| | from transformers import 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: |
| | |
| | |
| | batch = tokenizer(text, return_tensors="pt", padding=True) |
| | |
| | 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 |
