from sentence_transformers import SentenceTransformer
import torch

class EmbeddingModel:
    def __init__(self, model_name="embedingHF/Sentence_Transformer"):
        # Aapka apna HF model ya koi bhi pre-trained
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Loading model on {self.device}...")
        self.model = SentenceTransformer(model_name, device=self.device)
        print("Model loaded successfully!")
    
    def get_embedding(self, text: str):
        """Convert text to vector embedding"""
        embedding = self.model.encode(text, convert_to_tensor=True)
        return embedding.cpu().numpy().tolist()

# Global instance (ek baar load hoga, baar baar nahi)
model_instance = EmbeddingModel()