Upload Homepage.py

streamlit_app.py/pages/Homepage.py

@@ -0,0 +1,46 @@
+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 analyze 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.
+    """
+)
+
+