app.py

from pathlib import Path

import gradio as gr
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

lang_to_code = {
    "Akrikaans": "af",
    "Albanian": "sq",
    "Amharic": "am",
    "Arabic": "ar",
    "Armenian": "hy",
    "Assamese": "as",
    "Asturian": "ast",
    "Aymara": "ay",
    "Azerbaijani": "az",
    "Bashkir": "ba",
    "Belarusian": "be",
    "Bengali": "bn",
    "Bosnian": "bs",
    "Breton": "br",
    "Bulgarian": "bg",
    "Burmese": "my",
    "Catalan": "ca",
    "Cebuano": "ceb",
    "Central Khmer": "km",
    "Chinese": "zh",
    "Chokwe": "cjk",
    "Croatian": "hr",
    "Czech": "cs",
    "Danish": "da",
    "Dutch": "nl",
    "Dyula": "dyu",
    "English": "en",
    "Estonian": "et",
    "Finnish": "fi",
    "French": "fr",
    "Fulah": "ff",
    "Galician": "gl",
    "Ganda": "lg",
    "Georgian": "ka",
    "German": "de",
    "Greek": "el",
    "Gujarati": "gu",
    "Haitian Creole": "ht",
    "Hausa": "ha",
    "Hebrew": "he",
    "Hindi": "hi",
    "Hungarian": "hu",
    "Icelandic": "is",
    "Igbo": "ig",
    "Iloko": "ilo",
    "Indonesian": "id",
    "Irish": "ga",
    "Italian": "it",
    "Japanese": "ja",
    "Javanese": "jv",
    "Kabuverdianu": "kea",
    "Kachin": "kac",
    "Kamba": "kam",
    "Kannada": "kn",
    "Kazakh": "kk",
    "Kimbundu": "kmb",
    "Kongo": "kg",
    "Korean": "ko",
    "Kurdish": "ku",
    "Kyrgyz": "ky",
    "Lao": "lo",
    "Latvian": "lv",
    "Lingala": "ln",
    "Lithuanian": "lt",
    "Luo": "luo",
    "Luxembourgish": "lb",
    "Macedonian": "mk",
    "Malagasy": "mg",
    "Malay": "ms",
    "Malayalam": "ml",
    "Maltese": "mt",
    "Maori": "mi",
    "Marathi": "mr",
    "Mongolian": "mn",
    "Nepali": "ne",
    "Northern Kurdish": "kmr",
    "Northern Sotho": "ns",
    "Norwegian": "no",
    "Nyanja": "ny",
    "Occitan": "oc",
    "Oriya": "or",
    "Oromo": "om",
    "Pashto": "ps",
    "Persian": "fa",
    "Polish": "pl",
    "Portuguese": "pt",
    "Punjabi": "pa",
    "Quechua": "qu",
    "Romanian": "ro",
    "Russian": "ru",
    "Scottish Gaelic": "gd",
    "Serbian": "sr",
    "Shan": "shn",
    "Shona": "sn",
    "Sindhi": "sd",
    "Sinhala": "si",
    "Slovak": "sk",
    "Slovenian": "sl",
    "Somali": "so",
    "Spanish": "es",
    "Sundanese": "su",
    "Swahili": "sw",
    "Swati": "ss",
    "Swedish": "sv",
    "Tagalog": "tl",
    "Tajik": "tg",
    "Tamil": "ta",
    "Telugu": "te",
    "Thai": "th",
    "Tigrinya": "ti",
    "Tswana": "tn",
    "Turkish": "tr",
    "Ukrainian": "uk",
    "Umbundu": "umb",
    "Urdu": "ur",
    "Uzbek": "uz",
    "Vietnamese": "vi",
    "Welsh": "cy",
    "Western Frisian": "fy",
    "Wolof": "wo",
    "Xhosa": "xh",
    "Yiddish": "yi",
    "Yoruba": "yo",
    "Zulu": "zu",
}
lang_names = list(lang_to_code.keys())

# load model
model_path = Path("./model_files").resolve()
print(f"model_path: {model_path}")
tokenizer: M2M100Tokenizer = M2M100Tokenizer.from_pretrained(
    pretrained_model_name_or_path=str(model_path), local_files_only=True
)
model = M2M100ForConditionalGeneration.from_pretrained(
    pretrained_model_name_or_path=str(model_path), local_files_only=True
)


# fix tokenizer
tokenizer.lang_token_to_id = {
    t: i
    for t, i in zip(tokenizer.all_special_tokens, tokenizer.all_special_ids)
    if i > 5
}
tokenizer.lang_code_to_token = {s.strip("_"): s for s in tokenizer.lang_token_to_id}
tokenizer.lang_code_to_id = {
    s.strip("_"): i for s, i in tokenizer.lang_token_to_id.items()
}
tokenizer.id_to_lang_token = {i: s for s, i in tokenizer.lang_token_to_id.items()}


def translate(src_text: str, source_lang: str, target_lang: str) -> str:
    # get lang code
    src_lang = lang_to_code[source_lang]
    tgt_lang = lang_to_code[target_lang]

    # encode
    tokenizer.src_lang = src_lang
    encoded_input = tokenizer(src_text, return_tensors="pt")

    # inference
    generated_tokens = model.generate(
        **encoded_input,
        forced_bos_token_id=tokenizer.get_lang_id(tgt_lang),
        max_length=1024,
    )

    # decode
    pred_texts = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    pred_text = pred_texts[0]
    assert isinstance(pred_text, str)

    return pred_text


inputs = [
    gr.Textbox(lines=4, value="Hello world!", label="Input Text"),
    gr.Dropdown(lang_names, value="English", label="Source Language"),
    gr.Dropdown(lang_names, value="Korean", label="Target Language"),
]


outputs = gr.Textbox(lines=4, label="Output Text")


demo = gr.Interface(
    fn=translate,
    inputs=inputs,
    outputs=outputs,
    title="Flores101: Large-Scale Multilingual Machine Translation",
    description="[`seyoungsong/flores101_mm100_175M`](https://huggingface.co/seyoungsong/flores101_mm100_175M)",
)

if __name__ == "__main__":
    # https://huggingface.co/seyoungsong/flores101_mm100_175M
    # https://huggingface.co/spaces/seyoungsong/flores101_mm100_175M
    # gradio src/pretrained/gradio/app.py
    # http://127.0.0.1:7860
    demo.launch()