app.py

"""
Translation interface using the MADLAD-400 3B model.
Translates English text to evaluated languages from the MADLAD-400 paper.
"""

import warnings
from functools import lru_cache

import gradio as gr
import spaces
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

from langmap.langid_mapping import langid_to_language

MODEL_NAME = "google/madlad400-3b-mt"


def _get_device() -> torch.device:
    if torch.backends.mps.is_available():
        return torch.device("mps")
    if torch.cuda.is_available():
        return torch.device("cuda")
    warnings.warn("No GPU available. Running on CPU — translation will be very slow.", stacklevel=2)
    return torch.device("cpu")


@lru_cache(maxsize=1)
def _load_tokenizer() -> AutoTokenizer:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
    if tokenizer is None:
        raise RuntimeError(f"Failed to load tokenizer for {MODEL_NAME}")
    return tokenizer


@lru_cache(maxsize=1)
def _load_model() -> AutoModelForSeq2SeqLM:
    device = _get_device()
    return AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(device)


def _build_language_mappings() -> tuple[dict[str, str], list[str]]:
    tokenizer = _load_tokenizer()
    vocab = tokenizer.get_vocab()
    name_to_code = {v: k for k, v in langid_to_language.items() if k in vocab}
    return name_to_code, sorted(name_to_code.keys())


@spaces.GPU
def translate(text: str, target_language_name: str) -> str:
    tokenizer = _load_tokenizer()
    model = _load_model()
    device = _get_device()

    name_to_code, _ = _build_language_mappings()
    target_code = name_to_code.get(target_language_name)
    if target_code is None:
        raise ValueError(f"Unsupported language: {target_language_name}")

    input_ids = tokenizer(target_code + " " + text, return_tensors="pt").input_ids.to(device)
    outputs = model.generate(input_ids=input_ids, max_new_tokens=512)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    if not isinstance(result, str):
        raise TypeError(f"Expected str from decode, got {type(result)}")
    return result


def _build_demo() -> gr.Blocks:
    _, language_names = _build_language_mappings()

    with gr.Blocks(title="MADLAD-400 Translation") as demo:
        gr.Markdown(
            "# MADLAD-400 Translation\n"
            "Translate English into 183 languages using Google's MADLAD-400 3B model. "
            "[Paper](https://arxiv.org/pdf/2309.04662)"
        )

        target_language = gr.Dropdown(
            choices=language_names,
            value="French",
            label="Target language",
        )

        with gr.Row():
            input_text = gr.Textbox(
                label="English",
                placeholder="Enter English text here",
                lines=4,
            )
            output_text = gr.Textbox(
                label="Translation",
                lines=4,
                buttons=["copy"],
                interactive=False,
            )

        translate_btn = gr.Button("Translate", variant="primary")

        translate_btn.click(
            fn=translate,
            inputs=[input_text, target_language],
            outputs=output_text,
        )

        gr.Examples(
            examples=[
                ["Hello, how are you today?", "French"],
                ["The weather is beautiful.", "Japanese"],
                ["Thank you very much.", "Swahili"],
                ["Where is the train station?", "Hindi"],
            ],
            inputs=[input_text, target_language],
        )

    return demo


demo = _build_demo()


def main() -> None:
    demo.launch(theme=gr.themes.Soft())


if __name__ == "__main__":
    main()