app.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
import threading
import elevenlabs
from elevenlabs import set_api_key, generate, play, clone
# from flask_cors import CORS, cross_origin
# from flask_swagger_ui import get_swaggerui_blueprint
import uuid
import time

start_time = time.time()

class AnimationConfig:
    def __init__(self, driven_audio_path, source_image_path, result_folder,pose_style,expression_scale,enhancer,still,preprocess,ref_pose_video_path):
        self.driven_audio = driven_audio_path
        self.source_image = source_image_path
        self.ref_eyeblink = ref_pose_video_path
        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'


app = Flask(__name__)

TEMP_DIR = None

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



def main(args):
    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

    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/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)

   
    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)

    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 = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \
                                enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess)
    
    print('The generated video is named:')
    app.config['temp_response'] = base64_video
    app.config['final_video_path'] = temp_file_path
    return base64_video, temp_file_path

    # shutil.move(result, save_dir+'.mp4')


    if not args.verbose:
        shutil.rmtree(save_dir)

def create_temp_dir():
    return tempfile.TemporaryDirectory()

def save_uploaded_file(file, filename,TEMP_DIR):
    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="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")

def translate_text(text_prompt, target_language):
    response = client.chat.completions.create(
        model="gpt-4-0125-preview",
        messages=[{"role": "system", "content": "You are a helpful language translator assistant."},
            {"role": "user", "content": f"Translate completely without hallucination, end to end, and give the following text to {target_language} language and the text is: {text_prompt}"},
        ],
        max_tokens = len(text_prompt) + 200 # Use the length of the input text
        # temperature=0.3,
        # stop=["Translate:", "Text:"]
    )
    return response



@app.route("/run", methods=['POST'])
async def generate_video():
    global TEMP_DIR
    TEMP_DIR = create_temp_dir()
    if request.method == 'POST':
        source_image = request.files['source_image']
        text_prompt = request.form['text_prompt']
        print('Input text prompt: ',text_prompt)
        voice_cloning = request.form.get('voice_cloning', 'no')
        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 = int(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() == 'true'
        print('still', still)
        preprocess = request.form.get('preprocess', 'crop')
        print('preprocess selected: ',preprocess)
        ref_pose_video = request.files.get('ref_pose', None)

        if target_language != 'original_text':
            response = translate_text(text_prompt, target_language)
            # response = await translate_text_async(text_prompt, target_language)
            text_prompt = response.choices[0].message.content.strip()

        app.config['text_prompt'] = text_prompt
        print('Final text prompt: ',text_prompt)

        source_image_path = save_uploaded_file(source_image, 'source_image.png',TEMP_DIR)
        print(source_image_path)

        # driven_audio_path = await voice_cloning_async(voice_cloning, voice_gender, text_prompt, user_voice)

        if voice_cloning == 'no':
            if voice_gender == 'male':
                voice = 'onyx'
            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')

        elif voice_cloning == 'yes':
            user_voice = request.files['user_voice']

            with tempfile.NamedTemporaryFile(suffix=".wav", prefix="user_voice_",dir=TEMP_DIR.name, delete=False) as temp_file:
                user_voice_path = temp_file.name
                user_voice.save(user_voice_path)
                print('user_voice_path',user_voice_path)

            set_api_key("87792fce164425fbe1204e9fd1fe25cd")
            voice = clone(name = "User Cloned Voice",
                        files = [user_voice_path] )

            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)
                
            #     elevenlabs.save(audio, driven_audio_path)

        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)

    # Example of using the class with some hypothetical paths
    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)
        
    if torch.cuda.is_available() and not args.cpu:
        args.device = "cuda"
    else:
        args.device = "cpu"
        
    generation_thread = threading.Thread(target=main, args=(args,))
    app.config['generation_thread'] = generation_thread
    generation_thread.start()
    response_data = {"message": "Video generation started",
                    "process_id": generation_thread.ident}

    return jsonify(response_data)
    # base64_video = main(args)
    # return jsonify({"base64_video": base64_video})

    #else:
    #    return 'Unsupported HTTP method', 405

@app.route("/status", methods=["GET"])
def check_generation_status():
    global TEMP_DIR
    response = {"base64_video": "","text_prompt":"", "status": ""}
    process_id = request.args.get('process_id', None)

    # process_id is required to check the status for that specific process
    if process_id:
        generation_thread = app.config.get('generation_thread')
        if generation_thread and generation_thread.ident == int(process_id) and generation_thread.is_alive():
            return jsonify({"status": "in_progress"}), 200
        elif app.config.get('temp_response'):
            # app.config['temp_response']['status'] = 'completed'
            final_response = app.config['temp_response']
            response["base64_video"] = final_response
            response["text_prompt"] = app.config.get('text_prompt')
            response["status"] = "completed"

            final_video_path = app.config['final_video_path']
            print('final_video_path',final_video_path)


            if final_video_path and os.path.exists(final_video_path):
                os.remove(final_video_path)
                print("Deleted video file:", final_video_path)

            TEMP_DIR.cleanup()
            # print("Temporary Directory:", TEMP_DIR.name)
            # if TEMP_DIR:
            #     print("Contents of Temporary Directory:")
            #     for filename in os.listdir(TEMP_DIR.name):
            #         print(filename)
            # else:
            #     print("Temporary Directory is None or already cleaned up.")
            end_time = time.time()
            total_time = round(end_time - start_time, 2)
            print("Total time taken for execution:", total_time, " seconds")
            return jsonify(response)
    return jsonify({"error":"No process id provided"})

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