app_celery.py

from flask import Flask, request, jsonify
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 dotenv import load_dotenv
from celery import Celery
# from gevent import monkey
# monkey.patch_all()


# Load environment variables from .env file
load_dotenv()



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__)
# CORS(app)
app.config['broker_url'] = 'redis://localhost:6379/0'
app.config['result_backend'] = 'redis://localhost:6379/0'

celery = Celery(app.name, broker=app.config['broker_url'])
celery.conf.update(app.config)

TEMP_DIR = None
start_time = 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




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}")
    
    print('first_coeff_path ',first_coeff_new_path)
    print('crop_pic_path ',crop_pic_new_path)
    print('crop_info ',crop_info)

    if first_coeff_new_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_new_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_new_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_new_path, first_coeff_new_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

@celery.task
def process_video_for_chunk(audio_chunk_path, args_dict, chunk_index):
    print("Entered process_video_for_chunk")
    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)
    print("args",args)
    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: {str(e)}")
        raise
    # base64_video, video_chunk_path, duration = main(args)
    # return video_chunk_path


@app.route("/run", methods=['POST'])
def generate_video():
    global start_time
    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 = 'C:/Users/fd01076/Downloads/marc.png'
            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'}

    # if torch.cuda.is_available() and not args.cpu:
    #     args.device = "cuda"
    # else:
    #     args.device = "cpu"
        
    try:
        # base64_video, temp_file_path, duration = main(args)
        # final_video_path = app.config['final_video_path']
        # print('final_video_path',final_video_path)
        chunk_tasks = []
        for index, audio_chunk in enumerate(audio_chunks):
            print(f"Submitting chunk {index} with audio_chunk: {audio_chunk}")
            task = process_video_for_chunk.apply_async(args=[audio_chunk, args_dict, index])
            print(f"Task {task.id} submitted for chunk {index}")
            chunk_tasks.append(task)
            print("chunk_tasks",chunk_tasks)

        # video_chunk_paths = [task.get() for task in chunk_tasks]
        # print(f"Video chunks generated: {video_chunk_paths}")
        video_chunk_paths = []
        for task in chunk_tasks:
            try:
                video_chunk_path = task.get()  # Wait for the task to complete
                video_chunk_paths.append(video_chunk_path)
            except Exception as e:
                print(f"Error while fetching task result: {str(e)}")
                return jsonify({'status': 'error', 'message': str(e)}), 500

        print(f"Video chunks generated: {video_chunk_paths}")
        preprocess_dir = os.path.join("/tmp", "preprocess_data")
        custom_cleanup(TEMP_DIR.name, preprocess_dir)

        print("Temporary files cleaned up, but preprocess_data is retained.")
        return jsonify({
            'status': 'completed',
            'video_chunk_paths': video_chunk_paths
        })
        
        # return jsonify({
        #         'base64_video': base64_video,
        #         'text_prompt': text_prompt,
        #         'duration': duration,
        #         'status': 'completed'
        #     })
        
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)}), 500



@app.route("/health", methods=["GET"])
def health_status():
    response = {"online": "true"}
    return jsonify(response)
if __name__ == '__main__':
    app.run(debug=True)