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dy.py
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dy.py
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import websocket
import json, logging, os
import time
import threading
import schedule
import random
import asyncio
import traceback
import copy
from functools import partial
from flask import Flask, send_from_directory, render_template, request, jsonify
from flask_cors import CORS
# 按键监听语音聊天板块
import keyboard
import pyaudio
import wave
import numpy as np
import speech_recognition as sr
from aip import AipSpeech
import signal
import time
from utils.common import Common
from utils.logger import Configure_logger
from utils.my_handle import My_handle
from utils.config import Config
config = None
config_path = None
common = None
my_handle = None
last_liveroom_data = None
last_username_list = None
# 空闲时间计数器
global_idle_time = 0
def start_server():
global config, common, my_handle, last_liveroom_data, last_username_list, config_path
global do_listen_and_comment_thread, stop_do_listen_and_comment_thread_event
# 按键监听相关
do_listen_and_comment_thread = None
stop_do_listen_and_comment_thread_event = threading.Event()
# 冷却时间 0.5 秒
cooldown = 0.5
last_pressed = 0
config_path = "config.json"
config = Config(config_path)
common = Common()
# 日志文件路径
log_path = "./log/log-" + common.get_bj_time(1) + ".txt"
Configure_logger(log_path)
# 最新的直播间数据
last_liveroom_data = {
'OnlineUserCount': 0,
'TotalUserCount': 0,
'TotalUserCountStr': '0',
'OnlineUserCountStr': '0',
'MsgId': 0,
'User': None,
'Content': '当前直播间人数 0,累计直播间人数 0',
'RoomId': 0
}
# 最新入场的用户名列表
last_username_list = [""]
my_handle = My_handle(config_path)
if my_handle is None:
logging.error("程序初始化失败!")
os._exit(0)
# HTTP API线程
def http_api_thread():
app = Flask(__name__, static_folder='./')
CORS(app) # 允许跨域请求
@app.route('/send', methods=['POST'])
def send():
global my_handle, config
try:
try:
data_json = request.get_json()
logging.info(f"API收到数据:{data_json}")
if data_json["type"] == "reread":
my_handle.reread_handle(data_json)
elif data_json["type"] == "comment":
my_handle.process_data(data_json, "comment")
elif data_json["type"] == "tuning":
my_handle.tuning_handle(data_json)
return jsonify({"code": 200, "message": "发送数据成功!"})
except Exception as e:
logging.error(f"发送数据失败!{e}")
return jsonify({"code": -1, "message": f"发送数据失败!{e}"})
except Exception as e:
return jsonify({"code": -1, "message": f"发送数据失败!{e}"})
app.run(host=config.get("api_ip"), port=config.get("api_port"), debug=False)
# HTTP API线程并启动
schedule_thread = threading.Thread(target=http_api_thread)
schedule_thread.start()
# 添加用户名到最新的用户名列表
def add_username_to_last_username_list(data):
global last_username_list
# 添加数据到 最新入场的用户名列表
last_username_list.append(data)
# 保留最新的3个数据
last_username_list = last_username_list[-3:]
"""
按键监听板块
"""
# 录音功能(录音时间过短进入openai的语音转文字会报错,请一定注意)
def record_audio():
pressdown_num = 0
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
WAVE_OUTPUT_FILENAME = "out/record.wav"
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
frames = []
print("Recording...")
flag = 0
while 1:
while keyboard.is_pressed('RIGHT_SHIFT'):
flag = 1
data = stream.read(CHUNK)
frames.append(data)
pressdown_num = pressdown_num + 1
if flag:
break
print("Stopped recording.")
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()
if pressdown_num >= 5: # 粗糙的处理手段
return 1
else:
print("杂鱼杂鱼,好短好短(录音时间过短,按右shift重新录制)")
return 0
# THRESHOLD 设置音量阈值,默认值800.0,根据实际情况调整 silence_threshold 设置沉默阈值,根据实际情况调整
def audio_listen(volume_threshold=800.0, silence_threshold=15):
audio = pyaudio.PyAudio()
# 设置音频参数
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
CHUNK = 1024
stream = audio.open(
format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK,
input_device_index=int(config.get("talk", "device_index"))
)
frames = [] # 存储录制的音频帧
is_speaking = False # 是否在说话
silent_count = 0 # 沉默计数
speaking_flag = False #录入标志位 不重要
while True:
# 读取音频数据
data = stream.read(CHUNK)
audio_data = np.frombuffer(data, dtype=np.short)
max_dB = np.max(audio_data)
# print(max_dB)
if max_dB > volume_threshold:
is_speaking = True
silent_count = 0
elif is_speaking is True:
silent_count += 1
if is_speaking is True:
frames.append(data)
if speaking_flag is False:
logging.info("[录入中……]")
speaking_flag = True
if silent_count >= silence_threshold:
break
logging.info("[语音录入完成]")
# 将音频保存为WAV文件
'''with wave.open(WAVE_OUTPUT_FILENAME, 'wb') as wf:
wf.setnchannels(CHANNELS)
wf.setsampwidth(pyaudio.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))'''
return frames
# 执行录音、识别&提交
def do_listen_and_comment(status=True):
global stop_do_listen_and_comment_thread_event
config = Config(config_path)
# 是否启用按键监听,不启用的话就不用执行了
if False == config.get("talk", "key_listener_enable"):
return
while True:
try:
# 检查是否收到停止事件
if stop_do_listen_and_comment_thread_event.is_set():
logging.info(f'停止录音~')
break
config = Config(config_path)
# 根据接入的语音识别类型执行
if "baidu" == config.get("talk", "type"):
# 设置音频参数
FORMAT = pyaudio.paInt16
CHANNELS = config.get("talk", "CHANNELS")
RATE = config.get("talk", "RATE")
audio_out_path = config.get("play_audio", "out_path")
if not os.path.isabs(audio_out_path):
if not audio_out_path.startswith('./'):
audio_out_path = './' + audio_out_path
file_name = 'baidu_' + common.get_bj_time(4) + '.wav'
WAVE_OUTPUT_FILENAME = common.get_new_audio_path(audio_out_path, file_name)
# WAVE_OUTPUT_FILENAME = './out/baidu_' + common.get_bj_time(4) + '.wav'
frames = audio_listen(config.get("talk", "volume_threshold"), config.get("talk", "silence_threshold"))
# 将音频保存为WAV文件
with wave.open(WAVE_OUTPUT_FILENAME, 'wb') as wf:
wf.setnchannels(CHANNELS)
wf.setsampwidth(pyaudio.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
# 读取音频文件
with open(WAVE_OUTPUT_FILENAME, 'rb') as fp:
audio = fp.read()
# 初始化 AipSpeech 对象
baidu_client = AipSpeech(config.get("talk", "baidu", "app_id"), config.get("talk", "baidu", "api_key"), config.get("talk", "baidu", "secret_key"))
# 识别音频文件
res = baidu_client.asr(audio, 'wav', 16000, {
'dev_pid': 1536,
})
if res['err_no'] == 0:
content = res['result'][0]
# 输出识别结果
logging.info("识别结果:" + content)
user_name = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": user_name,
"content": content
}
my_handle.process_data(data, "talk")
else:
logging.error(f"百度接口报错:{res}")
elif "google" == config.get("talk", "type"):
# 创建Recognizer对象
r = sr.Recognizer()
try:
# 打开麦克风进行录音
with sr.Microphone() as source:
logging.info(f'录音中...')
# 从麦克风获取音频数据
audio = r.listen(source)
logging.info("成功录制")
# 进行谷歌实时语音识别 en-US zh-CN ja-JP
content = r.recognize_google(audio, language=config.get("talk", "google", "tgt_lang"))
# 输出识别结果
# logging.info("识别结果:" + content)
user_name = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": user_name,
"content": content
}
my_handle.process_data(data, "talk")
except sr.UnknownValueError:
logging.warning("无法识别输入的语音")
except sr.RequestError as e:
logging.error("请求出错:" + str(e))
elif "faster_whisper" == config.get("talk", "type"):
from faster_whisper import WhisperModel
# 设置音频参数
FORMAT = pyaudio.paInt16
CHANNELS = config.get("talk", "CHANNELS")
RATE = config.get("talk", "RATE")
audio_out_path = config.get("play_audio", "out_path")
if not os.path.isabs(audio_out_path):
if not audio_out_path.startswith('./'):
audio_out_path = './' + audio_out_path
file_name = 'faster_whisper_' + common.get_bj_time(4) + '.wav'
WAVE_OUTPUT_FILENAME = common.get_new_audio_path(audio_out_path, file_name)
# WAVE_OUTPUT_FILENAME = './out/faster_whisper_' + common.get_bj_time(4) + '.wav'
frames = audio_listen(config.get("talk", "volume_threshold"), config.get("talk", "silence_threshold"))
# 将音频保存为WAV文件
with wave.open(WAVE_OUTPUT_FILENAME, 'wb') as wf:
wf.setnchannels(CHANNELS)
wf.setsampwidth(pyaudio.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
# Run on GPU with FP16
model = WhisperModel(model_size_or_path=config.get("talk", "faster_whisper", "model_size"), \
device=config.get("talk", "faster_whisper", "device"), \
compute_type=config.get("talk", "faster_whisper", "compute_type"), \
download_root=config.get("talk", "faster_whisper", "download_root"))
segments, info = model.transcribe(WAVE_OUTPUT_FILENAME, beam_size=config.get("talk", "faster_whisper", "beam_size"))
logging.debug("识别语言为:'%s',概率:%f" % (info.language, info.language_probability))
content = ""
for segment in segments:
logging.info("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
content += segment.text + "。"
if content == "":
return
# 输出识别结果
logging.info("识别结果:" + content)
user_name = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": user_name,
"content": content
}
my_handle.process_data(data, "talk")
if not status:
return
except Exception as e:
logging.error(traceback.format_exc())
def on_key_press(event):
global do_listen_and_comment_thread, stop_do_listen_and_comment_thread_event
# 是否启用按键监听,不启用的话就不用执行了
if False == config.get("talk", "key_listener_enable"):
return
# if event.name in ['z', 'Z', 'c', 'C'] and keyboard.is_pressed('ctrl'):
# print("退出程序")
# os._exit(0)
# 按键CD
current_time = time.time()
if current_time - last_pressed < cooldown:
return
"""
触发按键部分的判断
"""
trigger_key_lower = None
stop_trigger_key_lower = None
# trigger_key是字母, 整个小写
if trigger_key.isalpha():
trigger_key_lower = trigger_key.lower()
# stop_trigger_key是字母, 整个小写
if stop_trigger_key.isalpha():
stop_trigger_key_lower = stop_trigger_key.lower()
if trigger_key_lower:
if event.name == trigger_key or event.name == trigger_key_lower:
logging.info(f'检测到单击键盘 {event.name},即将开始录音~')
elif event.name == stop_trigger_key or event.name == stop_trigger_key_lower:
logging.info(f'检测到单击键盘 {event.name},即将停止录音~')
stop_do_listen_and_comment_thread_event.set()
return
else:
return
else:
if event.name == trigger_key:
logging.info(f'检测到单击键盘 {event.name},即将开始录音~')
elif event.name == stop_trigger_key:
logging.info(f'检测到单击键盘 {event.name},即将停止录音~')
stop_do_listen_and_comment_thread_event.set()
return
else:
return
# 是否启用连续对话模式
if config.get("talk", "continuous_talk"):
stop_do_listen_and_comment_thread_event.clear()
do_listen_and_comment_thread = threading.Thread(target=do_listen_and_comment, args=(True,))
do_listen_and_comment_thread.start()
else:
stop_do_listen_and_comment_thread_event.clear()
do_listen_and_comment_thread = threading.Thread(target=do_listen_and_comment, args=(False,))
do_listen_and_comment_thread.start()
# 按键监听
def key_listener():
# 注册按键按下事件的回调函数
keyboard.on_press(on_key_press)
try:
# 进入监听状态,等待按键按下
keyboard.wait()
except KeyboardInterrupt:
os._exit(0)
# 从配置文件中读取触发键的字符串配置
trigger_key = config.get("talk", "trigger_key")
stop_trigger_key = config.get("talk", "stop_trigger_key")
if config.get("talk", "key_listener_enable"):
logging.info(f'单击键盘 {trigger_key} 按键进行录音喵~ 由于其他任务还要启动,如果按键没有反应,请等待一段时间')
# 创建并启动按键监听线程
thread = threading.Thread(target=key_listener)
thread.start()
# 定时任务
def schedule_task(index):
global config, common, my_handle, last_liveroom_data, last_username_list
logging.debug("定时任务执行中...")
hour, min = common.get_bj_time(6)
if 0 <= hour and hour < 6:
time = f"凌晨{hour}点{min}分"
elif 6 <= hour and hour < 9:
time = f"早晨{hour}点{min}分"
elif 9 <= hour and hour < 12:
time = f"上午{hour}点{min}分"
elif hour == 12:
time = f"中午{hour}点{min}分"
elif 13 <= hour and hour < 18:
time = f"下午{hour - 12}点{min}分"
elif 18 <= hour and hour < 20:
time = f"傍晚{hour - 12}点{min}分"
elif 20 <= hour and hour < 24:
time = f"晚上{hour - 12}点{min}分"
# 根据对应索引从列表中随机获取一个值
random_copy = random.choice(config.get("schedule")[index]["copy"])
# 假设有多个未知变量,用户可以在此处定义动态变量
variables = {
'time': time,
'user_num': last_liveroom_data["OnlineUserCount"],
'last_username': last_username_list[-1],
}
# 使用字典进行字符串替换
if any(var in random_copy for var in variables):
content = random_copy.format(**{var: value for var, value in variables.items() if var in random_copy})
else:
content = random_copy
data = {
"platform": "抖音",
"username": None,
"content": content
}
logging.info(f"定时任务:{content}")
my_handle.process_data(data, "schedule")
# 启动定时任务
def run_schedule():
try:
for index, task in enumerate(config.get("schedule")):
if task["enable"]:
# print(task)
# 设置定时任务,每隔n秒执行一次
schedule.every(task["time"]).seconds.do(partial(schedule_task, index))
except Exception as e:
logging.error(traceback.format_exc())
while True:
schedule.run_pending()
# time.sleep(1) # 控制每次循环的间隔时间,避免过多占用 CPU 资源
if any(item['enable'] for item in config.get("schedule")):
# 创建定时任务子线程并启动
schedule_thread = threading.Thread(target=run_schedule)
schedule_thread.start()
# 启动动态文案
async def run_trends_copywriting():
global config
try:
if False == config.get("trends_copywriting", "enable"):
return
logging.info(f"动态文案任务线程运行中...")
while True:
# 文案文件路径列表
copywriting_file_path_list = []
# 获取动态文案列表
for copywriting in config.get("trends_copywriting", "copywriting"):
# 获取文件夹内所有文件的文件绝对路径,包括文件扩展名
for tmp in common.get_all_file_paths(copywriting["folder_path"]):
copywriting_file_path_list.append(tmp)
# 是否开启随机播放
if config.get("trends_copywriting", "random_play"):
random.shuffle(copywriting_file_path_list)
# 遍历文案文件路径列表
for copywriting_file_path in copywriting_file_path_list:
# 获取文案文件内容
copywriting_file_content = common.read_file_return_content(copywriting_file_path)
# 是否启用提示词对文案内容进行转换
if copywriting["prompt_change_enable"]:
data_json = {
"username": "trends_copywriting",
"content": copywriting["prompt_change_content"] + copywriting_file_content
}
# 调用函数进行LLM处理,以及生成回复内容,进行音频合成,需要好好考虑考虑实现
data_json["content"] = my_handle.llm_handle(config.get("chat_type"), data_json)
else:
data_json = {
"username": "trends_copywriting",
"content": copywriting_file_content
}
# 空数据判断
if data_json["content"] != None and data_json["content"] != "":
# 发给直接复读进行处理
my_handle.reread_handle(data_json)
await asyncio.sleep(config.get("trends_copywriting", "play_interval"))
except Exception as e:
logging.error(traceback.format_exc())
if config.get("trends_copywriting", "enable"):
# 创建动态文案子线程并启动
threading.Thread(target=lambda: asyncio.run(run_trends_copywriting())).start()
# 闲时任务
async def idle_time_task():
global config, global_idle_time
try:
if False == config.get("idle_time_task", "enable"):
return
logging.info(f"闲时任务线程运行中...")
# 记录上一次触发的任务类型
last_mode = 0
comment_copy_list = None
local_audio_path_list = None
overflow_time = int(config.get("idle_time_task", "idle_time"))
# 是否开启了随机闲时时间
if config.get("idle_time_task", "random_time"):
overflow_time = random.randint(0, overflow_time)
logging.info(f"闲时时间={overflow_time}秒")
def load_data_list(type):
if type == "comment":
tmp = config.get("idle_time_task", "comment", "copy")
elif type == "local_audio":
tmp = config.get("idle_time_task", "local_audio", "path")
tmp2 = copy.copy(tmp)
return tmp2
comment_copy_list = load_data_list("comment")
local_audio_path_list = load_data_list("local_audio")
logging.debug(f"comment_copy_list={comment_copy_list}")
logging.debug(f"local_audio_path_list={local_audio_path_list}")
while True:
# 每隔一秒的睡眠进行闲时计数
await asyncio.sleep(1)
global_idle_time = global_idle_time + 1
# 闲时计数达到指定值,进行闲时任务处理
if global_idle_time >= overflow_time:
# 闲时计数清零
global_idle_time = 0
# 闲时任务处理
if config.get("idle_time_task", "comment", "enable"):
if last_mode == 0 or not config.get("idle_time_task", "local_audio", "enable"):
# 是否开启了随机触发
if config.get("idle_time_task", "comment", "random"):
if comment_copy_list != []:
# 随机打乱列表中的元素
random.shuffle(comment_copy_list)
comment_copy = comment_copy_list.pop(0)
else:
# 刷新list数据
comment_copy_list = load_data_list("comment")
# 随机打乱列表中的元素
random.shuffle(comment_copy_list)
comment_copy = comment_copy_list.pop(0)
else:
if comment_copy_list != []:
comment_copy = comment_copy_list.pop(0)
else:
# 刷新list数据
comment_copy_list = load_data_list("comment")
comment_copy = comment_copy_list.pop(0)
# 发送给处理函数
data = {
"platform": "抖音",
"username": "闲时任务",
"type": "comment",
"content": comment_copy
}
my_handle.process_data(data, "idle_time_task")
# 模式切换
last_mode = 1
overflow_time = int(config.get("idle_time_task", "idle_time"))
# 是否开启了随机闲时时间
if config.get("idle_time_task", "random_time"):
overflow_time = random.randint(0, overflow_time)
logging.info(f"闲时时间={overflow_time}秒")
continue
if config.get("idle_time_task", "local_audio", "enable"):
if last_mode == 1 or not config.get("idle_time_task", "comment", "enable"):
# 是否开启了随机触发
if config.get("idle_time_task", "local_audio", "random"):
if local_audio_path_list != []:
# 随机打乱列表中的元素
random.shuffle(local_audio_path_list)
local_audio_path = local_audio_path_list.pop(0)
else:
# 刷新list数据
local_audio_path_list = load_data_list("local_audio")
# 随机打乱列表中的元素
random.shuffle(local_audio_path_list)
local_audio_path = local_audio_path_list.pop(0)
else:
if local_audio_path_list != []:
local_audio_path = local_audio_path_list.pop(0)
else:
# 刷新list数据
local_audio_path_list = load_data_list("local_audio")
local_audio_path = local_audio_path_list.pop(0)
# 发送给处理函数
data = {
"platform": "抖音",
"username": "闲时任务",
"type": "local_audio",
"content": common.extract_filename(local_audio_path, False),
"file_path": local_audio_path
}
my_handle.process_data(data, "idle_time_task")
# 模式切换
last_mode = 0
overflow_time = int(config.get("idle_time_task", "idle_time"))
# 是否开启了随机闲时时间
if config.get("idle_time_task", "random_time"):
overflow_time = random.randint(0, overflow_time)
logging.info(f"闲时时间={overflow_time}秒")
continue
except Exception as e:
logging.error(traceback.format_exc())
# if config.get("idle_time_task", "enable"):
# 创建闲时任务子线程并启动
threading.Thread(target=lambda: asyncio.run(idle_time_task())).start()
def on_message(ws, message):
global last_liveroom_data, last_username_list, config, config_path
global global_idle_time
message_json = json.loads(message)
# logging.debug(message_json)
if "Type" in message_json:
type = message_json["Type"]
data_json = json.loads(message_json["Data"])
if type == 1:
# 闲时计数清零
global_idle_time = 0
user_name = data_json["User"]["Nickname"]
content = data_json["Content"]
logging.info(f'[📧直播间弹幕消息] [{user_name}]:{content}')
data = {
"platform": "抖音",
"username": user_name,
"content": content
}
my_handle.process_data(data, "comment")
pass
elif type == 2:
user_name = data_json["User"]["Nickname"]
count = data_json["Count"]
logging.info(f'[👍直播间点赞消息] {user_name} 点了{count}赞')
elif type == 3:
user_name = data_json["User"]["Nickname"]
logging.info(f'[🚹🚺直播间成员加入消息] 欢迎 {user_name} 进入直播间')
data = {
"platform": "抖音",
"username": user_name,
"content": "进入直播间"
}
# 添加用户名到最新的用户名列表
add_username_to_last_username_list(user_name)
my_handle.process_data(data, "entrance")
elif type == 4:
user_name = data_json["User"]["Nickname"]
logging.info(f'[➕直播间关注消息] 感谢 {data_json["User"]["Nickname"]} 的关注')
data = {
"platform": "抖音",
"username": user_name
}
my_handle.process_data(data, "follow")
pass
elif type == 5:
gift_name = data_json["GiftName"]
user_name = data_json["User"]["Nickname"]
# 礼物数量
num = data_json["GiftCount"]
# 礼物重复数量
repeat_count = data_json["RepeatCount"]
try:
# 暂时是写死的
data_path = "data/抖音礼物价格表.json"
# 读取JSON文件
with open(data_path, "r", encoding="utf-8") as file:
# 解析JSON数据
data_json = json.load(file)
if gift_name in data_json:
# 单个礼物金额 需要自己维护礼物价值表
discount_price = data_json[gift_name]
else:
logging.warning(f"数据文件:{data_path} 中,没有 {gift_name} 对应的价值,请手动补充数据")
discount_price = 1
except Exception as e:
logging.error(traceback.format_exc())
discount_price = 1
# 总金额
combo_total_coin = repeat_count * discount_price
logging.info(f'[🎁直播间礼物消息] 用户:{user_name} 赠送 {num} 个 {gift_name},单价 {discount_price}抖币,总计 {combo_total_coin}抖币')
data = {
"platform": "抖音",
"gift_name": gift_name,
"username": user_name,
"num": num,
"unit_price": discount_price / 10,
"total_price": combo_total_coin / 10
}
my_handle.process_data(data, "gift")
elif type == 6:
logging.info(f'[直播间数据] {data_json["Content"]}')
# {'OnlineUserCount': 50, 'TotalUserCount': 22003, 'TotalUserCountStr': '2.2万', 'OnlineUserCountStr': '50',
# 'MsgId': 7260517442466662207, 'User': None, 'Content': '当前直播间人数 50,累计直播间人数 2.2万', 'RoomId': 7260415920948906807}
# print(f"data_json={data_json}")
last_liveroom_data = data_json
# 当前在线人数
OnlineUserCount = data_json["OnlineUserCount"]
try:
# 是否开启了动态配置功能
if config.get("trends_config", "enable"):
for path_config in config.get("trends_config", "path"):
online_num_min = int(path_config["online_num"].split("-")[0])
online_num_max = int(path_config["online_num"].split("-")[1])
# 判断在线人数是否在此范围内
if OnlineUserCount >= online_num_min and OnlineUserCount <= online_num_max:
logging.debug(f"当前配置文件:{path_config['path']}")
# 如果配置文件相同,则跳过
if config_path == path_config["path"]:
break
config_path = path_config["path"]
config = Config(config_path)
my_handle.reload_config(config_path)
logging.info(f"切换配置文件:{config_path}")
break
except Exception as e:
logging.error(traceback.format_exc())
pass
elif type == 8:
logging.info(f'[分享直播间] 感谢 {data_json["User"]["Nickname"]} 分享了直播间')
pass
def on_error(ws, error):
logging.error("Error:", error)
def on_close(ws):
logging.debug("WebSocket connection closed")
def on_open(ws):
logging.debug("WebSocket connection established")
try:
# WebSocket连接URL
ws_url = "ws://127.0.0.1:8888"
logging.info(f"监听地址:{ws_url}")
# 不设置日志等级
websocket.enableTrace(False)
# 创建WebSocket连接
ws = websocket.WebSocketApp(ws_url,
on_message=on_message,
on_error=on_error,
on_close=on_close,
on_open=on_open)
# 运行WebSocket连接
ws.run_forever()
except KeyboardInterrupt:
logging.warning('程序被强行退出')
finally:
logging.warning('关闭ws连接...请确认您是否启动了抖音弹幕监听程序,ws服务正常运行!\n监听程序启动成功后,请重新运行程序进行对接使用!')
# os._exit(0)
# 等待子线程结束
schedule_thread.join()
# 退出程序
def exit_handler(signum, frame):
print("Received signal:", signum)
if __name__ == '__main__':
# 按键监听相关
do_listen_and_comment_thread = None
stop_do_listen_and_comment_thread_event = None
signal.signal(signal.SIGINT, exit_handler)
signal.signal(signal.SIGTERM, exit_handler)
start_server()