import ast
from typing import Dict, Union
from ibis.expr.types.relations import Table
from phenex.tables import PhenotypeTable, PHENOTYPE_TABLE_COLUMNS
from phenex.phenotypes.phenotype import Phenotype


class ScorePhenotype(Phenotype):
    def __init__(self, value, date):
        super().__init__()
        self.value_expression = value
        self.date_expression = date
        self.children = []  # Assuming no children for simplicity

    def _execute(self, tables: Dict[str, Table]) -> PhenotypeTable:
        """
        Executes the score phenotype processing logic.

        Args:
            tables (Dict[str, Table]): A dictionary where the keys are table names and the values are Table objects.

        Returns:
            PhenotypeTable: The resulting phenotype table containing the required columns.
        """
        # Evaluate the value expression
        value_result = self.evaluate_value(self.value_expression, tables)

        # Evaluate the date expression
        date_result = self.evaluate_date(self.date_expression, tables)

        # Create a PhenotypeTable with the results
        result_table = PhenotypeTable()
        result_table["value"] = value_result
        result_table["date"] = date_result

        return result_table

    def evaluate_value(self, value_expression, tables):
        """
        Evaluates the value expression.

        Args:
            value_expression: The value expression to evaluate.
            tables (Dict[str, Table]): A dictionary where the keys are table names and the values are Table objects.

        Returns:
            The result of the value expression evaluation.
        """
        # Implement the logic to evaluate the value expression
        # Here we assume value_expression is an arithmetic expression involving booleans
        # For simplicity, we use eval to evaluate the expression
        return ast.literal_eval(value_expression)

    def evaluate_date(self, date_expression, tables):
        """
        Evaluates the date expression.

        Args:
            date_expression: The date expression to evaluate.
            tables (Dict[str, Table]): A dictionary where the keys are table names and the values are Table objects.

        Returns:
            The result of the date expression evaluation.
        """
        # Implement the logic to evaluate the date expression
        # Here we just return the expression for simplicity
        return date_expression


# Example usage
age_gt_45 = 1
hypertension = 1
chf = 0

score = ScorePhenotype(
    value="2 * age_gt_45 + hypertension + chf", date="first|last|Phenotype"
)

# Assuming tables is a dictionary of table names to Table objects
tables = {}

result = score.execute(tables)
print(f"Value: {result['value']}")
print(f"Date: {result['date']}")
