app.py

from flask import Flask, request, jsonify, send_from_directory
import torch
import shutil
import os
import sys
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
import tempfile
from openai import OpenAI
from elevenlabs import set_api_key, generate, play, clone, Voice, VoiceSettings
# from flask_cors import CORS, cross_origin
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
# from time import strftime
# from argparse import Namespace
# from argparse import ArgumentParser
# from flask_swagger_ui import get_swaggerui_blueprint
# import threading
# import elevenlabs
# from src.utils.init_path import init_path



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 = 8
        self.expression_scale = expression_scale
        # self.input_yaw = [-15,0,10,5,0,-10,-5,0, 5,10]
        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
VIDEO_DIRECTORY = None
preprocessed_data = None
args = None
unique_id = 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, args):
    print("Entered Process Chunk Function")
    global audio2pose_checkpoint, audio2pose_yaml_path, audio2exp_checkpoint, audio2exp_yaml_path, wav2lip_checkpoint
    global path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting
    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 = "/home/user/app/preprocess_data/crop_info.json"
    # with open(crop_info_path , "rb") as f:
    #         crop_info = json.load(f)

    first_frame_dir = os.path.join(args.result_dir, 'first_frame_dir')
    os.makedirs(first_frame_dir, exist_ok=True)
    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)

    print(f"Loaded existing preprocessed data")
    print("first_coeff_path",first_coeff_path)
    print("crop_pic_path",crop_pic_path)
    print("crop_info",crop_info)
    torch.cuda.empty_cache()
    
    if args.ref_pose is not None:
        ref_pose_videoname = os.path.splitext(os.path.split(args.ref_pose)[-1])[0]
        ref_pose_frame_dir = os.path.join(args.result_dir, ref_pose_videoname)
        os.makedirs(ref_pose_frame_dir, exist_ok=True)
        ref_pose_coeff_path, _, _ =  preprocess_model.generate(args.ref_pose, ref_pose_frame_dir)
        print('ref_eyeblink_coeff_path',ref_pose_coeff_path)
    else:
        ref_pose_coeff_path = None
        print('ref_eyeblink_coeff_path',ref_pose_coeff_path)
        
    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)
    
    # Further processing with animate_from_coeff using the coeff_path
    animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint, 
                                            facerender_yaml_path, args.device)

    torch.cuda.empty_cache()
    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):
    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 openai_chat_avatar(text_prompt):
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": "Ensure answers are concise, human-like, and clear while maintaining quality. Use the fewest possible words, avoiding unnecessary articles, prepositions, and adjectives. Responses should be short but still address the question thoroughly without being verbose.Keep them to one sentence only"},
            {"role": "user", "content": f"Hi! I need help with something. {text_prompt}"},
        ],
        max_tokens = len(text_prompt) + 300 # Use the length of the input text
        # temperature=0.3,
        # stop=["Translate:", "Text:"]
    )
    return response

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')
        # user_voice_path = '/home/user/app/images/AUDIO-2024-10-04-09-51-34.m4a'
        # voice = clone(name = "User Cloned Voice",
        #             files = [user_voice_path] )
        # DeZH4ash9IU9gUcNjVXh
        voice = Voice(voice_id="KR6RRu8YgfxrhYocGuOc",name="Sachin",settings=VoiceSettings(
                        stability=0.71, similarity_boost=0.9, 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)
            # audio_duration = get_audio_duration(driven_audio_path)
            # print('Total Audio Duration in seconds',audio_duration)

    return driven_audio_path


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 = "/home/user/app/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)

    return preprocessed_data

@app.route("/run", methods=['POST'])
def generate_video():
    global start_time, VIDEO_DIRECTORY
    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/shared_image1.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', '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 chat_model_used == 'openai':
                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)

            else:
                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 "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"
        

    try:
        # preprocessed_data = run_preprocessing(args)
        base64_video, temp_file_path = process_chunk(driven_audio_path, args)
        final_video_path = app.config['final_video_path']
        print('final_video_path',final_video_path)

        if temp_file_path and temp_file_path.endswith('.mp4'):
            filename = os.path.basename(temp_file_path)
            os.makedirs('videos', exist_ok=True)
            VIDEO_DIRECTORY = os.path.abspath('videos')
            print("VIDEO_DIRECTORY: ",VIDEO_DIRECTORY)
            destination_path = os.path.join(VIDEO_DIRECTORY, filename)
            shutil.copy(temp_file_path, destination_path)
            video_url = f"/videos/{filename}"

            if final_video_path and os.path.exists(final_video_path):
                os.remove(final_video_path)
                print("Deleted video file:", final_video_path)
    
            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()
            time_taken = end_time - start_time
            print(f"Time taken for endpoint: {time_taken:.2f} seconds")

            return jsonify({
                    "message": "Video processed and saved successfully.",
                    "video_url": video_url,
                    "time_taken": time_taken,
                    "status": "success"
                })
        else:
            return jsonify({
                "message": "Failed to process the video.",
                "status": "error"
            }), 500

        
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)}), 500
    

@app.route("/videos/<string:filename>", methods=['GET'])
def serve_video(filename):
    global VIDEO_DIRECTORY
    return send_from_directory(VIDEO_DIRECTORY, filename, as_attachment=False)



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