Update data_processor.py

data_processor.py

@@ -1,14 +1,200 @@
+# import pandas as pd
+# import os
+# import re
+# from huggingface_hub import InferenceClient
+# # from graphviz import Digraph
+
+# class DataProcessor:
+#     INTERVENTION_COLUMN = 'Did the intervention happen today?'
+#     ENGAGED_STR = 'Engaged (Respect, Responsibility, Effort)'
+#     PARTIALLY_ENGAGED_STR = 'Partially Engaged (about 50%)'
+#     NOT_ENGAGED_STR = 'Not Engaged (less than 50%)'
+
+#     def __init__(self, student_metrics_df=None):
+#         self.hf_api_key = os.getenv('HF_API_KEY')
+#         if not self.hf_api_key:
+#             raise ValueError("HF_API_KEY not set in environment variables")
+#         self.client = InferenceClient(api_key=self.hf_api_key)
+#         self.student_metrics_df = student_metrics_df
+
+#     def read_excel(self, uploaded_file):
+#         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
+#         else:
+#             print("Warning: Neither 'Date of Session' nor 'Date' column found in the dataframe.")
+        
+#         df['Timestamp'] = self.safe_convert_to_datetime(df['Timestamp'], '%I:%M %p')
+#         df['Session Start Time'] = self.safe_convert_to_time(df['Session Start Time'], '%I:%M %p')
+#         df['Session End Time'] = self.safe_convert_to_time(df['Session End Time'], '%I:%M %p')
+#         return df
+
+#     def safe_convert_to_time(self, series, format_str='%I:%M %p'):
+#         try:
+#             converted = pd.to_datetime(series, format='%H:%M:%S', errors='coerce')
+#             if format_str:
+#                 return converted.dt.strftime(format_str)
+#             return converted
+#         except Exception as e:
+#             print(f"Error converting series to time: {e}")
+#             return series
+
+#     def safe_convert_to_datetime(self, series, format_str=None):
+#         try:
+#             converted = pd.to_datetime(series, errors='coerce')
+#             if format_str:
+#                 return converted.dt.strftime(format_str)
+#             return converted
+#         except Exception as e:
+#             print(f"Error converting series to datetime: {e}")
+#             return series
+
+#     def replace_student_names_with_initials(self, df):
+#         updated_columns = []
+#         for col in df.columns:
+#             if col.startswith('Student Attendance'):
+#                 match = re.match(r'Student Attendance \[(.+?)\]', col)
+#                 if match:
+#                     name = match.group(1)
+#                     initials = ''.join([part[0] for part in name.split()])
+#                     updated_columns.append(f'Student Attendance [{initials}]')
+#                 else:
+#                     updated_columns.append(col)
+#             else:
+#                 updated_columns.append(col)
+#         df.columns = updated_columns
+#         return df
+
+#     def compute_intervention_statistics(self, df):
+#         total_days = len(df)
+#         sessions_held = df[self.INTERVENTION_COLUMN].str.strip().str.lower().eq('yes').sum()
+#         intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
+#         return pd.DataFrame({
+#             'Intervention Dosage (%)': [round(intervention_frequency, 0)],
+#             'Intervention Sessions Held': [sessions_held],
+#             'Intervention Sessions Not Held': [total_days - sessions_held],
+#             'Total Number of Days Available': [total_days]
+#         })
+
+#     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)
+    
+#             # 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,
+#                 # 'Attendance #': 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":
+#             return "Address Attendance"
+#         elif row["Engagement ≥ 80%"] == "No":
+#             return "Address Engagement"
+#         return "Consider barriers, fidelity, and progress monitoring"
+
+
+
+
+
+
+
+import re
 import pandas as pd
 import os
-import re
 from huggingface_hub import InferenceClient
-# from graphviz import Digraph
 
 class DataProcessor:
     INTERVENTION_COLUMN = 'Did the intervention happen today?'
-    ENGAGED_STR = 'Engaged (Respect, Responsibility, Effort)'
-    PARTIALLY_ENGAGED_STR = 'Partially Engaged (about 50%)'
-    NOT_ENGAGED_STR = 'Not Engaged (less than 50%)'
+    ENGAGED_STR = 'Engaged'
+    PARTIALLY_ENGAGED_STR = 'Partially Engaged'
+    NOT_ENGAGED_STR = 'Not Engaged'
 
     def __init__(self, student_metrics_df=None):
         self.hf_api_key = os.getenv('HF_API_KEY')
@@ -17,6 +203,7 @@ class DataProcessor:
         self.client = InferenceClient(api_key=self.hf_api_key)
         self.student_metrics_df = student_metrics_df
 
+
     def read_excel(self, uploaded_file):
         return pd.read_excel(uploaded_file)
 
@@ -32,13 +219,6 @@ class DataProcessor:
         df['Session Start Time'] = self.safe_convert_to_time(df['Session Start Time'], '%I:%M %p')
         df['Session End Time'] = self.safe_convert_to_time(df['Session End Time'], '%I:%M %p')
         return df
-    
-    # def format_session_data(self, df):
-    #     df['Date of Session'] = pd.to_datetime(df['Date of Session'], errors='coerce').dt.date
-    #     df['Timestamp'] = self.safe_convert_to_datetime(df['Timestamp'], '%I:%M %p')
-    #     df['Session Start Time'] = self.safe_convert_to_time(df['Session Start Time'], '%I:%M %p')
-    #     df['Session End Time'] = self.safe_convert_to_time(df['Session End Time'], '%I:%M %p')
-    #     return df
 
     def safe_convert_to_time(self, series, format_str='%I:%M %p'):
         try:
@@ -87,6 +267,17 @@ class DataProcessor:
             'Total Number of Days Available': [total_days]
         })
 
+    def classify_engagement(self, engagement_str):
+        engagement_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()):
+            return self.PARTIALLY_ENGAGED_STR
+        elif engagement_str.startswith(self.NOT_ENGAGED_STR.lower()):
+            return self.NOT_ENGAGED_STR
+        else:
+            return 'Unknown'
+
     def compute_student_metrics(self, df):
         intervention_df = df[df[self.INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
         intervention_sessions_held = len(intervention_df)
@@ -98,7 +289,7 @@ class DataProcessor:
             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 [
+            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
@@ -109,19 +300,16 @@ class DataProcessor:
             attendance_pct = round(attendance_pct)
     
             engagement_counts = {
-                'Engaged': 0,
-                'Partially Engaged': 0,
-                'Not Engaged': 0,
+                self.ENGAGED_STR: 0,
+                self.PARTIALLY_ENGAGED_STR: 0,
+                self.NOT_ENGAGED_STR: 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
+                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
     
@@ -129,16 +317,16 @@ class DataProcessor:
             total_sessions = sum(engagement_counts.values())
             
             # Engagement (%)
-            engagement_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            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['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            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['Partially Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            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['Not Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            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
@@ -155,11 +343,10 @@ class DataProcessor:
                 'Attended ≥ 90%': attended_90,
                 'Engagement ≥ 80%': engaged_80,
                 'Attendance (%)': attendance_pct,
-                # 'Attendance #': sessions_attended,
                 'Engagement (%)': engagement_pct,
-                'Engaged (%)': engaged_pct,
-                'Partially Engaged (%)': partially_engaged_pct,
-                'Not Engaged (%)': not_engaged_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
             }
     
@@ -167,7 +354,7 @@ class DataProcessor:
         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