Update app.py

app.py

@@ -1,46 +1,49 @@
-import streamlit as st
-from st_pages import Page, show_pages
-
-st.set_page_config(page_title="Question Answering", page_icon="🏠")
-
-show_pages(
-    [
-        Page("app.py", "Home", "🏠"),
-        Page(
-            "QuestionAnswering.py", "Question Answering", "📝"
-        ),
-    ]
-)
-
-st.title("Project in Text Mining and Application")
-st.header("Question Answering use a pre-trained model - ELECTRA")
-st.markdown(
-    """
-    **Team members:**
-    | Student ID | Full Name                | Email                          |
-    | ---------- | ------------------------ | ------------------------------ |
-    | 1712603    | Lê Quang Nam             | 1712603@student.hcmus.edu.vn   |
-    | 19120582   | Lê Nhựt Minh             | 19120582@student.hcmus.edu.vn  |
-    | 19120600   | Bùi Nguyên Nghĩa         | 19120600@student.hcmus.edu.vn  |
-    | 21120198   | Nguyễn Thị Lan Anh       | 21120198@student.hcmus.edu.vn  |
-    """
-)
-
-st.header("The Need for Question Answering")
-st.markdown(
-    """
-    ...
-    """
-)
-
-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 Question Answering task, we choose two related classes: ElectraTokenizerFast and 
-    TFElectraForQuestionAnswering to implement.
-    """
+import streamlit as st
+from st_pages import Page, show_pages
+
+st.set_page_config(page_title="Question Answering", page_icon="🏠")
+
+show_pages(
+    [
+        Page("app.py", "Home", "🏠"),
+        Page(
+            "SampleQA.py", "Sample in Dataset", "📝"
+        ),
+        Page(
+            "QuestionAnswering.py", "Question Answering", "📝"
+        ),
+    ]
+)
+
+st.title("Project in Text Mining and Application")
+st.header("Question Answering use a pre-trained model - ELECTRA")
+st.markdown(
+    """
+    **Team members:**
+    | Student ID | Full Name                | Email                          |
+    | ---------- | ------------------------ | ------------------------------ |
+    | 1712603    | Lê Quang Nam             | 1712603@student.hcmus.edu.vn   |
+    | 19120582   | Lê Nhựt Minh             | 19120582@student.hcmus.edu.vn  |
+    | 19120600   | Bùi Nguyên Nghĩa         | 19120600@student.hcmus.edu.vn  |
+    | 21120198   | Nguyễn Thị Lan Anh       | 21120198@student.hcmus.edu.vn  |
+    """
+)
+
+st.header("The Need for Question Answering")
+st.markdown(
+    """
+    ...
+    """
+)
+
+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 Question Answering task, we choose two related classes: ElectraTokenizerFast and 
+    TFElectraForQuestionAnswering to implement.
+    """
 )