diff --git a/app2.py b/app2.py
new file mode 100644
index 0000000..fab2797
--- /dev/null
+++ b/app2.py
@@ -0,0 +1,375 @@
+# server.py
+from flask import Flask, request, jsonify
+from flask_sockets import Sockets
+import base64
+import time
+import json
+import gevent
+from gevent import pywsgi
+from geventwebsocket.handler import WebSocketHandler
+import os
+import re
+import numpy as np
+from threading import Thread
+import multiprocessing
+
+import argparse
+from nerf_triplane.provider import NeRFDataset_Test
+from nerf_triplane.utils import *
+from nerf_triplane.network import NeRFNetwork
+from nerfreal import NeRFReal
+
+import shutil
+import asyncio
+import edge_tts
+from typing import Iterator
+
+import requests
+
+app = Flask(__name__)
+sockets = Sockets(app)
+global nerfreal
+global tts_type
+global gspeaker
+
+
+async def main(voicename: str, text: str, render):
+    communicate = edge_tts.Communicate(text, voicename)
+
+    #with open(OUTPUT_FILE, "wb") as file:
+    first = True
+    async for chunk in communicate.stream():
+        if first:
+            #render.before_push_audio()
+            first = False
+        if chunk["type"] == "audio":
+            render.push_audio(chunk["data"])
+            #file.write(chunk["data"])
+        elif chunk["type"] == "WordBoundary":
+            pass                
+
+def get_speaker(ref_audio,server_url):
+    files = {"wav_file": ("reference.wav", open(ref_audio, "rb"))}
+    response = requests.post(f"{server_url}/clone_speaker", files=files)
+    return response.json()
+
+def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
+    start = time.perf_counter()
+    speaker["text"] = text
+    speaker["language"] = language
+    speaker["stream_chunk_size"] = stream_chunk_size  # you can reduce it to get faster response, but degrade quality
+    res = requests.post(
+        f"{server_url}/tts_stream",
+        json=speaker,
+        stream=True,
+    )
+    end = time.perf_counter()
+    print(f"xtts Time to make POST: {end-start}s")
+
+    if res.status_code != 200:
+        print("Error:", res.text)
+        return
+
+    first = True
+    for chunk in res.iter_content(chunk_size=960):
+        if first:
+            end = time.perf_counter()
+            print(f"xtts Time to first chunk: {end-start}s")
+            first = False
+        if chunk:
+            yield chunk
+
+    print("xtts response.elapsed:", res.elapsed)
+
+def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
+    start = time.perf_counter()
+    speaker["text"] = text
+    speaker["language"] = language
+    speaker["stream_chunk_size"] = stream_chunk_size  # you can reduce it to get faster response, but degrade quality
+    res = requests.get(f"{server_url}&text="+text,stream=True)
+    end = time.perf_counter()
+    print(f"xtts Time to make POST: {end-start}s")
+
+    if res.status_code != 200:
+        print("Error:", res.text)
+        return
+
+    first = True
+    for chunk in res.iter_content(chunk_size=960):
+        if first:
+            end = time.perf_counter()
+            print(f"xtts Time to first chunk: {end-start}s")
+            first = False
+        if chunk:
+            yield chunk
+
+    print("xtts response.elapsed:", res.elapsed)
+
+def stream_xtts(audio_stream,render):
+    for chunk in audio_stream:
+        if chunk is not None:
+            render.push_audio(chunk)
+
+def txt_to_audio(text_):
+    if tts_type == "edgetts":
+        voicename = "zh-CN-YunxiaNeural"
+        text = text_
+        t = time.time()
+        asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
+        print(f'-------edge tts time:{time.time()-t:.4f}s')
+    elif tts_type == "gpt": #xtts
+        stream_xtts(
+            gpt_sovits(
+                text_,
+                gspeaker,
+                "zh-cn", #en args.language,
+                "http://127.0.0.1:9880/tts_ava?ava=maimai&streaming_mode=true", #args.server_url,
+                "20" #args.stream_chunk_size
+            ),
+            nerfreal
+        )
+    else :#xtts
+        stream_xtts(
+            xtts(
+                text_,
+                gspeaker,
+                "zh-cn", #en args.language,
+                "http://localhost:9000", #args.server_url,
+                "20" #args.stream_chunk_size
+            ),
+            nerfreal
+        )
+@sockets.route('/humanecho')
+def echo_socket(ws):
+    # 获取WebSocket对象
+    #ws = request.environ.get('wsgi.websocket')
+    # 如果没有获取到，返回错误信息
+    if not ws:
+        print('未建立连接！')
+        return 'Please use WebSocket'
+    # 否则，循环接收和发送消息
+    else:
+        print('建立连接！')
+        while True:
+            message = ws.receive()           
+            
+            if not message or len(message)==0:
+                return '输入信息为空'
+            else:                                
+                txt_to_audio(message)
+
+
+def llm_response(message):
+    from llm.LLM import LLM
+    # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
+    # llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
+    llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
+    response = llm.chat(message)
+    print(response)
+    return response
+
+@sockets.route('/humanchat')
+def chat_socket(ws):
+    # 获取WebSocket对象
+    #ws = request.environ.get('wsgi.websocket')
+    # 如果没有获取到，返回错误信息
+    if not ws:
+        print('未建立连接！')
+        return 'Please use WebSocket'
+    # 否则，循环接收和发送消息
+    else:
+        print('建立连接！')
+        while True:
+            message = ws.receive()           
+            
+            if len(message)==0:
+                return '输入信息为空'
+            else:
+                res=llm_response(message)                           
+                txt_to_audio(res)                        
+
+def render():
+    nerfreal.render()                  
+               
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--pose', type=str, default="data/data_kf.json", help="transforms.json, pose source")
+    parser.add_argument('--au', type=str, default="data/au.csv", help="eye blink area")
+    parser.add_argument('--torso_imgs', type=str, default="", help="torso images path")
+
+    parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --exp_eye")
+
+    parser.add_argument('--data_range', type=int, nargs='*', default=[0, -1], help="data range to use")
+    parser.add_argument('--workspace', type=str, default='data/video')
+    parser.add_argument('--seed', type=int, default=0)
+
+    ### training options
+    parser.add_argument('--ckpt', type=str, default='data/pretrained/ngp_kf.pth')
+   
+    parser.add_argument('--num_rays', type=int, default=4096 * 16, help="num rays sampled per image for each training step")
+    parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
+    parser.add_argument('--max_steps', type=int, default=16, help="max num steps sampled per ray (only valid when using --cuda_ray)")
+    parser.add_argument('--num_steps', type=int, default=16, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
+    parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
+    parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
+    parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when NOT using --cuda_ray)")
+
+    ### loss set
+    parser.add_argument('--warmup_step', type=int, default=10000, help="warm up steps")
+    parser.add_argument('--amb_aud_loss', type=int, default=1, help="use ambient aud loss")
+    parser.add_argument('--amb_eye_loss', type=int, default=1, help="use ambient eye loss")
+    parser.add_argument('--unc_loss', type=int, default=1, help="use uncertainty loss")
+    parser.add_argument('--lambda_amb', type=float, default=1e-4, help="lambda for ambient loss")
+
+    ### network backbone options
+    parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
+    
+    parser.add_argument('--bg_img', type=str, default='white', help="background image")
+    parser.add_argument('--fbg', action='store_true', help="frame-wise bg")
+    parser.add_argument('--exp_eye', action='store_true', help="explicitly control the eyes")
+    parser.add_argument('--fix_eye', type=float, default=-1, help="fixed eye area, negative to disable, set to 0-0.3 for a reasonable eye")
+    parser.add_argument('--smooth_eye', action='store_true', help="smooth the eye area sequence")
+
+    parser.add_argument('--torso_shrink', type=float, default=0.8, help="shrink bg coords to allow more flexibility in deform")
+
+    ### dataset options
+    parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
+    parser.add_argument('--preload', type=int, default=0, help="0 means load data from disk on-the-fly, 1 means preload to CPU, 2 means GPU.")
+    # (the default value is for the fox dataset)
+    parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box[-bound, bound]^3, if > 1, will invoke adaptive ray marching.")
+    parser.add_argument('--scale', type=float, default=4, help="scale camera location into box[-bound, bound]^3")
+    parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
+    parser.add_argument('--dt_gamma', type=float, default=1/256, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
+    parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
+    parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
+    parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
+    parser.add_argument('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")
+
+    parser.add_argument('--init_lips', action='store_true', help="init lips region")
+    parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
+    parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")
+
+    parser.add_argument('--torso', action='store_true', help="fix head and train torso")
+    parser.add_argument('--head_ckpt', type=str, default='', help="head model")
+
+    ### GUI options
+    parser.add_argument('--gui', action='store_true', help="start a GUI")
+    parser.add_argument('--W', type=int, default=450, help="GUI width")
+    parser.add_argument('--H', type=int, default=450, help="GUI height")
+    parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
+    parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
+    parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
+
+    ### else
+    parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
+    parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
+    parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")
+
+    parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
+    parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")
+
+    parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
+
+    parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
+    parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
+    parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
+
+    parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
+    parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
+    parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
+
+    # asr
+    parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
+    parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
+    parser.add_argument('--asr_play', action='store_true', help="play out the audio")
+
+    #parser.add_argument('--asr_model', type=str, default='deepspeech')
+    parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto') #
+    # parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
+    # parser.add_argument('--asr_model', type=str, default='facebook/hubert-large-ls960-ft')
+
+    parser.add_argument('--push_url', type=str, default='rtmp://localhost/live/livestream')
+
+    parser.add_argument('--asr_save_feats', action='store_true')
+    # audio FPS
+    parser.add_argument('--fps', type=int, default=50)
+    # sliding window left-middle-right length (unit: 20ms)
+    parser.add_argument('-l', type=int, default=10)
+    parser.add_argument('-m', type=int, default=8)
+    parser.add_argument('-r', type=int, default=10)
+
+    parser.add_argument('--fullbody', action='store_true', help="fullbody human")
+    parser.add_argument('--fullbody_img', type=str, default='data/fullbody/img')
+    parser.add_argument('--fullbody_width', type=int, default=580)
+    parser.add_argument('--fullbody_height', type=int, default=1080)
+    parser.add_argument('--fullbody_offset_x', type=int, default=0)
+    parser.add_argument('--fullbody_offset_y', type=int, default=0)
+
+    parser.add_argument('--tts', type=str, default='edgetts') #xtts
+    parser.add_argument('--ref_file', type=str, default=None)
+    parser.add_argument('--xtts_server', type=str, default='http://localhost:9000')
+
+    opt = parser.parse_args()
+    app.config.from_object(opt)
+    #print(app.config['xtts_server'])
+
+    tts_type = opt.tts
+    if tts_type == "xtts":
+        print("Computing the latents for a new reference...")
+        gspeaker = get_speaker(opt.ref_file, opt.xtts_server)
+
+    # assert test mode
+    opt.test = True
+    opt.test_train = False
+    #opt.train_camera =True
+    # explicit smoothing
+    opt.smooth_path = True
+    opt.smooth_lips = True
+
+    assert opt.pose != '', 'Must provide a pose source'
+
+    # if opt.O:
+    opt.fp16 = True
+    opt.cuda_ray = True
+    opt.exp_eye = True
+    opt.smooth_eye = True
+
+    if opt.torso_imgs=='': #no img,use model output
+        opt.torso = True
+
+    # assert opt.cuda_ray, "Only support CUDA ray mode."
+    opt.asr = True
+
+    if opt.patch_size > 1:
+        # assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
+        assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
+    seed_everything(opt.seed)
+    print(opt)
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model = NeRFNetwork(opt)
+
+    criterion = torch.nn.MSELoss(reduction='none')
+    metrics = [] # use no metric in GUI for faster initialization...
+    print(model)
+    trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
+
+    test_loader = NeRFDataset_Test(opt, device=device).dataloader()
+    model.aud_features = test_loader._data.auds
+    model.eye_areas = test_loader._data.eye_area
+
+    # we still need test_loader to provide audio features for testing.
+    nerfreal = NeRFReal(opt, trainer, test_loader)
+    #txt_to_audio('我是中国人,我来自北京')
+    rendthrd = Thread(target=render)
+    rendthrd.start()
+
+    #############################################################################
+    print('start websocket server')
+
+    server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
+    server.serve_forever()
+    
+    
\ No newline at end of file