import torch
import pandas as pd
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
from model import LSTMModel


def load_model(model_path, vocab_size):
    model = LSTMModel(vocab_size)
    model.load_state_dict(torch.load(model_path))
    model.eval()
    return model


def predict(model, titles, texts, device):
    titles, texts = titles.to(device), texts.to(device)
    model.to(device)
    with torch.no_grad():
        outputs = model(titles, texts).squeeze()
    return outputs


def evaluate_model(model, data_loader, device, labels):
    model.to(device)
    model.eval()
    predictions = []
    labels = torch.tensor(labels).to(device)
    for titles, texts in data_loader:
        titles, texts = titles.to(device), texts.to(device)
        outputs = predict(model, titles, texts, device)
        predictions.extend(outputs.cpu().numpy())

    labels = labels.cpu().numpy()  # Convert labels to NumPy array for consistency
    predicted_labels = [1 if p > 0.5 else 0 for p in predictions]

    # Calculate metrics
    accuracy = accuracy_score(labels, predicted_labels)
    f1 = f1_score(labels, predicted_labels)
    auc_roc = roc_auc_score(labels, predictions)

    return accuracy, f1, auc_roc, labels, predicted_labels