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streamlit run app.py
import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Define a dictionary of available languages and their corresponding Hugging Face models
LANGUAGE_MODELS = {
"French": "Helsinki-NLP/opus-mt-en-fr",
"German": "Helsinki-NLP/opus-mt-en-de",
"Spanish": "Helsinki-NLP/opus-mt-en-es",
"Italian": "Helsinki-NLP/opus-mt-en-it",
"Portuguese": "Helsinki-NLP/opus-mt-en-pt",
# Add more languages and their models here
}
def load_model(language):
model_name = LANGUAGE_MODELS.get(language)
if model_name is None:
st.error("Selected language not supported.")
return None, None
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
def translate_text(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", padding=True)
translated = model.generate(**inputs)
translation = tokenizer.decode(translated[0], skip_special_tokens=True)
return translation
def main():
st.title("Language Translator")
st.write("This app translates English text into selected languages using Hugging Face models.")
# Select target language
target_language = st.selectbox("Select target language", list(LANGUAGE_MODELS.keys()))
# Input text
input_text = st.text_area("Enter text in English")
if st.button("Translate"):
if not input_text:
st.error("Please enter text to translate.")
else:
# Load the model and tokenizer for the selected language
tokenizer, model = load_model(target_language)
if tokenizer and model:
# Translate the text
translated_text = translate_text(input_text, tokenizer, model)
st.subheader("Translated Text")
st.write(translated_text)
if __name__ == "__main__":
main()
streamlit run app.py
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