from pathlib import Path

import joblib
import numpy as np


class CPVDivisionClassifier:
    def __init__(self, path: str | Path = "model.joblib"):
        self.artifact = joblib.load(path)

    def predict(self, texts: list[str], top_k: int = 3):
        x = self.artifact["features"].transform(texts)
        scores = self.artifact["classifier"].decision_function(x)
        calibration = self.artifact["calibration"]
        logits = np.clip(
            scores * np.asarray(calibration["slopes"])
            + np.asarray(calibration["intercepts"]),
            -35.0,
            35.0,
        )
        probabilities = 1.0 / (1.0 + np.exp(-logits))
        classes = np.asarray(self.artifact["classes"])
        output = []
        for row in probabilities:
            indices = np.argsort(row)[::-1][:top_k]
            output.append([
                {"division": str(classes[index]), "probability": float(row[index])}
                for index in indices
            ])
        return output