models.py

import torch
import logging
from transformers import BertTokenizer, BertForSequenceClassification


logger = logging.getLogger(__name__)


class ModelManager:
    """Lazy loading model manager"""
    _instance = None
    _model = None
    _tokenizer = None
    _device = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance
    
    @property
    def model(self):
        if self._model is None:
            self._load_model()
        return self._model
    
    @property
    def tokenizer(self):
        if self._tokenizer is None:
            self._load_model()
        return self._tokenizer
    
    @property
    def device(self):
        if self._device is None:
            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        return self._device
    
    def _load_model(self):
        """Load model and tokenizer"""
        try:
            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            self._tokenizer = BertTokenizer.from_pretrained("entropy25/sentimentanalysis")
            self._model = BertForSequenceClassification.from_pretrained("entropy25/sentimentanalysis")
            self._model.to(self._device)
            logger.info(f"Model loaded on {self._device}")
        except Exception as e:
            logger.error(f"Model loading failed: {e}")
            raise