import streamlit as st

st.set_page_config(page_title="Top2Vec", layout="wide") 

st.markdown(
    """
    # Introduction
    This is [space](https://huggingface.co/spaces) dedicated to using [top2vec](https://github.com/ddangelov/Top2Vec) and showing what features are available for semantic searching and topic modeling. 
    Please check out this [readme](https://github.com/ddangelov/Top2Vec#how-does-it-work) to better understand how it works.
    
    > Top2Vec is an algorithm for **topic modeling** and **semantic search**. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors.

    
    # Setup
    I used the [20 NewsGroups](https://huggingface.co/datasets/SetFit/20_newsgroups) dataset with `top2vec`. 
    I fit on the dataset and reduced the topics to 20. 
    The topics are created from top2vec, not the labels. 
    No analysis on the top 20 topics vs labels is provided.
    
    # Usage
    Check out
    - The [Topic Explorer](/Topic_Explorer) page to understand what topic were detected 
    - The [Document Explorer](/Document_Explorer) page to visually explore documents
    - The [Semantic Search](/Semantic_Search) page to search by meaning
    """
)