"""
BaseMLModel class
This is the base protocol:
- Any implementation will be its own seperate file
Key tasks:
- Template Model class for different model types
- Save model
- Load Model
- Generate Inference
"""

from numpy import ndarray
from pathlib import Path
from typing import Protocol, NamedTuple, Any
import pandas as pd
from training import S3FSClient


class MLModel(Protocol):
    """
    Base ML Model protocol
    """

    def load_model(self, filepath: Path) -> None:
        """
        Providing a path, this function will load the model to be used. Will load to internal variable
        """

    def save_model(
        self, output_filepath: Path, s3_client: S3FSClient | None = None
    ) -> None:
        """
        Providing a path, this function will save the model to be used.
        """

    def train_model(
        self, data: pd.DataFrame, target_column: str, hyperparameters: dict
    ) -> None:
        """
        For the given data and hyperparameters (specified to the model), a model is trained
        """

    def generate_predictions(self, data: pd.DataFrame) -> ndarray[Any, Any] | None:
        """
        For the given dataframe, model is loaded and predictions are generated
        """

    def model_evaluation(
        self,
        validation_data: pd.DataFrame,
        target_column: str,
        metrics_location: Path | None = None,
    ) -> pd.DataFrame | None:
        """
        For any validation data, a set of predictions and metrics are return
        """

    def optimise_model_for_deployment(self):
        """
        Perfomance post processing on Model to ensure ready for deployment
        """

    def model_metadata(self) -> dict | None:
        """
        Extract out model metadata as dictionary
        """