handler.py

import torch
from typing import Dict, List, Any
from tokenizers import Tokenizer
import sys
import os
import warnings

# Add the current directory to the system path to locate the model module
sys.path.append(os.path.dirname(__file__))

from model import build_transformer

warnings.simplefilter("ignore", category=FutureWarning)


class EndpointHandler:
    def __init__(self, path: str = ""):
        """
        Initialize the handler. Load the model and tokenizer.
        """
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.device = device

        # Paths for weights and tokenizers
        self.model_weights_path = os.path.join(path, "EN-IT.pt")
        self.tokenizer_src_path = os.path.join(path, "tokenizer_en.json")
        self.tokenizer_tgt_path = os.path.join(path, "tokenizer_it.json")

        # Load tokenizers
        self.tokenizer_src = Tokenizer.from_file(self.tokenizer_src_path)
        self.tokenizer_tgt = Tokenizer.from_file(self.tokenizer_tgt_path)

        # Build the transformer model
        self.model = build_transformer(
            src_vocab_size=self.tokenizer_src.get_vocab_size(),
            tgt_vocab_size=self.tokenizer_tgt.get_vocab_size(),
            src_seq_len=350,  # Match the trained model's sequence length
            tgt_seq_len=350,  # Match the trained model's sequence length
            d_model=512,
            num_layers=6,
            num_heads=8,
            dropout=0.1,
            d_ff=2048
        ).to(self.device)

        # Load the pretrained weights
        print(f"Loading weights from: {self.model_weights_path}")
        checkpoint = torch.load(self.model_weights_path, map_location=self.device)
        self.model.load_state_dict(checkpoint["model_state_dict"])
        self.model.eval()

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Process the incoming request and return the translation.
        """
        try:
            inputs = data.get("inputs", "")
            if not inputs:
                return [{"error": "No 'inputs' provided in request"}]

            source = self.tokenizer_src.encode(inputs)
            source = torch.cat([
                torch.tensor([self.tokenizer_src.token_to_id("[SOS]")], dtype=torch.int64),
                torch.tensor(source.ids, dtype=torch.int64),
                torch.tensor([self.tokenizer_src.token_to_id("[EOS]")], dtype=torch.int64),
                torch.tensor([self.tokenizer_src.token_to_id("[PAD]")] * (350 - len(source.ids) - 2), dtype=torch.int64)
            ], dim=0).to(self.device)

            source_mask = (source != self.tokenizer_src.token_to_id("[PAD]")).unsqueeze(0).unsqueeze(1).int().to(self.device)
            encoder_output = self.model.encode(source, source_mask)

            decoder_input = torch.empty(1, 1).fill_(self.tokenizer_tgt.token_to_id("[SOS]")).type_as(source).to(self.device)
            predicted_words = []

            while decoder_input.size(1) < 350:
                decoder_mask = torch.triu(
                    torch.ones((1, decoder_input.size(1), decoder_input.size(1))),
                    diagonal=1
                ).type(torch.int).type_as(source_mask).to(self.device)

                out = self.model.decode(encoder_output, source_mask, decoder_input, decoder_mask)
                prob = self.model.project(out[:, -1])
                _, next_word = torch.max(prob, dim=1)

                decoder_input = torch.cat(
                    [decoder_input, torch.empty(1, 1).type_as(source).fill_(next_word.item()).to(self.device)], dim=1)

                decoded_word = self.tokenizer_tgt.decode([next_word.item()])
                if next_word == self.tokenizer_tgt.token_to_id("[EOS]"):
                    break

                predicted_words.append(decoded_word)

            predicted_translation = " ".join(predicted_words).replace("[EOS]", "").strip()

            return [{"translation": predicted_translation}]
        except Exception as e:
            return [{"error": str(e)}]