Update data_processor.py

data_processor.py

@@ -182,8 +182,6 @@
 
 
 
-
-
 import re
 import pandas as pd
 import os
@@ -210,7 +208,6 @@ class DataProcessor:
         return pd.read_excel(uploaded_file)
 
     def format_session_data(self, df):
-        # Look for "Date of Session" or "Date" column
         date_column = next((col for col in df.columns if col in ["Date of Session", "Date"]), None)
         if date_column:
             df[date_column] = pd.to_datetime(df[date_column], errors='coerce').dt.date
@@ -276,10 +273,9 @@ class DataProcessor:
             'Intervention Sessions Not Held': [total_days - sessions_held],
             'Total Number of Days Available': [total_days]
         })
-    
 
     def classify_engagement(self, engagement_str):
-        engagement_str = engagement_str.lower()
+        engagement_str = str(engagement_str).lower()
         if engagement_str.startswith(self.ENGAGED_STR.lower()):
             return self.ENGAGED_STR
         elif engagement_str.startswith(self.PARTIALLY_ENGAGED_STR.lower()):
@@ -289,84 +285,82 @@ class DataProcessor:
         else:
             return 'Unknown'
 
-def compute_student_metrics(self, df):
-    intervention_column = self.find_intervention_column(df)
-    intervention_df = df[df[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 self.classify_engagement(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 = {
-            self.ENGAGED_STR: 0,
-            self.PARTIALLY_ENGAGED_STR: 0,
-            self.NOT_ENGAGED_STR: 0,
-            'Absent': 0
-        }
-
-        for x in student_data[col]:
-            classified_engagement = self.classify_engagement(x)
-            if classified_engagement in engagement_counts:
-                engagement_counts[classified_engagement] += 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[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)
-
-        partially_engaged_pct = (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
-        partially_engaged_pct = round(partially_engaged_pct)
-
-        not_engaged_pct = (engagement_counts[self.NOT_ENGAGED_STR] / 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)
-
-        # 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"
-
-        # Store metrics in the required order
-        student_metrics[student_name] = {
-            'Attended ≥ 90%': attended_90,
-            'Engagement ≥ 80%': engaged_80,
-            'Attendance (%)': attendance_pct,
-            'Engagement (%)': engagement_pct,
-            f'{self.ENGAGED_STR} (%)': engaged_pct,
-            f'{self.PARTIALLY_ENGAGED_STR} (%)': partially_engaged_pct,
-            f'{self.NOT_ENGAGED_STR} (%)': 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_student_metrics(self, df):
+        intervention_column = self.find_intervention_column(df)
+        intervention_df = df[df[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 self.classify_engagement(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 = {
+                self.ENGAGED_STR: 0,
+                self.PARTIALLY_ENGAGED_STR: 0,
+                self.NOT_ENGAGED_STR: 0,
+                'Absent': 0
+            }
+    
+            for x in student_data[col]:
+                classified_engagement = self.classify_engagement(x)
+                if classified_engagement in engagement_counts:
+                    engagement_counts[classified_engagement] += 1
+                else:
+                    engagement_counts['Absent'] += 1  # Count as Absent if not engaged
+    
+            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)
+    
+            partially_engaged_pct = (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
+            partially_engaged_pct = round(partially_engaged_pct)
+    
+            not_engaged_pct = (engagement_counts[self.NOT_ENGAGED_STR] / 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)
     
+            # 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"
+    
+            # Store metrics in the required order
+            student_metrics[student_name] = {
+                'Attended ≥ 90%': attended_90,
+                'Engagement ≥ 80%': engaged_80,
+                'Attendance (%)': attendance_pct,
+                'Engagement (%)': engagement_pct,
+                f'{self.ENGAGED_STR} (%)': engaged_pct,
+                f'{self.PARTIALLY_ENGAGED_STR} (%)': partially_engaged_pct,
+                f'{self.NOT_ENGAGED_STR} (%)': 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