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added permission wrapper

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ctih1 2025-07-23 10:19:08 +03:00
parent f7042ed8a7
commit f186e079da
29 changed files with 860 additions and 788 deletions
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110
assets/cogs/tf.py
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@ -13,20 +13,22 @@ 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
import tensorflow as tf
import keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential, load_model
from keras.layers import Embedding, LSTM, Dense
from keras.backend import clear_session
if tf.config.list_physical_devices('GPU'):
if tf.config.list_physical_devices("GPU"):
print("Using GPU acceleration")
elif tf.config.list_physical_devices('Metal'):
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(
"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
@ -38,24 +40,39 @@ class TFCallback(keras.callbacks.Callback):
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)
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."))
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))
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)}"))
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):
@ -63,11 +80,11 @@ class Ai:
if model_path:
self.__load_model(model_path)
self.is_loaded = model_path is not None
self.batch_size = 64
self.batch_size = 64
def generate_model_name(self):
return time.strftime('%d_%m_%Y-%H_%M', time.localtime())
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"))
@ -81,7 +98,7 @@ class Ai:
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")
@ -90,9 +107,11 @@ class Ai:
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))
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]
@ -107,41 +126,58 @@ class Learning(Ai):
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"])
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), "")
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 = {}
@ -152,4 +188,4 @@ tf_callback = None
async def setup(bot):
await bot.add_cog(Tf(bot))
await bot.add_cog(Tf(bot))