from sentence_transformers import SentenceTransformer
from transformers import pipeline
import torch

class MLModels:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(MLModels, cls).__new__(cls)
            cls._instance.device = 'cuda' if torch.cuda.is_available() else 'cpu'
            print(f"Loading models on {cls._instance.device}...")
            
            # Load Sentence Transformer
            cls._instance.st_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device=cls._instance.device)
            
            # Load FinBERT for sentiment
            cls._instance.finbert = pipeline("text-classification", model="yiyanghkust/finbert-tone", device=0 if cls._instance.device == 'cuda' else -1)
            
            # Load ClimateBERT for ESG sentiment (optional, can be heavy)
            # cls._instance.climatebert = pipeline("text-classification", model="climatebert/distilroberta-base-climate-sentiment", device=0 if cls._instance.device == 'cuda' else -1)
            
            print("Models loaded successfully.")
        return cls._instance

ml_models = MLModels()