import streamlit as st
from st_pages import Page, show_pages

st.set_page_config(page_title="Sentiment Analysis", page_icon="🏠")

show_pages(
    [
        Page("streamlit_app.py/Homepage.py", "Home", "🏠"),
        Page(
            "streamlit_app.py/pages/Sentiment_Analysis.py", "Sentiment Analysis", "📝"
        ),
    ]
)

st.title("Final Project in Machine Learning Course - Sentiment Analysis")
st.markdown(
    """
    **Team members:**
    | Student ID | Full Name                |
    | ---------- | ------------------------ |
    | 19120600   | Bùi Nguyên Nghĩa         |
    | 20120089   | Lê Xuân Hoàng            |
    | 20120422   | Nguyễn Thị Ánh Tuyết     |
    | 20120460   | Lê Nguyễn Hải Dương      |
    | 20120494   | Lê Xuân Huy              |
    """
)

st.header("The Need for Sentiment Analysis")
st.markdown(
    """
    Sentiment analysis algorithms are used to detect sentiment in a comment or a review.
    It is said that around 90% of consumers read online reviews before visiting a business or buying a product.
    These reviews can be positive or negative or neutral, and it is important to know what the customers are saying about your business.
    """
)

st.header("Technology used")
st.markdown(
    """
    In this demo, we used BERT as the model for sentiment analysis. BERT is a transformer-based model that was proposed in 2018 by Google.
    It is a pre-trained model that can be used for various NLP tasks such as sentiment analysis, question answering, etc.
    """
)