import pandas as pd

def generate_summary_stats_v2(file_path):
    # Load the DataFrame from the provided file path
    df = pd.read_csv(file_path)
    
    # Ensure date columns are correctly parsed
    df['Assessment_Start_Date'] = pd.to_datetime(df['Assessment_Start_Date'])
    df['Assessment_End_Date'] = pd.to_datetime(df['Assessment_End_Date'])
    
    # Add completion rate calculation
    completed_status = df['Assessment_Status'] == 'Completed'
    completion_rate_by_frequency = df[completed_status].groupby('Assessment_Frequency').size() / df.groupby('Assessment_Frequency').size()
    
    in_progress_status = df['Assessment_Status'] == 'In Progress'
    incomplete_status = df['Assessment_Status'] == 'Incomplete'
    
    # Calculate in-progress and incomplete rates by frequency
    in_progress_rate_by_frequency = df[in_progress_status].groupby('Assessment_Frequency').size() / df.groupby('Assessment_Frequency').size()
    incomplete_rate_by_frequency = df[incomplete_status].groupby('Assessment_Frequency').size() / df.groupby('Assessment_Frequency').size()
    
    # Fill NaN values (where no assessments are in-progress or incomplete for certain frequencies)
    completion_rate_by_frequency = completion_rate_by_frequency.fillna(0)
    in_progress_rate_by_frequency = in_progress_rate_by_frequency.fillna(0)
    incomplete_rate_by_frequency = incomplete_rate_by_frequency.fillna(0)
    
    # Round all numerical values to 2 decimal places
    completion_rate_by_frequency = completion_rate_by_frequency.round(2)
    in_progress_rate_by_frequency = in_progress_rate_by_frequency.round(2)
    incomplete_rate_by_frequency = incomplete_rate_by_frequency.round(2)

    summary_stats = {
        'Open Items and Red Flags': {
            'Total Open Items': round(df['Open_Items'].sum(), 2),
            'Average Open Items per Assessment': round(df['Open_Items'].mean(), 2),
            'Total Red Flags': round(df['Red_Flags'].sum(), 2),
            'Average Red Flags per Assessment': round(df['Red_Flags'].mean(), 2),
            'Max Red Flags in a Single Assessment': round(df['Red_Flags'].max(), 2),
            'Most Common Area with Red Flags': df[df['Red_Flags'] > 0]['Assessment_Area'].mode()[0]
        },
        'Assessment Frequency': {
            'Assessment Type Breakdown': df['Assessment_Frequency'].value_counts(normalize=True).round(2).to_dict(),
            'Average Time Between Assessments': round((df['Assessment_End_Date'] - df['Assessment_Start_Date']).dt.days.mean(), 2),
            'Average Assessment Duration': round(df['Assessment_End_Date'].sub(df['Assessment_Start_Date']).dt.days.mean(), 2),
            'Completion Rate by Frequency': completion_rate_by_frequency.to_dict(),
            'In Progress Rate by Frequency': in_progress_rate_by_frequency.to_dict(),
            'Incomplete Rate by Frequency': incomplete_rate_by_frequency.to_dict()
        },
        'Assessment Start and End Dates': {
            'Longest Assessment Duration (days)': round(df['Assessment_End_Date'].sub(df['Assessment_Start_Date']).dt.days.max(), 2),
            'Shortest Assessment Duration (days)': round(df['Assessment_End_Date'].sub(df['Assessment_Start_Date']).dt.days.min(), 2),
        },
        'Assessment Areas': {
            'Most Assessed Area': df['Assessment_Area'].value_counts().idxmax(),
            'Most Open Items in Area': df.groupby('Assessment_Area')['Open_Items'].sum().idxmax(),
            'Area with Most Red Flags': df.groupby('Assessment_Area')['Red_Flags'].sum().idxmax()
        },
        'Assessment Status': {
            'Assessment Status Distribution': df['Assessment_Status'].value_counts(normalize=True).round(2).to_dict(),
            'Incomplete Assessments': round(df[df['Assessment_Status'] == 'Incomplete'].shape[0], 2),
            'In Progress Assessments': round(df[df['Assessment_Status'] == 'In Progress'].shape[0], 2)
        },
        'Assessment Admin': {
            'Most Frequent Admin': df['Assessment_Admin'].mode()[0],
            'Admin with Fewest Red Flags': df.groupby('Assessment_Admin')['Red_Flags'].sum().idxmin(),
            'Admin with Most Open Items': df.groupby('Assessment_Admin')['Open_Items'].mean().idxmax()
        }
    }
    
    return summary_stats