app_parallel.py

from flask import Flask, request, jsonify, stream_with_context, send_file, send_from_directory, Response
import asyncio
import torch
import shutil
import os
import sys
from time import strftime
from src.utils.preprocess import CropAndExtract
from src.test_audio2coeff import Audio2Coeff
from src.facerender.animate import AnimateFromCoeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
# from src.utils.init_path import init_path
import tempfile
from openai import OpenAI
import elevenlabs
from elevenlabs import set_api_key, generate, play, clone, Voice, VoiceSettings
import uuid
import time
from PIL import Image
import moviepy.editor as mp
import requests
import json
import pickle
# from dotenv import load_dotenv
from concurrent.futures import ProcessPoolExecutor, as_completed, ThreadPoolExecutor
from stream_server import add_video,HLS_DIR, generate_m3u8
import math

# Load environment variables from .env file
# load_dotenv()

# Initialize ProcessPoolExecutor for parallel processing
executor = ThreadPoolExecutor(max_workers=2)
torch.cuda.empty_cache()

class AnimationConfig:
    def __init__(self, driven_audio_path, source_image_path, result_folder,pose_style,expression_scale,enhancer,still,preprocess,ref_pose_video_path, image_hardcoded):
        self.driven_audio = driven_audio_path
        self.source_image = source_image_path
        self.ref_eyeblink = None
        self.ref_pose = None
        self.checkpoint_dir = './checkpoints'
        self.result_dir = result_folder
        self.pose_style = pose_style
        self.batch_size = 2
        self.expression_scale = expression_scale
        self.input_yaw = None
        self.input_pitch = None
        self.input_roll = None
        self.enhancer = enhancer
        self.background_enhancer = None
        self.cpu = False
        self.face3dvis = False
        self.still = still  
        self.preprocess = preprocess
        self.verbose = False
        self.old_version = False
        self.net_recon = 'resnet50'
        self.init_path = None
        self.use_last_fc = False
        self.bfm_folder = './checkpoints/BFM_Fitting/'
        self.bfm_model = 'BFM_model_front.mat'
        self.focal = 1015.
        self.center = 112.
        self.camera_d = 10.
        self.z_near = 5.
        self.z_far = 15.
        self.device = 'cuda'
        self.image_hardcoded = image_hardcoded


app = Flask(__name__)

from flask_cors import CORS
CORS(app,origins=["*"])

TEMP_DIR = None
start_time = None
audio_chunks = []
preprocessed_data = None
args = None
unique_id  = None
m3u8_path = None
audio_duration = None
driven_audio_path = None

app.config['temp_response'] = None
app.config['generation_thread'] = None
app.config['text_prompt'] = None
app.config['final_video_path'] = None
app.config['final_video_duration'] = None

# Global paths
dir_path = os.path.dirname(os.path.realpath(__file__))
current_root_path = dir_path

path_of_lm_croper = os.path.join(current_root_path, 'checkpoints', 'shape_predictor_68_face_landmarks.dat')
path_of_net_recon_model = os.path.join(current_root_path, 'checkpoints', 'epoch_20.pth')
dir_of_BFM_fitting = os.path.join(current_root_path, 'checkpoints', 'BFM_Fitting')
wav2lip_checkpoint = os.path.join(current_root_path, 'checkpoints', 'wav2lip.pth')
audio2pose_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2pose_00140-model.pth')
audio2pose_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2pose.yaml')
audio2exp_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2exp_00300-model.pth')
audio2exp_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2exp.yaml')
free_view_checkpoint = os.path.join(current_root_path, 'checkpoints', 'facevid2vid_00189-model.pth.tar')


# Function for running the actual task (using preprocessed data)
def process_chunk(audio_chunk, preprocessed_data, args):
    print("Entered Process Chunk Function")
    global audio2pose_checkpoint, audio2pose_yaml_path, audio2exp_checkpoint, audio2exp_yaml_path, wav2lip_checkpoint
    global free_view_checkpoint
    if args.preprocess == 'full':
        mapping_checkpoint = os.path.join(current_root_path, 'checkpoints', 'mapping_00109-model.pth.tar')
        facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender_still.yaml')
    else:
        mapping_checkpoint = os.path.join(current_root_path, 'checkpoints', 'mapping_00229-model.pth.tar')
        facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender.yaml')

    first_coeff_path = preprocessed_data["first_coeff_path"]
    crop_pic_path = preprocessed_data["crop_pic_path"]
    crop_info_path = "/home/user/app/preprocess_data/crop_info.json"
    with open(crop_info_path , "rb") as f:
            crop_info = json.load(f)

    print(f"Loaded existing preprocessed data")
    print("first_coeff_path",first_coeff_path)
    print("crop_pic_path",crop_pic_path)
    print("crop_info",crop_info)
    torch.cuda.empty_cache()
    batch = get_data(first_coeff_path, audio_chunk, args.device, ref_eyeblink_coeff_path=None, still=args.still)
    audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path, 
                                audio2exp_checkpoint, audio2exp_yaml_path, 
                                wav2lip_checkpoint, args.device)
    coeff_path = audio_to_coeff.generate(batch, args.result_dir, args.pose_style, ref_pose_coeff_path=None)
    
    # Further processing with animate_from_coeff using the coeff_path
    animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, 
                                            facerender_yaml_path, args.device)

    torch.cuda.empty_cache()
    data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_chunk, 
                                args.batch_size, args.input_yaw, args.input_pitch, args.input_roll, 
                                expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess)
    torch.cuda.empty_cache()
    print("Will Enter Animation")
    result, base64_video, temp_file_path, _ = animate_from_coeff.generate(data, args.result_dir, args.source_image, crop_info, 
                                    enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess)

    # video_clip = mp.VideoFileClip(temp_file_path)
    # duration = video_clip.duration
    
    app.config['temp_response'] = base64_video
    app.config['final_video_path'] = temp_file_path
    # app.config['final_video_duration'] = duration
    torch.cuda.empty_cache()
    return base64_video, temp_file_path


def create_temp_dir():
    return tempfile.TemporaryDirectory()

def save_uploaded_file(file, filename,TEMP_DIR):
    print("Entered save_uploaded_file")
    unique_filename = str(uuid.uuid4()) + "_" + filename
    file_path = os.path.join(TEMP_DIR.name, unique_filename)
    file.save(file_path)
    return file_path


def custom_cleanup(temp_dir):
    # Iterate over the files and directories in TEMP_DIR
    for filename in os.listdir(temp_dir):
        file_path = os.path.join(temp_dir, filename)
        if os.path.isdir(file_path):
            shutil.rmtree(file_path)
        else:
            os.remove(file_path)
        print(f"Deleted: {file_path}")
    
    torch.cuda.empty_cache()
    import gc
    gc.collect()

# def get_audio_duration(audio_path):
#     audio_clip = mp.AudioFileClip(audio_path)
#     duration_in_seconds = audio_clip.duration
#     audio_clip.close()  # Don't forget to close the clip
#     return duration_in_seconds


def generate_audio(voice_cloning, voice_gender, text_prompt):
    print("generate_audio")
    if voice_cloning == 'no':
        if voice_gender == 'male':
            voice = 'echo'
            print('Entering Audio creation using elevenlabs')
            set_api_key('92e149985ea2732b4359c74346c3daee')

            audio = generate(text = text_prompt, voice = "Daniel", model = "eleven_multilingual_v2",stream=True, latency=4)
            with tempfile.NamedTemporaryFile(suffix=".mp3", prefix="text_to_speech_",dir=TEMP_DIR.name, delete=False) as temp_file:
                for chunk in audio:
                    temp_file.write(chunk)
                driven_audio_path = temp_file.name
                print('driven_audio_path',driven_audio_path)
                print('Audio file saved using elevenlabs')
                    
        else:
            voice = 'nova'

            print('Entering Audio creation using whisper')
            response = client.audio.speech.create(model="tts-1-hd",
                                            voice=voice,
                                            input = text_prompt)

            print('Audio created using whisper')
            with tempfile.NamedTemporaryFile(suffix=".wav", prefix="text_to_speech_",dir=TEMP_DIR.name, delete=False) as temp_file:
                driven_audio_path = temp_file.name
            
            response.write_to_file(driven_audio_path)
            print('Audio file saved using whisper')
    
    elif voice_cloning == 'yes':
        set_api_key('92e149985ea2732b4359c74346c3daee')
        # voice = clone(name = "User Cloned Voice",
        #             files = [user_voice_path] )
        voice = Voice(voice_id="CEii8R8RxmB0zhAiloZg",name="Marc",settings=VoiceSettings(
                        stability=0.71, similarity_boost=0.5, style=0.0, use_speaker_boost=True),)

        audio = generate(text = text_prompt, voice = voice, model = "eleven_multilingual_v2",stream=True, latency=4)
        with tempfile.NamedTemporaryFile(suffix=".mp3", prefix="cloned_audio_",dir=TEMP_DIR.name, delete=False) as temp_file:
            for chunk in audio:
                temp_file.write(chunk)
            driven_audio_path = temp_file.name
            print('driven_audio_path',driven_audio_path)
            # audio_duration = get_audio_duration(driven_audio_path)
            # print('Total Audio Duration in seconds',audio_duration)

    return driven_audio_path

def run_preprocessing(args):
    global path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting
    first_frame_dir = os.path.join(args.result_dir, 'first_frame_dir')
    os.makedirs(first_frame_dir, exist_ok=True)
    fixed_temp_dir = "/home/user/app/preprocess_data/"
    os.makedirs(fixed_temp_dir, exist_ok=True)
    preprocessed_data_path = os.path.join(fixed_temp_dir, "preprocessed_data.pkl")

    if os.path.exists(preprocessed_data_path) and args.image_hardcoded == "yes":
        print("Loading preprocessed data...")
        with open(preprocessed_data_path, "rb") as f:
            preprocessed_data = pickle.load(f)
        print("Loaded existing preprocessed data from:", preprocessed_data_path)

    return preprocessed_data
    
client = OpenAI(api_key="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")

def openai_chat_avatar(text_prompt):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": "Ensure answers are concise, human-like, and clear while maintaining quality. Use the fewest possible words, avoiding unnecessary articles, prepositions, and adjectives. Responses should be short but still address the question thoroughly without being verbose.Keep them to one sentence only"},
            {"role": "user", "content": f"Hi! I need help with something. {text_prompt}"},
        ],
        max_tokens = len(text_prompt) + 300 # Use the length of the input text
        # temperature=0.3,
        # stop=["Translate:", "Text:"]
    )
    return response
    
def split_audio(audio_path, TEMP_DIR, chunk_duration):
    audio_clip = mp.AudioFileClip(audio_path)
    total_duration = audio_clip.duration
    print("split_audio duration:",total_duration)
    number_of_chunks = math.ceil(total_duration / chunk_duration)
    print("Number of audio chunks:",number_of_chunks)
    audio_chunks = []
    for i in range(number_of_chunks):
        start_time = i * chunk_duration
        end_time = min(start_time + chunk_duration, total_duration)
        chunk = audio_clip.subclip(start_time, end_time)
        
        # Create a temporary file for the chunk
        with tempfile.NamedTemporaryFile(suffix=f"_chunk_{start_time}-{end_time}.wav", prefix="audio_chunk_", dir=TEMP_DIR.name, delete=False) as temp_file:
            chunk_path = temp_file.name
            chunk.write_audiofile(chunk_path)  # Specify codec if needed
            audio_chunks.append((start_time, chunk_path))
    
    audio_clip.close()  # Close the audio clip to release resources
    return audio_chunks, total_duration
    
# def extract_order_from_path(temp_file_path):
#     match = re.search(r'videostream(\d+)', temp_file_path)
#     return int(match.group(1)) if match else -1  # Return -1 if no match is found, handle appropriately.
# Generator function to yield chunk results as they are processed

def generate_chunks(audio_chunks, preprocessed_data, args, m3u8_path, audio_duration, start_time):
    global TEMP_DIR
    future_to_chunk = {executor.submit(process_chunk, chunk[1], preprocessed_data, args): chunk[0] for chunk in audio_chunks}
    processed_chunks = {chunk[0]: None for chunk in audio_chunks}
    print("processed_chunks:",processed_chunks)
    yielded_count = 1
    try:
        for chunk_idx, future in enumerate(as_completed(future_to_chunk)):
            idx = future_to_chunk[future]
            try:
                base64_video, temp_file_path = future.result()
                processed_chunks[idx] = temp_file_path
                for expected_start_time in sorted(processed_chunks.keys()):
                    if processed_chunks[expected_start_time] is not None:
                        add_video(processed_chunks[expected_start_time], m3u8_path, audio_duration)
                        end_time = time.time()
                        elapsed_time = end_time - start_time
                        event_data = json.dumps({
                            'start_time': expected_start_time,
                            'video_index': yielded_count,
                            'elapsed_time': elapsed_time
                        })
                        yield f"data: {event_data}\n\n"
                        processed_chunks[expected_start_time] = None
                        yielded_count += 1
                    else:
                        break
            except Exception as e:
                yield f"Task for chunk {idx} failed: {e}\n"
    finally:
        if TEMP_DIR:
            #close_m3u8(m3u8_path)
            custom_cleanup(TEMP_DIR.name)

def close_m3u8(m3u8_path: str):
    try:
        with open(m3u8_path, 'a') as m3u8_file:
            m3u8_file.write('#EXT-X-ENDLIST\n')
        print(f"Closed m3u8 file with end tag: {m3u8_path}")
    except Exception as e:
        print(f"Error closing m3u8 file: {e}")
      
@app.route("/run", methods=['POST'])
def parallel_processing():
    global start_time, driven_audio_path
    global audio_chunks, preprocessed_data, args, m3u8_path, audio_duration
    start_time = time.time()
    global TEMP_DIR
    TEMP_DIR = create_temp_dir()
    global unique_id
    unique_id = str(uuid.uuid4())
    print('request:',request.method)
    try:
        if request.method == 'POST':
            # source_image = request.files['source_image']
            image_path = '/home/user/app/images/marc_smile_enhanced.jpg'
            source_image = Image.open(image_path)
            text_prompt = request.form['text_prompt']
            
            print('Input text prompt: ',text_prompt)
            text_prompt = text_prompt.strip()
            if not text_prompt:
                return jsonify({'error': 'Input text prompt cannot be blank'}), 400
                
            voice_cloning = request.form.get('voice_cloning', 'yes')
            image_hardcoded = request.form.get('image_hardcoded', 'no')
            chat_model_used = request.form.get('chat_model_used', 'openai')
            target_language = request.form.get('target_language', 'original_text')
            print('target_language',target_language)
            pose_style = int(request.form.get('pose_style', 1))
            expression_scale = float(request.form.get('expression_scale', 1))
            enhancer = request.form.get('enhancer', None)
            voice_gender = request.form.get('voice_gender', 'male')
            still_str = request.form.get('still', 'False')
            still = still_str.lower() == 'false'
            print('still', still)
            preprocess = request.form.get('preprocess', 'crop')
            print('preprocess selected: ',preprocess)
            # ref_pose_video = request.files.get('ref_pose', None)

            response = openai_chat_avatar(text_prompt)
            text_prompt = response.choices[0].message.content.strip()
            app.config['text_prompt'] = text_prompt
            print('Final output text prompt using openai: ',text_prompt)
    
            source_image_path = save_uploaded_file(source_image, 'source_image.png',TEMP_DIR)
            print(source_image_path)
    
            driven_audio_path = generate_audio(voice_cloning, voice_gender, text_prompt)
        
            save_dir = tempfile.mkdtemp(dir=TEMP_DIR.name)
            result_folder = os.path.join(save_dir, "results")
            os.makedirs(result_folder, exist_ok=True)
    
            ref_pose_video_path = None
            args = AnimationConfig(driven_audio_path=driven_audio_path, source_image_path=source_image_path, result_folder=result_folder, pose_style=pose_style, expression_scale=expression_scale,enhancer=enhancer,still=still,preprocess=preprocess,ref_pose_video_path=ref_pose_video_path, image_hardcoded=image_hardcoded)
            preprocessed_data = run_preprocessing(args)
            # chunk_duration = 3
            # print(f"Splitting the audio into {chunk_duration}-second chunks...")
            # audio_chunks, audio_duration = split_audio(driven_audio_path, TEMP_DIR, chunk_duration=chunk_duration)
            # print(f"Audio has been split into {len(audio_chunks)} chunks: {audio_chunks}")
            start_time = 0
            audio_clip = mp.AudioFileClip(driven_audio_path)
            audio_duration = audio_clip.duration
            audio_chunks.append((start_time, driven_audio_path))

            os.makedirs('lives', exist_ok=True)
            print("Entering generate m3u8")
            m3u8_path = f'lives/{unique_id}.m3u8'
            #generate_m3u8(audio_duration, m3u8_path)
            
            return jsonify({'video_url': f'{unique_id}.m3u8'}), 200
                    
    except Exception as e:
        app.logger.error(f"An error occurred: {e}")
        return jsonify({'status': 'error', 'message': str(e)}), 500


@app.route("/stream", methods=["GET"])
def stream_results():
    global audio_chunks, preprocessed_data, args, m3u8_path, audio_duration, start_time
    print("audio_chunks",audio_chunks)
    print("preprocessed_data",preprocessed_data)
    print("args",args)
    try:
        return Response(stream_with_context(generate_chunks(audio_chunks, preprocessed_data, args, m3u8_path, audio_duration, start_time)),content_type='text/event-stream')
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)}), 500

@app.route("/live_stream/<string:playlist>", methods=['GET'])
async def get_concatenated_playlist(playlist: str):
    """
    Endpoint to serve the concatenated HLS playlist.

    Returns:
        FileResponse: The concatenated playlist file.
    """
    if playlist.endswith('.ts'):
        playlist_path = os.path.join('hls_videos', playlist)
    else:
        playlist_path = os.path.join('lives', playlist)
    if not os.path.exists(playlist_path):
        return jsonify({'status': 'error', "msg":"Playlist not found"}), 404

    return send_file(playlist_path, mimetype='application/vnd.apple.mpegurl')

# @app.route("/live_stream/<string:filename>", methods=["GET"])
# def live_stream(filename):
#     return send_from_directory(directory="hls_videos", filename=filename)

@app.route("/health", methods=["GET"])
def health_status():
    response = {"online": "true"}
    return jsonify(response)

if __name__ == '__main__':
    app.run(debug=True)