app.py

from functools import partial
from pathlib import Path

import gradio as gr
import spaces
from joeynmt.datasets import build_dataset
from joeynmt.helpers import (
    load_checkpoint,
    load_config,
    parse_train_args,
    resolve_ckpt_path,
)
from joeynmt.model import build_model
from joeynmt.prediction import predict
from joeynmt.tokenizers import build_tokenizer
from joeynmt.vocabulary import build_vocab

languages_scripts = {
    "Azeri Turkish in Persian": "AzeriTurkish-Persian",
    "Central Kurdish in Arabic": "Sorani-Arabic",
    "Central Kurdish in Persian": "Sorani-Persian",
    "Gilaki in Persian": "Gilaki-Persian",
    "Gorani in Arabic": "Gorani-Arabic",
    "Gorani in Central Kurdish": "Gorani-Sorani",
    "Gorani in Persian": "Gorani-Persian",
    "Kashmiri in Urdu": "Kashmiri-Urdu",
    "Mazandarani in Persian": "Mazandarani-Persian",
    "Northern Kurdish in Arabic": "Kurmanji-Arabic",
    "Northern Kurdish in Persian": "Kurmanji-Persian",
    "Sindhi in Urdu": "Sindhi-Urdu",
}


@spaces.GPU
def normalize(text, language_script):
    cfg_file = "./models/%s/config.yaml" % languages_scripts[language_script]
    ckpt = "./models/%s/best.ckpt" % languages_scripts[language_script]

    cfg = load_config(Path(cfg_file))
    # parse and validate cfg
    model_dir, load_model, device, n_gpu, num_workers, _, fp16 = parse_train_args(cfg["training"], mode="prediction")
    test_cfg = cfg["testing"]
    src_cfg = cfg["data"]["src"]
    trg_cfg = cfg["data"]["trg"]

    load_model = load_model if ckpt is None else Path(ckpt)
    ckpt = resolve_ckpt_path(load_model, model_dir)

    src_vocab, trg_vocab = build_vocab(cfg["data"], model_dir=model_dir)

    model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)

    # load model state from disk
    model_checkpoint = load_checkpoint(ckpt, device=device)
    model.load_state_dict(model_checkpoint["model_state"])

    if device.type == "cuda":
        model.to(device)

    tokenizer = build_tokenizer(cfg["data"])
    sequence_encoder = {
        src_cfg["lang"]: partial(src_vocab.sentences_to_ids, bos=False, eos=True),
        trg_cfg["lang"]: None,
    }

    test_cfg["batch_size"] = 1  # CAUTION: this will raise an error if n_gpus > 1
    test_cfg["batch_type"] = "sentence"

    test_data = build_dataset(
        dataset_type="stream",
        path=None,
        src_lang=src_cfg["lang"],
        trg_lang=trg_cfg["lang"],
        split="test",
        tokenizer=tokenizer,
        sequence_encoder=sequence_encoder,
    )
    test_data.set_item(text.strip())

    cfg = test_cfg
    _, _, hypotheses, trg_tokens, trg_scores, _ = predict(
        model=model,
        data=test_data,
        compute_loss=False,
        device=device,
        n_gpu=n_gpu,
        normalization="none",
        num_workers=num_workers,
        cfg=cfg,
        fp16=fp16,
    )

    return hypotheses[0]


title = """
<center><strong><font size='8'>Script Normalization for Unconventional Writing<font></strong></center>

<div align="center">
    <img src="https://raw.githubusercontent.com/sinaahmadi/ScriptNormalization/b80b8fd9e3b77d0e58443ebd506c42173486f9a6/Perso-Arabic_scripts.jpg" alt="Perso-Arabic scripts used by the target languages in our paper" width="400">
</div>

<h3 style="font-weight: 450; font-size: 1rem; margin: 0rem"> 
    [<a href="https://sinaahmadi.github.io/docs/articles/ahmadi2023acl.pdf" style="color:blue;">Paper (ACL 2023)</a>] 
    [<a href="https://sinaahmadi.github.io/docs/slides/ahmadi2023acl_slides.pdf" style="color:blue;">Slides</a>]
    [<a href="https://github.com/sinaahmadi/ScriptNormalization" style="color:blue;">GitHub</a>]
    [<a href="https://s3.amazonaws.com/pf-user-files-01/u-59356/uploads/2023-06-04/rw32pwp/ACL2023.mp4" style="color:blue;">Presentation</a>]
</h3>
"""

description = """
<ul>
    <li style="font-size:120%;">&quot;<em>mar7aba!</em>&quot;</li>
    <li style="font-size:120%;">&quot;<em>هاو ئار یوو؟</em>&quot;</li>
    <li style="font-size:120%;">&quot;<em>Μπιάνβενου α σετ ντεμό!</em>&quot;</li>
</ul>

<p style="font-size:120%;">What do all these sentences have in common?  Being greeted in Arabic with &quot;<em>mar7aba</em>&quot; written in the Latin script, then asked how you are (&quot;<em>هاو ئار یوو؟</em>&quot;) in English using the Perso-Arabic script of Kurdish and then, welcomed to this demo in French (&quot;<em>Μπιάνβενου α σετ ντεμό!</em>&quot;) written in Greek script. All these sentences are written in an <strong>unconventional</strong> script.</p>
<p style="font-size:120%;">Although you may find these sentences risible, unconventional writing is a common practice among millions of speakers in bilingual communities. In our paper entitled &quot;<a href="https://sinaahmadi.github.io/docs/articles/ahmadi2023acl.pdf" target="_blank"><strong>Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities</strong></a>&quot;, we shed light on this problem and propose an approach to normalize noisy text written in unconventional writing.</p>
<p style="font-size:120%;">This demo deploys a few models that are trained for <strong>the normalization of unconventional writing</strong>. Please note that this tool is not a spell-checker and cannot correct errors beyond character normalization. For better performance, you can apply hard-coded rules on the input and then pass it to the models, hence a hybrid system.</p>
<p style="font-size:120%;">For more information, you can check out the project on GitHub too: <a href="https://github.com/sinaahmadi/ScriptNormalization" target="_blank"><strong>https://github.com/sinaahmadi/ScriptNormalization</strong></a></p>
"""

examples = [
    [
        "بو شهرین نوفوسو ، 2014 نجی ایلين نوفوس ساییمی اساسيندا 41 نفر ایمیش .",
        "Azeri Turkish in Persian",
    ],  # "بۇ شهرین نۆفوسو ، 2014 نجی ایلين نۆفوس ساییمی اساسيندا 41 نفر ایمیش ."
    ["ياخوا تةمةن دريژبيت بوئةم ميللةتة", "Central Kurdish in Arabic"],
    ["یکیک له جوانیکانی ام شاره جوانه", "Central Kurdish in Persian"],
    ["نمک درهٰ مردوم گيلک ايسن ؤ اوشان زوان ني گيلکي ايسه .", "Gilaki in Persian"],
    ["شؤنةو اانةيةرة گةشت و گلي ناجارانةو اؤجالاني دةستش پنةكةرد", "Gorani in Arabic"],  # شۆنەو ئانەیەرە گەشت و گێڵی ناچارانەو ئۆجالانی دەستش پنەکەرد
    ["ڕوٙو زوانی ئەذایی چەنی پەیذابی ؟", "Gorani in Central Kurdish"],  # ڕوٙو زوانی ئەڎایی چەنی پەیڎابی ؟
    ["هنگامکان ظميٛ ر چمان ، بپا کريٛلي بيشان :", "Gorani in Persian"],  # هەنگامەکان وزمیٛ وەرو چەمان ، بەپاو کریٛڵی بیەشان :
    ["ربعی بن افکل اُسے اَکھ صُحابی .", "Kashmiri in Urdu"],  # ربعی بن افکل ٲسؠ اَکھ صُحابی .
    ["اینتا زون گنشکرون 85 میلیون نفر هسن", "Mazandarani in Persian"],  # اینتا زوون گِنِشکَرون 85 میلیون نفر هسنه
    ["بة رطكا هة صطئن ژ دل هاطة  بة لافكرن", "Northern Kurdish in Arabic"],  # پەرتوکا هەستێن ژ دل هاتە بەلافکرن
    ["ثرکى همرنگ نرميني دويت هندک قوناغين دي ببريت", "Northern Kurdish in Persian"],  # سەرەکی هەمەرەنگ نەرمینێ دڤێت هندەک قوناغێن دی ببڕیت
    ["ہتی کجھ اپ ۽ تمام دائون ترینون بیھندیون آھن .", "Sindhi in Urdu"],  # هتي ڪجھ اپ ۽ تمام ڊائون ٽرينون بيھنديون آھن .
]


demo = gr.Interface(
    title=title,
    description=description,
    fn=normalize,
    inputs=[
        gr.Textbox(lines=4, label="Noisy Text \U0001F974"),
        gr.Dropdown(label="Language in unconventional script", choices=sorted(list(languages_scripts.keys()))),
    ],
    outputs=gr.Textbox(label="Normalized Text \U0001F642"),
    examples=examples,
    examples_per_page=20,
)

demo.launch()