Final beautification

app.py

@@ -47,9 +47,10 @@ st.markdown(
 st.sidebar.markdown(
     body=
         "<div style='text-align: justify; color: #ffff00'>"
-        "<h1 style='color: #ffff00; font-size: 4rem;'>About</h1>The species <strong>Pinus sylvestris "
-        "(Scots Pine), Fagus sylvatica (European Beech), Picea abies (Norway Spruce), "
-        "and Betula pendula (Silver Birch)</strong> are native to Europe and parts "
+        "<h1 style='color: #ffff00; font-size: 4rem;'>About</h1>"
+        "The species <strong>Pinus sylvestris (Scots Pine), Fagus sylvatica "
+        "(European Beech), Picea abies (Norway Spruce), and Betula pendula "
+        "(Silver Birch)</strong> are native to Europe and parts "
         "of Asia but are also found in India (Parts of Himachal Pradesh, "
         "Uttarakhand, Jammu and Kashmir, Sikkim and Arunachal Pradesh). "
         "These temperate species, typically thriving in boreal and montane ecosystems, "
@@ -60,9 +61,9 @@ st.sidebar.markdown(
         "and environmental factors.<br><br>"
         "This AI-powered application employs the PointNet++ deep learning "
         "architecture, optimized for processing 3D point cloud data from "
-        "individual <code>.laz</code> files (fused aerial and terrestrial LiDAR) "
-        "to classify tree species up to four classes (<strong>Pinus sylvestris, "
-        "Fagus sylvatica, Picea abies, and Betula pendula</strong>) "
+        "individual <code>.las</code> <code>.laz</code> <code>.pcd</code> files "
+        "(fused aerial and terrestrial LiDAR) to classify tree species up to four classes "
+        "(<strong>Pinus sylvestris, Fagus sylvatica, Picea abies, and Betula pendula</strong>) "
         "with associated confidence scores. Additionally, it calculates critical "
         "metrics such as Diameter at Breast Height (DBH), actual height and "
         "customizable canopy volume, enabling precise refinement of predictions "
@@ -108,7 +109,8 @@ st.markdown(
         "a density-based clustering algorithm that groups densely packed points within "
         "a specified distance 'eps' and minimum points 'minpoints', while treating "
         "sparse points as noise. It effectively identifies arbitrarily shaped clusters "
-        "and handles outliers, making it suitable for spatial data and anomaly detection.",
+        "and handles outliers, making it suitable for spatial data and anomaly detection."
+        "</div><br>",
     unsafe_allow_html=True
 )