| import streamlit as st |
| from transformers import pipeline, AutoTokenizer |
|
|
| base="Helsinki-NLP/opus-mt-en-zh" |
| model="edwinlaw/opus-mt-cantonese-v2" |
| tokenizer = AutoTokenizer.from_pretrained(base) |
|
|
| def translate(text, src_lang, tgt_lang): |
| translator = pipeline( |
| "translation", |
| model=model, |
| tokenizer=tokenizer, |
| src_lang=src_lang, |
| tgt_lang=tgt_lang, |
| ) |
| translated_text = translator(text) |
| return translated_text |
|
|
| st.title("💬 Translate English into Cantonese:") |
| st.markdown( |
| """ |
| This translation engine is a fine-tuned version of the [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) model, initially designed for translating English to Mandarin Chinese. |
| |
| This [fine-tuned model](https://huggingface.co/edwinlaw/opus-mt-cantonese-v2) has been trained using 6000+ Cantonese sentences along with their English translations. |
| |
| Translations generated are intended for experimental use only. Correctness is not guaranteed. Proceed at your own risk. |
| """) |
|
|
| st.subheader('Enter a simple English sentence here:') |
| prompt = st.text_input('') |
|
|
| if prompt: |
| translation = translate(prompt, 'en', 'yue') |
| translated_txt = translation[0]['translation_text'] |
|
|
| st.write(translated_txt) |
|
|
| with st.expander('Chat History'): |
| st.info(translated_txt) |
|
|
