# app/handlers/toxicity_handler.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from typing import Optional, List
from app.core.config import settings

class ToxicityHandler:
    def __init__(self, model_checkpoint: str = settings.MODEL_CHECKPOINT, use_cuda: Optional[bool] = settings.USE_CUDA):
        self.device = torch.device('cuda' if torch.cuda.is_available() and use_cuda else 'cpu')
        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
        self.model.to(self.device)
        self.model.eval()

    def text_to_toxicity(self, text: str, aggregate: bool = True) -> float:
        """Вычисляет токсичность текста.

        Args:
            text (str): Входной текст.
            aggregate (bool): Если True, возвращает агрегированную оценку токсичности.

        Returns:
            float: Оценка токсичности.
        """
        with torch.no_grad():
            inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(self.device)
            logits = self.model(**inputs).logits
            proba = torch.sigmoid(logits)

        proba = proba.cpu().numpy()[0]
        if aggregate:
            return float(1 - proba[0] * (1 - proba[-1]))
        return proba.tolist()