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import streamlit as st | |
import requests | |
import os | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
from transformers import pipeline | |
st.markdown('<p style="text-align:center; font-size: 70px; margin-bottom: 50px; margin-top: 0px;"><b>LingoLink</b></p>', unsafe_allow_html=True) | |
# Load the model and tokenizer | |
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") | |
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") | |
def translate(text, src_lang, tgt_lang): | |
translator = pipeline( | |
"translation", | |
model=model, | |
tokenizer=tokenizer, | |
src_lang=src_lang, | |
tgt_lang=tgt_lang, | |
) | |
output = translator(text, max_length=400) | |
return output[0]["translation_text"] | |
lang_dict = { | |
"Assamese":"asm_Beng", | |
"Bengali":"ben_Beng", | |
"Chinese":"zho_Hans", | |
"Czech":"ces_Latn", | |
"Danish":"dan_Latn", | |
"English":"eng_Latn", | |
"German":"deu_Latn", | |
"Greek":"ell_Grek", | |
"Finnish":"fin_Latn", | |
"French":"fra_Latn", | |
"Gujarati":"guj_Gujr", | |
"Hindi":"hin_Deva", | |
"Japanese":"jpn_Jpan", | |
"Kannada":"kan_Knda", | |
"Korean":"kor_Hang", | |
"Maithili":"mai_Deva", | |
"Malayalam":"mal_Mlym", | |
"Odia":"ory_Orya", | |
"Portuguese":"por_Latn", | |
"Russian":"rus_Cyrl", | |
"Sanskrit":"san_Deva", | |
"Tamil":"tam_Taml", | |
"Telugu":"tel_Telu", | |
"Urdu":"urd_Arab", | |
} | |
languages = tuple(lang_dict.keys()) | |
src_lang = lang_dict[src_lang] | |
tgt_lang = lang_dict[tgt_lang] | |
def main(): | |
cols=st.columns(2) | |
with cols[0]: | |
src_lang = st.selectbox('From', languages) | |
with cols[1]: | |
tgt_lang = st.selectbox('To', languages) | |
text = st.text_area("Enter your text below:") | |
if st.button("Translate"): | |
if src_lang and tgt_lang and text: | |
result = translate(text, src_lang, tgt_lang) | |
st.text_area("", result) | |
else: | |
st.warning("Please provide source language, target language, and text to translate.") | |
if __name__ == "__main__": | |
main() | |