assets/cogs/README.md

@@ -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)
 

assets/cogs/tf.py.disabled

@@ -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))

bot.py

@@ -41,8 +41,6 @@ from discord.ext import commands
 from discord import app_commands
 from discord import Colour, Embed, File, Interaction, Message
 from discord.abc import Messageable
-
-from better_profanity import profanity
 from discord.ext import commands
 
 from modules.volta.main import _, set_language
@@ -386,14 +384,11 @@ async def on_message(message: discord.Message) -> None:
         await bot.process_commands(message)
         return
 
-    if profanity.contains_profanity(message.content):
-        return
-
     if message.content:
         if not USERTRAIN_ENABLED:
             return
-        formatted_message: str = append_mentions_to_18digit_integer(message.content)
+        formatted_message: str = message.content
-        cleaned_message: str = preprocess_message(formatted_message)
+        cleaned_message: str = formatted_message
         if cleaned_message:
             memory.append(cleaned_message)
             message_metadata = {

modules/sentenceprocessing.py

@@ -61,20 +61,15 @@ async def send_message(ctx, message=None, embed=None, file=None, edit=False, mes
                 sent_message = await ctx.send(file=file)
         return sent_message
 
-def append_mentions_to_18digit_integer(message):
-    pattern = r'\b\d{18}\b'
-    return re.sub(pattern, lambda match: "", message)
-
 def preprocess_message(message):
-    message = append_mentions_to_18digit_integer(message)
+    message = message
     doc = nlp(message)
     tokens = [token.text for token in doc if token.is_alpha or token.is_digit]
     return " ".join(tokens)
 
 def improve_sentence_coherence(sentence):
-    return re.sub(r'\bi\b', 'I', sentence)
+    return ""
 
 def rephrase_for_coherence(sentence):
-    words = sentence.split()
+    coherent_sentence = sentence
-    coherent_sentence = " ".join(words)
     return coherent_sentence