import torch
import torch.nn as nn


class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim=128, hidden_size=256, num_layers=2, dropout=0.2):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(
            num_embeddings=vocab_size, embedding_dim=embedding_dim)
        self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_size,
                            num_layers=num_layers, batch_first=True, dropout=dropout)
        self.fc = nn.Linear(hidden_size, 1)

    def forward(self, title, text):
        title_emb = self.embedding(title)
        text_emb = self.embedding(text)
        combined = torch.cat((title_emb, text_emb), dim=1)
        output, (hidden, _) = self.lstm(combined)
        out = self.fc(hidden[-1])
        return torch.sigmoid(out)