app.py

import requests
import os

import fasttext
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch

title = "Community Tab Language Detection & Translation"
description = """
When comments are created in the community tab, detect the language of the content.
Then, if the detected language is different from the user's language, display an option to translate it.
"""

LANG_ID_API_URL = "https://q5esh83u7boq5qwd.us-east-1.aws.endpoints.huggingface.cloud"
ACCESS_TOKEN = os.environ.get("ACCESS_TOKEN")
headers = {"Authorization": f"Bearer {ACCESS_TOKEN}"}

model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
device = 0 if torch.cuda.is_available() else -1
print(f"Is CUDA available: {torch.cuda.is_available()}")

language_code_map = {
    "English": "eng_Latn",
    "French": "fra_Latn",
    "German": "deu_Latn",
    "Spanish": "spa_Latn",
    "Korean": "kor_Hang",
    "Japanese": "jpn_Jpan"
}

def identify_language(text):
    model_file = "lid218e.bin"
    model_full_path = os.path.join(os.path.dirname(__file__), model_file)
    model = fasttext.load_model(model_full_path)
    predictions = model.predict(text, k=1) # e.g., (('__label__eng_Latn',), array([0.81148803]))
    
    PREFIX_LENGTH = 7 # To strip away '__label__' from language code
    language_code = predictions[0][0][PREFIX_LENGTH:]
    return language_code
    


def translate(text, src_lang, tgt_lang):
    src_lang_code = language_code_map[src_lang]
    tgt_lang_code = language_code_map[tgt_lang]

    translation_pipeline = pipeline(
        "translation", model=model, tokenizer=tokenizer, src_lang=src_lang_code, tgt_lang=tgt_lang_code, device=device)
    result = translation_pipeline(text)
    return result[0]['translation_text']


def query(text, src_lang, tgt_lang):
    translation = translate(text, src_lang, tgt_lang)
    lang_id_response = requests.post(LANG_ID_API_URL, headers=headers, json={
        "inputs": text, "wait_for_model": True, "use_cache": True})
    lang_id = lang_id_response.json()[0]
    
    language_code = identify_language(text)

    return [language_code, translation]


examples = [
    ["Hello, world", "English", "French"],
    ["Can I have a cheeseburger?", "English", "German"],
    ["Hasta la vista", "Spanish", "German"],
    ["동경에 휴가를 간다", "Korean", "Japanese"],
]

gr.Interface(
    query,
    [
        gr.Textbox(lines=2),
        gr.Radio(["English", "Spanish", "Korean"], value="English", label="Source Language"),
        gr.Radio(["French", "German", "Japanese"], value="French", label="Target Language")
    ],
    outputs=[
        gr.Textbox(lines=3, label="Detected Language"),
        gr.Textbox(lines=3, label="Translation")
    ],
    title=title,
    description=description,
    examples=examples
).launch()