import marimo as mo
import polars as pl


def check_progress(data):
    """Check if all responses are complete based on 'progress' column."""
    if data.collect().select(pl.col('progress').unique()).shape[0] == 1:
        return mo.md("""### Responses Complete: \n\n✅ All responses are complete (progress = 100) """)
    
    return mo.md("### Responses Complete: \n\n⚠️ There are incomplete responses (progress < 100) ⚠️")


def duration_validation(data):
    """Validate response durations to identify outliers."""
    # Identify any outliers in duration
    duration_stats = data.select(
        pl.col('duration').mean().alias('mean_duration'),
        pl.col('duration').std().alias('std_duration')
    ).collect()
    mean_duration = duration_stats['mean_duration'][0]
    std_duration = duration_stats['std_duration'][0]
    upper_outlier_threshold = mean_duration + 3 * std_duration
    lower_outlier_threshold = mean_duration - 3 * std_duration
    
    _d = data.with_columns(
        ((pl.col('duration') > upper_outlier_threshold) | (pl.col('duration') < lower_outlier_threshold)).alias('outlier_duration')
    )
    
    # Show durations with outlier flag is true
    outlier_data = _d.filter(pl.col('outlier_duration') == True).collect()

    if outlier_data.shape[0] == 0:
        return mo.md("### Duration Outliers: \n\n✅ No duration outliers detected")

    return mo.md(f"""
    ### Duration Outliers:
    
    **⚠️ Potential outliers detected based on response duration ⚠️**
    
    - Mean Duration: {mean_duration:.2f} seconds (approximately {mean_duration/60:.2f} minutes)
    - Standard Deviation of Duration: {std_duration:.2f} seconds
    - Upper Outlier Threshold (Mean + 3*Std): {upper_outlier_threshold:.2f} seconds
    - Lower Outlier Threshold (Mean - 3*Std): {lower_outlier_threshold:.2f} seconds
    - Number of Outlier Responses: {outlier_data.shape[0]}
    
    Outliers:
    
    {mo.ui.table(outlier_data)}
    
    
    **⚠️ NOTE: These have not been removed from the dataset ⚠️**
    
    """)