app.py

import gradio as gr
import uuid
import os
from typing import Optional
import tempfile
from pydub import AudioSegment
import re
import subprocess
import numpy as np
import soundfile as sf
import sounddevice as sd
import time
import sox
from io import BytesIO
import asyncio
import aiohttp
from moviepy.editor import VideoFileClip
import threading
import socketio
import base64

ASR_API = "http://astarwiz.com:9998/asr"
TTS_SPEAK_SERVICE = 'http://astarwiz.com:9603/speak'
TTS_WAVE_SERVICE = 'http://astarwiz.com:9603/wave'


#bSegByPunct = True 
bSegByPunct = False

LANGUAGE_MAP = {
    "en": "English",
    "ma": "Malay",
    "ta": "Tamil",
    "zh": "Chinese"
}

DEVELOPER_PASSWORD = os.getenv("DEV_PWD")
RAPID_API_KEY = os.getenv("RAPID_API_KEY")

AVAILABLE_SPEAKERS = {
    "en": ["MS"],
    "ma": ["msFemale"],
    "ta": ["ta_female1"],
    "zh": ["childChinese2"]
}


audio_update_event = asyncio.Event()
acc_cosy_audio = None
# cosy voice tts related; 
#TTS_SOCKET_SERVER = "http://localhost:9244"
TTS_SOCKET_SERVER = "http://astarwiz.com:9244"

sio = socketio.AsyncClient()

@sio.on('connect')
def on_connect():
    print('Connected to server')

@sio.on('disconnect')
def on_disconnect():
    print('Disconnected from server')

@sio.on('audio_chunk')
async def on_audio_chunk(data):
    global translation_update, audio_update, acc_cosy_audio

    translated_seg_txt = data['trans_text']
    with translation_lock:
        translation_update["content"] = translation_update["content"] + " " + translated_seg_txt
        translation_update["new"] = True

    audio_base64 = data['audio']
    audio_bytes = base64.b64decode(audio_base64)
    audio_np = np.frombuffer(audio_bytes, dtype=np.int16)
    
    if (acc_cosy_audio is None):
        acc_cosy_audio = audio_np
    else:
        acc_cosy_audio = np.concatenate((acc_cosy_audio, audio_np))
        
    with audio_lock:
        audio_update["content"] = (22050, audio_np)
        audio_update["new"] = True

    #audio_float = audio_np.astype(np.float32) / 32767.0
    #audio_queue.append(audio_float)
    #accumulated_audio.extend(audio_float)
    

@sio.on('tts_complete')
async def on_tts_complete():
    await sio.disconnect()
    print("Disconnected from server after TTS completion")
    
    audio_update_event.set()
    


# Global variables for storing update information
transcription_update = {"content": "", "new": False}
translation_update = {"content": "", "new": False}
audio_update = {"content": None, "new": False}

# Locks for thread-safe operations
transcription_lock = threading.Lock()
translation_lock = threading.Lock()
audio_lock = threading.Lock()

def replace_audio_in_video(video_path, audio_path, output_path):
    command = [
        'ffmpeg',
        '-i', video_path,
        '-i', audio_path,
        '-c:v', 'copy',
        '-map', '0:v:0',
        '-map', '1:a:0',
        '-shortest',
        output_path
    ]
    subprocess.run(command, check=True)
    return output_path

async def replace_audio_and_generate_video(temp_video_path, gradio_audio):
    print ("gradio_audio:", gradio_audio)
    if not temp_video_path or gradio_audio is None:
        return "Both video and audio are required to replace audio.", None

    if not os.path.exists(temp_video_path):
        return "Video file not found.", None

    # Unpack the Gradio audio output
    sample_rate, audio_data = gradio_audio

    # Ensure audio_data is a numpy array
    if not isinstance(audio_data, np.ndarray):
        audio_data = np.array(audio_data)

    # Create a temporary WAV file for the original audio
    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_audio_file:
        original_audio_path = temp_audio_file.name
        sf.write(original_audio_path, audio_data, sample_rate)

    # Get video duration
    video_clip = VideoFileClip(temp_video_path)
    video_duration = video_clip.duration
    video_clip.close()

    # Get audio duration
    audio_duration = len(audio_data) / sample_rate

    # Calculate tempo factor
    tempo_factor = audio_duration / video_duration

    # Create a temporary WAV file for the tempo-adjusted audio
    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_audio_file:
        adjusted_audio_path = temp_audio_file.name

    # Adjust audio tempo
    tfm = sox.Transformer()
    tfm.tempo(tempo_factor, 's')
    tfm.build(original_audio_path, adjusted_audio_path)

    # Generate output video path
    output_video_path = os.path.join(tempfile.gettempdir(), f"output_{uuid.uuid4()}.mp4")

    try:
        replace_audio_in_video(temp_video_path, adjusted_audio_path, output_video_path)
        return "Audio replaced successfully.", output_video_path
    except subprocess.CalledProcessError as e:
        return f"Error replacing audio: {str(e)}", None
    finally:
        os.unlink(original_audio_path)  # Clean up the original audio file
        os.unlink(adjusted_audio_path)  # Clean up the adjusted audio file
 
async def fetch_youtube_id(youtube_url: str) -> str:
    if 'v=' in youtube_url:
        return youtube_url.split("v=")[1].split("&")[0]
    elif 'youtu.be/' in youtube_url:
        return youtube_url.split("youtu.be/")[1]
    elif 'shorts' in youtube_url:
        return youtube_url.split("/")[-1]
    else:
        raise Exception("Unsupported URL format")

async def download_youtube_audio(youtube_url: str, output_dir: Optional[str] = None) -> Optional[tuple[str, str]]:
    video_id = await fetch_youtube_id(youtube_url)
    
    if not video_id:
        return None

    if output_dir is None:
        output_dir = tempfile.gettempdir()

    output_filename = os.path.join(output_dir, f"{video_id}.mp3")
    temp_filename = os.path.join(output_dir, f"{video_id}.mp4")
    if os.path.exists(output_filename) and os.path.exists(temp_filename):
        return (output_filename, temp_filename)

    url = "https://youtube86.p.rapidapi.com/api/youtube/links"
    headers = {
        'Content-Type': 'application/json',
        'x-rapidapi-host': 'youtube86.p.rapidapi.com',
        'x-rapidapi-key': RAPID_API_KEY
    }
    data = {
        "url": youtube_url
    }
    
    async with aiohttp.ClientSession() as session:
        async with session.post(url, headers=headers, json=data) as response:
            if response.status == 200:
                result = await response.json()
                for url in result[0]['urls']:
                    if url.get('isBundle'):
                        audio_url = url['url']
                        extension = url['extension']
                        print ("audio_url :", audio_url)
                        async with session.get(audio_url) as audio_response:
                            print ("audio_response:", audio_response) 
                            if audio_response.status == 200:
                                content = await audio_response.read()
                                temp_filename = os.path.join(output_dir, f"{video_id}.{extension}")
                                with open(temp_filename, 'wb') as audio_file:
                                    audio_file.write(content)
                                
                                audio = AudioSegment.from_file(temp_filename, format=extension)
                                audio = audio.set_frame_rate(16000)
                                audio.export(output_filename, format="mp3", parameters=["-ar", "16000"])
                                return (output_filename, temp_filename)
            else:
                print("Error:", response.status, await response.text())
                return None
punctuation_marks = r'([\.!?！？。])'
def split_text_with_punctuation(text):
    # Split the text using the punctuation marks, keeping the punctuation marks
    split_text = re.split(punctuation_marks, text)
    # Combine each punctuation mark with the preceding segment
    combined_segments = []
    
    # Loop through the split text in steps of 2
    for i in range(0, len(split_text) - 1, 2):
        combined_segments.append(split_text[i] + split_text[i + 1])
    
    # Handle any remaining text that doesn't have a punctuation following it
    if len(split_text) % 2 != 0 and split_text[-1]:
        combined_segments.append(split_text[-1])
    
    # Split any segment that exceeds 50 words
    final_segments = []
    for segment in combined_segments:
        words = segment.split()  # Split each segment into words
        if len(words) > 50:
            # Split the segment into chunks of no more than 50 words
            for j in range(0, len(words), 50):
                final_segments.append(' '.join(words[j:j+50]))
        else:
            final_segments.append(segment)
    
    return [segment for segment in final_segments if segment]  # Filter out empty strings

def extract_segments(text):
    pattern = r'\[(\d+\.\d+)s\s*->\s*(\d+\.\d+)s\]\s*(.*?)(?=\[\d+\.\d+s|\Z)'
    matches = re.findall(pattern, text, re.DOTALL)
    
    if not matches:
        return []
    
    segments = []
    for start, end, content in matches:
        segments.append({
            'start': float(start),
            'end': float(end),
            'text': content.strip()
        })
    
    return segments

def adjust_tempo_pysox_array(gradio_audio, duration):
    # Unpack the Gradio audio output
    sample_rate, audio_data = gradio_audio
    # Ensure audio_data is a numpy array
    if not isinstance(audio_data, np.ndarray):
        audio_data = np.array(audio_data)
    # Calculate the current duration of the audio in seconds
    current_duration = len(audio_data) / sample_rate
    # Calculate the necessary tempo factor to match the desired duration
    tempo_factor = current_duration / duration
    # Create a pysox Transformer
    tfm = sox.Transformer()
    tfm.tempo(tempo_factor)
    # Use pysox to transform the audio directly in memory
    adjusted_audio = tfm.build_array(input_array=audio_data, sample_rate_in=sample_rate)
    # Trim or pad the audio to exactly match the desired duration
    target_length = int(sample_rate * duration)
    if len(adjusted_audio) > target_length:
        adjusted_audio = adjusted_audio[:target_length]  # Trim if too long
    else:
        # Pad with zeros if too short
        adjusted_audio = np.pad(adjusted_audio, (0, target_length - len(adjusted_audio)), mode='constant')
    # Return the processed audio in the Gradio format (sample_rate, adjusted_audio)
    return sample_rate, adjusted_audio

async def inference_via_llm_api(input_text, min_new_tokens=2, max_new_tokens=64):
    print(input_text)
    one_vllm_input = f"<|im_start|>system\nYou are a translation expert.<|im_end|>\n<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant"
    vllm_api = 'http://astarwiz.com:2333/' + "v1/completions"
    data = {
        "prompt": one_vllm_input,
        'model': "./Edu-4B-NewTok-V2-20240904/",
        'min_tokens': min_new_tokens,
        'max_tokens': max_new_tokens,
        'temperature': 0.1,
        'top_p': 0.75,
        'repetition_penalty': 1.1,
        "stop_token_ids": [151645, ],
    }
    async with aiohttp.ClientSession() as session:
        async with session.post(vllm_api, headers={"Content-Type": "application/json"}, json=data) as response:
            if response.status == 200:
                result = await response.json()
                if "choices" in result:
                    return result["choices"][0]['text'].strip()
            return "The system got some error during vLLM generation. Please try it again."
async def upload_file(file_path, upload_url):
    print(f"1. Client sends request: {time.time()}")
    async with aiohttp.ClientSession() as session:
        with open(file_path, 'rb') as f:
            form_data = aiohttp.FormData()
            form_data.add_field('file', f, filename=os.path.basename(file_path))
    
            async with session.post(upload_url, data=form_data) as response:
                print(f"5. Client receives headers: {time.time()}")
                print(f"Status: {response.status}")
    
                result = await response.json()
                print(f"7. Client fully received and parsed response: {time.time()}")
                if response.status == 200: 
                    return result
                else:
                    return {"file_id",""}
async def transcribe_and_speak(audio, source_lang, target_lang, youtube_url=None, target_speaker=None, progress_tracker=None):
    global transcription_update, translation_update, audio_update, acc_cosy_audio,audio_update_event
    transcription_update = {"content": "", "new": True}
    translation_update = {"content": "", "new": True}
    audio_update = {"content": None, "new": True}
    acc_cosy_audio =None
    video_path = None
    audio_update_event.clear()
    #progress = gr.Progress();
    
    #progress(0.1, "started:")
    if youtube_url:
        audio = await download_youtube_audio(youtube_url)
        if audio is None:
            return "Failed to download YouTube audio.", None, None, video_path,(22050, accumulated_audio)
        audio, video_path = audio 
    if not audio:
        return "Please provide an audio input or a valid YouTube URL.", None, None, video_path,(22050, accumulated_audio)

    # ASR
    #progress(0.2, "ASR started:")
    file_id = str(uuid.uuid4())
    data = aiohttp.FormData()
    data.add_field('file', open(audio, 'rb'))
    data.add_field('language', 'ms' if source_lang == 'ma' else source_lang)
    if bSegByPunct:
        data.add_field('model_name', 'whisper-large-v2-local-cs')  
        data.add_field('with_timestamp', 'false')
    else:
        data.add_field('model_name', 'official-v3')
        data.add_field('with_timestamp', 'true')

    async with aiohttp.ClientSession() as session:
        async with session.post(ASR_API, data=data) as asr_response:
            if asr_response.status == 200:
                result = await asr_response.json()
                transcription = result['text']
                with transcription_lock:
                    transcription_update["content"] = transcription
                    transcription_update["new"] = True
            else:
                return "ASR failed", None, None, video_path,(22050, accumulated_audio)
    #progress(0.4, "ASR done:")
    

    # use cosy voice if  target_lang  == 'en'  or target_lang == 'zh'

    if  target_lang  == 'en'  or target_lang == 'zh':        
        try:
            if not sio.connected:
                server_url = TTS_SOCKET_SERVER 
                await sio.connect(server_url)
                print(f"Connected to {server_url}")
              
            # Handle the audio file
            file_id=""
            if audio and os.path.exists(audio):
                print("upload_url")
                upload_url = f"{server_url}/upload"  # Adjust this URL as needed
                print("before call upload_file:")
                upload_result = await upload_file(audio, upload_url)
                #print (type(upload_result))
                print ("upload_result:", upload_result)
                file_id = upload_result['file_id']

 
            # use defualt voice 
            tts_request = {
                'text': transcription,
                'overwrite_prompt': False,
                'promptText':"",
                'promptAudio':file_id,
                'sourceLang':source_lang,
                'targetLang':target_lang
            }
            await sio.emit('tts_request', tts_request)
            
            # wait until all cosy voice tts is done :
            await audio_update_event.wait() 
            print('cosy tts complete,',audio_update)
            
            return transcription, translation_update["content"], audio_update["content"], video_path, (22050, acc_cosy_audio)

        except Exception as e:
            print(f"Failed to process request: {str(e)}")
            print("let use vits then")



    if bSegByPunct:
        split_result = split_text_with_punctuation(transcription)
    else:
        split_result = extract_segments(transcription);

    translate_segments = []
    accumulated_audio = None
    sample_rate = 22050
    global is_playing
    for i, segment in enumerate(split_result):
        if bSegByPunct:
            translation_prompt = f"Translate the following text from {LANGUAGE_MAP[source_lang]} to {LANGUAGE_MAP[target_lang]}: {segment}"
        else:
            translation_prompt = f"Translate the following text from {LANGUAGE_MAP[source_lang]} to {LANGUAGE_MAP[target_lang]}: {segment['text']}"
        translated_seg_txt = await inference_via_llm_api(translation_prompt)
        translate_segments.append(translated_seg_txt)
        print(f"Translation: {translated_seg_txt}")
        with translation_lock:
            translation_update["content"] = " ".join(translate_segments)
            translation_update["new"] = True
        
        # Generate TTS for each translated segment
        #progress(0.4 + (0.5 * (i + 1) / len(split_result)), "translation and tts  in progress :")
        
        tts_params = {
            'language': target_lang,
            'speed': 1.1,
            'speaker': target_speaker or AVAILABLE_SPEAKERS[target_lang][0],
            'text': translated_seg_txt
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.get(TTS_SPEAK_SERVICE, params=tts_params) as tts_response:
                if tts_response.status == 200:
                    audio_file = await tts_response.text()
                    audio_file = audio_file.strip()
                    audio_url = f"{TTS_WAVE_SERVICE}?file={audio_file}"
                    async with session.get(audio_url) as response:
                        content = await response.read()
                        audio_chunk, sr = sf.read(BytesIO(content))
                        #print ('audio_chunk:', type(audio_chunk),audio_chunk) 
                        #print ('audio_chunk:, src:', segment['end'] -segment['start'], ' tts:', len(audio_chunk)/sr) 
                        # _, audio_chunk = adjust_tempo_pysox_array( (sr, audio_chunk), segment['end'] -segment['start'])
                        

                        if accumulated_audio is None:
                            accumulated_audio = audio_chunk
                            sample_rate = sr
                        else:
                            accumulated_audio = np.concatenate((accumulated_audio, audio_chunk))
                        
                        with audio_lock:
                            audio_update["content"] = (sample_rate, audio_chunk)
                            audio_update["new"] = True
                else:
                    print(f"TTS failed for segment: {translated_seg_txt}")

    translated_text = " ".join(translate_segments)
    
    #progress(1, "all done.")    
    print("sigal the playing could stop now. all tts generated")
    is_playing =False; 
    if accumulated_audio is not None:
        return transcription, translated_text, audio_update["content"], video_path, (sample_rate,accumulated_audio)
    else:
        return transcription, translated_text, "TTS failed", video_path, (sample_rate, accumulated_audio)

"""
async def run_speech_translation(audio, source_lang, target_lang, youtube_url, target_speaker):
    temp_video_path = None
    transcription, translated_text, audio_chunksr, temp_video_path = await transcribe_and_speak(audio, source_lang, target_lang, youtube_url, target_speaker)
    return transcription, translated_text, audio_chunksr, temp_video_path
"""
async def update_transcription():
    global transcription_update
    with transcription_lock:
        if transcription_update["new"]:
            content = transcription_update["content"]
            transcription_update["new"] = False
            return content
    return gr.update()

async def update_translation():
    global translation_update
    with translation_lock:
        if translation_update["new"]:
            content = translation_update["content"]
            translation_update["new"] = False
            return content
    return gr.update()

async def update_audio():
    global audio_update
    with audio_lock:
        if audio_update["new"]:
            content = audio_update["content"]
            audio_update["new"] = False
            return content
    return gr.update()

def disable_button():
    # Disable the button during processing
    return gr.update(interactive=False)

with gr.Blocks() as demo:
    gr.Markdown("# Speech Translation")
    
    gr.Markdown("Speak into the microphone, upload an audio file, or provide a YouTube URL. The app will translate and speak it back to you.")
    
    with gr.Row():
        user_audio_input = gr.Audio(sources=["microphone", "upload"], type="filepath")
        user_youtube_url = gr.Textbox(label="YouTube URL (optional)")
    
    with gr.Row():
        user_source_lang = gr.Dropdown(choices=["en", "ma", "ta", "zh"], label="Source Language", value="en")
        user_target_lang = gr.Dropdown(choices=["en", "ma", "ta", "zh"], label="Target Language", value="zh")
        user_target_speaker = gr.Dropdown(choices=AVAILABLE_SPEAKERS['zh'], label="Target Speaker", value="childChinese2")

    with gr.Row():
        user_button = gr.Button("Translate and Speak", interactive=False)
    
    with gr.Row():
        user_transcription_output = gr.Textbox(label="Transcription")
        user_translation_output = gr.Textbox(label="Translation")
        user_audio_output = gr.Audio(label="Translated Speech", visible =False)
        user_audio_final = gr.Audio(label="Final total Speech")
    status_message = gr.Textbox(label="Status", interactive=False)

    user_video_output = gr.HTML(label="YouTube Video")

    replace_audio_button = gr.Button("Replace Audio", interactive=False, visible =False)
    final_video_output = gr.Video(label="Video with Replaced Audio",visible=False)

    temp_video_path = gr.State()
    translation_progress = gr.State(0.0)

    async def update_button_state(audio, youtube_url, progress):
        print(audio, youtube_url, progress)
        # Button is interactive if there's input and progress is 0 or 1 (not in progress)
        print ("progress:", audio, youtube_url,bool(audio) , bool(youtube_url), progress == 0 or progress == 1)
        return gr.Button(interactive=(bool(audio) or bool(youtube_url)) and (progress == 0 or progress == 1))

    user_audio_input.change(
        fn=update_button_state,
        inputs=[user_audio_input, user_youtube_url, translation_progress],
        outputs=user_button
    )
    user_youtube_url.change(
        fn=update_button_state,
        inputs=[user_audio_input, user_youtube_url, translation_progress],
        outputs=user_button
    )


    async def run_speech_translation_wrapper(audio, source_lang, target_lang, youtube_url, target_speaker,progress):
        
        progress = 0.1
        temp_video_path = None
        transcription, translated_text, audio_chunksr, temp_video_path, accumulated_aud_buf = await transcribe_and_speak(audio, source_lang, target_lang, youtube_url, target_speaker)
        progress = 1
        return  transcription, translated_text, audio_chunksr, temp_video_path, "Translation complete", accumulated_aud_buf, gr.update(interactive=True) 

    user_button.click(
        fn=disable_button,
        inputs=[],
        outputs=[user_button]  # Disable the button during processing
    ).then(
        fn=run_speech_translation_wrapper,
        inputs=[user_audio_input, user_source_lang, user_target_lang, user_youtube_url, user_target_speaker, translation_progress],
        outputs=[user_transcription_output, user_translation_output, user_audio_output, temp_video_path, status_message,user_audio_final,user_button]
    )

    async def update_replace_audio_button(audio_url, video_path):
        print("update replace:", audio_url, video_path)
        return gr.Button(interactive=bool(audio_url) and bool(video_path))

    user_audio_output.change(
        fn=update_replace_audio_button,
        inputs=[user_audio_output, temp_video_path],
        outputs=[replace_audio_button]
    )

    replace_audio_button.click(
        fn=replace_audio_and_generate_video,
        inputs=[temp_video_path, user_audio_final],
        outputs=[status_message, final_video_output]
    )
    
    async def update_video_embed(youtube_url):
        if youtube_url:
            try:
                video_id = await fetch_youtube_id(youtube_url)
                return f'<iframe width="560" height="315" src="https://www.youtube.com/embed/{video_id}" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>'
            except Exception as e:
                print(f"Error embedding video: {e}")
        return ""

    user_youtube_url.change(
        fn=update_video_embed,
        inputs=[user_youtube_url],
        outputs=[user_video_output]
    )

    async def update_target_speakers(target_lang):
        return gr.Dropdown(choices=AVAILABLE_SPEAKERS[target_lang], value=AVAILABLE_SPEAKERS[target_lang][0])

    user_target_lang.change(
        fn=update_target_speakers,
        inputs=[user_target_lang],
        outputs=[user_target_speaker]
    )

    async def periodic_update():
        transcription = await update_transcription()
        translation = await update_translation()
        audio = await update_audio()            
        return (
            transcription,
            translation,
            audio
        )

    demo.load(
        periodic_update,
        inputs=[],
        outputs=[
            user_transcription_output,
            user_translation_output,
            user_audio_output,            
        ],
        every=0.1
    )

    # JavaScript for client-side queue and playback handling
    user_audio_output.change(
        None,  # No backend change needed, we only handle frontend actions
        inputs=user_audio_output,  # Set the user_audio_output as input to capture its audio changes
        outputs=None,
        js="""
        async (audioFilePath) => {
        // Debug: Log received audio file path
        console.log("Received audio file path:", audioFilePath);

        if (!window.audioQueue) {
            window.audioQueue = [];
            window.isPlaying = false;
        }

        // Ensure the correct URL for the audio file is available
        if (audioFilePath && audioFilePath.url) {
            console.log("Processing audio file...");

            try {
                // Fetch and decode the audio file
                const response = await fetch(audioFilePath.url);
                if (!response.ok) {
                    console.error("Failed to fetch audio file:", response.statusText);
                    return;
                }

                const audioData = await response.arrayBuffer();
                const audioContext = new AudioContext();
                const decodedData = await audioContext.decodeAudioData(audioData);

                // Split the decoded audio buffer into two chunks
                const totalDuration = decodedData.duration;
                const midPoint = Math.floor(decodedData.length / 2);  // Midpoint for splitting
                const sampleRate = decodedData.sampleRate;

                // Create two separate AudioBuffers for each chunk
                const firstHalfBuffer = audioContext.createBuffer(decodedData.numberOfChannels, midPoint, sampleRate);
                const secondHalfBuffer = audioContext.createBuffer(decodedData.numberOfChannels, decodedData.length - midPoint, sampleRate);

                // Copy data from original buffer to the two new buffers
                for (let channel = 0; channel < decodedData.numberOfChannels; channel++) {
                    firstHalfBuffer.copyToChannel(decodedData.getChannelData(channel).slice(0, midPoint), channel, 0);
                    secondHalfBuffer.copyToChannel(decodedData.getChannelData(channel).slice(midPoint), channel, 0);
                }

                // Add both chunks to the queue
                window.audioQueue.push(firstHalfBuffer);
                window.audioQueue.push(secondHalfBuffer);
                console.log("Two audio chunks added to queue. Queue length:", window.audioQueue.length);

                // Function to play the next audio chunk from the queue
                const playNextChunk = async () => {
                    console.log("Attempting to play next chunk. isPlaying:", window.isPlaying);

                    if (!window.isPlaying && window.audioQueue.length > 0) {
                        console.log("Starting playback...");
                        window.isPlaying = true;

                        // Get the next audio buffer from the queue
                        const audioBuffer = window.audioQueue.shift();
                        console.log("Playing audio chunk from buffer.");

                        const source = audioContext.createBufferSource();
                        source.buffer = audioBuffer;
                        source.connect(audioContext.destination);

                        // When the audio finishes playing, play the next chunk
                        source.onended = () => {
                            console.log("Audio chunk finished playing.");
                            window.isPlaying = false;
                            playNextChunk();  // Play the next audio chunk in the queue
                        };

                        source.start(0);  // Start playing the current chunk
                        console.log("Audio chunk started.");
                    } else {
                        console.log("Already playing or queue is empty.");
                    }
                };

                // Start playing the next chunk if not already playing
                playNextChunk();

            } catch (error) {
                console.error("Error during audio playback:", error);
                window.isPlaying = false;
            }
        } else {
            console.log("No valid audio file path received.");
        }
    }
    """
    )

demo.queue()

#demo.launch(auth=(os.getenv("DEV_USER"), os.getenv("DEV_PWD")))
asyncio.run(demo.launch(auth=(os.getenv("DEV_USER"), os.getenv("DEV_PWD"))))