from app.model_utils import load_model, load_data
from sklearn.metrics import accuracy_score
import torch

def validate_model():
    model, tokenizer = load_model()
    _, val_dataset, _ = load_data(tokenizer)

    correct = 0
    total = 0
    for item in val_dataset:
        input_ids = item["input_ids"].unsqueeze(0)
        attention_mask = item["attention_mask"].unsqueeze(0)
        label = item["label"].item()

        with torch.no_grad():
            output = model(input_ids=input_ids, attention_mask=attention_mask)
            pred = torch.argmax(output.logits, dim=1).item()

        correct += int(pred == label)
        total += 1

    return {"accuracy": correct / total}