app.py

import streamlit as st
from transformers import MarianMTModel, MarianTokenizer

# Define available languages with MarianMT models
LANGUAGES = {
 'Spanish': 'es',
 'French': 'fr',
 'German': 'de',
 'Chinese': 'zh',
 'Hindi': 'hi',
 'Arabic': 'ar',
 'Japanese': 'ja',
 'Russian': 'ru',
 'Italian': 'it',
 'Portuguese': 'pt',
 # Add more languages if needed
}

# Function to load the model based on the selected language
@st.cache_resource
def load_model(src_lang='en', tgt_lang='es'):
 model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}'
 model = MarianMTModel.from_pretrained(model_name)
 tokenizer = MarianTokenizer.from_pretrained(model_name)
 return model, tokenizer

# Function to translate text
def translate_text(model, tokenizer, text):
 inputs = tokenizer.encode(text, return_tensors='pt', truncation=True, padding=True)
 translated = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
 translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
 return translated_text

# Streamlit app
st.title("Language Translator")
st.write("Translate English text to any language using Hugging Face models.")

# Input text
text = st.text_area("Enter text in English to translate:")

# Language selection
language = st.selectbox("Choose target language", list(LANGUAGES.keys()))

if st.button("Translate"):
 if text:
 # Load model and tokenizer based on selected language
 tgt_lang = LANGUAGES[language]
 model, tokenizer = load_model('en', tgt_lang)
 
 # Perform translation
 translated_text = translate_text(model, tokenizer, text)
 
 # Display the translation
 st.write(f"**Translated text ({language}):**")
 st.write(translated_text)
 else:
 st.write("Please enter text to translate.")