Translator / app.py
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Create app.py
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# Install necessary libraries first:
# pip install streamlit transformers
import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Function to load model and tokenizer
@st.cache_resource
def load_translation_model(src_lang, tgt_lang):
model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
# Function to translate text
def translate_text(text, tokenizer, model):
tokens = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
translated = model.generate(**tokens)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
# List of supported language codes
languages = {
"English": "en",
"French": "fr",
"Spanish": "es",
"German": "de",
"Italian": "it",
"Dutch": "nl",
"Chinese (Simplified)": "zh",
"Russian": "ru",
"Arabic": "ar",
"Hindi": "hi",
}
# Streamlit UI
st.title("Language Translation App")
st.write("Translate text between multiple languages using an open-source translation model.")
# Language selection
source_language = st.selectbox("Select source language", list(languages.keys()), index=0)
target_language = st.selectbox("Select target language", list(languages.keys()), index=1)
# Input text
input_text = st.text_area("Enter text to translate", height=150)
# Load model and tokenizer
if source_language != target_language and input_text.strip():
src_lang_code = languages[source_language]
tgt_lang_code = languages[target_language]
try:
tokenizer, model = load_translation_model(src_lang_code, tgt_lang_code)
translated_text = translate_text(input_text, tokenizer, model)
st.subheader("Translated Text")
st.success(translated_text)
except Exception as e:
st.error(f"Error: {e}")
else:
st.info("Please select different source and target languages and enter text to translate.")