import polars as pl
from pathlib import Path
import pandas as pd
from typing import Union

def extract_qid(val):
    """Extracts the 'ImportId' from a string representation of a dictionary."""

    if isinstance(val, str) and val.startswith('{') and val.endswith('}'):
        val = eval(val)
    return val['ImportId']


def extract_qid_descr_map(results_file: Union[str, Path]) -> dict:
    """Extract mapping of Qualtrics ImportID to Question Description from results file."""
    if isinstance(results_file, str):
        results_file = Path(results_file)

    if '1_1-16-2026' in results_file.as_posix():
        df_questions = pd.read_csv(results_file, nrows=1)
        df_questions
    
        return df_questions.iloc[0].to_dict()
        
    
    else:
        # First row contains Qualtrics Editor question names (ie 'B_VOICE SEL. 18-8')
    
        # Second row which contains the question content
        # Third row contains the Export Metadata (ie '{"ImportId":"startDate","timeZone":"America/Denver"}')
        df_questions = pd.read_csv(results_file, nrows=1, skiprows=1)
    
        
    
        # transpose df_questions
        df_questions = df_questions.T.reset_index()
        df_questions.columns = ['Description', 'export_metadata']
        df_questions['ImportID'] = df_questions['export_metadata'].apply(extract_qid)
    
        df_questions = df_questions[['ImportID', 'Description']]
    
        return dict(zip(df_questions['ImportID'], df_questions['Description']))


def load_csv_with_qid_headers(file_path: Union[str, Path]) -> pl.DataFrame:
    """
    Load CSV where column headers are in row 3 as dict strings with ImportId.
    
    The 3rd row contains metadata like '{"ImportId":"startDate","timeZone":"America/Denver"}'.
    This function extracts the ImportId from each column and uses it as the column name.
    
    Parameters:
    file_path (Path): Path to the CSV file to load.
    
    Returns:
    pl.DataFrame: Polars DataFrame with ImportId as column names.
    """
    if isinstance(file_path, str):
        file_path = Path(file_path)

    # Read the 3rd row (index 2) which contains the metadata dictionaries
    # Use header=None to get raw values instead of treating them as column names
    df_meta = pd.read_csv(file_path, nrows=1, skiprows=2, header=None)
    
    # Extract ImportIds from each column value in this row
    new_columns = [extract_qid(val) for val in df_meta.iloc[0]]
    
    # Now read the actual data starting from row 4 (skip first 3 rows)
    df = pl.read_csv(file_path, skip_rows=3)
    
    # Rename columns with the extracted ImportIds
    df.columns = new_columns
    
    return df