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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}")
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