import streamlit as st # Set page config FIRST st.set_page_config(page_title="Translation App", page_icon="🌎") from transformers import MarianMTModel, MarianTokenizer # Language-specific model names MODEL_MAPPING = { "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", "Chinese": "Helsinki-NLP/opus-mt-en-zh", "Arabic": "Helsinki-NLP/opus-mt-en-ar", "Hindi": "Helsinki-NLP/opus-mt-en-hi", "Urdu": "Helsinki-NLP/opus-mt-en-ur", "Russian": "Helsinki-NLP/opus-mt-en-ru", "Japanese": "Helsinki-NLP/opus-mt-en-ja", } @st.cache_resource def load_model(model_name): tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) return tokenizer, model # Streamlit app UI st.title("🌎 Multilingual Translation Application") st.write("Enter your question or information below and select target languages:") # Input text input_text = st.text_area("Your Question or Information", height=150) # Language selection selected_languages = st.multiselect("Select languages to translate into", list(MODEL_MAPPING.keys())) # Translate button if st.button("Translate"): if not input_text: st.warning("Please enter some text to translate.") elif not selected_languages: st.warning("Please select at least one language.") else: for lang in selected_languages: model_name = MODEL_MAPPING.get(lang) if model_name: try: # Load model for selected language tokenizer, model = load_model(model_name) # Perform translation translated = model.generate(**tokenizer(input_text, return_tensors="pt", padding=True)) output_text = tokenizer.decode(translated[0], skip_special_tokens=True) st.subheader(f"Translation in {lang}:") st.success(output_text) except Exception as e: st.error(f"Error translating to {lang}: {str(e)}") else: st.error(f"No model available for {lang}")