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import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Specified different languages
LANGUAGES = {
"English": "en",
"French": "fr",
"German": "de",
"Spanish": "es",
"Italian": "it",
"Portuguese": "pt",
"Russian": "ru",
"Chinese": "zh",
"Japanese": "ja",
"Arabic": "ar",
}
# Helper function to load the model and tokenizer
@st.cache_resource
def load_translation_model(src_lang, tgt_lang): # Corrected parameter names
model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}" # Corrected typo in model_name
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
# Translation function
def translate_text(tokenizer, model, text):
inputs = tokenizer(text, return_tensors='pt', padding=True)
translated = model.generate(**inputs)
return tokenizer.decode(translated[0], skip_special_tokens=True)
# Streamlit App
st.title("Language Translation APP 🌍")
st.write("Translate text between multiple languages using open-source models.")
# Language selection
col1, col2 = st.columns(2)
with col1:
source_language = st.selectbox("Select Source Language 🌐", list(LANGUAGES.keys()))
with col2:
target_language = st.selectbox("Select Target Language 🌍", list(LANGUAGES.keys()))
# Input Text
text_to_translate = st.text_area("Enter text to translate", height=150)
# Translate button
if st.button("Translate"):
if source_language == target_language:
st.warning("Source and target language must be different")
elif not text_to_translate.strip():
st.warning("Please enter some text to translate")
else:
src_lang = LANGUAGES[source_language]
tgt_lang = LANGUAGES[target_language]
try:
# Load model and tokenizer
tokenizer, model = load_translation_model(src_lang, tgt_lang)
# Perform translation
translated_text = translate_text(tokenizer, model, text_to_translate)
# Display result
st.subheader("Translated Text 🔄:")
st.write(translated_text)
except Exception as e:
st.error(f"Error: {str(e)}")
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