Update app.py

app.py

@@ -8,6 +8,7 @@ st.set_page_config(
 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.image("pages/Cervical-Cancer-Cells.jpg", caption='', width=650)
 
 
 st.header("How does this app work?")
@@ -38,3 +39,5 @@ Current methods in early detection like the Pap smear test can be slow and labor
 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=250)