import pysera
import os
from workflow.module import Module
from workflow.io_port import InPort, OutPort, NIFTIImageType, CSVTableType
# Codes Go Below:


class FeatureExtractor(Module):
    """
    Pure logical node for radiomics feature extraction.
    No Dagster, no Engine, no state.
    """

    def __init__(self):
        super().__init__("FeatureExtractor")

        self.addInPort(InPort("image", NIFTIImageType()))
        self.addInPort(InPort("mask", NIFTIImageType()))

        self.addOutPort(OutPort("features", CSVTableType(columns=["name", "value"])))

    def run(self, context):
        image = context.get_asset_value("FeatureExtractor.image")
        mask = context.get_asset_value("FeatureExtractor.mask")

        output_dir = "results"
        os.makedirs(output_dir, exist_ok=True)

        result = pysera.process_batch(
            image_input=image,
            mask_input=mask,
            output_path=output_dir,
            num_workers="auto",
            enable_parallelism=True,
            apply_preprocessing=True,
            categories="all",
            dimensions="1st,2_5d,3d",
            feature_value_mode="REAL_VALUE",
            extraction_mode="handcrafted_feature",
            report="info",
        )

        df = result.get("features_extracted")

        features = []
        if df is not None:
            for idx, row in df.iterrows():
                features.append({"name": row[0], "value": row[1]})

        return {"features": features}