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import streamlit as st | |
from bertopic import BERTopic | |
import re | |
import pandas as pd | |
from sklearn.feature_extraction.text import CountVectorizer | |
st.set_page_config(page_title='eRupt Topic Trendy (e-Commerce x Social Media)', page_icon=None, layout='centered', initial_sidebar_state='auto') | |
st.markdown("<h1 style='text-align: center;'>Topic Trendy</h1>", unsafe_allow_html=True) | |
BerTopic_model = BERTopic.load("my_topics_model") | |
input_text = st.text_area("Enter product topic here") | |
topic = pd.read_csv('./Data/tiktok_utf8.csv') | |
timestamps = topic.date.to_list() | |
tiktok = topic.text.to_list() | |
vectorizer_model = CountVectorizer(stop_words="english") | |
topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model) | |
topics, probs = topic_model.fit_transform(tiktok) | |
similar_topics, similarity = topic_model.find_topics(input_text, top_n=20) | |
most_similar = similar_topics[0] | |
print(similar_topics[0]) | |
print("Most Similar Topic Info: \n{}".format(topic_model.get_topic(most_similar))) | |
print("Similarity Score: {}".format(similarity[0])) | |
answer_as_string = topic_model.get_topic(most_similar) | |
st.text_area("Most Similar Topic List is Here",answer_as_string,key="topic_list") | |
st.image('https://freepngimg.com/download/keyboard/6-2-keyboard-png-file.png',use_column_width=True) | |
st.markdown("<h6 style='text-align: center; color: #808080;'>Created By LiHE</a></h6>", unsafe_allow_html=True) | |