299 lines
No EOL
10 KiB
Python
299 lines
No EOL
10 KiB
Python
import discord
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from discord.ext import commands
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import os
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from typing import List, TypedDict
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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 re
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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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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. 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 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 = 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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return strftime('%d_%m_%Y-%H_%M', localtime())
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def generate_model_abs_path(self, name:str):
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name = name or self.generate_model_name()
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return os.path.join(".","models",self.generate_model_name(),"model.h5")
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def generate_tokenizer_abs_path(self, name:str):
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name = name or self.generate_model_name()
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return os.path.join(".","models",name,"tokenizer.pkl")
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def generate_info_abs_path(self,name:str):
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name = name or self.generate_model_name()
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return os.path.join(".","models",name,"info.json")
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def save_model(self,model, tokenizer, history, _name:str=None):
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name:str = _name or self.generate_model_name()
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os.makedirs(os.path.join(".","models",name), exist_ok=True)
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with open(self.generate_info_abs_path(name),"w") as f:
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json.dump(history.history,f)
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with open(self.generate_tokenizer_abs_path(name), "wb") as f:
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pickle.dump(tokenizer,f)
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model.save(self.generate_model_abs_path(name))
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def __load_model(self, model_path:str):
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clear_session()
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self.model = load_model(os.path.join(model_path,"model.h5"))
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model_name:str = self.get_model_name_from_path(model_path)
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try:
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with open(self.generate_tokenizer_abs_path(model_name),"rb") as f:
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self.tokenizer = pickle.load(f)
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except FileNotFoundError:
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print("Failed to load tokenizer for model... Using default")
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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_sequences(json.load(f))
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self.is_loaded = True
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def reload_model(self):
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clear_session()
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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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class Learning(Ai):
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def __init__(self):
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super().__init__()
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def __generate_labels_and_inputs(self,memory: List[str], tokenizer=None) -> tuple:
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if not tokenizer:
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tokenizer = Tokenizer()
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tokenizer.fit_on_texts(memory)
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sequences = tokenizer.texts_to_sequences(memory)
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x = []
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y = []
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for seq in sequences:
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for i in range(1, len(seq)):
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x.append(seq[:i])
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y.append(seq[i])
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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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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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y = np.array(y)
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model = Sequential()
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model.add(Embedding(input_dim=VOCAB_SIZE,output_dim=128,input_length=maxlen))
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model.add(LSTM(64))
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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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self.save_model(model, tokenizer, history)
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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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with open(tokenizer_path, "rb") as f:
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tokenizer = pickle.load(f)
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X,y,_ = self.__generate_labels_and_inputs(memory, tokenizer)
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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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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) # Idelaly, 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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class Generation(Ai):
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def __init__(self):
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super().__init__()
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def generate_sentence(self, word_amount:int, seed:str):
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if not self.is_loaded:
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return False
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for _ in range(word_amount):
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token_list = self.tokenizer.texts_to_sequences([seed])[0]
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token_list = pad_sequences([token_list], maxlen=self.model.layers[0].input_shape[1], padding="pre")
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output_word = "" # Sometimes model fails to predict the word, so using a fallback
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predicted_probs = self.model.predict(token_list, verbose=0)
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predicted_word_index = np.argmax(predicted_probs, axis=-1)[0]
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for word, index in self.tokenizer.word_index.items():
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if index == predicted_word_index:
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output_word = word
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break
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seed += " " + output_word
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return seed
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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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model_dropdown_items = []
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settings: SETTINGS_TYPE = {}
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learning:Learning
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generation: Generation
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class Settings:
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def __init__(self):
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self.settings_path:str = os.path.join(".","models","settings.json")
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def load(self):
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global settings
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try:
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with open(self.settings_path,"r") as f:
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settings = json.load(f)
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except FileNotFoundError:
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with open(self.settings_path,"w") as f:
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json.dump({},f)
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def change_model(self,new_model_base_path:str):
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global settings
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new_model_path = os.path.join(".","models",new_model_base_path)
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with open(self.settings_path,"r") as f:
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settings = json.load(f)
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settings["model_path"] = new_model_path
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with open(self.settings_path, "w") as f:
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json.dump(settings,f)
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class Dropdown(discord.ui.Select):
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def __init__(self, items:List[str]):
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global model_dropdown_items
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model_dropdown_items = []
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for item in items:
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model_dropdown_items.append(
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discord.SelectOption(label=item)
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)
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super().__init__(placeholder="Select model", options=model_dropdown_items)
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async def callback(self, interaction: discord.Interaction):
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if int(interaction.user.id) != int(os.getenv("ownerid")):
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await interaction.message.channel.send("KILL YOURSELF")
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Settings().change_model(self.values[0])
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await interaction.message.channel.send(f"Changed model to {self.values[0]}")
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class DropdownView(discord.ui.View):
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def __init__(self, timeout, models):
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super().__init__(timeout=timeout)
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self.add_item(Dropdown(models))
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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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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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@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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@commands.command()
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async def create(self,ctx):
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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 ctx.send("Trained succesfully!")
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@commands.command()
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async def train(self,ctx):
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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 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 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)) |