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
from PIL import Image
import moviepy.editor as mp
import requests
import json
import pickle
# from videoretalking import inference_function
# import base64
# import gfpgan_enhancer



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)

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):
    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)

    # face_path = temp_file_path
    # audio_path = new_audio_path
    # temp_file = tempfile.NamedTemporaryFile(delete=False, dir=TEMP_DIR.name, suffix='.mp4')
    # video_lipsync_file_path = temp_file.name
    # output_path = video_lipsync_file_path

    # # Call the function
    # inference_function.video_lipsync_correctness(
    #     face=face_path,
    #     audio_path=audio_path,
    #     face3d_net_path = path_of_net_recon_model,
    #     outfile=output_path,
    #     tmp_dir="temp",
    #     crop=[0, -1, 0, -1],
    #     re_preprocess=True,  # Set to True if you want to reprocess; False otherwise
    #     exp_img="neutral",  # Can be 'smile', 'neutral', or path to an expression image
    #     one_shot=False,
    #     up_face="original",  # Options: 'original', 'sad', 'angry', 'surprise'
    #     LNet_batch_size=16,
    #     without_rl1=False
    # )
    
    # print('The video with lip sync is generated')
    # print("GFPGAN Activated")
    
    # gfpgan_enhancer.process_video_with_gfpgan(output_path, output_path)
    # audio_clip = mp.AudioFileClip(new_audio_path) 
    # video_clip = mp.VideoFileClip(output_path)
    # # Combine audio and video
    # final_clip = video_clip.set_audio(audio_clip)

    # temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', dir=TEMP_DIR.name, delete=False)
    # temp_file.close()
    # final_video_path = temp_file.name
    # final_clip.write_videofile(final_video_path)

    # with open(final_video_path, 'rb') as f:
    #     video_content = f.read()

    # base64_lipsync_video = base64.b64encode(video_content).decode('utf-8')

    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

    # 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-4o-mini",
        messages=[{"role": "system", "content": "You are a helpful language translator assistant."},
            {"role": "user", "content": f"Translate completely without hallucination, end to end and the ouput should just be the translation of the text prompt and nothing else, 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

def openai_chat_avatar(text_prompt):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": "Answer in Portuguese language always 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": "af6b2bc719a14478adcff1d71c19dc00",
    "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}")

@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 = '/home/user/app/images/screen.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', 'no')
            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 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()

            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 = await voice_cloning_async(voice_cloning, voice_gender, text_prompt, user_voice)
    
            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':
                # user_voice = request.files['user_voice']
                user_voice = '/home/user/app/images/Recording.m4a'
    
                with tempfile.NamedTemporaryFile(suffix=".wav", prefix="user_voice_",dir=TEMP_DIR.name, delete=False) as temp_file:
                    with open(user_voice, 'rb') as source_file:
                        file_contents = source_file.read()
                        temp_file.write(file_contents)

                    temp_file.flush()
                    user_voice_path = temp_file.name
                    # user_voice.save(user_voice_path)
                    print('user_voice_path',user_voice_path)
    
                set_api_key("92e149985ea2732b4359c74346c3daee")
                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)
                    
    except Exception as e:
        app.logger.error(f"An error occurred: {e}")
        return "An error occurred", 500
    
    # 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, image_hardcoded=image_hardcoded)
        
    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)


@app.route("/status", methods=["GET"])
def check_generation_status():
    global TEMP_DIR
    global start_time
    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["duration"] = app.config.get('final_video_duration')
            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()
            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.")
            
            end_time = time.time()
            total_time = round(end_time - start_time, 2)
            print("Total time taken for execution:", total_time, " seconds")
            response["time_taken"] = total_time
            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)