example.py

import coremltools as ct
import numpy as np
from transformers import AutoTokenizer
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

def translate_text(text, source_lang="eng_Latn", target_lang="deu_Latn"):
    """
    Translate text using CoreML models
    
    Args:
        text: Text to translate (up to ~150-180 words)
        source_lang: Source language code (e.g., "eng_Latn", "fra_Latn")
        target_lang: Target language code (e.g., "deu_Latn", "spa_Latn")
    
    Returns:
        Translated text
    """
    MAX_LEN = 256
    
    # Load models (do this once, reuse for multiple translations)
    encoder = ct.models.MLModel("NLLB_Encoder_256.mlpackage", 
                                compute_units=ct.ComputeUnit.ALL)
    decoder = ct.models.MLModel("NLLB_Decoder_256.mlpackage",
                                compute_units=ct.ComputeUnit.ALL)
    
    # Load tokenizer from local directory
    tokenizer = AutoTokenizer.from_pretrained("./tokenizer")
    tokenizer.src_lang = source_lang
    
    # Encode
    inputs = tokenizer(text, return_tensors="np", 
                      padding="max_length", 
                      max_length=MAX_LEN, 
                      truncation=True)
    
    enc_outputs = encoder.predict({
        "input_ids": inputs["input_ids"].astype(np.int32),
        "attention_mask": inputs["attention_mask"].astype(np.int32)
    })
    
    encoder_hidden_states = enc_outputs[list(enc_outputs.keys())[0]]
    
    # Decode
    forced_bos = tokenizer.convert_tokens_to_ids(target_lang)
    current_tokens = [2, forced_bos]
    
    for i in range(MAX_LEN - 2):
        decoder_input = np.full((1, MAX_LEN), tokenizer.pad_token_id, dtype=np.int32)
        decoder_input[0, :len(current_tokens)] = current_tokens
        
        dec_outputs = decoder.predict({
            "decoder_input_ids": decoder_input,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": inputs["attention_mask"].astype(np.int32)
        })
        
        logits = dec_outputs[list(dec_outputs.keys())[0]]
        next_token = int(np.argmax(logits[0, len(current_tokens) - 1, :]))
        
        if next_token == 2:
            break
        
        current_tokens.append(next_token)
    
    return tokenizer.decode(current_tokens[2:], skip_special_tokens=True)


if __name__ == "__main__":
    # Example usage
    text = "Hello, how are you today?"
    translation = translate_text(text, source_lang="eng_Latn", target_lang="deu_Latn")
    print(f"English: {text}")
    print(f"German: {translation}")