| 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() |