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main.py
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main.py
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import os
import threading
import schedule
import random
import asyncio, aiohttp
import traceback
import copy
import json, re
from functools import partial
import http.cookies
from typing import *
# 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
import http.server
import socketserver
from utils.my_log import logger
from utils.common import Common
from utils.config import Config
from utils.my_handle import My_handle
"""
___ _
|_ _| | ____ _ _ __ ___ ___
| || |/ / _` | '__/ _ \/ __|
| || < (_| | | | (_) \__ \
|___|_|\_\__,_|_| \___/|___/
"""
config = 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
# 配置文件路径
config_path = "config.json"
# web服务线程
async def web_server_thread(web_server_port):
Handler = http.server.SimpleHTTPRequestHandler
with socketserver.TCPServer(("", web_server_port), Handler) as httpd:
logger.info(f"Web运行在端口:{web_server_port}")
logger.info(f"可以直接访问Live2D页, http://127.0.0.1:{web_server_port}/Live2D/")
httpd.serve_forever()
"""
_oo0oo_
o8888888o
88" . "88
(| -_- |)
0\ = /0
___/`---'\___
.' \\| |// '.
/ \\||| : |||// \
/ _||||| -:- |||||- \
| | \\\ - /// | |
| \_| ''\---/'' |_/ |
\ .-\__ '-' ___/-. /
___'. .' /--.--\ `. .'___
."" '< `.___\_<|>_/___.' >' "".
| | : `- \`.;`\ _ /`;.`/ - ` : | |
\ \ `_. \_ __\ /__ _/ .-` / /
=====`-.____`.___ \_____/___.-`___.-'=====
`=---='
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
佛祖保佑 永不宕机 永无BUG
"""
# 点火起飞
def start_server():
global config, common, my_handle, last_username_list, config_path, last_liveroom_data
global do_listen_and_comment_thread, stop_do_listen_and_comment_thread_event, faster_whisper_model, sense_voice_model, is_recording
# 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
# 正在录音中 标志位
is_recording = False
# 获取 httpx 库的日志记录器
# httpx_logger = logging.getLogger("httpx")
# 设置 httpx 日志记录器的级别为 WARNING
# httpx_logger.setLevel(logging.WARNING)
# 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:
logger.error("程序初始化失败!")
os._exit(0)
# Live2D线程
try:
if config.get("live2d", "enable"):
web_server_port = int(config.get("live2d", "port"))
threading.Thread(target=lambda: asyncio.run(web_server_thread(web_server_port))).start()
except Exception as e:
logger.error(traceback.format_exc())
os._exit(0)
if platform != "wxlive":
# HTTP API线程
def http_api_thread():
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from utils.models import SendMessage, LLMMessage, CallbackMessage, CommonResult
# 定义FastAPI应用
app = FastAPI()
# 允许跨域
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# 定义POST请求路径和处理函数
@app.post("/send")
async def send(msg: SendMessage):
global my_handle, config
try:
tmp_json = msg.dict()
logger.info(f"API收到数据:{tmp_json}")
data_json = tmp_json["data"]
if "type" not in data_json:
data_json["type"] = tmp_json["type"]
if data_json["type"] in ["reread", "reread_top_priority"]:
my_handle.reread_handle(data_json, type=data_json["type"])
elif data_json["type"] == "comment":
my_handle.process_data(data_json, "comment")
elif data_json["type"] == "tuning":
my_handle.tuning_handle(data_json)
elif data_json["type"] == "gift":
my_handle.gift_handle(data_json)
elif data_json["type"] == "entrance":
my_handle.entrance_handle(data_json)
return CommonResult(code=200, message="成功")
except Exception as e:
logger.error(f"发送数据失败!{e}")
return CommonResult(code=-1, message=f"发送数据失败!{e}")
@app.post("/llm")
async def llm(msg: LLMMessage):
global my_handle, config
try:
data_json = msg.dict()
logger.info(f"API收到数据:{data_json}")
resp_content = my_handle.llm_handle(data_json["type"], data_json, webui_show=False)
return CommonResult(code=200, message="成功", data={"content": resp_content})
except Exception as e:
logger.error(f"调用LLM失败!{e}")
return CommonResult(code=-1, message=f"调用LLM失败!{e}")
@app.post("/callback")
async def callback(msg: CallbackMessage):
global my_handle, config, global_idle_time
try:
data_json = msg.dict()
logger.info(f"API收到数据:{data_json}")
# 音频播放完成
if data_json["type"] in ["audio_playback_completed"]:
# 如果等待播放的音频数量大于10
if data_json["data"]["wait_play_audio_num"] > int(config.get("idle_time_task", "wait_play_audio_num_threshold")):
logger.info(f'等待播放的音频数量大于限定值,闲时任务的闲时计时由 {global_idle_time} -> {int(config.get("idle_time_task", "idle_time_reduce_to"))}秒')
# 闲时任务的闲时计时 清零
global_idle_time = int(config.get("idle_time_task", "idle_time_reduce_to"))
return CommonResult(code=200, message="callback处理成功!")
except Exception as e:
logger.error(f"callback处理失败!{e}")
return CommonResult(code=-1, message=f"callback处理失败!{e}")
logger.info("HTTP API线程已启动!")
uvicorn.run(app, host="0.0.0.0", port=config.get("api_port"))
# HTTP API线程并启动
inside_http_api_thread = threading.Thread(target=http_api_thread)
inside_http_api_thread.start()
# 添加用户名到最新的用户名列表
def add_username_to_last_username_list(data):
"""
data(str): 用户名
"""
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 = []
logger.info("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
logger.info("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:
logger.info("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 = config.get("talk", "CHANNELS")
RATE = config.get("talk", "RATE")
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
logger.info("[即将开始录音……]")
while True:
# 播放中不录音
if config.get("talk", "no_recording_during_playback"):
# 存在待合成音频 或 已合成音频还未播放 或 播放中 或 在数据处理中
if my_handle.is_audio_queue_empty() != 15 or my_handle.is_handle_empty() == 1:
time.sleep(float(config.get("talk", "no_recording_during_playback_sleep_interval")))
continue
# 读取音频数据
data = stream.read(CHUNK)
audio_data = np.frombuffer(data, dtype=np.short)
max_dB = np.max(audio_data)
# logger.info(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:
logger.info("[录入中……]")
speaking_flag = True
if silent_count >= silence_threshold:
break
logger.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, faster_whisper_model, sense_voice_model, is_recording
try:
is_recording = True
config = Config(config_path)
# 是否启用按键监听,不启用的话就不用执行了
if not config.get("talk", "key_listener_enable"):
is_recording = False
return
# 针对faster_whisper情况,模型加载一次共用,减少开销
if "faster_whisper" == config.get("talk", "type") :
from faster_whisper import WhisperModel
if faster_whisper_model is None:
logger.info("faster_whisper 模型加载中,请稍后...")
# Run on GPU with FP16
faster_whisper_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"))
logger.info("faster_whisper 模型加载完毕,可以开始说话了喵~")
elif "sensevoice" == config.get("talk", "type") :
from funasr import AutoModel
logger.info("sensevoice 模型加载中,请稍后...")
asr_model_path = config.get("talk", "sensevoice", "asr_model_path")
vad_model_path = config.get("talk", "sensevoice", "vad_model_path")
if sense_voice_model is None:
sense_voice_model = AutoModel(model=asr_model_path,
vad_model=vad_model_path,
vad_kwargs={"max_single_segment_time": int(config.get("talk", "sensevoice", "vad_max_single_segment_time"))},
trust_remote_code=True, device=config.get("talk", "sensevoice", "device"), remote_code="./sensevoice/model.py")
logger.info("sensevoice 模型加载完毕,可以开始说话了喵~")
while True:
try:
# 检查是否收到停止事件
if stop_do_listen_and_comment_thread_event.is_set():
logger.info('停止录音~')
is_recording = False
break
config = Config(config_path)
# 根据接入的语音识别类型执行
if config.get("talk", "type") in ["baidu", "faster_whisper", "sensevoice"]:
# 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 = 'asr_' + common.get_bj_time(4) + '.wav'
WAVE_OUTPUT_FILENAME = common.get_new_audio_path(audio_out_path, file_name)
# WAVE_OUTPUT_FILENAME = './out/asr_' + 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))
if config.get("talk", "type") == "baidu":
# 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
logger.info("识别结果:" + content)
username = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": username,
"content": content
}
my_handle.process_data(data, "talk")
else:
logger.error(f"百度接口报错:{res}")
elif config.get("talk", "type") == "faster_whisper":
logger.debug("faster_whisper模型加载中...")
language = config.get("talk", "faster_whisper", "language")
if language == "自动识别":
language = None
segments, info = faster_whisper_model.transcribe(WAVE_OUTPUT_FILENAME, language=language, beam_size=config.get("talk", "faster_whisper", "beam_size"))
logger.debug("识别语言为:'%s',概率:%f" % (info.language, info.language_probability))
content = ""
for segment in segments:
logger.info("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
content += segment.text + "。"
if content == "":
# 恢复录音标志位
is_recording = False
return
# 输出识别结果
logger.info("识别结果:" + content)
username = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": username,
"content": content
}
my_handle.process_data(data, "talk")
elif config.get("talk", "type") == "sensevoice":
res = sense_voice_model.generate(
input=WAVE_OUTPUT_FILENAME,
cache={},
language=config.get("talk", "sensevoice", "language"),
text_norm=config.get("talk", "sensevoice", "text_norm"),
batch_size_s=int(config.get("talk", "sensevoice", "batch_size_s")),
batch_size=int(config.get("talk", "sensevoice", "batch_size"))
)
def remove_angle_brackets_content(input_string: str):
# 使用正则表达式来匹配并删除 <> 之间的内容
return re.sub(r'<.*?>', '', input_string)
content = remove_angle_brackets_content(res[0]['text'])
# 输出识别结果
logger.info("识别结果:" + content)
username = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": username,
"content": content
}
my_handle.process_data(data, "talk")
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:
logger.info('录音中...')
# 从麦克风获取音频数据
audio = r.listen(source)
logger.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
# logger.info("识别结果:" + content)
username = config.get("talk", "username")
data = {
"platform": "本地聊天",
"username": username,
"content": content
}
my_handle.process_data(data, "talk")
except sr.UnknownValueError:
logger.warning("无法识别输入的语音")
except sr.RequestError as e:
logger.error("请求出错:" + str(e))
is_recording = False
if not status:
return
except Exception as e:
logger.error(traceback.format_exc())
is_recording = False
return
except Exception as e:
logger.error(traceback.format_exc())
is_recording = False
return
def on_key_press(event):
global do_listen_and_comment_thread, stop_do_listen_and_comment_thread_event, is_recording
# 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'):
# logger.info("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:
logger.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:
logger.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:
logger.info(f'检测到单击键盘 {event.name},即将开始录音~')
elif event.name == stop_trigger_key:
logger.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
if False == is_recording:
# 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()
else:
logger.warning("正在录音中...请勿重复点击录音捏!")
# 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"):
logger.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
logger.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
if len(config.get("schedule")[index]["copy"]) <= 0:
return None
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': "N",
'last_username': last_username_list[-1],
}
# 有用户数据情况的平台特殊处理
if platform in ["dy", "tiktok"]:
variables['user_num'] = last_liveroom_data["OnlineUserCount"]
# 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
content = common.brackets_text_randomize(content)
data = {
"platform": platform,
"username": "定时任务",
"content": content
}
logger.info(f"定时任务:{content}")
my_handle.process_data(data, "schedule")
# schedule.clear(index)
# 启动定时任务
def run_schedule():
global config
try:
for index, task in enumerate(config.get("schedule")):
if task["enable"]:
# logger.info(task)
min_seconds = int(task["time_min"])
max_seconds = int(task["time_max"])
def schedule_random_task(index, min_seconds, max_seconds):
schedule.clear(index)
# 在min_seconds和max_seconds之间随机选择下一次任务执行的时间
next_time = random.randint(min_seconds, max_seconds)
# logger.info(f"Next task {index} scheduled in {next_time} seconds at {time.ctime()}")
schedule_task(index)
schedule.every(next_time).seconds.do(schedule_random_task, index, min_seconds, max_seconds).tag(index)
schedule_random_task(index, min_seconds, max_seconds)
except Exception as e:
logger.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
logger.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)
logger.debug(f"copywriting_file_path_list={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("trends_copywriting", "llm_type"), data_json)
else:
copywriting_file_content = common.brackets_text_randomize(copywriting_file_content)
data_json = {
"username": "trends_copywriting",
"content": copywriting_file_content
}
logger.debug(f'copywriting_file_content={copywriting_file_content},content={data_json["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, filter=True, type="trends_copywriting")
await asyncio.sleep(config.get("trends_copywriting", "play_interval"))
except Exception as e:
logger.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, common
try:
if False == config.get("idle_time_task", "enable"):
return
logger.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
copywriting_copy_list = None
comment_copy_list = None
local_audio_path_list = None
overflow_time_min = int(config.get("idle_time_task", "idle_time_min"))
overflow_time_max = int(config.get("idle_time_task", "idle_time_max"))
overflow_time = random.randint(overflow_time_min, overflow_time_max)
logger.info(f"下一个闲时任务将在{overflow_time}秒后执行")
def load_data_list(type):
if type == "copywriting":
tmp = config.get("idle_time_task", "copywriting", "copy")
elif type == "comment":
tmp = config.get("idle_time_task", "comment", "copy")
elif type == "local_audio":
tmp = config.get("idle_time_task", "local_audio", "path")
logger.debug(f"type={type}, tmp={tmp}")
tmp2 = copy.copy(tmp)
return tmp2
# 加载数据到list
copywriting_copy_list = load_data_list("copywriting")
comment_copy_list = load_data_list("comment")
local_audio_path_list = load_data_list("local_audio")
logger.debug(f"copywriting_copy_list={copywriting_copy_list}")
logger.debug(f"comment_copy_list={comment_copy_list}")
logger.debug(f"local_audio_path_list={local_audio_path_list}")
def do_task(last_mode, copywriting_copy_list, comment_copy_list, local_audio_path_list):
global global_idle_time
# 闲时计数清零
global_idle_time = 0
# 闲时任务处理
if config.get("idle_time_task", "copywriting", "enable"):
if last_mode == 0:
# 是否开启了随机触发
if config.get("idle_time_task", "copywriting", "random"):
logger.debug("切换到文案触发模式")
if copywriting_copy_list != []:
# 随机打乱列表中的元素
random.shuffle(copywriting_copy_list)
copywriting_copy = copywriting_copy_list.pop(0)
else:
# 刷新list数据
copywriting_copy_list = load_data_list("copywriting")
# 随机打乱列表中的元素
random.shuffle(copywriting_copy_list)
if copywriting_copy_list != []:
copywriting_copy = copywriting_copy_list.pop(0)
else:
return last_mode, copywriting_copy_list, comment_copy_list, local_audio_path_list
else:
logger.debug(copywriting_copy_list)
if copywriting_copy_list != []:
copywriting_copy = copywriting_copy_list.pop(0)
else:
# 刷新list数据
copywriting_copy_list = load_data_list("copywriting")
if copywriting_copy_list != []:
copywriting_copy = copywriting_copy_list.pop(0)
else:
return last_mode, copywriting_copy_list, comment_copy_list, local_audio_path_list
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"
# 动态变量替换
# 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': "N",
'last_username': last_username_list[-1],
}
# 有用户数据情况的平台特殊处理
if platform in ["dy", "tiktok"]:
variables['user_num'] = last_liveroom_data["OnlineUserCount"]
# 使用字典进行字符串替换
if any(var in copywriting_copy for var in variables):
copywriting_copy = copywriting_copy.format(**{var: value for var, value in variables.items() if var in copywriting_copy})
# [1|2]括号语法随机获取一个值,返回取值完成后的字符串
copywriting_copy = common.brackets_text_randomize(copywriting_copy)
# 发送给处理函数
data = {
"platform": platform,
"username": "闲时任务-文案模式",
"type": "reread",
"content": copywriting_copy
}
my_handle.process_data(data, "idle_time_task")
# 模式切换
last_mode = 1
overflow_time = random.randint(overflow_time_min, overflow_time_max)
logger.info(f"下一个闲时任务将在{overflow_time}秒后执行")
return last_mode, copywriting_copy_list, comment_copy_list, local_audio_path_list
else:
last_mode = 1
if config.get("idle_time_task", "comment", "enable"):
if last_mode == 1:
# 是否开启了随机触发
if config.get("idle_time_task", "comment", "random"):
logger.debug("切换到弹幕触发LLM模式")
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)
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"
# 动态变量替换
# 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': "N",
'last_username': last_username_list[-1],
}
# 有用户数据情况的平台特殊处理
if platform in ["dy", "tiktok"]: