import streamlit as st
from transformers import pipeline
from gtts import gTTS
import tempfile
import os

# Set up the translator using a Hugging Face model
@st.cache_resource
def load_model(model_name):
    return pipeline("translation", model=model_name)

# Title of the app
st.title("Language Translator App")

# Add input fields for text and target language selection
st.subheader("Enter the text you want to translate:")
input_text = st.text_area("Text to translate", height=150)

# Dropdown for language selection
language_options = {
    "French": "Helsinki-NLP/opus-mt-en-fr",
    "German": "Helsinki-NLP/opus-mt-en-de",
    "Romanian": "Helsinki-NLP/opus-mt-en-ro",
    "Spanish": "Helsinki-NLP/opus-mt-en-es",
    "Italian": "Helsinki-NLP/opus-mt-en-it"
}

st.subheader("Select the target language:")
target_language = st.selectbox("Target Language", list(language_options.keys()))

# Translate button
if st.button("Translate"):
    # Load the chosen model based on the selected language
    selected_model = language_options[target_language]
    translator = load_model(selected_model)
    
    # Perform translation
    if input_text:
        translation = translator(input_text)
        translated_text = translation[0]['translation_text']
        
        # Display the translated text
        st.subheader(f"Translated Text in {target_language}:")
        st.write(translated_text)
        
        # Convert the translated text to speech
        tts = gTTS(text=translated_text, lang=target_language[:2].lower())  # Use the first two letters of the language for the TTS language code
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
            temp_file_path = temp_file.name
            tts.save(temp_file_path)
            # Play the converted speech
            st.audio(temp_file_path, format="audio/mp3")

        # Clean up temporary file
        os.remove(temp_file_path)
    else:
        st.write("Please enter some text to translate.")