Update app.py

app.py

@@ -1,4 +1,48 @@
 import streamlit as st
+from st_pages import Page, show_pages
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.set_page_config(page_title="Information Retrieval", page_icon="🏠")
+
+show_pages(
+    [
+        Page("streamlit_app.py/Homepage.py", "Home", "🏠"),
+        Page(
+            "streamlit_app.py/pages/Information_Retrieval.py", "Information Retrieval", "📝"
+        ),
+    ]
+)
+
+st.title("Project in Text Minining and Application - Information Retrieval")
+st.markdown(
+    """
+    **Team members:**
+    | Student ID | Full Name                | Email                          |
+    | ---------- | ------------------------ | ------------------------------ |
+    | 1712603    | Lê Quang Nam             | [email protected]   |
+    | 19120582   | Lê Nhựt Minh             | [email protected]  |
+    | 19120600   | Bùi Nguyên Nghĩa         | [email protected]  |
+    | 21120198   | Nguyễn Thị Lan Anh       | [email protected]  |
+    """
+)
+
+st.header("The Need for Information Retrieval")
+st.markdown(
+    """
+    The task of classifying whether a question and a context paragraph are related to 
+    each other is based on two main steps: word embedding and classifier. Both of these 
+    steps together constitute the process of analyzing and evaluating the relationship 
+    between the question and the context.
+    """
+)
+
+st.header("Technology used")
+st.markdown(
+    """
+    The ELECTRA model, specifically the "google/electra-small-discriminator" used here, 
+    is a deep learning model in the field of natural language processing (NLP) developed 
+    by Google. This model is an intelligent variation of the supervised learning model 
+    based on the Transformer architecture, designed to understand and process natural language efficiently.
+    For this text classification task, we choose two related classes: ElectraTokenizer and 
+    FElectraForSequenceClassification to implement.
+    """
+)