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| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| from gensim import corpora, models | |
| from PIL import Image | |
| # Load the saved models and data | |
| dictionary = joblib.load('doc2bow.sav') | |
| lda_model = joblib.load('ldamodel.sav') | |
| # Function to preprocess input text and get topic distribution | |
| def get_topics(text): | |
| bow_vector = dictionary(text.split()) | |
| topics = lda_model[bow_vector] | |
| return topics | |
| # Function to get top keywords for a topic | |
| def get_top_keywords(topic, num_keywords=10): | |
| topic = lda_model.show_topic(topic, topn=num_keywords) | |
| keywords = [f"{word} ({weight:.3f})" for word, weight in topic] | |
| return keywords | |
| # Streamlit app | |
| def main(): | |
| st.title("Aplikasi Diskusi Topic Produk Menggunakan Latent Dirichlet Allocation pada Artikel Berita📰") | |
| # Sidebar with title and description | |
| st.sidebar.title("Aplikasi Diskusi Topic Produk") | |
| st.sidebar.write("Menemukan topik di artikel berita.") | |
| # Input text area for user to enter their text | |
| user_input = st.text_area("Masukkan artikel berita:") | |
| # Submit button | |
| if st.button("Kirim"): | |
| if user_input: | |
| # Process the user's input and get topic distribution | |
| topics = get_topics(user_input) | |
| # Display the top topics | |
| st.subheader("🔥Top Topik🔥") | |
| for topic in topics: | |
| st.write(f"**📍Topik {topic[0] + 1}** (Skor: {topic[1]:.4f})") | |
| top_keywords = get_top_keywords(topic[0]) | |
| st.markdown(", ".join(top_keywords)) | |
| st.write("---") | |
| # Add a footer | |
| st.sidebar.markdown("---") | |
| st.sidebar.write("© 2023 Aplikasi Diskusi Topic Produk") | |
| if __name__ == "__main__": | |
| main() |