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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import langid
import os
load_dotenv()
os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
os.environ["HF_HOME"] = "/code/.cache/huggingface"
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
translator = pipeline('translation', model=model, tokenizer=tokenizer, max_length=400)
class LanguageTextModel(BaseModel):
languageText: str
sourceLanguageCode: str
targetLanguageCode: str
language_code_mapping = {
'en': 'eng_Latn',
'hi': 'hin_Deva',
'bn': 'ben_Beng',
'bho': 'bho_Deva',
'ur': 'urd_Arab',
'ta': 'tam_Taml',
'te': 'tel_Telu',
'ml': 'mal_Mlym',
'es': 'spa_Latn',
'fr': 'fra_Latn',
'de': 'deu_Latn',
'zh-cn': 'zho_Hans',
'ru': 'rus_Cyrl',
'pt': 'por_Latn',
'ja': 'jpn_Jpan',
'ko': 'kor_Hang',
'it': 'ita_Latn',
'nl': 'nld_Latn',
'el': 'ell_Grek',
'pl': 'pol_Latn',
'tr': 'tur_Latn',
'sv': 'swe_Latn',
'da': 'dan_Latn',
'fi': 'fin_Latn',
'hu': 'hun_Latn',
'cs': 'ces_Latn',
'no': 'nob_Latn',
'ro': 'ron_Latn',
'sk': 'slk_Latn',
'hr': 'hrv_Latn',
'bg': 'bul_Cyrl',
'uk': 'ukr_Cyrl',
'sr': 'srp_Cyrl',
'he': 'heb_Hebr',
'ar': 'arb_Arab',
'th': 'tha_Thai',
'vi': 'vie_Latn',
'id': 'ind_Latn',
'ms': 'zsm_Latn',
'tl': 'tgl_Latn',
'sw': 'swh_Latn',
'am': 'amh_Ethi',
'so': 'som_Latn',
'ha': 'hau_Latn',
'yo': 'yor_Latn',
'zu': 'zul_Latn',
'xh': 'xho_Latn',
'ig': 'ibo_Latn',
'uz': 'uzb_Latn',
'kk': 'kaz_Cyrl',
}
@app.get('/')
def welcome():
return {
'success': True,
'message': 'Server of "NLLB language translator" is up and running successfully'
}
@app.post('/translate')
async def translate_text(allInput: LanguageTextModel):
try:
detected_language, confidence = langid.classify(allInput.languageText)
if detected_language not in language_code_mapping:
return {
"success": False,
"message": "Detected Language is not supported."
}
detected_source_language_code = language_code_mapping[detected_language]
if detected_source_language_code != allInput.sourceLanguageCode:
return {
"success": False,
"message": "Wrong combination of source language code and input text."
}
response = translator(allInput.languageText, src_lang=allInput.sourceLanguageCode, tgt_lang=allInput.targetLanguageCode)
return {
"success": True,
"translated_text": response[0]['translation_text']
}
except Exception as e:
print(f"Error: {e}")
return {
"success": False,
"message": "Something went wrong. Please try again after sometime."
}
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