Update app.py

app.py

@@ -1,27 +1,43 @@
 import streamlit as st
 
 st.set_page_config(
-    page_title="title_name",
-    page_icon="icon",
+    page_title="U-Net Architecture Prototype",
+    page_icon="👋",
 )
 
-st.title("title_name")
-st.write("project_desc")
-st.write("project_mem/lead")
+st.title("Enhancing the Performance of SVM and CNN Models in Detection and Classification of Cervical Cells in Pap Smear Images Using U-Net Architecture for Image Segmentation")
+st.write("A prototype for our U-Net Architecture for Image Segmentation of Cervical Cancer Cells, and SVM and CNN for Classification of Cervical Cancer Cells")
+st.write("Thesis Project by Group DJY of Mapua University")
+
 
 st.header("How does this app work?")
 st.write("""
+Once you upload an image of cervical cancer cells using any of our models,
+they analyze the cell structure and classify the type of cancer present.
+The system will then predict the type of cancer cells based on the analysis.  
 """)
 
 st.subheader("How to use this app?")
 st.markdown("""
+1. Select any one model from the sidebar
+2. Upload the image of your cervical cancer cells & wait for a few seconds
+3. The model will analyze and classify the type of cancer present.
+4. The model will output the predicted the type of cancer cells based on the analysis
 """)
 
 st.sidebar.info("Please select a page above 👆")
 
 st.sidebar.write("""
 ### Background
+Cervical cancer is a significant health issue that profoundly impacts women's lives, making awareness and education crucial. 
+It is a leading cause of cancer-related morbidity and mortality among women globally. 
+Understanding the importance of regular screenings and vaccinations can empower individuals to take proactive steps in prevention. 
+Additionally, raising awareness about the signs and symptoms of cervical cancer fosters early detection, which is vital for effective treatment. 
+Through community support and education, we can work together to reduce the burden of this disease and promote healthier futures for women everywhere.
 
 ### The Problem
-
+Cervical cancer poses a significant risk to women's health, emphasizing the urgent need for early detection to prevent its progression. 
+Current methods like the Pap smear test can be slow and labor-intensive, prompting researchers to develop classification models to assist medical professionals. 
+However, many of these models face challenges with image segmentation, particularly in cases of overlapping cells. 
+This prototype seeks to improve upon existing machine learning models by incorporating the U-Net architecture, designed for precise image segmentation, to enhance the identification of cancerous cells in cervical samples, ultimately facilitating faster and more accurate diagnoses.
 """)