app_parallel.py

from flask import Flask, request, jsonify, stream_with_context
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

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

# Initialize ProcessPoolExecutor for parallel processing
executor = ThreadPoolExecutor(max_workers=3)


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__)
# CORS(app)

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

# 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 = preprocessed_data["crop_info"]
    with open(crop_info_path , "rb") as f:
            crop_info = pickle.load(f)

    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)
    
    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, 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

# Preprocessing step that runs only once
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 = "/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 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)
    else:
        print("Running preprocessing...")
        preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, args.device)
        first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(args.source_image, 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": 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}")

    return preprocessed_data
    
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((start_time, chunk_path))
    
    return audio_chunks

# Generator function to yield chunk results as they are processed
def generate_chunks(audio_chunks, preprocessed_data, args):
    future_to_chunk = {executor.submit(process_chunk, chunk[1], preprocessed_data, args): chunk[0] for chunk in audio_chunks}
    
    for future in as_completed(future_to_chunk):
        idx = future_to_chunk[future]  # Get the original chunk that was processed
        try:
            base64_video, temp_file_path = future.result()  # Get the result of the completed task
            yield json.dumps({'start_time': idx, 'path': temp_file_path}).encode('utf-8')
        except Exception as e:
            yield f"Task for chunk {idx} failed: {e}\n"

@app.route("/run", methods=['POST'])
def parallel_processing():
    global start_time
    start_time = time.time()
    global TEMP_DIR
    global audio_chunks
    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', '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)

            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
            # 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 jsonify({'status': 'error', 'message': str(e)}), 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)
    
    preprocessed_data = run_preprocessing(args)
    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}")
  
    try:
        return stream_with_context(generate_chunks(audio_chunks, preprocessed_data, args))
        # base64_video, temp_file_path, duration = process_chunk(driven_audio_path, preprocessed_data, args)
    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)