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import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Define a dictionary to map language names to model identifiers
models = {
'French': 'Helsinki-NLP/opus-mt-en-fr',
'Spanish': 'Helsinki-NLP/opus-mt-en-es',
'German': 'Helsinki-NLP/opus-mt-en-de',
'Italian': 'Helsinki-NLP/opus-mt-en-it',
'Urdu': 'Helsinki-NLP/opus-mt-en-ur',
'Arabic': 'Helsinki-NLP/opus-mt-en-ar',
# Add more language models if needed
}
def load_model(model_name):
"""Load the model and tokenizer based on the selected model name."""
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)
return model, tokenizer
def translate_text(text, model, tokenizer):
"""Translate text using the provided model and tokenizer."""
inputs = tokenizer.encode(text, return_tensors="pt")
translated = model.generate(inputs)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
def main():
st.title("Multilingual Translator")
# User input for English text
text_to_translate = st.text_area("Enter text in English:")
# Language options
selected_language = st.selectbox("Select target language:", list(models.keys()))
if st.button("Translate"):
if text_to_translate:
# Load the selected model
model_name = models[selected_language]
model, tokenizer = load_model(model_name)
translated_text = translate_text(text_to_translate, model, tokenizer)
st.write(f"**Translation in {selected_language}:**")
st.write(translated_text)
else:
st.warning("Please enter text to translate.")
if __name__ == "__main__":
main()
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