app.py

"""
Main entry point for the Audio Translation Web Application
Handles file upload, processing pipeline, and UI rendering
"""

# Configure logging first
import logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("app.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

import streamlit as st
import os
import time
import subprocess
from utils.stt import transcribe_audio
from utils.translation import translate_text
from utils.tts_dummy import generate_speech

# Hugging Face Spaces Setup Automation
def setup_huggingface_space():
    """Automatically configure Hugging Face Space requirements"""
    logger.info("Running Hugging Face space setup")
    st.sidebar.header("Space Configuration")
    
    try:
        subprocess.run(["espeak-ng", "--version"], check=True, capture_output=True)
        logger.info("espeak-ng verification successful")
    except (FileNotFoundError, subprocess.CalledProcessError):
        logger.error("Missing espeak-ng dependency")
        st.sidebar.error("""
        **Missing System Dependencies!** Add this to your Space settings:
        ```txt
        apt-get update && apt-get install -y espeak-ng
        ```
        """)
        st.stop()

    model_dir = "./kokoro"
    required_files = [
        f"{model_dir}/kokoro-v0_19.pth",
        f"{model_dir}/voices/af_bella.pt"
    ]
    
    if not all(os.path.exists(f) for f in required_files):
        logger.error("Missing model files in %s", model_dir)
        st.sidebar.warning("""
        **Missing Model Files!** Add this to your Space settings:
        ```txt
        git clone https://huggingface.co/hexgrad/Kokoro-82M ./kokoro
        ```
        """)
        st.stop()

# Initialize environment configurations
os.makedirs("temp/uploads", exist_ok=True)
os.makedirs("temp/outputs", exist_ok=True)

def configure_page():
    """Set up Streamlit page configuration"""
    logger.info("Configuring Streamlit page")
    st.set_page_config(
        page_title="Audio Translator",
        page_icon="🎧",
        layout="wide",
        initial_sidebar_state="expanded"
    )
    st.markdown("""
        <style>
            .reportview-container {margin-top: -2em;}
            #MainMenu {visibility: hidden;}
            .stDeployButton {display:none;}
            .stAlert {padding: 20px !important;}
        </style>
    """, unsafe_allow_html=True)

def handle_file_processing(upload_path):
    """
    Execute the complete processing pipeline:
    1. Speech-to-Text (STT)
    2. Machine Translation
    3. Text-to-Speech (TTS)
    """
    logger.info(f"Starting processing for: {upload_path}")
    progress_bar = st.progress(0)
    status_text = st.empty()
    
    try:
        # STT Phase
        logger.info("Beginning STT processing")
        status_text.markdown("🔍 **Performing Speech Recognition...**")
        with st.spinner("Initializing Whisper model..."):
            english_text = transcribe_audio(upload_path)
        progress_bar.progress(30)
        logger.info(f"STT completed. Text length: {len(english_text)} characters")

        # Translation Phase
        logger.info("Beginning translation")
        status_text.markdown("🌐 **Translating Content...**")
        with st.spinner("Loading translation model..."):
            chinese_text = translate_text(english_text)
        progress_bar.progress(60)
        logger.info(f"Translation completed. Translated length: {len(chinese_text)} characters")

        # TTS Phase
        logger.info("Beginning TTS generation")
        status_text.markdown("🎵 **Generating Chinese Speech...**")
        with st.spinner("Initializing TTS engine..."):
            output_path = generate_speech(chinese_text, language="zh")
        progress_bar.progress(100)
        logger.info(f"TTS completed. Output file: {output_path}")
        
        
        # Display results

        # Display results
        status_text.success("✅ Processing Complete!")
        return english_text, chinese_text, output_path
        
    except Exception as e:
        logger.error(f"Processing failed: {str(e)}", exc_info=True)
        status_text.error(f"❌ Processing Failed: {str(e)}")
        st.exception(e)
        raise

def render_results(english_text, chinese_text, output_path):
    """Display processing results in organized columns"""
    logger.info("Rendering results")
    st.divider()
    
    col1, col2 = st.columns([2, 1])
    with col1:
        st.subheader("Recognition Results")
        st.code(english_text, language="text")
        
        st.subheader("Translation Results")
        st.code(chinese_text, language="text")

    with col2:
        st.subheader("Audio Output")
        st.audio(output_path)
        with open(output_path, "rb") as f:
            st.download_button(
                label="Download Audio",
                data=f,
                file_name="translated_audio.wav",
                mime="audio/wav"
            )

def main():
    """Main application workflow"""
    logger.info("Starting application")
    # setup_huggingface_space()  # First-run configuration checks
    configure_page()
    st.title("🎧 High-Quality Audio Translation System")
    st.markdown("Upload English Audio → Get Chinese Speech Output")

    uploaded_file = st.file_uploader(
        "Select Audio File (MP3/WAV)",
        type=["mp3", "wav"],
        accept_multiple_files=False
    )

    if uploaded_file:
        logger.info(f"File uploaded: {uploaded_file.name}")
        upload_path = os.path.join("temp/uploads", uploaded_file.name)
        with open(upload_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
        
        results = handle_file_processing(upload_path)
        if results:
            render_results(*results)

if __name__ == "__main__":
    main()