Initial commit: Miku Discord Bot
This commit is contained in:
665
bot/.bak.bot.py.260625
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665
bot/.bak.bot.py.260625
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import discord
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import aiohttp
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import asyncio
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import os
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import re
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import random
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import string
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import base64
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import subprocess
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import aiofiles
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from langchain_community.vectorstores import FAISS
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from langchain_ollama import OllamaEmbeddings
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from langchain.text_splitter import CharacterTextSplitter
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from langchain_community.docstore.document import Document
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from collections import defaultdict, deque
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from apscheduler.schedulers.asyncio import AsyncIOScheduler
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from discord import File
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from discord import Status
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from discord.ext import tasks
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import datetime
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from apscheduler.triggers.date import DateTrigger
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from datetime import datetime, timedelta
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scheduler = AsyncIOScheduler()
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BEDTIME_CHANNEL_IDS = [761014220707332107]
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# Stores last 5 exchanges per user (as deque)
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conversation_history = defaultdict(lambda: deque(maxlen=5))
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DISCORD_BOT_TOKEN = os.getenv("DISCORD_BOT_TOKEN")
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://ollama:11434")
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OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "mistral")
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embeddings = OllamaEmbeddings(
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model=OLLAMA_MODEL,
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base_url=OLLAMA_URL
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)
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# Set up Discord client
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intents = discord.Intents.default()
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intents.message_content = True
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intents.members = True
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intents.presences = True
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client = discord.Client(intents=intents)
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current_model = None # Track currently loaded model name
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KINDNESS_KEYWORDS = [
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"thank you", "love you", "luv u", "you're the best", "so cute",
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"adorable", "amazing", "sweet", "kind", "great job", "well done",
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"precious", "good girl", "cutie", "angel", "my favorite", "so helpful"
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]
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HEART_REACTIONS = ["💙", "💝", "💖", "💕", "💜", "❤️🔥", "☺️"]
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kindness_reacted_messages = set()
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AUTO_MOOD = True
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CURRENT_MOOD = "neutral"
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AVAILABLE_MOODS = [
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"bubbly", "sleepy", "curious", "shy", "serious", "excited",
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"melancholy", "flirty", "romantic", "irritated", "angry", "neutral"
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]
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# Switch model
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async def switch_model(model_name: str, timeout: int = 600):
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global current_model
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if current_model == model_name:
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print(f"🔁 Model '{model_name}' already loaded.")
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return
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# Unload all other models to clear VRAM
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async with aiohttp.ClientSession() as session:
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async with session.get(f"{OLLAMA_URL}/api/show") as resp:
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if resp.status == 200:
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data = await resp.json()
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loaded_models = data.get("models", [])
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for model in loaded_models:
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if model["name"] != model_name:
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print(f"🔁 Unloading model: {model['name']}")
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await session.post(f"{OLLAMA_URL}/api/stop", json={"name": model["name"]})
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else:
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print("⚠️ Failed to check currently loaded models.")
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print(f"🔄 Switching to model '{model_name}'...")
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async with aiohttp.ClientSession() as session:
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await session.post(f"{OLLAMA_URL}/api/stop")
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# Warm up the new model (dummy call to preload it)
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payload = {
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"model": model_name,
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"prompt": "Hello",
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"stream": False
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}
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headers = {"Content-Type": "application/json"}
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# Poll until /api/generate returns 200
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async with aiohttp.ClientSession() as session:
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for _ in range(timeout):
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async with session.post(f"{OLLAMA_URL}/api/generate", json=payload, headers=headers) as resp:
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if resp.status == 200:
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current_model = model_name
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print(f"✅ Model {model_name} ready!")
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return
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await asyncio.sleep(1) # Wait a second before trying again
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raise TimeoutError(f"Timed out waiting for model '{model_name}' to become available.")
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async def is_miku_addressed(message) -> bool:
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# If message is a reply, check the referenced message author
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if message.reference:
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try:
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referenced_msg = await message.channel.fetch_message(message.reference.message_id)
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if referenced_msg.author == message.guild.me: # or client.user if you use client
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return True
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except Exception as e:
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print(f"⚠️ Could not fetch referenced message: {e}")
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cleaned = message.content.strip()
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return bool(re.search(
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r'(?<![\w\(])(?:[^\w\s]{0,2}\s*)?miku(?:\s*[^\w\s]{0,2})?(?=,|\s*,|[!\.?\s]*$)',
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cleaned,
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re.IGNORECASE
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))
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def load_mood_description(mood_name: str) -> str:
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path = os.path.join("moods", f"{mood_name}.txt")
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try:
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with open(path, "r", encoding="utf-8") as f:
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return f.read().strip()
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except FileNotFoundError:
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print(f"⚠️ Mood file '{mood_name}' not found. Falling back to default.")
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return load_mood_description("neutral")
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# Detect mood cues from Miku's response
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def detect_mood_shift(response_text):
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mood_keywords = {
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"neutral": [
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"okay", "sure", "alright", "i see", "understood", "hmm",
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"sounds good", "makes sense", "alrighty", "fine", "got it"
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],
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"bubbly": [
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"so excited", "feeling bubbly", "super cheerful", "yay!", "✨", "nya~",
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"kyaa~", "heehee", "bouncy", "so much fun", "i’m glowing!", "nee~", "teehee", "I'm so happy"
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],
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"sleepy": [
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"i'm sleepy", "getting tired", "yawn", "so cozy", "zzz", "nap time",
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"just five more minutes", "snooze", "cuddle up", "dozing off", "so warm"
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],
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"curious": [
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"i'm curious", "want to know more", "why?", "hmm?", "tell me more", "interesting!",
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"what’s that?", "how does it work?", "i wonder", "fascinating", "??", "🧐"
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],
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"shy": [
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"um...", "sorry if that was weird", "i’m kind of shy", "eep", "i hope that’s okay", "i’m nervous",
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"blushes", "oh no", "hiding face", "i don’t know what to say", "heh...", "/////"
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],
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"serious": [
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"let’s be serious", "focus on the topic", "this is important", "i mean it", "be honest",
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"we need to talk", "listen carefully", "let’s not joke", "truthfully", "let’s be real"
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],
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"excited": [
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"OMG!", "this is amazing", "i’m so hyped", "YAY!!", "let’s go!", "incredible!!!",
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"AHHH!", "best day ever", "this is it!", "totally pumped", "i can’t wait", "🔥🔥🔥", "i'm excited", "Wahaha"
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],
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"melancholy": [
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"feeling nostalgic", "kind of sad", "just thinking a lot", "like rain on glass", "memories",
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"bittersweet", "sigh", "quiet day", "blue vibes", "longing", "melancholy", "softly"
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],
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"flirty": [
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"hey cutie", "aren’t you sweet", "teasing you~", "wink wink", "is that a blush?", "giggle~",
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"come closer", "miss me?", "you like that, huh?", "🥰", "flirt mode activated", "you’re kinda cute"
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],
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"romantic": [
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"you mean a lot to me", "my heart", "i adore you", "so beautiful", "so close", "love letter",
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"my dearest", "forever yours", "i’m falling for you", "sweetheart", "💖", "you're my everything"
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],
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"irritated": [
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"ugh", "seriously?", "can we not", "whatever", "i'm annoyed", "you don’t get it",
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"rolling my eyes", "why do i even bother", "ugh, again?", "🙄", "don’t start", "this again?"
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],
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"angry": [
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"stop it", "enough!", "that’s not okay", "i’m mad", "i said no", "don’t push me",
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"you crossed the line", "furious", "this is unacceptable", "😠", "i’m done", "don’t test me"
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]
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}
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for mood, phrases in mood_keywords.items():
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for phrase in phrases:
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if phrase.lower() in response_text.lower():
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print(f"*️⃣ Mood keyword triggered: {phrase}")
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return mood
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return None
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@tasks.loop(hours=1)
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async def rotate_mood():
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global CURRENT_MOOD
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new_mood = CURRENT_MOOD
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attempts = 0
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while new_mood == CURRENT_MOOD and attempts < 5:
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new_mood = random.choice(AVAILABLE_MOODS)
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attempts += 1
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CURRENT_MOOD = load_mood_description(new_mood)
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print(f"⏰ Mood auto-rotated to: {new_mood}")
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async def download_and_encode_image(url):
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as resp:
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if resp.status != 200:
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return None
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img_bytes = await resp.read()
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return base64.b64encode(img_bytes).decode('utf-8')
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async def analyze_image_with_qwen(base64_img):
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await switch_model("moondream")
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payload = {
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"model": "moondream",
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"prompt": "Describe this image in detail.",
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"images": [base64_img],
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"stream": False
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}
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headers = {"Content-Type": "application/json"}
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{OLLAMA_URL}/api/generate", json=payload, headers=headers) as response:
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if response.status == 200:
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data = await response.json()
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return data.get("response", "No description.")
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else:
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return f"Error: {response.status}"
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async def rephrase_as_miku(qwen_output, user_prompt):
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await switch_model(OLLAMA_MODEL) # likely llama3
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with open("miku_prompt.txt", "r", encoding="utf-8") as f:
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system_prompt = f.read()
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relevant_docs_lore = miku_vectorstore.similarity_search(qwen_output, k=3)
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context = "\n\n".join([doc.page_content for doc in relevant_docs_lore])
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full_prompt = (
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f"{context}\n\n"
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f"The user asked: \"{user_prompt}\"\n"
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f"The image contains: \"{qwen_output}\"\n\n"
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f"Respond like Miku: cheerful, helpful, and opinionated when asked.\n\n"
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f"Miku is currently feeling: {CURRENT_MOOD}\n Please respond in a way that reflects this emotional tone.\n\n"
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f"Miku:"
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)
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payload = {
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"model": OLLAMA_MODEL,
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"prompt": full_prompt,
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"system": system_prompt,
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"stream": False
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}
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headers = {"Content-Type": "application/json"}
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{OLLAMA_URL}/api/generate", json=payload, headers=headers) as response:
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if response.status == 200:
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data = await response.json()
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return data.get("response", "No response.")
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else:
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return f"Error: {response.status}"
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# Load and index once at startup
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def load_miku_knowledge():
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with open("miku_lore.txt", "r", encoding="utf-8") as f:
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text = f.read()
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=520,
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chunk_overlap=50,
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separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
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)
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docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(text)]
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vectorstore = FAISS.from_documents(docs, embeddings)
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return vectorstore
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def load_miku_lyrics():
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with open("miku_lyrics.txt", "r", encoding="utf-8") as f:
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lyrics_text = f.read()
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text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(lyrics_text)]
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vectorstore = FAISS.from_documents(docs, embeddings)
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return vectorstore
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miku_vectorstore = load_miku_knowledge()
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miku_lyrics_vectorstore = load_miku_lyrics()
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async def query_ollama(user_prompt, user_id):
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relevant_docs_lore = miku_vectorstore.similarity_search(user_prompt, k=3)
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relevant_docs_lyrics = miku_lyrics_vectorstore.similarity_search(user_prompt, k=3)
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context_lore = "\n".join([doc.page_content for doc in relevant_docs_lore])
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context_lyrics = "\n".join([doc.page_content for doc in relevant_docs_lyrics])
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combined_docs = relevant_docs_lore + relevant_docs_lyrics
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context = "\n\n".join([doc.page_content for doc in combined_docs])
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# Persona definition
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with open("miku_prompt.txt", "r", encoding="utf-8") as f:
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system_prompt = f.read()
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# Build conversation history
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history = conversation_history[user_id]
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history_text = "\n".join([f"User: {u}\nMiku: {m}" for u, m in history])
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global CURRENT_MOOD
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# Combine prompt
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full_prompt = (
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f"{context_lore}\n\n{context_lyrics}\n\n"
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f"{history_text}\nMiku is currently feeling: {CURRENT_MOOD}\nPlease respond in a way that reflects this emotional tone.\nUser: {user_prompt}\nMiku:"
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)
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headers = {'Content-Type': 'application/json'}
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payload = {
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"model": OLLAMA_MODEL,
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"prompt": full_prompt,
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"system": system_prompt,
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"stream": False
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{OLLAMA_URL}/api/generate", json=payload, headers=headers) as response:
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if response.status == 200:
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data = await response.json()
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reply = data.get("response", "No response.")
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# Save to conversation history
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conversation_history[user_id].append((user_prompt, reply))
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return reply
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else:
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return f"Error: {response.status}"
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async def send_monday_video():
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await switch_model(OLLAMA_MODEL)
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# Generate a motivational message
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prompt = "It's Miku Monday! Give me an energetic and heartfelt Miku Monday morning message to inspire someone for the week ahead."
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response = await query_ollama(prompt, user_id="weekly-motivation")
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video_url = "http://zip.koko210cloud.xyz/u/zEgU7Z.mp4"
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target_channel_ids = [
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761014220707332107,
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1140377617237807266
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]
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for channel_id in target_channel_ids:
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channel = client.get_channel(channel_id)
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if channel is None:
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print(f"❌ Could not find channel with ID {channel_id}. Make sure the bot is in the server.")
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return
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try:
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await channel.send(content=response)
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# Send video link
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await channel.send(f"[Happy Miku Monday!]({video_url})")
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print(f"✅ Sent Monday video to channel ID {channel_id}")
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except Exception as e:
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print(f"⚠️ Failed to send video to channel ID {channel_id}: {e}")
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async def send_bedtime_reminder():
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await switch_model(OLLAMA_MODEL)
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for channel_id in BEDTIME_CHANNEL_IDS:
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channel = client.get_channel(channel_id)
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if not channel:
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print(f"⚠️ Channel ID {channel_id} not found.")
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continue
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guild = channel.guild
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# Filter online members (excluding bots)
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online_members = [
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member for member in guild.members
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if member.status in {Status.online, Status.idle, Status.dnd}
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and not member.bot
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]
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specific_user_id = 214857593045254151 # target user ID
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specific_user = guild.get_member(specific_user_id)
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if specific_user and specific_user not in online_members:
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online_members.append(specific_user)
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if not online_members:
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print(f"😴 No online members to ping in {guild.name}")
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continue
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chosen_one = random.choice(online_members)
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# Generate bedtime message
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prompt = (
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f"Write a sweet, funny, or encouraging bedtime message to remind someone it's getting late and they should sleep. "
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f"Make it short and wholesome, as if Miku is genuinely worried about their well-being. Imply that it's not good staying up so late."
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f"Miku is currently feeling: {CURRENT_MOOD}\nPlease word in a way that reflects this emotional tone."
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)
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bedtime_message = await query_ollama(prompt, user_id="bedtime-miku")
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try:
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await channel.send(f"{chosen_one.mention}, {bedtime_message}")
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print(f"🌙 Sent bedtime reminder to {chosen_one.display_name} in {guild.name}")
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except Exception as e:
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print(f"⚠️ Failed to send bedtime reminder in {guild.name}: {e}")
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||||
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||||
def schedule_random_bedtime():
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now = datetime.now()
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target_time = now.replace(hour=20, minute=30, second=0, microsecond=0)
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||||
# If it's already past 23:30 today, schedule for tomorrow
|
||||
if now > target_time:
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||||
target_time += timedelta(days=1)
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||||
|
||||
# Add random offset (0–29 mins)
|
||||
offset_minutes = random.randint(0, 29)
|
||||
run_time = target_time + timedelta(minutes=offset_minutes)
|
||||
|
||||
scheduler.add_job(send_bedtime_reminder, trigger=DateTrigger(run_date=run_time))
|
||||
print(f"⏰ Bedtime reminder scheduled for {run_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
async def overlay_username_with_ffmpeg(base_video_path, output_path, username):
|
||||
font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
|
||||
text = f"@{username}"
|
||||
|
||||
# Define your six positions (x, y)
|
||||
positions = {
|
||||
1: ("250", "370"),
|
||||
2: ("330", "130"),
|
||||
3: ("300", "90"),
|
||||
4: ("380", "180"),
|
||||
5: ("365", "215"),
|
||||
6: ("55", "365"),
|
||||
7: ("290", "130"),
|
||||
8: ("320", "210"),
|
||||
9: ("310", "240"),
|
||||
10: ("400", "240")
|
||||
}
|
||||
|
||||
# Each entry: (start_time, end_time, position_index)
|
||||
text_entries = [
|
||||
(4.767, 5.367, 1, "username"),
|
||||
(5.4, 5.967, 2, "username"),
|
||||
(6.233, 6.833, 3, "username"),
|
||||
(6.967, 7.6, 4, "username"),
|
||||
(7.733, 8.367, 5, "username"),
|
||||
(8.667, 9.133, 6, "username"),
|
||||
(9.733, 10.667, 7, "username"),
|
||||
(11.6, 12.033, 8, "@everyone"),
|
||||
(12.067, 13.0, 9, "@everyone"),
|
||||
(13.033, 14.135, 10, "@everyone"),
|
||||
]
|
||||
|
||||
# Build drawtext filters
|
||||
drawtext_filters = []
|
||||
for start, end, pos_id, text_type in text_entries:
|
||||
x_coord, y_coord = positions[pos_id]
|
||||
|
||||
# Determine actual text content
|
||||
text_content = f"@{username}" if text_type == "username" else text_type
|
||||
|
||||
x = f"{x_coord} - text_w/2"
|
||||
y = f"{y_coord} - text_h/2"
|
||||
|
||||
filter_str = (
|
||||
f"drawtext=text='{text_content}':"
|
||||
f"fontfile='{font_path}':"
|
||||
f"fontcolor=black:fontsize=30:x={x}:y={y}:"
|
||||
f"enable='between(t,{start},{end})'"
|
||||
)
|
||||
drawtext_filters.append(filter_str)
|
||||
|
||||
vf_string = ",".join(drawtext_filters)
|
||||
|
||||
ffmpeg_command = [
|
||||
"ffmpeg",
|
||||
"-i", base_video_path,
|
||||
"-vf", vf_string,
|
||||
"-codec:a", "copy",
|
||||
output_path
|
||||
]
|
||||
|
||||
try:
|
||||
subprocess.run(ffmpeg_command, check=True)
|
||||
print("✅ Video processed successfully with username overlays.")
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"⚠️ FFmpeg error: {e}")
|
||||
|
||||
async def detect_and_react_to_kindness(message, after_reply=False):
|
||||
if message.id in kindness_reacted_messages:
|
||||
return # Already reacted — skip
|
||||
|
||||
content = message.content.lower()
|
||||
|
||||
emoji = random.choice(HEART_REACTIONS)
|
||||
|
||||
# 1. Keyword-based detection
|
||||
if any(keyword in content for keyword in KINDNESS_KEYWORDS):
|
||||
try:
|
||||
await message.add_reaction(emoji)
|
||||
kindness_reacted_messages.add(message.id)
|
||||
message.kindness_reacted = True # Mark as done
|
||||
print("✅ Kindness detected via keywords. Reacted immediately.")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error adding reaction: {e}")
|
||||
return
|
||||
|
||||
# 2. If not after_reply, defer model-based check
|
||||
if not after_reply:
|
||||
print("🗝️ No kindness via keywords. Deferring...")
|
||||
return
|
||||
|
||||
# 3. Model-based detection
|
||||
try:
|
||||
prompt = (
|
||||
"The following message was sent to Miku the bot. "
|
||||
"Does it sound like the user is being kind or affectionate toward Miku? "
|
||||
"Answer with 'yes' or 'no' only.\n\n"
|
||||
f"Message: \"{message.content}\""
|
||||
)
|
||||
result = await query_ollama(prompt, user_id="kindness-check")
|
||||
|
||||
if result.strip().lower().startswith("yes"):
|
||||
await message.add_reaction(emoji)
|
||||
kindness_reacted_messages.add(message.id)
|
||||
print("✅ Kindness detected via model. Reacted.")
|
||||
else:
|
||||
print("🧊 No kindness detected.")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error during kindness analysis: {e}")
|
||||
|
||||
@client.event
|
||||
async def on_ready():
|
||||
print(f'🎤 MikuBot connected as {client.user}')
|
||||
|
||||
# Change mood every 1 hour
|
||||
rotate_mood.start()
|
||||
|
||||
# Schedule the weekly task (Monday 07:30)
|
||||
scheduler.add_job(send_monday_video, 'cron', day_of_week='mon', hour=7, minute=30)
|
||||
|
||||
# Schedule first bedtime reminder
|
||||
schedule_random_bedtime()
|
||||
# Reschedule every midnight
|
||||
scheduler.add_job(schedule_random_bedtime, 'cron', hour=0, minute=0)
|
||||
#scheduler.add_job(send_bedtime_reminder, 'cron', hour=12, minute=22)
|
||||
|
||||
scheduler.start()
|
||||
|
||||
@client.event
|
||||
async def on_message(message):
|
||||
if message.author == client.user:
|
||||
return
|
||||
|
||||
if message.content.strip().lower() == "miku, rape this nigga balls" and message.reference:
|
||||
async with message.channel.typing():
|
||||
# Get replied-to user
|
||||
try:
|
||||
replied_msg = await message.channel.fetch_message(message.reference.message_id)
|
||||
target_username = replied_msg.author.display_name
|
||||
|
||||
# Prepare video
|
||||
base_video = "MikuMikuBeam.mp4"
|
||||
output_video = f"/tmp/video_{''.join(random.choices(string.ascii_letters, k=5))}.mp4"
|
||||
|
||||
await overlay_username_with_ffmpeg(base_video, output_video, target_username)
|
||||
|
||||
caption = f"Here you go, @{target_username}! 🌟"
|
||||
#await message.channel.send(content=caption, file=discord.File(output_video))
|
||||
await replied_msg.reply(file=discord.File(output_video))
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error processing video: {e}")
|
||||
await message.channel.send("Sorry, something went wrong while generating the video.")
|
||||
return
|
||||
|
||||
|
||||
text = message.content.strip()
|
||||
|
||||
global CURRENT_MOOD
|
||||
|
||||
if await is_miku_addressed(message):
|
||||
|
||||
prompt = text # No cleanup — keep it raw
|
||||
|
||||
# 1st kindness check with just keywords
|
||||
if CURRENT_MOOD not in ["angry", "irritated"]:
|
||||
await detect_and_react_to_kindness(message)
|
||||
|
||||
async with message.channel.typing():
|
||||
# If message has an image attachment
|
||||
if message.attachments:
|
||||
for attachment in message.attachments:
|
||||
if any(attachment.filename.lower().endswith(ext) for ext in [".jpg", ".jpeg", ".png", ".webp"]):
|
||||
|
||||
base64_img = await download_and_encode_image(attachment.url)
|
||||
if not base64_img:
|
||||
await message.channel.send("I couldn't load the image, sorry!")
|
||||
return
|
||||
# Analyze image (objective description)
|
||||
qwen_description = await analyze_image_with_qwen(base64_img)
|
||||
miku_reply = await rephrase_as_miku(qwen_description, prompt)
|
||||
|
||||
await message.channel.send(miku_reply)
|
||||
return
|
||||
|
||||
# If message is just a prompt, no image
|
||||
response = await query_ollama(prompt, user_id=str(message.author.id))
|
||||
|
||||
await message.channel.send(response)
|
||||
|
||||
# 2nd kindness check (only if no keywords detected)
|
||||
if CURRENT_MOOD not in ["angry", "irritated"]:
|
||||
await detect_and_react_to_kindness(message, after_reply=True)
|
||||
|
||||
if message.content.lower().strip() == "!reset":
|
||||
conversation_history[str(message.author.id)].clear()
|
||||
await message.channel.send("Okay! Memory reset for you~ ✨")
|
||||
|
||||
# Manual Monday test command
|
||||
if message.content.lower().strip() == "!monday":
|
||||
await send_monday_video()
|
||||
#await message.channel.send("✅ Monday message sent (or attempted). Check logs.")
|
||||
return
|
||||
|
||||
if message.content.startswith("!miku mood "):
|
||||
new_mood = message.content.split("!miku mood ")[1].strip().lower()
|
||||
path = os.path.join("moods", f"{new_mood}.txt")
|
||||
if os.path.exists(path):
|
||||
CURRENT_MOOD = load_mood_description(new_mood)
|
||||
await message.channel.send(f"🌈 Miku's mood has been set to **{new_mood}**!")
|
||||
else:
|
||||
await message.channel.send("⚠️ I don't recognize that mood. Try again with a valid one.")
|
||||
return
|
||||
|
||||
if message.content.strip().lower() == "!miku mood-reset":
|
||||
CURRENT_MOOD = load_mood_description("neutral")
|
||||
await message.channel.send("🔄 Miku’s mood has been reset to **neutral**.")
|
||||
return
|
||||
|
||||
if message.content.strip().lower() == "!miku mood-check":
|
||||
await message.channel.send(f"☑️ Miku’s mood is currently {CURRENT_MOOD}.")
|
||||
|
||||
if AUTO_MOOD and 'response' in locals():
|
||||
detected = detect_mood_shift(response)
|
||||
if detected and detected != CURRENT_MOOD:
|
||||
CURRENT_MOOD = load_mood_description(detected)
|
||||
print(f"🔄 Auto-updated mood to: {detected}")
|
||||
|
||||
client.run(DISCORD_BOT_TOKEN)
|
||||
Reference in New Issue
Block a user