Update app.py

app.py

@@ -11,14 +11,14 @@ st.write("Thesis Project by Group DJY of Mapua University")
 st.image("pages/Cervical-Cancer-Cells.jpg", caption='', width=700)
 
 
-st.header("How does this app work?")
+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.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
@@ -29,15 +29,15 @@ st.markdown("""
 st.sidebar.info("Please select a page above 👆")
 
 st.sidebar.write("""
-### Background
+### 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 is vital for effective treatment. 
 
-### The Problem
+### The Problem 💡
 Current methods in early detection 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.
 """)
 
-st.sidebar.image("pages/Mapua-logo.png", width=300)
+st.sidebar.image("pages/Mapua-logo.png", width=250)