from sientia_model.wrappers.sientia_model import SientiaModel
import pandas as pd
import numpy as np
from typing import Any

class DummyWrapper(SientiaModel):
    def _predict(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
        self._log("info", f"Predicting dummy model for {self.model_type}")
        # Return a simple prediction (mean or 0.5) to allow metrics computation
        preds = pd.DataFrame({self.target: [0.5] * len(data)}, index=data.index)
        return preds, {}
        
    def _transform(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
        return data, {}
        
    def _train_transformer(self, train_data: pd.DataFrame, val_data: pd.DataFrame) -> None:
        pass
        
    def _train_model(self, x: pd.DataFrame, y: pd.DataFrame, x_val: pd.DataFrame | None = None, y_val: pd.DataFrame | None = None) -> None:
        self.target = y.columns[0]
        
    def _retrain_transformer(self, data: pd.DataFrame) -> None:
        pass
        
    def _retrain_model(self, x: pd.DataFrame, y: pd.DataFrame | None) -> None:
        pass