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import streamlit as st | |
from transformers import MarianMTModel, MarianTokenizer | |
def translate_text(text, src_lang, tgt_lang): | |
model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}' | |
model = MarianMTModel.from_pretrained(model_name) | |
tokenizer = MarianTokenizer.from_pretrained(model_name) | |
translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True)) | |
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) | |
return translated_text | |
def main(): | |
st.title("Language Translator App") | |
# Language options | |
language_dict = { | |
"English": "en", | |
"French": "fr", | |
"German": "de", | |
"Spanish": "es", | |
"Italian": "it", | |
"Russian": "ru", | |
"Chinese": "zh", | |
"Japanese": "ja", | |
"Korean": "ko", | |
"Arabic": "ar", | |
} | |
src_lang = st.selectbox("Select Source Language", list(language_dict.keys())) | |
tgt_lang = st.selectbox("Select Target Language", list(language_dict.keys())) | |
# Text input | |
text = st.text_area("Enter Text to Translate") | |
# Translate button | |
if st.button("Translate"): | |
if text: | |
translated_text = translate_text(text, language_dict[src_lang], language_dict[tgt_lang]) | |
st.write("Translated Text:") | |
st.success(translated_text) | |
else: | |
st.warning("Please enter text to translate.") | |
if __name__ == "__main__": | |
main() | |