import streamlit as st
from transformers import MarianMTModel, MarianTokenizer

# Function to load a translation model
@st.cache_resource
def load_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 model, tokenizer

def translate_text(text, model, tokenizer):
    inputs = tokenizer.encode(text, return_tensors="pt", truncation=True)
    outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_text

# Streamlit UI
st.title("Multi-Language Translator")

st.sidebar.header("Translation Settings")

# Input source and target languages
src_lang = st.sidebar.text_input("Enter source language code (e.g., 'en' for English):", "en")
tgt_lang = st.sidebar.text_input("Enter target language code (e.g., 'ur' for Urdu):", "ur")

# Validate language codes
if not src_lang.strip() or not tgt_lang.strip():
    st.sidebar.error("Please enter valid source and target language codes.")
else:
    # Load the selected model
    try:
        st.sidebar.write(f"Model: `{src_lang}-{tgt_lang}`")
        model, tokenizer = load_model(src_lang.strip(), tgt_lang.strip())

        # Input text
        text_to_translate = st.text_area("Enter text to translate:")

        # Translate and display output
        if text_to_translate.strip():
            with st.spinner("Translating..."):
                translated_text = translate_text(text_to_translate, model, tokenizer)
            st.markdown(f"### Translated Text:\n{translated_text}")
    except Exception as e:
        st.error(f"Error loading model for `{src_lang}-{tgt_lang}`: {str(e)}")