features/image_classifier/model_loader.py

import tensorflow as tf
from tensorflow.keras.models import load_model as keras_load_model
import os
from huggingface_hub import snapshot_download
import shutil

# Constants
REPO_ID = "can-org/AI-VS-HUMAN-IMAGE-classifier"
MODEL_DIR = "./IMG_models"
MODEL_PATH = os.path.join(MODEL_DIR, 'latest-my_cnn_model.h5')  # adjust path as needed

_model_img = None  # global model variable

def warmup():
    global _model_img
    if not os.path.exists(MODEL_DIR):
        download_model_Repo()
    _model_img = load_model()

def download_model_Repo():
    if os.path.exists(MODEL_DIR):
        return
    snapshot_path = snapshot_download(repo_id=REPO_ID)
    os.makedirs(MODEL_DIR, exist_ok=True)
    shutil.copytree(snapshot_path, MODEL_DIR, dirs_exist_ok=True)

def load_model():
    if not os.path.exists(MODEL_DIR):
        download_model_Repo()

    # Check for GPU availability
    gpus = tf.config.list_physical_devices('GPU')
    if gpus:
        # GPU is available, load model normally
        print("GPU detected, loading model on GPU.")
        model = keras_load_model(MODEL_PATH)
    else:
        # No GPU, force CPU usage
        print("No GPU detected, forcing model loading on CPU.")
        with tf.device('/CPU:0'):
            model = keras_load_model(MODEL_PATH)
    return model