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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name2 = "Mhassanen/nllb-200-600M-En-Ar-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name2, src_lang="eng_Latn", tgt_lang="arz_Arab")
model = AutoModelForSeq2SeqLM.from_pretrained(model_name2)
def translate2(text):
inputs = tokenizer(text, return_tensors="pt", padding=True)
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
return translated_text[0]
# st.set_page_config(page_title="", page_icon="logo.png")
with st.sidebar:
# st.image("logo.png", width=70)
# st.markdown('<div style="position: absolute; left: 5px;"></div>', unsafe_allow_html=True)
st.markdown("## About")
st.markdown("---")
st.markdown('''
This App powered by [Mhassanen/nllb-200-600M-En-Ar-finetuned](https://huggingface.co/Mhassanen/nllb-200-600M-En-Ar-finetuned) Language model
''')
# st.markdown("---")
st.title("English to Arabic Translation")
text_to_translate = st.text_area("Enter text in English:")
if st.button("Translate"):
if text_to_translate:
with st.spinner("Translating..."):
translation = translate2(text_to_translate)
st.success("Translation completed!")
st.text_area("Translated text in Arabic:", translation, height=200)
else:
st.warning("Please enter some text to translate.")
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