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_image3.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/", 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)