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import streamlit as st
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
# Load the model and tokenizer once at the start
@st.cache_resource
def load_model_and_tokenizer():
model_name = "facebook/m2m100_418M"
tokenizer = M2M100Tokenizer.from_pretrained(model_name)
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
return model, tokenizer
model, tokenizer = load_model_and_tokenizer()
# Streamlit UI
st.title("English to Multiple Language Translator")
st.write("Translate English text into different languages using AI.")
# Input text
input_text = st.text_area("Enter English text:", value="")
# Sidebar for language selection
st.sidebar.title("Settings")
st.sidebar.subheader("Select Target Language")
language_options = {
"French": "fr",
"Spanish": "es",
"German": "de",
"Chinese": "zh",
"Arabic": "ar",
"Hindi": "hi",
"Japanese": "ja",
"Russian": "ru",
"Portuguese": "pt",
"Italian": "it"
}
selected_language = st.sidebar.selectbox("Target Language", list(language_options.keys()))
if st.button("Translate"):
if input_text:
# Interactive loading state
with st.spinner('Translating... Please wait...'):
# Set target language
target_language = language_options[selected_language]
tokenizer.src_lang = "en"
encoded_input = tokenizer(input_text, return_tensors="pt")
# Generate translation
generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(target_language))
translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
# Display translated text
st.write(f"**Translated text ({selected_language}):**")
st.write(translated_text)
else:
st.write("Please enter text to translate.")
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