Update app_concurrent.py

app_concurrent.py

@@ -1,567 +1,569 @@
-from flask import Flask, request, jsonify, Response, stream_with_context
-import torch
-import shutil
-import os
-import sys
-from argparse import ArgumentParser
-from time import strftime
-from argparse import Namespace
-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, AsyncOpenAI
-import threading
-import elevenlabs
-from elevenlabs import set_api_key, generate, play, clone, Voice, VoiceSettings
-# from flask_cors import CORS, cross_origin
-# from flask_swagger_ui import get_swaggerui_blueprint
-import uuid
-import time
-from PIL import Image
-import moviepy.editor as mp
-import requests
-import json
-import pickle
-from celery import Celery
-import concurrent.futures
-import multiprocessing
-
-# Get the number of CPU cores
-cpu_cores = multiprocessing.cpu_count()
-print(f"Number of available CPU cores: {cpu_cores}")
-
-
-
-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 = ref_pose_video_path
-        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 = 'cpu'
-        self.image_hardcoded = image_hardcoded
-
-
-app = Flask(__name__)
-
-MAX_WORKERS = cpu_cores-1
-TEMP_DIR = None
-start_time = None
-chunk_tasks = []
-futures = []
-
-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
-
-
-
-
-def main(args):
-    print("Entered main function")
-    pic_path = args.source_image
-    audio_path = args.driven_audio
-    save_dir = args.result_dir
-    pose_style = args.pose_style
-    device = args.device
-    batch_size = args.batch_size
-    input_yaw_list = args.input_yaw
-    input_pitch_list = args.input_pitch
-    input_roll_list = args.input_roll
-    ref_eyeblink = args.ref_eyeblink
-    ref_pose = args.ref_pose
-    preprocess = args.preprocess
-    image_hardcoded = args.image_hardcoded
-
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-    current_root_path = dir_path
-    print('current_root_path ',current_root_path)
-
-    # sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess)
-
-    path_of_lm_croper = os.path.join(current_root_path, args.checkpoint_dir, 'shape_predictor_68_face_landmarks.dat')
-    path_of_net_recon_model = os.path.join(current_root_path, args.checkpoint_dir, 'epoch_20.pth')
-    dir_of_BFM_fitting = os.path.join(current_root_path, args.checkpoint_dir, 'BFM_Fitting')
-    wav2lip_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'wav2lip.pth')
-
-    audio2pose_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, '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, args.checkpoint_dir, '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, args.checkpoint_dir, 'facevid2vid_00189-model.pth.tar')
-
-    if preprocess == 'full':
-        mapping_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, '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, args.checkpoint_dir, 'mapping_00229-model.pth.tar')
-        facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender.yaml')
-
-
-    # preprocess_model = CropAndExtract(sadtalker_paths, device)
-    #init model
-    print(path_of_net_recon_model)
-    preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device)
-
-    # audio_to_coeff = Audio2Coeff(sadtalker_paths,  device)
-    audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path, 
-                                audio2exp_checkpoint, audio2exp_yaml_path, 
-                                wav2lip_checkpoint, device)
-    # animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device)
-    animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, 
-                                            facerender_yaml_path, device)
-
-    first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
-    os.makedirs(first_frame_dir, exist_ok=True)
-    # first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\
-                                                                            #  source_image_flag=True, pic_size=args.size)
-
-
-    # fixed_temp_dir = "/tmp/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 image_hardcoded == "yes":
-    #     print("Loading preprocessed data...")
-    #     with open(preprocessed_data_path, "rb") as f:
-    #         preprocessed_data = pickle.load(f)
-    #     first_coeff_new_path = preprocessed_data["first_coeff_path"]
-    #     crop_pic_new_path = preprocessed_data["crop_pic_path"]
-    #     crop_info_path = preprocessed_data["crop_info_path"]
-    #     with open(crop_info_path, "rb") as f:
-    #         crop_info = pickle.load(f)
-            
-    #     print(f"Loaded existing preprocessed data from: {preprocessed_data_path}")
-    
-    # else:
-    #     print("Running preprocessing...")
-    #     first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess, source_image_flag=True)
-    #     first_coeff_new_path = os.path.join(fixed_temp_dir, os.path.basename(first_coeff_path))
-    #     crop_pic_new_path = os.path.join(fixed_temp_dir, os.path.basename(crop_pic_path))
-    #     crop_info_new_path = os.path.join(fixed_temp_dir, "crop_info.pkl")
-    #     shutil.move(first_coeff_path, first_coeff_new_path)
-    #     shutil.move(crop_pic_path, crop_pic_new_path)
-
-    #     with open(crop_info_new_path, "wb") as f:
-    #         pickle.dump(crop_info, f)
-            
-    #     preprocessed_data = {"first_coeff_path": first_coeff_new_path,
-    #                         "crop_pic_path": crop_pic_new_path,
-    #                         "crop_info_path": crop_info_new_path}
-
-
-    #     with open(preprocessed_data_path, "wb") as f:
-    #         pickle.dump(preprocessed_data, f)
-    #     print(f"Preprocessed data saved to: {preprocessed_data_path}")
-
-    first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess, source_image_flag=True)
-
-    
-    print('first_coeff_path ',first_coeff_path)
-    print('crop_pic_path ',crop_pic_path)
-    print('crop_info ',crop_info)
-
-    if first_coeff_path is None:
-        print("Can't get the coeffs of the input")
-        return
-
-    if ref_eyeblink is not None:
-        ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0]
-        ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname)
-        os.makedirs(ref_eyeblink_frame_dir, exist_ok=True)
-        # ref_eyeblink_coeff_path, _, _ =  preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False)
-        ref_eyeblink_coeff_path, _, _ =  preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir)
-    else:
-        ref_eyeblink_coeff_path=None
-        print('ref_eyeblink_coeff_path',ref_eyeblink_coeff_path)
-
-    if ref_pose is not None:
-        if ref_pose == ref_eyeblink:
-            ref_pose_coeff_path = ref_eyeblink_coeff_path
-        else:
-            ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0]
-            ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname)
-            os.makedirs(ref_pose_frame_dir, exist_ok=True)
-            # ref_pose_coeff_path, _, _ =  preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False)
-            ref_pose_coeff_path, _, _ =  preprocess_model.generate(ref_pose, ref_pose_frame_dir)
-    else:
-        ref_pose_coeff_path=None
-        print('ref_eyeblink_coeff_path',ref_pose_coeff_path)
-
-    batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still)
-    coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
-
-
-    if args.face3dvis:
-        from src.face3d.visualize import gen_composed_video
-        gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4'))
-  
-    # data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path,
-                                # batch_size, input_yaw_list, input_pitch_list, input_roll_list,
-                                # expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size)
-
-
-    data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, 
-                                batch_size, input_yaw_list, input_pitch_list, input_roll_list,
-                                expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess)
-
-    # result, base64_video,temp_file_path= animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \
-                                # enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size)
-
-
-    result, base64_video,temp_file_path,new_audio_path = animate_from_coeff.generate(data, save_dir, pic_path, 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
-    
-    return base64_video, temp_file_path, duration
-
-
-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
-
-# client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
-# def openai_chat_avatar(text_prompt):
-#     response = client.chat.completions.create(
-#         model="gpt-4o-mini",
-#         messages=[{"role": "system", "content": "Answer using the minimum words you can ever use."},
-#             {"role": "user", "content": f"Hi! I need help with something. Can you assist me with the following: {text_prompt}"},
-#         ],
-#         max_tokens = len(text_prompt) + 300 # Use the length of the input text
-#         # temperature=0.3,
-#         # stop=["Translate:", "Text:"]
-#     )
-#     return response
-
-def ryzedb_chat_avatar(question):
-    url = "https://inference.dev.ryzeai.ai/chat/stream"
-    question = question + ". Summarize and Answer using the minimum words you can ever use."
-    payload = json.dumps({
-    "input": {
-    "chat_history": [],
-    "app_id": os.getenv('RYZE_APP_ID'),
-    "question": question
-    },
-    "config": {}
-    })
-    headers = {
-        'Content-Type': 'application/json'
-    }
-    
-    try:
-        # Send the POST request
-        response = requests.request("POST", url, headers=headers, data=payload)
-        
-        # Check for successful request
-        response.raise_for_status()
-        
-        # Return the response JSON
-        return response.text
-    
-    except requests.exceptions.RequestException as e:
-        print(f"An error occurred: {e}")
-        return None
-
-def custom_cleanup(temp_dir, exclude_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)
-        # Skip the directory we want to exclude
-        if file_path != exclude_dir:
-            try:
-                if os.path.isdir(file_path):
-                    shutil.rmtree(file_path)
-                else:
-                    os.remove(file_path)
-                print(f"Deleted: {file_path}")
-            except Exception as e:
-                print(f"Failed to delete {file_path}. Reason: {e}")
-
-
-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)
-
-    return driven_audio_path
-
-def split_audio(audio_path, chunk_duration=5):
-    audio_clip = mp.AudioFileClip(audio_path)
-    total_duration = audio_clip.duration
-
-    audio_chunks = []
-    for start_time in range(0, int(total_duration), chunk_duration):
-        end_time = min(start_time + chunk_duration, total_duration)
-        chunk = audio_clip.subclip(start_time, end_time)
-        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)
-            audio_chunks.append(chunk_path)
-    
-    return audio_chunks
-
-
-def process_video_for_chunk_sync(audio_chunk_path, args_dict, chunk_index):
-    """
-    Synchronous function to process a video chunk. This will be submitted to concurrent.futures ProcessPoolExecutor.
-    """
-    print("Entered process_video_for_chunk_sync")
-    args = AnimationConfig(
-        driven_audio_path=args_dict['driven_audio_path'],
-        source_image_path=args_dict['source_image_path'],
-        result_folder=args_dict['result_folder'],
-        pose_style=args_dict['pose_style'],
-        expression_scale=args_dict['expression_scale'],
-        enhancer=args_dict['enhancer'],
-        still=args_dict['still'],
-        preprocess=args_dict['preprocess'],
-        ref_pose_video_path=args_dict['ref_pose_video_path'],
-        image_hardcoded=args_dict['image_hardcoded']
-    )
-    args.driven_audio = audio_chunk_path
-    chunk_save_dir = os.path.join(args.result_dir, f"chunk_{chunk_index}")
-    os.makedirs(chunk_save_dir, exist_ok=True)
-    
-    try:
-        base64_video, video_chunk_path, duration = main(args)
-        print(f"Main function returned: {video_chunk_path}, {duration}")
-        return video_chunk_path
-    except Exception as e:
-        print(f"Error in process_video_for_chunk_sync: {str(e)}")
-        raise
-
-
-@app.route("/run", methods=['POST'])
-def generate_video():
-    global start_time
-    global chunk_tasks
-    global futures
-    start_time = time.time()
-    global TEMP_DIR
-    TEMP_DIR = create_temp_dir()
-    print('request:',request.method)
-    try:
-        if request.method == 'POST':
-            # source_image = request.files['source_image']
-            image_path = '/home/user/app/images/out.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', 'yes')
-            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)
-
-            if chat_model_used == 'ryzedb':
-                response = ryzedb_chat_avatar(text_prompt)
-                events  = response.split('\r\n\r\n')
-                content = None
-                for event in events:
-                # Split each event block by "\r\n" to get the lines
-                    lines = event.split('\r\n')
-                    if len(lines) > 1 and lines[0] == 'event: data':
-                        # Extract the JSON part from the second line and parse it
-                        json_data = lines[1].replace('data: ', '')
-                        try:
-                            data = json.loads(json_data)
-                            text_prompt = data.get('content')
-                            app.config['text_prompt'] = text_prompt
-                            print('Final output text prompt using ryzedb: ',text_prompt)
-                            break  # Exit the loop once content is found
-                        except json.JSONDecodeError:
-                            continue
-
-            else:
-                # 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)
-            chunk_duration = 5
-            print(f"Splitting the audio into {chunk_duration}-second chunks...")
-            audio_chunks = split_audio(driven_audio_path, chunk_duration=chunk_duration)
-            print(f"Audio has been split into {len(audio_chunks)} chunks: {audio_chunks}")
-        
-            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
-            if ref_pose_video:
-                with tempfile.NamedTemporaryFile(suffix=".mp4", prefix="ref_pose_",dir=TEMP_DIR.name, delete=False) as temp_file:
-                    ref_pose_video_path = temp_file.name
-                    ref_pose_video.save(ref_pose_video_path)
-                    print('ref_pose_video_path',ref_pose_video_path)
-                    
-    except Exception as e:
-        app.logger.error(f"An error occurred: {e}")
-        return "An error occurred", 500
-    
-    # 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)
-    args_dict = {
-    '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,
-    'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
-
-    executor = concurrent.futures.ProcessPoolExecutor(max_workers=MAX_WORKERS)    
-    try:
-        for index, audio_chunk in enumerate(audio_chunks):
-            print(f"Submitting chunk {index} with audio_chunk: {audio_chunk}")
-            future = executor.submit(process_video_for_chunk_sync, audio_chunk, args_dict, index)
-            futures.append(future)
-        return jsonify({'status': 'Video generation started'}), 200
-
-        
-    except Exception as e:
-        return jsonify({'status': 'error', 'message': str(e)}), 500
-    
-@app.route("/stream", methods=['GET'])
-def stream_video_chunks():
-    global futures
-    print("futures:", futures)
-
-    @stream_with_context
-    def generate_chunks():
-        video_chunk_paths = []
-        for future in concurrent.futures.as_completed(futures):  # Wait for each future to complete
-            try:
-                video_chunk_path = future.result()  # Get the result (video chunk path)
-                video_chunk_paths.append(video_chunk_path)
-                yield f'data: {video_chunk_path}\n\n'  # Stream the chunk path to frontend
-                app.logger.info(f"Chunk generated and sent: {video_chunk_path}")
-                os.remove(video_chunk_path)  # Optionally delete the chunk after sending
-            except Exception as e:
-                app.logger.error(f"Error while fetching future result: {str(e)}")
-                yield f'data: error\n\n'
-
-        preprocess_dir = os.path.join("/tmp", "preprocess_data")
-        custom_cleanup(TEMP_DIR.name, preprocess_dir)
-        app.logger.info("Temporary files cleaned up, but preprocess_data is retained.")
-
-    # Return the generator that streams the data as it becomes available
-    return Response(generate_chunks(), content_type='text/event-stream')
-
-
-
-@app.route("/health", methods=["GET"])
-def health_status():
-    response = {"online": "true"}
-    return jsonify(response)
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, request, jsonify, Response, stream_with_context
+import torch
+import shutil
+import os
+import sys
+from argparse import ArgumentParser
+from time import strftime
+from argparse import Namespace
+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, AsyncOpenAI
+import threading
+import elevenlabs
+from elevenlabs import set_api_key, generate, play, clone, Voice, VoiceSettings
+# from flask_cors import CORS, cross_origin
+# from flask_swagger_ui import get_swaggerui_blueprint
+import uuid
+import time
+from PIL import Image
+import moviepy.editor as mp
+import requests
+import json
+import pickle
+from celery import Celery
+import concurrent.futures
+import multiprocessing
+
+
+# Get the number of CPU cores
+cpu_cores = multiprocessing.cpu_count()
+print(f"Number of available CPU cores: {cpu_cores}")
+
+
+
+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 = ref_pose_video_path
+        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 = 'cpu'
+        self.image_hardcoded = image_hardcoded
+
+
+app = Flask(__name__)
+
+MAX_WORKERS = cpu_cores-1
+TEMP_DIR = None
+start_time = None
+chunk_tasks = []
+futures = []
+
+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
+
+
+
+
+def main(args):
+    print("Entered main function")
+    pic_path = args.source_image
+    audio_path = args.driven_audio
+    save_dir = args.result_dir
+    pose_style = args.pose_style
+    device = args.device
+    batch_size = args.batch_size
+    input_yaw_list = args.input_yaw
+    input_pitch_list = args.input_pitch
+    input_roll_list = args.input_roll
+    ref_eyeblink = args.ref_eyeblink
+    ref_pose = args.ref_pose
+    preprocess = args.preprocess
+    image_hardcoded = args.image_hardcoded
+
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    current_root_path = dir_path
+    print('current_root_path ',current_root_path)
+
+    # sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess)
+
+    path_of_lm_croper = os.path.join(current_root_path, args.checkpoint_dir, 'shape_predictor_68_face_landmarks.dat')
+    path_of_net_recon_model = os.path.join(current_root_path, args.checkpoint_dir, 'epoch_20.pth')
+    dir_of_BFM_fitting = os.path.join(current_root_path, args.checkpoint_dir, 'BFM_Fitting')
+    wav2lip_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'wav2lip.pth')
+
+    audio2pose_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, '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, args.checkpoint_dir, '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, args.checkpoint_dir, 'facevid2vid_00189-model.pth.tar')
+
+    if preprocess == 'full':
+        mapping_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, '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, args.checkpoint_dir, 'mapping_00229-model.pth.tar')
+        facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender.yaml')
+
+
+    # preprocess_model = CropAndExtract(sadtalker_paths, device)
+    #init model
+    print(path_of_net_recon_model)
+    preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device)
+
+    # audio_to_coeff = Audio2Coeff(sadtalker_paths,  device)
+    audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path, 
+                                audio2exp_checkpoint, audio2exp_yaml_path, 
+                                wav2lip_checkpoint, device)
+    # animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device)
+    animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, 
+                                            facerender_yaml_path, device)
+
+    first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
+    os.makedirs(first_frame_dir, exist_ok=True)
+    # first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\
+                                                                            #  source_image_flag=True, pic_size=args.size)
+
+
+    # fixed_temp_dir = "/tmp/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 image_hardcoded == "yes":
+    #     print("Loading preprocessed data...")
+    #     with open(preprocessed_data_path, "rb") as f:
+    #         preprocessed_data = pickle.load(f)
+    #     first_coeff_new_path = preprocessed_data["first_coeff_path"]
+    #     crop_pic_new_path = preprocessed_data["crop_pic_path"]
+    #     crop_info_path = preprocessed_data["crop_info_path"]
+    #     with open(crop_info_path, "rb") as f:
+    #         crop_info = pickle.load(f)
+            
+    #     print(f"Loaded existing preprocessed data from: {preprocessed_data_path}")
+    
+    # else:
+    #     print("Running preprocessing...")
+    #     first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess, source_image_flag=True)
+    #     first_coeff_new_path = os.path.join(fixed_temp_dir, os.path.basename(first_coeff_path))
+    #     crop_pic_new_path = os.path.join(fixed_temp_dir, os.path.basename(crop_pic_path))
+    #     crop_info_new_path = os.path.join(fixed_temp_dir, "crop_info.pkl")
+    #     shutil.move(first_coeff_path, first_coeff_new_path)
+    #     shutil.move(crop_pic_path, crop_pic_new_path)
+
+    #     with open(crop_info_new_path, "wb") as f:
+    #         pickle.dump(crop_info, f)
+            
+    #     preprocessed_data = {"first_coeff_path": first_coeff_new_path,
+    #                         "crop_pic_path": crop_pic_new_path,
+    #                         "crop_info_path": crop_info_new_path}
+
+
+    #     with open(preprocessed_data_path, "wb") as f:
+    #         pickle.dump(preprocessed_data, f)
+    #     print(f"Preprocessed data saved to: {preprocessed_data_path}")
+
+    first_coeff_path, crop_pic_path, crop_info =  preprocess_model.generate(pic_path, first_frame_dir, args.preprocess, source_image_flag=True)
+
+    
+    print('first_coeff_path ',first_coeff_path)
+    print('crop_pic_path ',crop_pic_path)
+    print('crop_info ',crop_info)
+
+    if first_coeff_path is None:
+        print("Can't get the coeffs of the input")
+        return
+
+    if ref_eyeblink is not None:
+        ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0]
+        ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname)
+        os.makedirs(ref_eyeblink_frame_dir, exist_ok=True)
+        # ref_eyeblink_coeff_path, _, _ =  preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False)
+        ref_eyeblink_coeff_path, _, _ =  preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir)
+    else:
+        ref_eyeblink_coeff_path=None
+        print('ref_eyeblink_coeff_path',ref_eyeblink_coeff_path)
+
+    if ref_pose is not None:
+        if ref_pose == ref_eyeblink:
+            ref_pose_coeff_path = ref_eyeblink_coeff_path
+        else:
+            ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0]
+            ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname)
+            os.makedirs(ref_pose_frame_dir, exist_ok=True)
+            # ref_pose_coeff_path, _, _ =  preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False)
+            ref_pose_coeff_path, _, _ =  preprocess_model.generate(ref_pose, ref_pose_frame_dir)
+    else:
+        ref_pose_coeff_path=None
+        print('ref_eyeblink_coeff_path',ref_pose_coeff_path)
+
+    batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still)
+    coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
+
+
+    if args.face3dvis:
+        from src.face3d.visualize import gen_composed_video
+        gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4'))
+  
+    # data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path,
+                                # batch_size, input_yaw_list, input_pitch_list, input_roll_list,
+                                # expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size)
+
+
+    data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, 
+                                batch_size, input_yaw_list, input_pitch_list, input_roll_list,
+                                expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess)
+
+    # result, base64_video,temp_file_path= animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \
+                                # enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size)
+
+
+    result, base64_video,temp_file_path,new_audio_path = animate_from_coeff.generate(data, save_dir, pic_path, 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
+    
+    return base64_video, temp_file_path, duration
+
+
+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
+
+# client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+
+# def openai_chat_avatar(text_prompt):
+#     response = client.chat.completions.create(
+#         model="gpt-4o-mini",
+#         messages=[{"role": "system", "content": "Answer using the minimum words you can ever use."},
+#             {"role": "user", "content": f"Hi! I need help with something. Can you assist me with the following: {text_prompt}"},
+#         ],
+#         max_tokens = len(text_prompt) + 300 # Use the length of the input text
+#         # temperature=0.3,
+#         # stop=["Translate:", "Text:"]
+#     )
+#     return response
+
+def ryzedb_chat_avatar(question):
+    url = "https://inference.dev.ryzeai.ai/chat/stream"
+    question = question + ". Summarize and Answer using the minimum words you can ever use."
+    payload = json.dumps({
+    "input": {
+    "chat_history": [],
+    "app_id": os.getenv('RYZE_APP_ID'),
+    "question": question
+    },
+    "config": {}
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    
+    try:
+        # Send the POST request
+        response = requests.request("POST", url, headers=headers, data=payload)
+        
+        # Check for successful request
+        response.raise_for_status()
+        
+        # Return the response JSON
+        return response.text
+    
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+def custom_cleanup(temp_dir, exclude_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)
+        # Skip the directory we want to exclude
+        if file_path != exclude_dir:
+            try:
+                if os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+                else:
+                    os.remove(file_path)
+                print(f"Deleted: {file_path}")
+            except Exception as e:
+                print(f"Failed to delete {file_path}. Reason: {e}")
+
+
+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)
+
+    return driven_audio_path
+
+def split_audio(audio_path, chunk_duration=5):
+    audio_clip = mp.AudioFileClip(audio_path)
+    total_duration = audio_clip.duration
+
+    audio_chunks = []
+    for start_time in range(0, int(total_duration), chunk_duration):
+        end_time = min(start_time + chunk_duration, total_duration)
+        chunk = audio_clip.subclip(start_time, end_time)
+        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)
+            audio_chunks.append(chunk_path)
+    
+    return audio_chunks
+
+
+def process_video_for_chunk_sync(audio_chunk_path, args_dict, chunk_index):
+    """
+    Synchronous function to process a video chunk. This will be submitted to concurrent.futures ProcessPoolExecutor.
+    """
+    print("Entered process_video_for_chunk_sync")
+    args = AnimationConfig(
+        driven_audio_path=args_dict['driven_audio_path'],
+        source_image_path=args_dict['source_image_path'],
+        result_folder=args_dict['result_folder'],
+        pose_style=args_dict['pose_style'],
+        expression_scale=args_dict['expression_scale'],
+        enhancer=args_dict['enhancer'],
+        still=args_dict['still'],
+        preprocess=args_dict['preprocess'],
+        ref_pose_video_path=args_dict['ref_pose_video_path'],
+        image_hardcoded=args_dict['image_hardcoded']
+    )
+    args.driven_audio = audio_chunk_path
+    chunk_save_dir = os.path.join(args.result_dir, f"chunk_{chunk_index}")
+    os.makedirs(chunk_save_dir, exist_ok=True)
+    
+    try:
+        base64_video, video_chunk_path, duration = main(args)
+        print(f"Main function returned: {video_chunk_path}, {duration}")
+        return video_chunk_path
+    except Exception as e:
+        print(f"Error in process_video_for_chunk_sync: {str(e)}")
+        raise
+
+
+@app.route("/run", methods=['POST'])
+def generate_video():
+    global start_time
+    global chunk_tasks
+    global futures
+    start_time = time.time()
+    global TEMP_DIR
+    TEMP_DIR = create_temp_dir()
+    print('request:',request.method)
+    try:
+        if request.method == 'POST':
+            # source_image = request.files['source_image']
+            image_path = '/home/user/app/images/out.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', 'yes')
+            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)
+
+            if chat_model_used == 'ryzedb':
+                response = ryzedb_chat_avatar(text_prompt)
+                events  = response.split('\r\n\r\n')
+                content = None
+                for event in events:
+                # Split each event block by "\r\n" to get the lines
+                    lines = event.split('\r\n')
+                    if len(lines) > 1 and lines[0] == 'event: data':
+                        # Extract the JSON part from the second line and parse it
+                        json_data = lines[1].replace('data: ', '')
+                        try:
+                            data = json.loads(json_data)
+                            text_prompt = data.get('content')
+                            app.config['text_prompt'] = text_prompt
+                            print('Final output text prompt using ryzedb: ',text_prompt)
+                            break  # Exit the loop once content is found
+                        except json.JSONDecodeError:
+                            continue
+
+            else:
+                # 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)
+            chunk_duration = 5
+            print(f"Splitting the audio into {chunk_duration}-second chunks...")
+            audio_chunks = split_audio(driven_audio_path, chunk_duration=chunk_duration)
+            print(f"Audio has been split into {len(audio_chunks)} chunks: {audio_chunks}")
+        
+            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
+            if ref_pose_video:
+                with tempfile.NamedTemporaryFile(suffix=".mp4", prefix="ref_pose_",dir=TEMP_DIR.name, delete=False) as temp_file:
+                    ref_pose_video_path = temp_file.name
+                    ref_pose_video.save(ref_pose_video_path)
+                    print('ref_pose_video_path',ref_pose_video_path)
+                    
+    except Exception as e:
+        app.logger.error(f"An error occurred: {e}")
+        return "An error occurred", 500
+    
+    # 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)
+    args_dict = {
+    '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,
+    'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
+
+    executor = concurrent.futures.ProcessPoolExecutor(max_workers=MAX_WORKERS)    
+    try:
+        for index, audio_chunk in enumerate(audio_chunks):
+            print(f"Submitting chunk {index} with audio_chunk: {audio_chunk}")
+            future = executor.submit(process_video_for_chunk_sync, audio_chunk, args_dict, index)
+            futures.append(future)
+        return jsonify({'status': 'Video generation started'}), 200
+
+        
+    except Exception as e:
+        return jsonify({'status': 'error', 'message': str(e)}), 500
+    
+@app.route("/stream", methods=['GET'])
+def stream_video_chunks():
+    global futures
+    print("futures:", futures)
+
+    @stream_with_context
+    def generate_chunks():
+        video_chunk_paths = []
+        for future in concurrent.futures.as_completed(futures):  # Wait for each future to complete
+            try:
+                video_chunk_path = future.result()  # Get the result (video chunk path)
+                video_chunk_paths.append(video_chunk_path)
+                yield f'data: {video_chunk_path}\n\n'  # Stream the chunk path to frontend
+                app.logger.info(f"Chunk generated and sent: {video_chunk_path}")
+                os.remove(video_chunk_path)  # Optionally delete the chunk after sending
+            except Exception as e:
+                app.logger.error(f"Error while fetching future result: {str(e)}")
+                yield f'data: error\n\n'
+
+        preprocess_dir = os.path.join("/tmp", "preprocess_data")
+        custom_cleanup(TEMP_DIR.name, preprocess_dir)
+        app.logger.info("Temporary files cleaned up, but preprocess_data is retained.")
+
+    # Return the generator that streams the data as it becomes available
+    return Response(generate_chunks(), content_type='text/event-stream')
+
+
+
+@app.route("/health", methods=["GET"])
+def health_status():
+    response = {"online": "true"}
+    return jsonify(response)
+if __name__ == '__main__':
+    multiprocessing.set_start_method('spawn', force=True)
+    app.run(debug=True)