import streamlit as st from transformers import MarianMTModel, MarianTokenizer from datasets import load_dataset from gtts import gTTS import os # Streamlit app st.title("Text Translator with Voice") # Input text user_text = st.text_input("Enter the text you want to translate:") # Define a dictionary of language codes and their full names language_names = { 'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chichewa': 'ny', 'Chinese (Simplified)': 'zh-cn', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Filipino': 'tl', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Odia': 'or', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tajik': 'tg', 'Tamil': 'ta', 'Tatar': 'tt', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Turkmen': 'tk', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uyghur': 'ug', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu' } # Function to load the translation model and tokenizer def load_translation_model(language_pair): model_name = f'Helsinki-NLP/opus-mt-{language_pair}' model = MarianMTModel.from_pretrained(model_name) tokenizer = MarianTokenizer.from_pretrained(model_name) return model, tokenizer # Function to translate text def translate_text(text, target_language_code): try: # Load model and tokenizer for the selected language language_pair = f'en-{target_language_code}' model, tokenizer = load_translation_model(language_pair) # Tokenize the input text inputs = tokenizer(text, return_tensors="pt", padding=True) # Translate the text translated_tokens = model.generate(**inputs) translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True) return translated_text except Exception as e: return f"Translation failed: {str(e)}" # Target language selection st.subheader("Select the target language for translation:") selected_target_language = st.selectbox("Select a target language:", list(language_names.keys())) # Display the translated text and speak it if user_text: translated_text = translate_text(user_text, language_names[selected_target_language]) # Apply advanced CSS for better display in a bordered box styled_text = f'