Update data_processor.py

data_processor.py

@@ -1,172 +1,3 @@
-# import pandas as pd
-# import os
-# import re
-# from huggingface_hub import InferenceClient
-
-# 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):
-#         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)
-
-#     def read_excel(self, uploaded_file):
-#         return pd.read_excel(uploaded_file)
-
-#     def format_session_data(self, df):
-#         df['Date of Session'] = self.safe_convert_to_datetime(df['Date of Session'], '%m/%d/%Y')
-#         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')
-#         df = df[['Date of Session', 'Timestamp'] + [col for col in df.columns if col not in ['Date of Session', 'Timestamp']]]
-#         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)
-#                     name_parts = name.split()
-#                     if len(name_parts) == 1:
-#                         initials = name_parts[0][0]
-#                     else:
-#                         initials = ''.join([part[0] for part in name_parts])
-#                     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()
-#         sessions_not_held = df[self.INTERVENTION_COLUMN].str.strip().str.lower().eq('no').sum()
-#         intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
-#         intervention_frequency = round(intervention_frequency, 0)
-
-#         stats = {
-#             'Intervention Frequency (%)': [intervention_frequency],
-#             'Intervention Sessions Held': [sessions_held],
-#             'Intervention Sessions Not Held': [sessions_not_held],
-#             'Total Number of Days Available': [total_days]
-#         }
-#         return pd.DataFrame(stats)
-
-#     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
-
-
-
 import pandas as pd
 import os
 import re
@@ -340,18 +171,4 @@ class DataProcessor:
             return "Address Attendance"
         elif row["Engagement ≥ 80%"] == "No":
             return "Address Engagement"
-        return "Consider addressing logistical barriers, improving fidelity, and/or collecting progress monitoring data"
-
-    # def build_tree_diagram(self, row):
-    #     dot = Digraph()
-    #     dot.node("Q1", "Has the student attended ≥ 90% of interventions?")
-    #     dot.node("Q2", "Has the student been engaged ≥ 80% of intervention time?")
-    #     dot.node("A1", "Address Attendance", shape="box")
-    #     dot.node("A2", "Address Engagement", shape="box")
-    #     dot.node("A3", "Consider addressing logistical barriers", shape="box")
-    #     if row["Attended ≥ 90%"] == "No":
-    #         dot.edge("Q1", "A1", label="No")
-    #     else:
-    #         dot.edge("Q1", "Q2", label="Yes")
-    #         dot.edge("Q2", "A2" if row["Engagement ≥ 80%"] == "No" else "A3", label="Yes")
-    #     return dot
+        return "Consider addressing logistical barriers, improving fidelity, and/or collecting progress monitoring data"