model/model_utils.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

def load_model():
    model_name = "mrm8488/codebert-base-finetuned-stackoverflow"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    model.eval()

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    return tokenizer, model, device

def generate_explanation(code, tokenizer, model, device):
    inputs = tokenizer(code, return_tensors="pt", truncation=True, padding=True).to(device)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = logits.argmax().item()
    return f"This code is classified as category ID: {predicted_class_id} (label may vary based on fine-tuning objective)"