Delete streamlit_app.py/Homepage.py

streamlit_app.py/Homepage.py

@@ -1,49 +0,0 @@
-import streamlit as st
-from st_pages import Page, show_pages
-
-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.
-    """
-)
-