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): 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') 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 Frequency (%)': [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 addressing logistical barriers, improving fidelity, and/or collecting progress monitoring data"