app.py

import os
import re
from datetime import datetime
from typing import Dict

import gradio
import sign_language_translator as slt

DESCRIPTION = """Enter your text and select languages from the dropdowns, then click Submit to generate a video. [`Library Repository`](https://github.com/sign-language-translator/sign-language-translator)

The text is preprocessed, tokenized and rearranged and then each token is mapped to a prerecorded video which are concatenated and returned. [`Model Code`](https://github.com/sign-language-translator/sign-language-translator/blob/main/sign_language_translator/models/text_to_sign/concatenative_synthesis.py)

> *NOTE*: This model only supports a fixed vocabulary. See the [`*-dictionary-mapping.json`](https://github.com/sign-language-translator/sign-language-datasets/tree/main/parallel_texts) files for supported words.
> This version needs to re-encode the generated video so that will take some extra time after translation.
> Since this is a rule-based model, you will have to add *context* to ambiguous words (e.g. glass(material) vs glass(container)).
""".strip()

TITLE = "Concatenative Synthesis: Rule Based Text to Sign Language Translator"

CUSTOM_JS = """<script>
const rtlLanguages = ["ur", "ar"];

function updateTextareaDir(language) {
    const sourceTextarea = document.getElementById("source-textbox").querySelector("textarea");

    if (rtlLanguages.includes(language)) {
        sourceTextarea.setAttribute("dir", "rtl");
    } else {
        sourceTextarea.setAttribute("dir", "ltr");
    }
}
</script>"""
# todo: add dropdown keyboard custom component with key mapping

CUSTOM_CSS = """
#auto-complete-button {
    border-color: var(--button-primary-border-color-hover);
}
"""

HF_TOKEN = os.getenv("HF_TOKEN")
request_logger = (
    gradio.HuggingFaceDatasetSaver(
        HF_TOKEN,
        "sltAI/crowdsourced-text-to-sign-language-rule-based-translation-corpus",
    )
    if HF_TOKEN
    else gradio.CSVLogger()
)

translation_model = slt.models.ConcatenativeSynthesis("ur", "pk-sl", "video")
language_models: Dict[str, slt.models.BeamSampling] = {}


def auto_complete_text(model_code: str, text: str):
    if model_code not in language_models:
        lm = slt.get_model(model_code)
        language_models[model_code] = slt.models.BeamSampling(
            lm,  # type: ignore
            start_of_sequence_token=getattr(lm, "start_of_sequence_token", "<"),  # type: ignore
            end_of_sequence_token=getattr(lm, "end_of_sequence_token", ">"),  # type: ignore
        )

    # todo: better tokenize/detokenize
    tokens = [w for w in re.split(r"\b", text) if w]
    lm = language_models[model_code]
    lm.max_length = len(tokens) + 10
    completion, _ = lm.complete(tokens or None)
    if completion[0] == lm.start_of_sequence_token:  # type: ignore
        completion = completion[1:]  # type: ignore
    if completion[-1] == lm.end_of_sequence_token:  # type: ignore
        completion = completion[:-1]  # type: ignore
    new_text = "".join(completion)

    return new_text


def text_to_video(
    text: str,
    text_language: str,
    sign_language: str,
    output_path: str = "output.mp4",
    codec="h264",  # ToDo: install h264 codec for opencv
):
    translation_model.text_language = text_language
    translation_model.sign_language = sign_language

    video = translation_model.translate(text)
    video.save(output_path, overwrite=True, codec=codec)

    # ToDo: video.watermark("Sign Language Translator\nAI Generated Video")


def translate(text: str, text_lang: str, sign_lang: str):
    log = [
        text,
        text_lang,
        sign_lang,
        None,
        datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
    ]
    try:
        path = "output.mp4"
        text_to_video(text, text_lang, sign_lang, output_path=path, codec="mp4v")
        request_logger.flag(log)
        return path

    except Exception as exc:
        log[3] = str(exc)
        request_logger.flag(log)
        raise gradio.Error(f"Error during translation: {exc}")


with gradio.Blocks(title=TITLE, head=CUSTOM_JS, css=CUSTOM_CSS) as gradio_app:
    gradio.Markdown(f"# {TITLE}")
    gradio.Markdown(DESCRIPTION)
    with gradio.Row():
        with gradio.Column():
            gradio.Markdown("## Input Text")
            with gradio.Row():
                with gradio.Column():
                    gradio.Markdown("Write here (in selected language):")
                    source_textbox = gradio.Textbox(
                        lines=1,
                        placeholder="Enter Text Here...",
                        label="Spoken Language Sentence",
                        show_copy_button=True,
                        elem_id="source-textbox",
                    )
                with gradio.Column():
                    gradio.Markdown("Generate sample text instead:")
                    with gradio.Row():
                        language_model_dropdown = gradio.Dropdown(
                            choices=[
                                slt.ModelCodes.MIXER_LM_NGRAM_URDU.value,
                                slt.ModelCodes.TRANSFORMER_LM_UR_SUPPORTED.value,
                            ],
                            value=slt.ModelCodes.MIXER_LM_NGRAM_URDU.value,
                            label="Language Model for auto-complete",
                        )
                    with gradio.Row():
                        clear_button = gradio.ClearButton(
                            source_textbox, api_name=False
                        )
                        auto_complete_button = gradio.Button(
                            "Auto-Complete", elem_id="auto-complete-button"
                        )
                        auto_complete_button.click(
                            auto_complete_text,
                            inputs=[language_model_dropdown, source_textbox],
                            outputs=[source_textbox],
                            api_name=False,
                        )

            gradio.Markdown("## Select Languages")
            with gradio.Row():
                text_lang_dropdown = gradio.Dropdown(
                    choices=[code.value for code in slt.TextLanguageCodes],
                    value=slt.TextLanguageCodes.URDU.value,
                    label="Text Language",
                    elem_id="text-lang-dropdown",
                )
                text_lang_dropdown.change(
                    None, inputs=text_lang_dropdown, js="updateTextareaDir"
                )
                sign_lang_dropdown = gradio.Dropdown(
                    choices=[code.value for code in slt.SignLanguageCodes],
                    value=slt.SignLanguageCodes.PAKISTAN_SIGN_LANGUAGE.value,
                    label="Sign Language",
                )
                # todo: sign format: video/landmarks (tabs?)

        with gradio.Column():
            gradio.Markdown("## Output Sign Language")
            output_video = gradio.Video(
                format="mp4",
                label="Synthesized Sign Language Video",
                autoplay=True,
                show_download_button=True,
                include_audio=False,
            )

    with gradio.Row():
        translate_button = gradio.Button("Translate", variant="primary")
        translate_button.click(
            translate,
            inputs=[source_textbox, text_lang_dropdown, sign_lang_dropdown],
            outputs=[output_video],
            api_name="translate",
        )

    gradio.Examples(
        [
            ["یہ بہت اچھا ہے۔", "ur", "pakistan-sign-language"],
            ["وہ کام آسان تھا۔", "ur", "pakistan-sign-language"],
            ["पाँच घंटे।", "hi", "pakistan-sign-language"],
            # ["आप कैसे हैं?", "hi", "pakistan-sign-language"],
        ],
        inputs=[source_textbox, text_lang_dropdown, sign_lang_dropdown],
        outputs=output_video,
    )
    request_logger.setup(
        [
            source_textbox,
            text_lang_dropdown,
            sign_lang_dropdown,
            gradio.Markdown(label="Exception"),
            gradio.Markdown(label="Timestamp"),
        ],
        "flagged",
    )

    gradio_app.load(None, inputs=[text_lang_dropdown], js="updateTextareaDir")

if __name__ == "__main__":
    gradio_app.launch()