import marimo

__generated_with = "0.19.2"
app = marimo.App(width="medium")


@app.cell
def _():
    import marimo as mo
    import polars as pl
    import pandas as pd
    import plotly as plt
    from pathlib import Path

    import utils
    return Path, mo, pd, utils


@app.cell
def _(Path):
    # results_file = Path('data/exports/OneDrive_1_1-16-2026/JPMC_Chase Brand Personality_Quant Round 1_TestData_Labels.csv')
    # results_file = Path('data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase Brand Personality_Quant Round 1_January 21, 2026_Soft Launch_Labels.csv')
    results_file = Path('data/exports/1-23-26/JPMC_Chase Brand Personality_Quant Round 1_January 23, 2026_Labels.csv')
    qsf_file = 'data/exports/OneDrive_1_1-16-2026/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
    return qsf_file, results_file


@app.cell
def _(qsf_file, results_file, utils):
    survey = utils.QualtricsSurvey(results_file, qsf_file)
    data_all = survey.load_data()
    return (survey,)


@app.cell
def _(mo):
    mo.md(r"""
    # Mapping Question <-> Internal-ID
    Questions are too long to use as headers of the df
    """)
    return


@app.cell
def _(survey):
    qid_descr_map = survey.qid_descr_map
    qid_descr_map
    return (qid_descr_map,)


@app.cell
def _(mo):
    mo.md(r"""
    ## Save mapping to a validation.csv so it can be compared across exports
    """)
    return


@app.cell(hide_code=True)
def _(pd, qid_descr_map, results_file):
    validate_df = pd.DataFrame.from_dict(qid_descr_map, orient='index', columns=['Description']).reset_index().rename(columns={'index': 'ImportID'})
    # add column source_file
    validate_df['SourceFile'] = results_file.as_posix()

    validate_df
    return (validate_df,)


@app.cell
def _(mo):
    mo.md(r"""
    # Compare with other exports
    """)
    return


@app.cell
def _(Path, pd, validate_df):
    validate_record_csv = Path('./validation_qid_descr_map.csv')

    if not validate_record_csv.exists():
        validate_df.to_csv(validate_record_csv, index=False)
        combined_df = validate_df

    else:
        existing_df = pd.read_csv(validate_record_csv)
        combined_df = pd.concat([existing_df, validate_df])

        # remove records that are full duplicates, keeping the last one
        combined_df = combined_df.drop_duplicates(keep='last').reset_index(drop=True)
        combined_df.to_csv(validate_record_csv, index=False)

    # Sort rows by ImportID
    combined_df = combined_df.sort_values(by='ImportID').reset_index(drop=True)
    combined_df
    return (validate_record_csv,)


@app.cell
def _(mo):
    mo.md(r"""
    ## Identify mismatches
    """)
    return


@app.function
def validate_mappings(_df):
    validation_issues = {
        'MismatchedDescriptions': [],
        'MissingImportID': []
    }
    for import_id, group in _df.groupby('ImportID'):
        # Check for mismatched descriptions
        descriptions = group['Description'].unique()
        if len(descriptions) > 1:
            validation_issues['MismatchedDescriptions'].append({
                'ImportID': import_id,
                'Descriptions': descriptions.tolist(),
                'SourceFiles': group['SourceFile'].tolist()
            })

        # Check for new or missing ImportIDs
        source_files = group['SourceFile'].unique()
        if len(source_files) < len(_df['SourceFile'].unique()):
            validation_issues['MissingImportID'].append({
                'ImportID': import_id,
                'Descriptions': descriptions.tolist(),
                'SourceFiles': group['SourceFile'].tolist()
            })
    return validation_issues


@app.cell
def _(pd, validate_record_csv):
    # As-is (no modifications for known issues)
    _df = pd.read_csv(validate_record_csv)
    validation_issues = validate_mappings(_df)

    validation_issues      
    return


@app.cell
def _(mo):
    mo.md(r"""
    ## Inspect & Dismiss Errors
    """)
    return


@app.cell
def _(pd, validate_record_csv):
    # Known issue with the 'OneDrive_1_1-16-2026' export, where each of the descriptions is prepended with a '<Qualtrics Editor Question name> - ' string. Drop that, then recompare

    _df = pd.read_csv(validate_record_csv)

    # Remove the prefix from the descriptions in rows where the SourceFile contains 'OneDrive_1_1-16-2026'
    _df.loc[_df['SourceFile'].str.contains('OneDrive_1_1-16-2026'), 'Description'] = _df.loc[_df['SourceFile'].str.contains('OneDrive_1_1-16-2026'), 'Description'].apply(lambda x: ' - '.join(x.split(' - ')[1:]) if ' - ' in x else x)

    validation_issues_fixed = validate_mappings(_df)
    validation_issues_fixed
    return


if __name__ == "__main__":
    app.run()