Update data_processor.py

data_processor.py

@@ -1,186 +1,3 @@
-# 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