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
# 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
# import re
# from videoretalking import inference_function
# import base64
# import gfpgan_enhancer
# import threading
# import elevenlabs
# from argparse import Namespace
# from argparse import ArgumentParser
# from time import strftime
# 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 = 64
        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
VIDEO_DIRECTORY = 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, 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 = "/home/user/app/preprocess_data/crop_info.json"
    with open(crop_info_path , "rb") as f:
            crop_info = json.load(f)

    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()
    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)

    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 ryzedb_chat_avatar(question, app_id):
    url = "https://inference.dev.ryzeai.ai/chat/stream"
    question = question + ". Summarize your answer using the minimum words you can ever use."
    payload = json.dumps({
    "input": {
    "chat_history": [],
    "app_id": 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)
            # 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

# def remove_brackets(text):
#     # Use regex to remove content in brackets at the end of the text
#     cleaned_text = re.sub(r'\s*\[.*?\]\s*$', '', text)
#     return cleaned_text.strip()

@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 image (3).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', 'ryzedb')
            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_id = request.form['app_id']
            if not app_id:
                return jsonify({'error': 'App ID cannot be blank'}), 400


            if chat_model_used == 'ryzedb':
                start_time_ryze = time.time()
                response = ryzedb_chat_avatar(text_prompt, app_id)
                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
                            end_time_ryze = time.time()
                            diff = end_time_ryze - start_time_ryze
                            print('Final output text prompt using ryzedb: ',text_prompt)
                            print('Time to get response from ryzedb: ',diff)
                            break  # Exit the loop once content is found
                        except json.JSONDecodeError:
                            continue

            elif chat_model_used == 'self':
                text_prompt = text_prompt.strip()
                
            else:
                print("No Ryze database found")
            
            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
    
    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}

    try:
        preprocessed_data = run_preprocessing(args)
        base64_video, temp_file_path = process_chunk(driven_audio_path, preprocessed_data, 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,
                    "text_prompt": text_prompt,
                    "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("/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)