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import streamlit as st
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from bertopic import BERTopic
import streamlit.components.v1 as components
from sentence_transformers import SentenceTransformer
from umap import UMAP
from hdbscan import HDBSCAN
from sklearn.feature_extraction.text import CountVectorizer
# Initialize BERTopic model
model = BERTopic()
st.subheader("Topic Modeling with Topic-Wizard")
uploaded_file = st.file_uploader("Choose a text file", type=["txt"])
if uploaded_file is not None:
st.session_state["text"] = uploaded_file.getvalue().decode("utf-8")
st.write("OR")
input_text = st.text_area(
label="Enter text separated by newlines",
value="",
key="text",
height=150,
)
button = st.button("Get Segments")
if button and (uploaded_file is not None or input_text != ""):
if uploaded_file is not None:
texts = st.session_state["text"].split("\n")
else:
texts = input_text.split("\n")
# Fit BERTopic model
topics, probabilities = model.fit_transform(texts)
# Create embeddings
embeddings_model = SentenceTransformer("distilbert-base-nli-mean-tokens")
embeddings = embeddings_model.encode(texts)
# Reduce dimensionality of embeddings using UMAP
umap_model = UMAP(n_neighbors=15, n_components=2, metric="cosine")
umap_embeddings = umap_model.fit_transform(embeddings)
# Cluster topics using HDBSCAN
cluster = HDBSCAN(
min_cluster_size=15, metric="euclidean", cluster_selection_method="eom"
).fit(umap_embeddings)
# Visualize BERTopic results with Streamlit
st.title("BERTopic Visualization")
# Display top N most representative topics and their documents
num_topics = st.sidebar.slider("Select number of topics to display", 1, 20, 5, 1)
topic_words, topic_docs = model.get_topics(num_topics=num_topics, with_documents=True)
for i, topic in enumerate(topic_words):
st.write(f"## Topic {i}")
st.write("Keywords:", ", ".join(topic))
st.write("Documents:")
for doc in topic_docs[i][:5]:
st.write("-", texts[doc])
# Display topic clusters
st.write("## Topic Clusters")
components.html(cluster.labels_.tolist(), height=500, width=800)
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