app/models/loader.py

import gc
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification

from app.core.device import DEVICE

_cache: dict = {}


def load_image_model(cfg: dict):
    """Lazy-load a model by config key. Returns (processor, model) or None on failure."""
    key = cfg["key"]
    if key in _cache:
        return _cache[key]

    print(f"Loading {cfg['desc']} ({cfg['name']})...")
    try:
        proc  = AutoImageProcessor.from_pretrained(cfg["name"])
        model = AutoModelForImageClassification.from_pretrained(cfg["name"]).to(DEVICE)
        model.eval()
        _cache[key] = (proc, model)
        print(f"{key} ready, labels: {model.config.id2label}")
    except Exception as e:
        print(f"Failed to load {key}: {e}")
        _cache[key] = None

    return _cache[key]


def unload_all():
    global _cache
    for entry in _cache.values():
        if entry is not None:
            proc, model = entry
            del model
            del proc
    _cache = {}
    gc.collect()
    if torch.backends.mps.is_available():
        torch.mps.empty_cache()
    elif torch.cuda.is_available():
        torch.cuda.empty_cache()