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why did they call the song rule #34 if it has nothing to do with rule34

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WhatDidYouExpect 2025-07-18 00:49:04 +02:00
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commit dec83f1513
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3
assets/cogs/README.md
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@ -8,9 +8,6 @@ by PowerPCFan
[Cog Manager](https://github.com/WhatDidYouExpect/goober/blob/main/cogs/cogmanager.py)
by expect
[TensorFlow integration](https://github.com/WhatDidYouExpect/goober/blob/main/cogs/tf.py)
by SuperSilly2 (requires Python 3.7 - 3.10, tensorflow-metal/tensorflow-gpu and tensorflow/tensorflow-macos)
[Web Scraper](https://raw.githubusercontent.com/WhatDidYouExpect/goober/refs/heads/main/cogs/webscraper.py)
by expect (requires goober version 0.11.7.2 or higher)

155
assets/cogs/tf.py.disabled
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@ -1,155 +0,0 @@
import discord
from discord.ext import commands
import os
import numpy as np
import json
import pickle
import functools
import re
import time
import asyncio
ready = True
MODEL_MATCH_STRING = r"[0-9]{2}_[0-9]{2}_[0-9]{4}-[0-9]{2}_[0-9]{2}"
try:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Embedding, LSTM, Dense
from tensorflow.keras.backend import clear_session
if tf.config.list_physical_devices('GPU'):
print("Using GPU acceleration")
elif tf.config.list_physical_devices('Metal'):
print("Using Metal for macOS acceleration")
except ImportError:
print("ERROR: Failed to import TensorFlow. Ensure you have the correct dependencies:")
print("tensorflow>=2.15.0")
print("For macOS (Apple Silicon): tensorflow-metal")
ready = False
class TFCallback(keras.callbacks.Callback):
def __init__(self, bot, progress_embed: discord.Embed, message):
self.embed = progress_embed
self.bot = bot
self.message = message
self.times = [time.time()]
async def send_message(self, message: str, description: str, **kwargs):
if "epoch" in kwargs:
self.times.append(time.time())
avg_epoch_time = np.mean(np.diff(self.times))
description = f"ETA: {round(avg_epoch_time)}s"
self.embed.add_field(name=f"<t:{round(time.time())}:t> - {message}", value=description, inline=False)
await self.message.edit(embed=self.embed)
def on_train_end(self, logs=None):
self.bot.loop.create_task(self.send_message("Training stopped", "Training has been stopped."))
def on_epoch_begin(self, epoch, logs=None):
self.bot.loop.create_task(self.send_message(f"Starting epoch {epoch}", "This might take a while", epoch=True))
def on_epoch_end(self, epoch, logs=None):
self.bot.loop.create_task(self.send_message(f"Epoch {epoch} ended", f"Accuracy: {round(logs.get('accuracy', 0.0), 4)}"))
class Ai:
def __init__(self):
model_path = settings.get("model_path")
if model_path:
self.__load_model(model_path)
self.is_loaded = model_path is not None
self.batch_size = 64
def generate_model_name(self):
return time.strftime('%d_%m_%Y-%H_%M', time.localtime())
def __load_model(self, model_path):
clear_session()
self.model = load_model(os.path.join(model_path, "model.h5"))
model_name = os.path.basename(model_path)
try:
with open(os.path.join(model_path, "tokenizer.pkl"), "rb") as f:
self.tokenizer = pickle.load(f)
except FileNotFoundError:
print("Failed to load tokenizer, using default.")
self.tokenizer = Tokenizer()
with open("memory.json", "r") as f:
self.tokenizer.fit_on_texts(json.load(f))
self.is_loaded = True
def reload_model(self):
clear_session()
model_path = settings.get("model_path")
if model_path:
self.__load_model(model_path)
self.is_loaded = True
async def run_async(self, func, bot, *args, **kwargs):
return await bot.loop.run_in_executor(None, functools.partial(func, *args, **kwargs))
class Learning(Ai):
def create_model(self, memory, epochs=2):
memory = memory[:2000]
tokenizer = Tokenizer()
tokenizer.fit_on_texts(memory)
sequences = tokenizer.texts_to_sequences(memory)
X, y = [], []
for seq in sequences:
for i in range(1, len(seq)):
X.append(seq[:i])
y.append(seq[i])
maxlen = max(map(len, X))
X = pad_sequences(X, maxlen=maxlen, padding="pre")
y = np.array(y)
model = Sequential([
Embedding(input_dim=VOCAB_SIZE, output_dim=128, input_length=maxlen),
LSTM(64),
Dense(VOCAB_SIZE, activation="softmax")
])
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
history = model.fit(X, y, epochs=epochs, batch_size=64, callbacks=[tf_callback])
self.save_model(model, tokenizer, history)
def save_model(self, model, tokenizer, history, name=None):
name = name or self.generate_model_name()
model_dir = os.path.join("models", name)
os.makedirs(model_dir, exist_ok=True)
with open(os.path.join(model_dir, "info.json"), "w") as f:
json.dump(history.history, f)
with open(os.path.join(model_dir, "tokenizer.pkl"), "wb") as f:
pickle.dump(tokenizer, f)
model.save(os.path.join(model_dir, "model.h5"))
class Generation(Ai):
def generate_sentence(self, word_amount, seed):
if not self.is_loaded:
return False
for _ in range(word_amount):
token_list = self.tokenizer.texts_to_sequences([seed])[0]
token_list = pad_sequences([token_list], maxlen=self.model.input_shape[1], padding="pre")
predicted_word_index = np.argmax(self.model.predict(token_list, verbose=0), axis=-1)[0]
output_word = next((w for w, i in self.tokenizer.word_index.items() if i == predicted_word_index), "")
seed += " " + output_word
return seed
VOCAB_SIZE = 100_000
settings = {}
learning = Learning()
generation = Generation()
tf_callback = None
async def setup(bot):
await bot.add_cog(Tf(bot))