort_values(
centrality_option, ascending=False).head(10)
# Prepare for plot
top_central_nodes = top_central_nodes.reset_index()
top_central_nodes['index'] = top_central_nodes['index'].astype(str)
top_central_nodes['Topic_Name'] = top_central_nodes['Topic_Name'].apply(
lambda x: ', '.join(x.split('_')[1:]))
top_central_nodes['Text'] = top_central_nodes['Text'].apply(
text_processing)
# Plot the Top 10 Central nodes
fig = px.bar(top_central_nodes, x=centrality_option, y='index',
color='Topic_Name', hover_data=['Text'], orientation='h')
fig.update_layout(yaxis={'categoryorder': 'total ascending', 'visible': False, 'showticklabels': False},
font={'size': 15}, height=800)
return fig
# Progress bar printer
# https://github.com/BugzTheBunny/streamlit_logging_output_example/blob/main/app.py
# https://discuss.streamlit.io/t/cannot-print-the-terminal-output-in-streamlit/6602/34
@contextmanager
def st_redirect(src, dst):
placeholder = st.empty()
output_func = getattr(placeholder, dst)
with StringIO() as buffer:
old_write = src.write
def new_write(b):
if getattr(current_thread(), REPORT_CONTEXT_ATTR_NAME, None):
buffer.write(b)
time.sleep(1)
buffer.seek(0) # returns pointer to 0 position
output_func(b)
else:
old_write(b)
try:
src.write = new_write
yield
finally:
src.write = old_write
@contextmanager
def st_stdout(dst):
"this will show the prints"
with st_redirect(sys.stdout, dst):
yield
@contextmanager
def st_stderr(dst):
"This will show the logging"
with st_redirect(sys.stderr, dst):
yield
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