Merge pull request #3 from ctih1/main
This commit is contained in:
WhatDidYouExpect
GitHub
commit
cda46777a7
2 changed files with 116 additions and 72 deletions
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@ -9,4 +9,4 @@ by PowerPCFan
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by expect
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[TensorFlow integration](https://github.com/WhatDidYouExpect/goober/blob/main/customcommands/tf.py)
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by SuperSilly2 (requires Python 3.11, tensorflow-metal/tensorflow-gpu and tensorflow/tensorflow-macos)
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by SuperSilly2 (requires Python 3.7 - 3.10, tensorflow-metal/tensorflow-gpu and tensorflow/tensorflow-macos)
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@ -6,39 +6,71 @@ import numpy as np
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import json
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from time import strftime, localtime
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import pickle
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import functools
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import re
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from discord import app_commands
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Embedding, LSTM, Dense
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from tensorflow.keras.models import load_model
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from tensorflow.keras.backend import clear_session
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import time
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import asyncio
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ready: bool = True
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MODEL_MATCH_STRING = "[0-9]{2}_[0-9]{2}_[0-9]{4}-[0-9]{2}_[0-9]{2}"
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try:
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tf.config.optimizer.set_jit(False)
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import tensorflow as tf
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from tensorflow import keras
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from keras.preprocessing.text import Tokenizer
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from keras_preprocessing.sequence import pad_sequences
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from keras.models import Sequential
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from keras.layers import Embedding, LSTM, Dense
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from keras.models import load_model
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from keras.backend import clear_session
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tf.config.optimizer.set_jit(True)
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except ImportError:
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print("ERROR: Failed to import TensorFlow.")
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print("ERROR: Failed to import Tensorflow. Here is a list of required dependencies:",(
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"tensorflow==2.10.0"
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"(for Nvidia users: tensorflow-gpu==2.10.0)"
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"(for macOS: tensorflow-metal==0.6.0, tensorflow-macos==2.10.0)"
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"numpy~=1.23"
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))
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ready = False
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class TFCallback(keras.callbacks.Callback):
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def __init__(self,bot, progress_embed:discord.Embed, message):
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self.embed:discord.Embed = progress_embed
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self.bot:commands.Bot = bot
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self.message = message
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self.times:List[int] = [time.time()]
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def on_train_begin(self, logs=None):
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pass
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async def send_message(self,message:str, description:str, **kwargs):
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if "epoch" in kwargs:
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self.times.append(time.time())
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average_epoch_time:int = np.average(np.diff(np.array(self.times)))
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description = f"ETA: {round(average_epoch_time)}s"
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self.embed.add_field(name=f"<t:{round(time.time())}:t> - {message}",value=description,inline=False)
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await self.message.edit(embed=self.embed)
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def on_train_end(self,logs=None):
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self.bot.loop.create_task(self.send_message("Training stopped", "training has been stopped."))
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def on_epoch_begin(self, epoch, logs=None):
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self.bot.loop.create_task(self.send_message(f"Starting epoch {epoch}","This might take a while", epoch=True))
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def on_epoch_end(self, epoch, logs=None):
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self.bot.loop.create_task(self.send_message(f"Epoch {epoch} ended",f"Accuracy: {round(logs.get('accuracy',0.0),4)}"))
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class Ai:
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def __init__(self):
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model_path = settings.get("model_path")
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if model_path:
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self.__load_model(model_path)
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self.is_loaded = model_path is not None
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self.batch_size = 32
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self.batch_size = 64
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def get_model_name_from_path(self,path:str):
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print(path)
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match:re.Match = re.search(MODEL_MATCH_STRING, path)
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print(match.start)
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return path[match.start():][:match.end()]
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def generate_model_name(self) -> str:
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@ -84,7 +116,7 @@ class Ai:
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self.tokenizer = Tokenizer()
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with open("memory.json","r") as f:
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self.tokenizer.fit_on_texts(json.load(f))
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self.tokenizer.fit_on_sequences(json.load(f))
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self.is_loaded = True
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def reload_model(self):
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@ -92,7 +124,11 @@ class Ai:
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model_path:str = settings.get("model_path")
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if model_path:
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self.model = self.__load_model(model_path)
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self.is_loaded = True
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async def run_async(self,func,bot,*args,**kwargs):
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func = functools.partial(func,*args,**kwargs)
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return await bot.loop.run_in_executor(None,func)
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class Learning(Ai):
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def __init__(self):
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@ -113,7 +149,8 @@ class Learning(Ai):
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return x,y, tokenizer
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def create_model(self,memory: List[str], iters:int=2):
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def create_model(self,memory: list, iters:int=2):
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memory = memory[:2000]
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X,y,tokenizer = self.__generate_labels_and_inputs(memory)
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maxlen:int = max([len(x) for x in X])
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x_pad = pad_sequences(X, maxlen=maxlen, padding="pre")
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@ -126,8 +163,10 @@ class Learning(Ai):
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model.add(Dense(VOCAB_SIZE, activation="softmax"))
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model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
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history = model.fit(x_pad, y, epochs=iters, batch_size=32)
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history = model.fit(x_pad, y, epochs=iters, batch_size=64, callbacks=[tf_callback])
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self.save_model(model, tokenizer, history)
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return
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def add_training(self,memory: List[str], iters:int=2):
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tokenizer_path = os.path.join(settings.get("model_path"),"tokenizer.pkl")
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@ -140,8 +179,9 @@ class Learning(Ai):
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x_pad = pad_sequences(X, maxlen=maxlen, padding="pre")
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y = np.array(y)
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history = self.model.fit(x_pad,y, epochs=iters, validation_data=(x_pad,y), batch_size=64) # Ideally, validation data would be separate from the actual data
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history = self.model.fit(x_pad,y, epochs=iters, validation_data=(x_pad,y), batch_size=64, callbacks=[tf_callback]) # Ideally, validation data would be seperate from the actual data
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self.save_model(self.model,tokenizer,history,self.get_model_name_from_path(settings.get("model_path")))
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return
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class Generation(Ai):
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def __init__(self):
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@ -169,16 +209,16 @@ class Generation(Ai):
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VOCAB_SIZE = 100_000
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SETTINGS_TYPE = TypedDict("SETTINGS_TYPE", {
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"model_path":str, # path to the base folder of the model, aka .../models/05-01-2025-22_31/
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"tokenizer_path":str,
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})
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tf_callback:TFCallback
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model_dropdown_items = []
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settings: SETTINGS_TYPE = {}
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target_message:int
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learning:Learning
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generation: Generation
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@ -233,64 +273,68 @@ class DropdownView(discord.ui.View):
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class Tf(commands.Cog):
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@staticmethod
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def needs_ready(func):
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def inner(args:tuple, kwargs:dict):
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if not ready:
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raise AttributeError("Not ready!")
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a = func(*args, **kwargs)
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return a
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return inner
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def __init__(self,bot):
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global learning, generation
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global ready
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global learning, generation, ready
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os.makedirs(os.path.join(".","models"),exist_ok=True)
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Settings().load()
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self.bot = bot
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learning = Learning()
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generation = Generation()
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@commands.command()
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async def start(self,ctx):
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await ctx.defer()
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await ctx.send("hi")
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@app_commands.command(name="start", description="Starts the bot")
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async def start(self, interaction: discord.Interaction):
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await interaction.response.send_message("hi")
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@commands.command()
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async def generate(self,ctx,seed:str,word_amount:int=5):
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await ctx.defer()
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await ctx.send(generation.generate_sentence(word_amount,seed))
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@app_commands.command(name="generate", description="Generates a sentence")
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async def generate(self, interaction: discord.Interaction, seed: str, word_amount: int = 5):
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await interaction.response.defer()
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sentence = generation.generate_sentence(word_amount, seed)
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await interaction.followup.send(sentence)
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@app_commands.command(name="create", description="Trains the model with memory")
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async def create(self, interaction: discord.Interaction):
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await interaction.response.defer()
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@commands.command()
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async def create(self,ctx:commands.Context, epochs:int=3):
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global tf_callback
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await ctx.defer()
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with open("memory.json","r") as f:
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memory:List[str] = json.load(f)
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learning.create_model(memory) # TODO: CHANGE
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await interaction.followup.send("Trained successfully!")
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await ctx.send("Initializing tensorflow")
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embed = discord.Embed(title="Creating a model...", description="Progress of creating a model")
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embed.set_footer(text="Note: Progress tracking might report delayed / wrong data, since the function is run asynchronously")
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target_message:discord.Message = await ctx.send(embed=embed)
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@app_commands.command(name="train", description="Trains the model further with memory")
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async def train(self, interaction: discord.Interaction):
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await interaction.response.defer()
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tf_callback = TFCallback(self.bot,embed,target_message)
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await learning.run_async(learning.create_model,self.bot,memory,epochs)
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embed = target_message.embeds[0]
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embed.add_field(name=f"<t:{round(time.time())}:t> Finished",value="Model saved.")
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await target_message.edit(embed=embed)
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@commands.command()
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async def train(self,ctx, epochs:int=2):
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global tf_callback
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await ctx.defer()
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with open("memory.json","r") as f:
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memory:List[str] = json.load(f)
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learning.add_training(memory, 2)
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await interaction.followup.send("Finished training!")
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@app_commands.command(name="change", description="Change the model")
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async def change(self, interaction: discord.Interaction, model: str = None):
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embed = discord.Embed(title="Training model...", description="Progress of training model")
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target_message = await ctx.send(embed=embed)
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tf_callback = TFCallback(self.bot,embed,target_message)
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await learning.run_async(learning.add_training,self.bot,memory,epochs)
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await ctx.send("Finished!")
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@commands.command()
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async def change(self,ctx,model:str=None):
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embed = discord.Embed(title="Change model",description="Which model would you like to use?")
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if model is None:
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models:List[str] = os.listdir(os.path.join(".","models"))
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models = [folder for folder in models if re.match(MODEL_MATCH_STRING,folder)]
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if len(models) == 0:
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models = ["No models available."]
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await interaction.response.send_message(embed=embed, view=DropdownView(90, models))
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await ctx.send(embed=embed,view=DropdownView(90,models))
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learning.reload_model()
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generation.reload_model()
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async def setup(bot):
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await bot.add_cog(Tf(bot))
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