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Add app.py and datasets
Browse files- Data/tiktok_utf8.csv +0 -0
- app.py +35 -0
- requirements.txt +1 -0
Data/tiktok_utf8.csv
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app.py
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import streamlit as st
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from bertopic import BERTopic
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import re
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import pandas as pd
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from sklearn.feature_extraction.text import CountVectorizer
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st.set_page_config(page_title='eRupt Topic Trendy (e-Commerce x Social Media)', page_icon=None, layout='centered', initial_sidebar_state='auto')
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st.markdown("<h1 style='text-align: center;'>Topic Trendy</h1>", unsafe_allow_html=True)
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BerTopic_model = BERTopic.load("my_topics_model")
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input_text = st.text_area("Enter product topic here")
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topic = pd.read_csv('./Data/tiktok_utf8.csv')
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timestamps = topic.date.to_list()
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tiktok = topic.text.to_list()
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vectorizer_model = CountVectorizer(stop_words="english")
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topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
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topics, probs = topic_model.fit_transform(tiktok)
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similar_topics, similarity = topic_model.find_topics(input_text, top_n=20)
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most_similar = similar_topics[0]
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print(similar_topics[0])
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print("Most Similar Topic Info: \n{}".format(topic_model.get_topic(most_similar)))
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print("Similarity Score: {}".format(similarity[0]))
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answer_as_string = topic_model.get_topic(most_similar)
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st.text_area("Most Similar Topic List is Here",answer_as_string,key="topic_list")
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st.image('https://freepngimg.com/download/keyboard/6-2-keyboard-png-file.png',use_column_width=True)
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st.markdown("<h6 style='text-align: center; color: #808080;'>Created By LiHE</a></h6>", unsafe_allow_html=True)
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requirements.txt
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bertopic==0.10.0
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