load_model.py

import transformers
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")

def predict_probs(model, text, 
                  labels=['World', 'Sports', 'Business', 'Sci/Tech']):
    with torch.no_grad():
        tokens = tokenizer(text, padding="max_length", truncation=True, return_tensors='pt').to(device)
        logits = model(**tokens).logits
        probs = torch.nn.functional.softmax(logits)[0]
    
    return {labels[i]: float(probs[i]) for i in range(min(len(probs), len(labels)))}


def load_model(labels_count=4):
    model = AutoModelForSequenceClassification.from_pretrained("pretrained_acc935/", num_labels=labels_count).to(device)
    return model


__all__ = ['predict_probs', 'load_model']