Update app.py

app.py

@@ -277,7 +277,7 @@ def compute_intervention_statistics(df):
 
     # Intervention Frequency (%)
     intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
-    intervention_frequency = round(intervention_frequency, 2)
+    intervention_frequency = round(intervention_frequency, 0)
 
     # Reorder columns as specified
     stats = {
@@ -301,11 +301,14 @@ def plot_intervention_statistics(intervention_stats):
     ax.bar(['Intervention Sessions'], [sessions_not_held], bottom=[sessions_held], label='Not Held', color='#91D6B8')
 
     # Display values on the bars
-    ax.text(0, sessions_held / 2, str(sessions_held), ha='center', va='center', color='white')
-    ax.text(0, sessions_held + sessions_not_held / 2, str(sessions_not_held), ha='center', va='center', color='black')
+    ax.text(0, sessions_held / 2, str(sessions_held), ha='center', va='center', color='white', 
+            fontweight='bold', fontsize=14)
+    ax.text(0, sessions_held + sessions_not_held / 2, str(sessions_not_held), ha='center', va='center', color='black', 
+            fontweight='bold', fontsize=14)
 
     # Update chart settings
     ax.set_ylabel('Frequency')
+    ax.set_title('Intervention Sessions Held vs Not Held', fontsize=16)  # Optional: Increased title font size
     # Reverse the legend order to match the new stacking order
     handles, labels = ax.get_legend_handles_labels()
     ax.legend(handles[::-1], labels[::-1])
@@ -313,62 +316,6 @@ def plot_intervention_statistics(intervention_stats):
 
     return fig
 
-# def compute_student_metrics(df):
-#     # Filter DataFrame for sessions where intervention happened
-#     intervention_df = df[df[INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
-#     intervention_sessions_held = len(intervention_df)
-
-#     # Get list of student columns
-#     student_columns = [col for col in df.columns if col.startswith('Student Attendance')]
-
-#     student_metrics = {}
-
-#     for col in student_columns:
-#         student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
-#         # Get the attendance data for the student
-#         student_data = intervention_df[[col]].copy()
-
-#         # Treat blank entries as 'Absent'
-#         student_data[col] = student_data[col].fillna('Absent')
-
-#         # Assign attendance values
-#         attendance_values = student_data[col].apply(lambda x: 1 if x in [
-#             ENGAGED_STR,
-#             PARTIALLY_ENGAGED_STR,
-#             NOT_ENGAGED_STR
-#         ] else 0)
-
-#         # Number of Sessions Attended
-#         sessions_attended = attendance_values.sum()
-
-#         # Attendance (%)
-#         attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
-#         attendance_pct = round(attendance_pct)  # Round to whole number
-
-#         # For engagement calculation, include only sessions where attendance is not 'Absent'
-#         valid_engagement_indices = attendance_values[attendance_values == 1].index
-#         engagement_data = student_data.loc[valid_engagement_indices, col]
-
-#         # Assign engagement values
-#         engagement_values = engagement_data.apply(lambda x: 1 if x == ENGAGED_STR
-#                                                   else 0.5 if x == PARTIALLY_ENGAGED_STR else 0)
-
-#         # Sum of Engagement Values
-#         sum_engagement_values = engagement_values.sum()
-
-#         # Number of Sessions Attended for engagement (should be same as sessions_attended)
-#         number_sessions_attended = len(valid_engagement_indices)
-
-#         # Engagement (%)
-#         engagement_pct = (sum_engagement_values / number_sessions_attended) * 100 if number_sessions_attended > 0 else 0
-#         engagement_pct = round(engagement_pct) # Round to whole number
-
-#         # Store metrics
-#         student_metrics[student_name] = {
-#             'Attendance (%)': attendance_pct,
-#             'Engagement (%)': engagement_pct
-#         }
-
 def compute_student_metrics(df):
     # Filter DataFrame for sessions where intervention happened
     intervention_df = df[df[INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
@@ -442,7 +389,7 @@ def compute_student_metrics(df):
         # Store metrics in the required order
         student_metrics[student_name] = {
             'Attendance (%)': attendance_pct,
-            'Attendance': sessions_attended,  # Raw number of sessions attended
+            'Attendance #': sessions_attended,  # Raw number of sessions attended
             'Engagement (%)': engagement_pct,
             'Engaged (%)': engaged_pct,
             'Partially Engaged (%)': partially_engaged_pct,
@@ -450,12 +397,22 @@ def compute_student_metrics(df):
             'Absent (%)': absent_pct
         }
 
-        # return student_metrics
+    # # Create a DataFrame from student_metrics
+    # student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
+    # student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
+    # return student_metrics_df
 
     # Create a DataFrame from student_metrics
     student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
     student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
-    return student_metrics_df
+    
+    # Use Pandas styler to bold specific columns and then export to HTML
+    styled_df = student_metrics_df.style \
+      .set_properties(**{'font-weight': 'bold'}, subset=pd.IndexSlice[:, ['Student', 'Attendance (%)', 'Engagement (%)']]) \
+      .set_table_styles([{'selector': 'th.colheading', 'props': [('font-weight', 'bold')]}], overwrite=False)
+    
+    # Return the styled DataFrame as an HTML string
+    return styled_df.to_html()
 
 def plot_student_metrics(student_metrics_df):
     # Create the figure and axis
@@ -463,6 +420,7 @@ def plot_student_metrics(student_metrics_df):
 
     # Width for the bars
     bar_width = 0.35  # Width of the bars
+
     index = range(len(student_metrics_df))  # Index for each student
 
     # Plot Attendance and Engagement bars side by side
@@ -494,6 +452,9 @@ def plot_student_metrics(student_metrics_df):
     ax.legend(loc='upper right', bbox_to_anchor=(1.25, 1), borderaxespad=0.)
     ax.set_xticks(index)  # Set x-ticks to the index
     ax.set_xticklabels(student_metrics_df['Student'], rotation=0, ha='right')  # Set student names as x-tick labels
+    # Set the y-axis limits and tick locations
+    ax.set_ylim(0, 100)  # Range from 0 to 100
+    ax.yaxis.set_ticks(range(0, 101, 10))  # Increments of 10
 
     # Display the plot
     plt.tight_layout()  # Adjust layout to fit elements