Add help text for sliders

app.py

@@ -25,20 +25,7 @@ side_bg_ext = "png"
 
 st.markdown(
     f"""
-    <div id="footer">
-        &copy;Copyright: WII, Technology Laboratory<br>
-        Authors: Shashank Sawan &amp; Paras Shah
-    </div>
     <style>
-        #footer {{
-            position: fixed;
-            bottom: 0;
-            width: 95%;
-            padding: 5px;
-            text-align: right;
-            font-size: 14px;
-            z-index: 5;
-        }}
         [data-testid="stSidebar"] {{
             background: url(data:image/{side_bg_ext};base64,{base64.b64encode(open(side_bg, "rb").read()).decode()});
             color: #ffff00;
@@ -81,7 +68,9 @@ st.sidebar.markdown(
         "customizable canopy volume, enabling precise refinement of predictions "
         "and analyses. By integrating species-specific and volumetric insights, "
         "the tool enhances ecological research workflows, facilitating data-driven "
-        "decision-making.</div>"
+        "decision-making.<br><br>"
+        "<div style='text-align: right; font-size: 10px;'>&copy;Copyright: WII, "
+        "Technology Laboratory<br>Authors: Shashank Sawan &amp; Paras Shah</div></div>"
         ,
     unsafe_allow_html=True,
 )
@@ -106,18 +95,22 @@ with col2:
         help=
             "Adjust the Z-threshold value to calculate the canopy volume " 
             "within specified limits, it uses Quickhull and DBSCAN algorithms. "
-            
-            "The Quickhull algorithm computes the convex hull of a set of points "
-            "by identifying extreme points to form an initial boundary and recursively "
-            "refining it by adding the farthest points until all points lie within the "
-            "convex boundary. It uses a divide-and-conquer approach, similar to QuickSort. "
-            
-            "DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is 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."
     )
+st.markdown(
+    body=
+        "<div style='text-align: justify; font-size: 13px;'>"
+        "The <b>Quickhull algorithm</b> computes the convex hull of a set of points "
+        "by identifying extreme points to form an initial boundary and recursively "
+        "refining it by adding the farthest points until all points lie within the "
+        "convex boundary. It uses a divide-and-conquer approach, similar to QuickSort. "
+        "<br>"
+        "<b>DBSCAN (Density-Based Spatial Clustering of Applications with Noise)</b> is "
+        "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.",
+    unsafe_allow_html=True
+)
     
 col1, col2 = st.columns(2)
 with col1:
@@ -132,15 +125,19 @@ with col2:
         help=
             "Adjust to calculate the DBH value within specified limits, "
             "it utilizes Least square circle fitting method Levenberg-Marquardt "
-            "optimization technique."
-            
-            "The Least Squares Circle Fitting method is used to find the "
-            "best-fitting circle to a set of 2D points by minimizing the sum of "
-            "squared distances between each point and the circle's circumference."
-            "Levenberg-Marquardt Optimization is used to fit models (like circles) "
-            "to point cloud data by minimizing the error between the model and the "
-            "actual points."
+            "optimization technique."   
     )
+st.markdown(
+    body=
+        "<div style='text-align: justify; font-size:13px;'>"
+        "The <b>Least Squares Circle Fitting method</b> is used to find the "
+        "best-fitting circle to a set of 2D points by minimizing the sum of "
+        "squared distances between each point and the circle's circumference. "
+        "<b>Levenberg-Marquardt Optimization</b> is used to fit models "
+        "(like circles) to point cloud data by minimizing the error between "
+        "the model and the actual points.</div>",
+        unsafe_allow_html=True
+)
     
 proceed = None
 if uploaded_file:
@@ -256,7 +253,6 @@ if proceed:
                 predicted_label = CLASSES[predicted_class]
             
         st.write(f"**Predicted class: {predicted_label}**")
-        # st.write(f"Class Probabilities: {probabilities.numpy().tolist()}")
         st.write(f"**Confidence score: {confidence_score:.2f}%**")
         st.write(f"**Height of tree: {height:.2f}m**")
         st.write(f"**Canopy volume: {canopy_volume:.2f}m\u00b3**")