app/services/model_service.py

import json
import numpy as np
from sentence_transformers import SentenceTransformer
from typing import List, Dict, Tuple, Any, Optional

# Global variables to store model and data
_model = None
_question_embeddings = None
_answer_embeddings = None
_qa_data = None


def initialize_model() -> None:
    """
    Initialize the model once and store it in a global variable.
    """
    global _model
    if _model is None:
        _model = SentenceTransformer("pkshatech/GLuCoSE-base-ja")
    return _model


def get_model() -> SentenceTransformer:
    """
    Get the loaded model or initialize it if not loaded.
    """
    global _model
    if _model is None:
        _model = initialize_model()
    return _model


def load_embeddings() -> Tuple[np.ndarray, np.ndarray, List[Dict[str, str]]]:
    """
    Load embeddings and QA data from files.
    """
    global _question_embeddings, _answer_embeddings, _qa_data

    try:
        _question_embeddings = np.load("data/question_embeddings.npy")
        _answer_embeddings = np.load("data/answer_embeddings.npy")

        with open("data/qa_data.json", "r", encoding="utf-8") as f:
            _qa_data = json.load(f)

        return _question_embeddings, _answer_embeddings, _qa_data
    except FileNotFoundError as e:
        print(f"Warning: Embeddings not found. {str(e)}")
        return None, None, None
    except Exception as e:
        print(f"Error loading embeddings: {str(e)}")
        return None, None, None


def get_embeddings() -> (
    Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[List[Dict[str, str]]]]
):
    """
    Get the loaded embeddings or load them if not loaded.
    """
    global _question_embeddings, _answer_embeddings, _qa_data

    if _question_embeddings is None or _answer_embeddings is None or _qa_data is None:
        _question_embeddings, _answer_embeddings, _qa_data = load_embeddings()

    return _question_embeddings, _answer_embeddings, _qa_data


def reload_embeddings() -> bool:
    """
    Reload embeddings from files.
    """
    global _question_embeddings, _answer_embeddings, _qa_data

    try:
        _question_embeddings, _answer_embeddings, _qa_data = load_embeddings()
        print(f"Embeddings reloaded successfully. {len(_qa_data)} QA pairs available.")
        return True
    except Exception as e:
        print(f"Error reloading embeddings: {str(e)}")
        return False