app.py

"""
File: app.py

Description: Translate text...

Author: Didier Guillevic
Date: 2024-09-07
"""
import spaces
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

import gradio as gr
import langdetect

from deep_translator import GoogleTranslator
from model_spacy import nlp_xx

import model_translation
m2m100 = model_translation.ModelM2M100()


def translate_with_Helsinki(
        chunks, src_lang, tgt_lang, input_max_length, output_max_length) -> str:
    """Translate the chunks with the Helsinki model
    """
    if src_lang not in translation.src_langs:
        return (
            f"ISSUE: currently no model for language '{src_lang}'. "
             "If wrong language, please specify language."
        )
    logger.info(f"LANG: {src_lang}, TEXT: {chunks[0][:50]}...")
    tokenizer, model = translation.get_tokenizer_model_for_src_lang(src_lang)

    translated_chunks = []
    for chunk in chunks:
        # NOTE: The 'fa' (Persian) model has multiple target languages to choose from.
        # We need to specifiy the desired languages among: fra ita por ron spa
        #   https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-fa-itc
        # Prepend text with >>fra<< in order to translate in French.
        if src_lang == 'fa':
            chunk = ">>fra<< " + chunk

        inputs = tokenizer(
            chunk, return_tensors="pt", max_length=input_max_length,
            truncation=True, padding="longest").to(model.device)
        outputs = model.generate(**inputs, max_length=output_max_length)
        translated_chunk = tokenizer.batch_decode(
            outputs, skip_special_tokens=True)[0]
        #logger.info(f"Text: {chunk}")
        #logger.info(f"Translation: {translated_chunk}")
        translated_chunks.append(translated_chunk)

    return '\n'.join(translated_chunks)


def translate_text(
        text: str,
        src_lang: str,
        tgt_lang: str
    ) -> str:
    """Translate the given text into English or French
    """
    # src_lang among the supported languages?
    # - make sure src_lang is not None
    src_lang = src_lang if (src_lang and src_lang != "auto") else langdetect.detect(text)
    if src_lang not in model_translation.language_codes.values():
        logging.error(f"Language detected {src_lang} not among supported language")
    
    # tgt_lang: make sure it is not None. Default to 'en' if not set.
    if tgt_lang not in model_translation.tgt_language_codes.values():
        tgt_lang = 'en'

    # translate
    translated_text_m2m100 = m2m100.translate(text, src_lang, tgt_lang)
    translated_text_google_translate = GoogleTranslator(
        source='auto', target='en').translate(text=text)

    return (
        translated_text_m2m100,
        translated_text_google_translate
    )


#
# User interface
#
with gr.Blocks() as demo:

    gr.Markdown("""
        ## Text translation v0.0.3
    """)
    # Input
    input_text = gr.Textbox(
        lines=5,
        placeholder="Enter text to translate",
        label="Text to translate",
        render=True
    )

    # Output
    output_text_m2m100 = gr.Textbox(
        lines=4,
        label="Facebook m2m100 (418M)",
        render=True
    )
    output_text_google_translate = gr.Textbox(
        lines=4,
        label="Google Translate",
        render=True
    )

    # Source and target languages
    with gr.Row():
        src_lang = gr.Dropdown(
            choices=model_translation.language_codes.items(),
            value="auto",
            label="Source language",
            render=True
        )
        tgt_lang = gr.Dropdown(
            choices=model_translation.tgt_language_codes.items(),
            value="en",
            label="Target language",
            render=True
        )

    # Submit button
    translate_btn = gr.Button("Translate")
    translate_btn.click(
        fn=translate_text,
        inputs=[input_text, src_lang, tgt_lang],
        outputs=[output_text_m2m100, output_text_google_translate]
    )

    with gr.Accordion("Documentation", open=False):
        gr.Markdown("""
            - Models: serving Facebook M2M100 (418M) and Google Translate.
        """)

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