from sentence_transformers import SentenceTransformer

class SentenceEncoder:
    def __init__(self, model_name='l3cube-pune/indic-sentence-similarity-sbert'):
        try:
            self.model = SentenceTransformer(model_name)
            print(f"✅ Model '{model_name}' loaded successfully.")
        except Exception as e:
            print(f"❌ Error loading model: {e}")
            self.model = None

    def encode(self, texts, batch_size=32, show_progress_bar=False):
        if self.model is None:
            return None
        return self.model.encode(texts, batch_size=batch_size, show_progress_bar=show_progress_bar, convert_to_tensor=True)