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/marc.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 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/marc_voice.mp3' 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} try: base64_video, temp_file_path, duration = main(args) 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) 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.") return jsonify({ 'base64_video': base64_video, 'text_prompt': text_prompt, 'duration': duration, 'status': 'completed' }) except Exception as e: return jsonify({'status': 'error', 'message': str(e)}), 500 # 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)