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21
LICENSE
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21
LICENSE
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MIT License
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Copyright (c) 2025 Dairo Carrasquilla
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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69
README.md
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69
README.md
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# 🎰 Miloto AI - Lottery Prediction System
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`Miloto AI` es un sistema de predicción de números de lotería basado en redes neuronales. Usa modelos LSTM con múltiples salidas para intentar predecir combinaciones ganadoras de un archivo histórico de resultados.
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⚠️ Este proyecto **no garantiza resultados reales ni premios**, pero es una excelente herramienta de aprendizaje sobre redes neuronales, modelos multi-salida y entrenamiento de modelos con Keras/TensorFlow.
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---
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## 🚀 Getting Started
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### 📁 Requisitos
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* Python 3.8+
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* TensorFlow 2.x
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* NumPy
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* [art](https://pypi.org/project/art/) (para generar banners ASCII)
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### 📦 Instalación
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```bash
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pip install -r requirements.txt
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```
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### 📄 Formato del archivo `data.txt`
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Debes tener un archivo llamado `data.txt` en el mismo directorio, con este formato:
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```
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5,12,18,23,35
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8,10,14,21,39
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...
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```
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Cada línea representa una combinación de números ganadores, separados por comas. Asegúrate de que el archivo no tenga encabezados ni otros caracteres.
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El formato debe ser **exactamente** así para que el script funcione correctamente.
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Si no tienes un archivo de datos, puedes crear uno manualmente o usar un generador de números aleatorios.
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---
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## ⚙️ Ejecución
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```bash
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python miloto_ai.py
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```
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El script:
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1. Limpia la consola.
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2. Muestra una pantalla de bienvenida.
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3. Carga y valida los datos.
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4. Construye y entrena un modelo LSTM multi-output.
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5. Muestra una barra de progreso animada por época.
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6. Genera una predicción de una nueva combinación de números.
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---
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## 📌 Disclaimer
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Este proyecto es **puramente educativo** y no tiene ninguna capacidad mágica para adivinar resultados de lotería reales.
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* No está afiliado a ningún sistema de lotería oficial.
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* No garantiza ningún tipo de premio.
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* Usar esto para apuestas reales es bajo tu **propio riesgo** (y probablemente mala idea).
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---
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## 🤖 Créditos
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Desarrollado por Dairo Carrasquilla.
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Inspirado en la idea absurda pero divertida de predecir el azar con redes neuronales.
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10
data.txt
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data.txt
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1,8,22,37,38
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5,15,17,21,25
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4,13,14,21,31
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13,23,26,30,36
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4,16,27,29,32
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2,5,13,17,26
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12,21,22,37,39
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10,20,28,29,38
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2,18,28,33,38
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7,8,10,29,31
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150
miloto_ai.py
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miloto_ai.py
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from tensorflow import keras
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from keras import layers
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from art import text2art
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import tensorflow as tf
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import numpy as np
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import socket
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import sys
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# Bloquear el acceso a Internet
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socket.socket = lambda *args, **kwargs: (_ for _ in ()).throw(Exception("Internet access is disabled"))
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# === Callback Personalizado para Salida Estilizada de Entrenamiento ===
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class CustomTrainingCallback(tf.keras.callbacks.Callback):
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def __init__(self):
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super().__init__()
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self.total_epochs = None
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self.bar_length = 40
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def on_train_begin(self, logs=None):
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self.total_epochs = self.params['epochs']
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print("\033[1;36m" + "=" * 60 + "\033[0m")
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print("\033[1;35mTraining Progress:\033[0m")
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def on_epoch_end(self, epoch, logs=None):
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current = epoch + 1
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percent = current / self.total_epochs
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filled_len = int(self.bar_length * percent)
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bar = '█' * filled_len + '·' * (self.bar_length - filled_len)
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sys.stdout.write(f"\r\033[1;34m[Epoch {current}/{self.total_epochs}] [{bar}] {percent*100:.1f}%\033[0m")
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sys.stdout.flush()
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if current == self.total_epochs:
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print("\n\033[1;36m" + "=" * 60 + "\033[0m\n")
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# === Funciones de Interfaz ===
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def print_banner():
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print("\033[1;34m" + text2art("Miloto AI", font="block"))
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print("\033[1;33m" + "Lottery Prediction Artificial Intelligence".center(60) + "\033[0m")
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print("\033[1;36m" + "=" * 60 + "\033[0m")
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def print_status(message):
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print(f"\033[1;34m[•]\033[0m \033[1;37m{message}\033[0m")
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def print_intro():
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print_banner()
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print_status("Starting LotteryAI Prediction System...")
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print("\033[1;36m" + "=" * 60 + "\033[0m")
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# === Funciones de Carga y Entrenamiento ===
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def load_data():
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try:
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if not tf.io.gfile.exists('data.txt'):
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raise FileNotFoundError("data.txt not found")
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data = np.genfromtxt('data.txt', delimiter=',', dtype=int)
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if data.size == 0:
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raise ValueError("data.txt is empty")
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data[data == -1] = 0
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train_size = int(0.8 * len(data))
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if train_size == 0:
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raise ValueError("Dataset too small to split")
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train_data = data[:train_size]
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val_data = data[train_size:]
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max_value = np.max(data)
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return train_data, val_data, max_value
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except Exception as e:
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print(f"\033[1;31mError loading data: {str(e)}\033[0m")
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sys.exit(1)
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def create_model(num_features, max_value):
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try:
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model = keras.Sequential([
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layers.Embedding(input_dim=max_value+1, output_dim=32),
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layers.LSTM(64, return_sequences=False),
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layers.Dense(num_features, activation='softmax')
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])
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model.compile(
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loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy']
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)
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return model
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except Exception as e:
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print(f"\033[1;31mError creating model: {str(e)}\033[0m")
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sys.exit(1)
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def train_model(model, train_data, val_data):
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try:
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callback = CustomTrainingCallback()
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history = model.fit(
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train_data,
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train_data,
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validation_data=(val_data, val_data),
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epochs=100,
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verbose=0, # Desactiva la salida estándar
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callbacks=[callback]
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)
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return history
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except Exception as e:
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print(f"\033[1;31mError training model: {str(e)}\033[0m")
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sys.exit(1)
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def predict_numbers(model, val_data, num_features):
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try:
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predictions = model.predict(val_data)
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indices = np.argsort(predictions, axis=1)[:, -num_features:]
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predicted_numbers = np.take_along_axis(val_data, indices, axis=1)
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return predicted_numbers
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except Exception as e:
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print(f"\033[1;31mError predicting numbers: {str(e)}\033[0m")
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sys.exit(1)
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def print_predicted_numbers(predicted_numbers):
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print_status("Generating predictions...")
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print("\033[1;36m" + "=" * 60 + "\033[0m")
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print("\033[1;32m" + "🎯 PREDICTED NUMBERS 🎯".center(60) + "\033[0m")
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print("\033[1;36m" + "-" * 60 + "\033[0m")
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if predicted_numbers.size > 0:
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print(f"\033[1;37m{', '.join(map(str, predicted_numbers[0]))}\033[0m")
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else:
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print("\033[1;31mNo predictions available\033[0m")
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print("\033[1;36m" + "=" * 60 + "\033[0m")
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def main():
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try:
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print("\x1b[H\x1b[2J\x1b[3J") # Limpia la consola
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print_intro()
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train_data, val_data, max_value = load_data()
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if train_data.ndim < 2:
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raise ValueError("Training data has invalid dimensions")
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num_features = train_data.shape[1]
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model = create_model(num_features, max_value)
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train_model(model, train_data, val_data)
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predicted_numbers = predict_numbers(model, val_data, num_features)
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print_predicted_numbers(predicted_numbers)
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except Exception as e:
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print("\033[1;31m[!] Error:\033[0m", str(e))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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3
requirements.txt
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3
requirements.txt
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tensorflow>=2.10
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numpy>=1.22
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art>=5.9
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