streamlit 
transformers 
torch
sentencepiece
sacremoses

import streamlit as st
from transformers import pipeline

# Initialize the translation pipeline
@st.cache_resource
def load_translator():
    return pipeline("translation", model="Helsinki-NLP/opus-mt-en-{target}")

# Supported languages (ISO 639-1 codes mapped to language names)
supported_languages = {
    "fr": "French",
    "es": "Spanish",
    "de": "German",
    "zh": "Chinese",
    "hi": "Hindi",
    "ar": "Arabic",
    "ru": "Russian",
    "ja": "Japanese",
    "ko": "Korean",
    "it": "Italian",
}

# Streamlit App
st.title("Language Translator App")
st.write("Translate text from English to a selected target language using Hugging Face models.")

# Input text from user
input_text = st.text_area("Enter text in English:", placeholder="Type here...")

# Language selection
target_language = st.selectbox(
    "Select target language:",
    options=list(supported_languages.keys()),
    format_func=lambda lang: supported_languages[lang],
)

# Translate button
if st.button("Translate"):
    if input_text.strip() == "":
        st.error("Please enter text to translate.")
    else:
        translator = load_translator()
        # Replace `{target}` with the user-selected language in the model
        translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{target_language}")
        translation = translator(input_text)[0]["translation_text"]
        st.success("Translated Text:")
        st.write(translation)