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