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| 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 | |