Update data_processor.py

data_processor.py

@@ -243,9 +243,89 @@ class DataProcessor:
             'Total Number of Days Available': [total_days]
         })
 
-    def compute_student_metrics(self):
-        # Add metrics processing logic here
-        pass
+    def compute_student_metrics(self, df):
+        intervention_df = df[df[self.INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
+        intervention_sessions_held = len(intervention_df)
+        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()
+            student_data = intervention_df[[col]].copy()
+            student_data[col] = student_data[col].fillna('Absent')
+
+            attendance_values = student_data[col].apply(lambda x: 1 if x in [
+                self.ENGAGED_STR,
+                self.PARTIALLY_ENGAGED_STR,
+                self.NOT_ENGAGED_STR
+            ] else 0)
+
+            sessions_attended = attendance_values.sum()
+            attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
+            attendance_pct = round(attendance_pct)
+
+            engagement_counts = {
+                'Engaged': 0,
+                'Partially Engaged': 0,
+                'Not Engaged': 0,
+                'Absent': 0
+            }
+
+            for x in student_data[col]:
+                if x == self.ENGAGED_STR:
+                    engagement_counts['Engaged'] += 1
+                elif x == self.PARTIALLY_ENGAGED_STR:
+                    engagement_counts['Partially Engaged'] += 1
+                elif x == self.NOT_ENGAGED_STR:
+                    engagement_counts['Not Engaged'] += 1
+                else:
+                    engagement_counts['Absent'] += 1 # Count as Absent if not engaged
+
+            # Calculate percentages for engagement states
+            total_sessions = sum(engagement_counts.values())
+            
+            # Engagement (%)
+            engagement_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            engagement_pct = round(engagement_pct)
+    
+            engaged_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            engaged_pct = round(engaged_pct)
+    
+            partially_engaged_pct = (engagement_counts['Partially Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            partially_engaged_pct = round(partially_engaged_pct)
+    
+            not_engaged_pct = (engagement_counts['Not Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            not_engaged_pct = round(not_engaged_pct)
+    
+            absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
+            absent_pct = round(absent_pct)
+    
+            # Store metrics in the required order
+            student_metrics[student_name] = {
+                'Attendance (%)': attendance_pct,
+                'Attendance #': sessions_attended,  # Raw number of sessions attended
+                'Engagement (%)': engagement_pct,
+                'Engaged (%)': engaged_pct,
+                'Partially Engaged (%)': partially_engaged_pct,
+                'Not Engaged (%)': not_engaged_pct,
+                'Absent (%)': absent_pct
+            }
+
+        # 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
+    
+    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
+        
+        # Round the averages to make them whole numbers
+        attendance_avg_stats = round(attendance_avg_stats)
+        engagement_avg_stats = round(engagement_avg_stats)
+        
+        return attendance_avg_stats, engagement_avg_stats
     
     def evaluate_student(self, row, attendance_threshold=90, engagement_threshold=80):
         if row["Attended ≥ 90%"] == "No":