model.py

# Save this as model.py
import torch
import torch.nn as nn
from sentence_transformers import SentenceTransformer

class SummaryClassifier(nn.Module):
    def __init__(self, embedder, num_classes, dropout=0.1):
        """
        Initializes the classifier.
        Args:
            embedder: A pre-loaded SentenceTransformer model.
            num_classes (int): The number of output classes.
            dropout (float): Dropout probability.
        """
        super().__init__()
        self.embedder = embedder
        embedding_dim = embedder.get_sentence_embedding_dimension()
        
        self.head = nn.Sequential(
            nn.Dropout(dropout),
            nn.Linear(embedding_dim, 128),
            nn.ReLU(),
            nn.Linear(128, num_classes)
        )
        
        # Freeze the embedder parameters
        for p in self.embedder.parameters():
            p.requires_grad = False

    def forward(self, texts, return_embeddings=False):
        """
        Forward pass.
        Args:
            texts (list[str]): A list of input strings.
            return_embeddings (bool): Whether to return embeddings alongside logits.
        Returns:
            torch.Tensor: The output logits.
            (Optional) torch.Tensor: The sentence embeddings.
        """
        # Automatically use the same device as the model's 'head'
        target_device = next(self.head.parameters()).device
        
        embeddings = self.embedder.encode(
            texts,
            convert_to_tensor=True,
            show_progress_bar=False,
            device=str(target_device)  
        )
        
        logits = self.head(embeddings)
        
        if return_embeddings:
            return logits, embeddings
        return logits