AI-Discord-Bot/src/bot.py

# bot.py

import os
import discord
import yaml
from discord.ext import commands
from textwrap import wrap
from dotenv import load_dotenv
import random
import yaml
from scheduler import start_scheduler
from logger import setup_logger
logger = setup_logger("bot")

from ai import unload_model, load_model, get_current_model

dotenv_path = os.path.join(os.path.dirname(__file__), '..', '.env')
load_dotenv(dotenv_path)
logger.info(f"🔍 Loaded MODEL_NAME from .env: {os.getenv('MODEL_NAME')}")

MODEL_NAME = os.getenv("MODEL_NAME", "llama3:latest")
logger.info(f"🔍 Loaded MODEL_NAME from .env: {MODEL_NAME}")

# 🧽 Try to unload any currently loaded model
logger.info(f"🧹 Attempting to clear VRAM before loading {MODEL_NAME}...")
unload_model(MODEL_NAME)

# 🚀 Load target model from .env
if load_model(MODEL_NAME):
    logger.info(f"🚀 Model `{MODEL_NAME}` preloaded on startup.")
else:
    logger.warning(f"⚠️ Failed to preload model `{MODEL_NAME}`.")

logger.info(f"✅ Final model in use: {MODEL_NAME}")

from ai import get_ai_response, load_model
MODEL_NAME = os.getenv("MODEL_NAME", "llama3:latest")

if load_model(MODEL_NAME):
    logger.info(f"🚀 Model `{MODEL_NAME}` preloaded on startup.")
else:
    logger.warning(f"⚠️ Failed to preload model `{MODEL_NAME}`.")

logger.info(f"✅ Final model in use: {MODEL_NAME}")

from personality import apply_personality, set_persona
from discord.ext.commands import (
    cooldown,
    BucketType,
    CooldownMapping,
    CommandOnCooldown
)

base_dir = os.path.dirname(__file__)
settings_path = os.path.join(base_dir, "settings.yml")

with open(settings_path, "r", encoding="utf-8") as f:
    settings = yaml.safe_load(f)

ROAST_COOLDOWN_SECONDS = settings["cooldowns"]["roast"]
GLOBAL_COOLDOWN_SECONDS = settings["cooldowns"]["global"]
COOLDOWN_MSG_TEMPLATE = settings["messages"]["cooldown"]

TOKEN = os.getenv("DISCORD_TOKEN")
if not TOKEN:
    logger.error("❌ DISCORD_TOKEN not set in .env file.")
    raise SystemExit("DISCORD_TOKEN not set.")

intents = discord.Intents.default()
intents.message_content = True

bot = commands.Bot(command_prefix="!", intents=intents)

@bot.event
async def on_command_error(ctx, error):
    if isinstance(error, CommandOnCooldown):
        retry_secs = round(error.retry_after, 1)
        template = random.choice(COOLDOWN_MSG_TEMPLATE) if isinstance(COOLDOWN_MSG_TEMPLATE, list) else COOLDOWN_MSG_TEMPLATE
        msg = template.replace("{seconds}", str(retry_secs))

        logger.info(f"Command {ctx.command} on cooldown. Retry after {retry_secs} seconds.")
        await ctx.send(msg)
    else:
        raise error

# Global cooldown bucket
global_cooldown = CooldownMapping.from_cooldown(1, GLOBAL_COOLDOWN_SECONDS, BucketType.user)

@bot.check
async def global_command_cooldown(ctx):
    bucket = global_cooldown.get_bucket(ctx.message)
    retry_after = bucket.update_rate_limit()

    if retry_after:
        raise CommandOnCooldown(bucket, retry_after, BucketType.user)
    return True

@bot.command()
async def ping(ctx):
    await ctx.send("🏓 Pong!")

@bot.command()
async def chat(ctx, *, prompt):
    await ctx.send("🤖 Thinking...")
    reply = get_ai_response(prompt)
    MAX_DISCORD_MESSAGE_LENGTH = 2000

    # Split long replies into chunks that fit Discord limits
    chunks = wrap(reply, MAX_DISCORD_MESSAGE_LENGTH)

    # Log only if the response is being chunked
    if len(chunks) > 1:
        logger.warning(f"💬 Splitting response into {len(chunks)} chunks due to length.")

    for chunk in chunks:
        await ctx.send(chunk)

@bot.command()
async def setpersona(ctx, *, description):
    set_persona(description)
    await ctx.send("✅ Persona updated! New style will be used in replies.")

@bot.command(name='roast')
@cooldown(rate=1, per=ROAST_COOLDOWN_SECONDS, type=BucketType.user)
async def roast(ctx):
    # Get the mentioned user (or fallback to the author)
    target = ctx.message.mentions[0].mention if ctx.message.mentions else ctx.author.mention

    # Build the roast prompt
    prompt = f"Roast {target}. Be dramatic, insulting, and sarcastic. Speak in your usual chaotic RGB catgirl personality."

    # Get AI response
    response = get_ai_response(prompt)

    # Send the roast back to the channel
    await ctx.send(f"😼 {response}")

@bot.command(name="clearmodel")
async def clear_model(ctx):
    from ai import unload_model, get_current_model
    model = get_current_model()
    success = unload_model(model)
    msg = f"✅ Unloaded model: `{model}`" if success else f"❌ Failed to unload model: `{model}`"
    await ctx.send(msg)

@bot.command(name="model")
async def current_model(ctx):
    from ai import get_current_model
    model = get_current_model()
    await ctx.send(f"📦 Current model: `{model}`")

@bot.command(name="setmodel")
async def set_model(ctx, *, model_name):
    from ai import get_current_model, load_model, unload_model

    current_model = get_current_model()
    if model_name == current_model:
        return await ctx.send(f"⚠️ `{model_name}` is already active.")

    await ctx.send(f"🔄 Switching from `{current_model}` to `{model_name}`…")

    # 1) Soft-unload old model from VRAM only
    if unload_model(current_model):
        await ctx.send(f"🧽 Unloaded `{current_model}` from VRAM.")
    else:
        await ctx.send(f"⚠️ Couldn’t unload `{current_model}` (it may not have been loaded).")

    # 2) Load the new one
    if not load_model(model_name):
        return await ctx.send(f"❌ Failed to pull `{model_name}`. Make sure it’s in `ollama list`.")

    # 3) Update runtime AND .env on disk
    os.environ["MODEL_NAME"] = model_name
    env_path = os.path.join(os.path.dirname(__file__), '..', '.env')
    # Read and rewrite .env
    lines = []
    with open(env_path, 'r', encoding='utf-8') as f:
        for line in f:
            if line.startswith("MODEL_NAME="):
                lines.append(f"MODEL_NAME={model_name}\n")
            else:
                lines.append(line)
    with open(env_path, 'w', encoding='utf-8') as f:
        f.writelines(lines)

    await ctx.send(f"✅ Model switched to `{model_name}` and `.env` updated.")

@bot.command(name="models")
async def list_models(ctx):
    import requests
    from ai import TAGS_ENDPOINT

    try:
        resp = requests.get(TAGS_ENDPOINT)
        models = [m["name"] for m in resp.json().get("models", [])]
        if models:
            await ctx.send("🧠 Available models:\n" + "\n".join(f"- `{m}`" for m in models))
        else:
            await ctx.send("❌ No models found.")
    except Exception as e:
        await ctx.send(f"❌ Failed to fetch models: {e}")

@bot.event
async def on_ready():
    print(f"✅ Logged in as {bot.user.name}")
    logger.info(f"Logged in as {bot.user.name}")
    bot.loop.create_task(start_scheduler(bot))

bot.run(TOKEN)