Update data_processor.py

data_processor.py

@@ -188,9 +188,6 @@
 
         
 
-
-
-
 import re
 import pandas as pd
 import os
@@ -277,7 +274,7 @@ class DataProcessor:
         return self.intervention_column
 
     def compute_intervention_statistics(self, df):
-        intervention_column = self.find_intervention_column(df)
+        intervention_column = self.get_intervention_column(df)
         total_days = len(df)
         sessions_held = df[intervention_column].str.strip().str.lower().eq('yes').sum()
         intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
@@ -337,9 +334,6 @@ class DataProcessor:
     
             total_sessions = sum(engagement_counts.values())
             
-            engagement_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-            engagement_pct = round(engagement_pct)
-    
             engaged_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
             engaged_pct = round(engaged_pct)
     
@@ -352,11 +346,15 @@ class DataProcessor:
             absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
             absent_pct = round(absent_pct)
     
+            # Engagement percentage is based on Engaged and Partially Engaged sessions
+            engagement_pct = ((engagement_counts[self.ENGAGED_STR] + engagement_counts[self.PARTIALLY_ENGAGED_STR]) / total_sessions * 100) if total_sessions > 0 else 0
+            engagement_pct = round(engagement_pct)
+    
             # Determine if the student attended ≥ 90% of sessions
             attended_90 = "Yes" if attendance_pct >= 90 else "No"
     
             # Determine if the student was engaged ≥ 80% of the time
-            engaged_80 = "Yes" if engaged_pct >= 80 else "No"
+            engaged_80 = "Yes" if engagement_pct >= 80 else "No"
     
             # Store metrics in the required order
             student_metrics[student_name] = {
@@ -377,10 +375,10 @@ class DataProcessor:
 
     def compute_average_metrics(self, student_metrics_df):
         # Calculate the attendance and engagement average percentages across students
-        attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()  # Calculate the average attendance percentage
-        engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()  # Calculate the average engagement percentage
+        attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()  # Average attendance percentage
+        engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()  # Average engagement percentage
         
-        # Round the averages to make them whole numbers
+        # Round the averages to whole numbers
         attendance_avg_stats = round(attendance_avg_stats)
         engagement_avg_stats = round(engagement_avg_stats)
         
@@ -391,4 +389,5 @@ class DataProcessor:
             return "Address Attendance"
         elif row["Engagement ≥ 80%"] == "No":
             return "Address Engagement"
-        return "Consider barriers, fidelity, and progress monitoring"
+        else:
+            return "Consider barriers, fidelity, and progress monitoring"