app.py

import streamlit as st
from transformers import MarianMTModel, MarianTokenizer

# App Title and Description
st.title("🌐 Universal Language Translator App")
st.write("""
Translate text from any language to any other language using an open-source multilingual model.  
This app supports many languages and ensures a user-friendly experience.
""")

# Instructions
st.sidebar.header("📝 Instructions")
st.sidebar.write("""
1. Enter the text you want to translate in the input box.
2. Select the source language of the input text.
3. Choose the target language for translation.
4. Click the "Translate" button to see the translated text.
""")

# Supported Languages
languages = {
    "English": "en",
    "Spanish": "es",
    "French": "fr",
    "German": "de",
    "Chinese": "zh",
    "Hindi": "hi",
    "Arabic": "ar",
    "Russian": "ru",
    "Italian": "it",
    "Portuguese": "pt",
    "Japanese": "ja",
    "Korean": "ko",
    "Dutch": "nl",
    "Bengali": "bn",
    "Turkish": "tr",
    "Urdu": "ur",
    "Greek": "el",
    "Polish": "pl",
    "Thai": "th",
    "Vietnamese": "vi",
    "Hebrew": "he",
    "Swahili": "sw",
    "Amharic": "am",
    "Tamil": "ta",
    "Telugu": "te",
    "Punjabi": "pa",
    "Malayalam": "ml",
}

# UI for Input and Language Selection
source_text = st.text_area("Enter text to translate:", height=150, placeholder="Type here...")
source_language = st.selectbox("Source Language:", options=list(languages.keys()), index=0)
target_language = st.selectbox("Target Language:", options=list(languages.keys()), index=1)

if st.button("Translate"):
    if source_text.strip() == "":
        st.error("Please enter some text to translate.")
    else:
        try:
            # Define model name dynamically
            model_name = f"Helsinki-NLP/opus-mt-{languages[source_language]}-{languages[target_language]}"
            
            # Load tokenizer and model
            tokenizer = MarianTokenizer.from_pretrained(model_name)
            model = MarianMTModel.from_pretrained(model_name)
            
            # Translate
            inputs = tokenizer(source_text, return_tensors="pt", padding=True, truncation=True)
            translated_tokens = model.generate(**inputs)
            translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
            
            # Display Translated Text
            st.subheader("🔄 Translated Text:")
            st.text_area("Translation Output:", value=translated_text, height=150)
        except Exception as e:
            st.error("Translation failed. Please ensure the selected languages are supported.")
            st.error(str(e))

# Footer
st.write("---")
st.markdown("💡 **Developed by Abdullah**")