| import streamlit as st | |
| from transformers import pipeline, AutoTokenizer | |
| base="Helsinki-NLP/opus-mt-en-zh" | |
| model="edwinlaw/opus-mt-cantonese-v1" | |
| 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:") | |
| prompt = st.text_input('English sentence here') | |
| 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) | |