Update app.py

changed the html

app.py

@@ -888,29 +888,37 @@ When the equivalent ratios are easy to compute (integral values), ER is much mor
 <p style="color: white;">To use the demo, please follow these steps:</p>
 
 <ol style="color: white;">
-    <li style="color: white;">Select a percentage of schools to analyze (selecting a large percentage may take a long time). Note that the selected percentage is applied to both High Graduation Rate (HGR) schools and Low Graduation Rate (LGR schools).
-    </li>
-    <li style="color: white;">Select a fine-tuned model:
+<li style="color: white;">Select a percentage of schools to analyze (selecting a large percentage may take a long time). Note that the selected percentage is applied to both High Graduation Rate (HGR) schools and Low Graduation Rate (LGR schools).
+</li>
+    <li style="color: white;">Select a fine-tuned model:</li>
         <ul style="color: white;">
             <li style="color: white;">ASTRA-FT-HGR: Fine-tuned with 10 % data from schools that have a high graduation rate.</li>
             <li style="color: white;">ASTRA-FT-Skills: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills encoded using Bayesian Knowledge Tracing</li>
             <li style="color: white;">ASTRA-FT-Temporal: Fine-tuned with 10% of initial problems from both HGR and LGR schools + temporal features measuring student engagement with MATHia</li>
             <li style="color: white;">ASTRA-FT-Unified: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills + temporal features</li>
         </ul>
-    </li>
-
-    <li style="color: white;">The results from the fine-tuned model are displayed in the dashboard:
-        <ul>
-            <li style="color: white;">The model accuracy is computed using the ROC-AUC metric.
-</li>
-            <li style="color: white;">The results are shown for HGR, LGR schools and  for different problem types (ER/ME). 
+    
+    
+ <li style="color: white;">
+  The results from the fine-tuned model are displayed in the dashboard:
+  <ul>
+    <li style="color: white;">The model accuracy is computed using the ROC-AUC metric.</li>
+    <li style="color: white;">The results are shown for HGR, LGR schools and for different problem types (ER/ME).</li>
+    <li style="color: white;">The distribution over how students utilized the optional tasks (whether they utilized ER/ME, used both of them or none of them) is shown for each problem type.</li>
+  </ul>
 </li>
-<li style="color: white;">The distribution over how students utilized the optional tasks (whether they utilized ER/ME, used both of them or none of them) is shown for each problem type. 
+
+    
+<li style="color: white;">
+  Generate strategies from the AI model and visualize the strategies .csv files using the path analysis web-app. 
+  Refer to the 
+  <a href="https://drive.google.com/file/d/1wussSAYV5WfRSgYNEDgxWNqMUNoiu65u/view" 
+     style="color: #1E90FF; text-decoration: none;">
+     user guide
+  </a> 
+  for more details on how to interpret the strategy visualization.
 </li>
-        </ul>
-    </li>
-        <li style="color: white;">Generate strategies from the AI model and visualize the strategies .csv files using the path analysis web-app. 
-        Refer to the <a href="https://drive.google.com/file/d/1wussSAYV5WfRSgYNEDgxWNqMUNoiu65u/view" style="color: #1E90FF; text-decoration: none;">user guide </a> for more details on how to interpret the strategy visualization.
+
 
 </ol>
 """