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# File: language_translation_app.py
import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Function to load the model and tokenizer
@st.cache_resource
def load_model_and_tokenizer(model_name):
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
# Function to perform translation
def translate_text(input_text, src_lang, tgt_lang):
model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
try:
tokenizer, model = load_model_and_tokenizer(model_name)
inputs = tokenizer(input_text, return_tensors="pt", padding=True)
outputs = model.generate(**inputs)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return translated_text
except Exception as e:
return f"Error: Unable to translate. Details: {e}"
# Language options supported by Helsinki-NLP
language_pairs = {
"English": "en",
"French": "fr",
"German": "de",
"Spanish": "es",
"Chinese": "zh",
"Hindi": "hi",
"Urdu": "ur",
"Arabic": "ar",
"Russian": "ru",
"Italian": "it",
# Add more as required
}
# Streamlit App
def main():
st.title("Language Translation App")
st.write("Translate text between multiple languages instantly.")
# Input and Output language selection
input_language = st.selectbox("Select Input Language", language_pairs.keys())
output_language = st.selectbox("Select Output Language", language_pairs.keys())
# User input for the text
input_text = st.text_area("Enter text to translate", height=200)
if st.button("Translate"):
if input_text.strip():
src_lang = language_pairs[input_language]
tgt_lang = language_pairs[output_language]
translated_text = translate_text(input_text, src_lang, tgt_lang)
st.subheader("Translated Text")
st.write(translated_text)
else:
st.warning("Please enter text to translate.")
if __name__ == "__main__":
main() |