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from transformers import MBartForConditionalGeneration, MBart50Tokenizer, MarianTokenizer, MarianMTModel
import streamlit as st
@st.cache(allow_output_mutation=True, suppress_st_warning=True)
def download_model():
model_name = f'Helsinki-NLP/opus-mt-en-ur'
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)
return model, tokenizer
st.title('English to Urdu Translator')
st.markdown("[Developd By: Aziz Ahmad](https://www.linkedin.com/in/theazizahmad/)", unsafe_allow_html=True)
text = st.text_area("Enter Text:", value='', height=None, max_chars=None, key=None)
model, tokenizer = download_model()
if st.button('Translate to Urdu'):
if text == '':
st.write('Please enter English text for translation')
else:
# Tokenize the text
batch = tokenizer(text, return_tensors="pt", padding=True)
# tokenized text maximum allowed size of 512
batch["input_ids"] = batch["input_ids"][:, :512]
batch["attention_mask"] = batch["attention_mask"][:, :512]
translation_encoded = model.generate(**batch)
translation = tokenizer.batch_decode(translation_encoded, skip_special_tokens=True)
st.write('', str(translation).strip('][\''))
else: pass |