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streamlit
transformers
torch
sentencepiece
sacremoses
import streamlit as st
from transformers import pipeline
# Initialize the translation pipeline
@st.cache_resource
def load_translator():
return pipeline("translation", model="Helsinki-NLP/opus-mt-en-{target}")
# Supported languages (ISO 639-1 codes mapped to language names)
supported_languages = {
"fr": "French",
"es": "Spanish",
"de": "German",
"zh": "Chinese",
"hi": "Hindi",
"ar": "Arabic",
"ru": "Russian",
"ja": "Japanese",
"ko": "Korean",
"it": "Italian",
}
# Streamlit App
st.title("Language Translator App")
st.write("Translate text from English to a selected target language using Hugging Face models.")
# Input text from user
input_text = st.text_area("Enter text in English:", placeholder="Type here...")
# Language selection
target_language = st.selectbox(
"Select target language:",
options=list(supported_languages.keys()),
format_func=lambda lang: supported_languages[lang],
)
# Translate button
if st.button("Translate"):
if input_text.strip() == "":
st.error("Please enter text to translate.")
else:
translator = load_translator()
# Replace `{target}` with the user-selected language in the model
translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{target_language}")
translation = translator(input_text)[0]["translation_text"]
st.success("Translated Text:")
st.write(translation)
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