app.py

import streamlit as st
from transformers import pipeline
import soundfile as sf

# Initialize translation model (English <-> Urdu)
translation_model = pipeline("translation_en_to_ur", model="Helsinki-NLP/opus-mt-en-ur")
reverse_translation_model = pipeline("translation_ur_to_en", model="Helsinki-NLP/opus-mt-ur-en")

# Initialize text-to-speech model from Hugging Face
tts_model = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech")

# Function to translate text and provide feedback
def translate_and_speak(text, direction):
    if direction == "English to Urdu":
        translated_text = translation_model(text)[0]['translation_text']
    else:
        translated_text = reverse_translation_model(text)[0]['translation_text']
    
    # Use TTS to synthesize speech from translated text
    audio = tts_model(translated_text)
    audio_path = "output.wav"
    sf.write(audio_path, audio["array"], 22050)  # Save audio to file
    return translated_text, audio_path

# Streamlit app UI
st.title("AI-Powered Language Tutor")
st.write("An interactive tutor to help you practice English-Urdu translations with speech feedback!")

# User input for translation
text_input = st.text_area("Enter Text", "Hello, how are you?")
direction = st.radio("Choose Translation Direction", ["English to Urdu", "Urdu to English"])

# Button to process the text and play audio
if st.button("Translate and Speak"):
    translated_text, audio_path = translate_and_speak(text_input, direction)
    
    # Display translated text and audio
    st.subheader("Translated Text:")
    st.write(translated_text)
    
    st.subheader("Generated Speech:")
    st.audio(audio_path, format="audio/wav")