main.py

from transformers import AutoImageProcessor, ResNetForImageClassification
import torch
from datasets import load_dataset
import joblib

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
print(image)

processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")

loaded_model = joblib.load("model.sav")
inputs = processor(image, return_tensors="pt")

with torch.no_grad():
    logits = loaded_model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(loaded_model.config.id2label[predicted_label])