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 import aiohttp import random # 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 = 8 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": "Summarize the following paragraph into a complete and accurate single sentence with no more than 15 words. The summary should capture the gist of the paragraph and make sense and remove the citation and document name from the end."}, {"role": "user", "content": f"Please summarize the following paragraph with 30 words or fewer, ensuring it makes sense and captures the gist make sure to include the order_ids : {text_prompt}"}, ], max_tokens = len(text_prompt), # Limit the response to a reasonable length for a summary ) return response def ryzedb_chat_avatar(question, app_id): url = "https://inference.dev.ryzeai.ai/v2/chat/stream" print("ryze db question",question) # thread_id = str(random.randint(1000, 99999)) # print("Generated thread_id:", thread_id) payload = { "input": { "app_id": app_id, "query": question, "chat_history": [] }, "config": { "thread_id": "123456" }} headers = { 'Content-Type': 'application/json' } response = requests.post(url, json=payload, headers=headers, stream=True) try: raw_text = response.text.strip() if raw_text.startswith("data:"): raw_text = raw_text[len("data:"):].strip() json_data = json.loads(raw_text) response = json_data.get("content", "") return response except Exception as e: print("Error parsing response:", e) return "" 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_monolingual_v1",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_monolingual_v1",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() def extract_content(line): print("line :", line) if line.startswith("data:"): print("Entering extract content") json_part = line[len("data:"):].strip() if json_part: try: parsed = json.loads(json_part) return parsed.get("content") except json.JSONDecodeError: pass return None @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() text_prompt = ryzedb_chat_avatar(text_prompt, app_id) print("Response from inference",text_prompt) # text_prompt = extract_content(response) # text_prompt = text_prompt.replace('\n', ' ').replace('\\n', ' ').strip() if "No information available" in text_prompt: text_prompt = re.sub(r'\\+', '', text_prompt) 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 ryzedb: ',text_prompt) print('app_id',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/", 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)