app.py

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.")