import streamlit as st from transformers import MarianMTModel, MarianTokenizer # Define available models for translation 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", "Dutch": "Helsinki-NLP/opus-mt-en-nl", "Russian": "Helsinki-NLP/opus-mt-en-ru", "Chinese": "Helsinki-NLP/opus-mt-en-zh", "Japanese": "Helsinki-NLP/opus-mt-en-ja", "Korean": "Helsinki-NLP/opus-mt-en-ko", "Arabic": "Helsinki-NLP/opus-mt-en-ar", "Hindi": "Helsinki-NLP/opus-mt-en-hi", "Thai": "Helsinki-NLP/opus-mt-en-th", "Vietnamese": "Helsinki-NLP/opus-mt-en-vi", "Swahili": "Helsinki-NLP/opus-mt-en-sw" } def load_model_and_tokenizer(model_name): model = MarianMTModel.from_pretrained(model_name) tokenizer = MarianTokenizer.from_pretrained(model_name) return model, tokenizer def translate_text(text, target_lang): model_name = models.get(target_lang, None) if not model_name: return "Translation model for the selected language is not available." try: model, tokenizer = load_model_and_tokenizer(model_name) inputs = tokenizer(text, return_tensors="pt", padding=True) translated = model.generate(**inputs) result = tokenizer.decode(translated[0], skip_special_tokens=True) return result except Exception as e: return f"Error during translation: {str(e)}" def main(): st.title("Universal Language Translator") # User input input_text = st.text_area("Enter text in English:", "") # Language selection target_language = st.selectbox("Select target language:", list(models.keys())) if st.button("Translate"): if input_text: result = translate_text(input_text, target_language) st.write(f"Translation ({target_language}):") st.write(result) else: st.warning("Please enter some text to translate.") if __name__ == "__main__": main()