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 from videoretalking import inference_function 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 = 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' app = Flask(__name__) CORS(app) TEMP_DIR = 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 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') face_path = "/home/user/app/images/download_1.mp4" # Replace with the path to your face image or video audio_path = "/home/user/app/images/audio_1.mp3" # Replace with the path to your audio file temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') # You can change suffix based on your file type temp_file_path = temp_file.name output_path = temp_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 ) # # 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 video is generated') # 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 chat_avatar(text_prompt): response = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "system", "content": "You are an interactive, conversational and helpful chatbot. Your role is to assist users by providing clear, engaging, and relevant only one liner responses responses based on their queries. Regardless of the language used by the user, you should always respond in English."}, {"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 @app.route("/run", methods=['POST']) def generate_video(): 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/vibhu2.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') 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() response = chat_avatar(text_prompt) 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 = '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) 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["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() # 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)