first commit

LICENSE

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+MIT License
+
+Copyright (c) 2025 Dairo Carrasquilla
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# 🎰 Baloto AI - Lottery Prediction System
+
+`Baloto 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.
+
+⚠️ 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.
+
+---
+
+## 🚀 Getting Started
+
+### 📁 Requisitos
+
+* Python 3.8+
+* TensorFlow 2.x
+* NumPy
+* [art](https://pypi.org/project/art/) (para generar banners ASCII)
+
+### 📦 Instalación
+
+```bash
+pip install -r requirements.txt
+```
+
+### 📄 Formato del archivo `data.txt`
+
+Debes tener un archivo llamado `data.txt` en el mismo directorio, con este formato:
+
+```
+5,12,18,23,35,4
+8,10,14,21,39,12
+...
+```
+
+* Las primeras 5 columnas son los números principales.
+* La sexta columna es el número **bonus**.
+
+---
+
+## ⚙️ Ejecución
+
+```bash
+python baloto_ai.py
+```
+
+El script:
+
+1. Limpia la consola.
+2. Muestra una pantalla de bienvenida.
+3. Carga y valida los datos.
+4. Construye y entrena un modelo LSTM multi-output.
+5. Muestra una barra de progreso animada por época.
+6. Genera una predicción de una nueva combinación de números.
+
+---
+
+## 📌 Disclaimer
+
+Este proyecto es **puramente educativo** y no tiene ninguna capacidad mágica para adivinar resultados de lotería reales.
+
+* No está afiliado a ningún sistema de lotería oficial.
+* No garantiza ningún tipo de premio.
+* Usar esto para apuestas reales es bajo tu **propio riesgo** (y probablemente mala idea).
+
+---
+
+## 🤖 Créditos
+
+Desarrollado por Dairo Carrasquilla.
+Inspirado en la idea absurda pero divertida de predecir el azar con redes neuronales.

baloto_ai.py

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+from tensorflow import keras
+from keras import layers
+from art import text2art
+import tensorflow as tf
+import numpy as np
+import shutil
+import socket
+import sys
+
+# Bloquear el acceso a Internet
+socket.socket = lambda *args, **kwargs: (_ for _ in ()).throw(Exception("Internet access is disabled"))
+
+# === Callback Personalizado para Salida Estilizada de Entrenamiento ===
+class CustomTrainingCallback(tf.keras.callbacks.Callback):
+    def __init__(self):
+        super().__init__()
+        self.total_epochs = None
+        self.bar_length = 40
+
+    def on_train_begin(self, logs=None):
+        self.total_epochs = self.params['epochs']
+        print("\033[1;36m" + "=" * 60 + "\033[0m")
+        print("\033[1;35mTraining Progress:\033[0m")
+
+    def on_epoch_end(self, epoch, logs=None):
+        current = epoch + 1
+        percent = current / self.total_epochs
+        filled_len = int(self.bar_length * percent)
+        bar = '█' * filled_len + '·' * (self.bar_length - filled_len)
+        sys.stdout.write(f"\r\033[1;34m[Epoch {current}/{self.total_epochs}] [{bar}] {percent*100:.1f}%\033[0m")
+        sys.stdout.flush()
+
+        if current == self.total_epochs:
+            print("\n\033[1;36m" + "=" * 60 + "\033[0m\n")
+
+
+# === Funciones de Interfaz ===
+def print_banner():
+    print("\033[1;34m" + text2art("Baloto AI", font="block"))
+    print("\033[1;33m" + "Lottery Prediction Artificial Intelligence".center(60) + "\033[0m")
+    print("\033[1;36m" + "=" * 60 + "\033[0m")
+
+def print_status(message):
+    print(f"\033[1;34m[•]\033[0m \033[1;37m{message}\033[0m")
+
+def print_intro():
+    print_banner()
+    print_status("Starting LotteryAI Prediction System...")
+    print("\033[1;36m" + "=" * 60 + "\033[0m")
+
+# === Funciones de Entrenamiento ===
+def load_data():
+    print_status("Loading training data...")
+    if not tf.io.gfile.exists('data.txt'):
+        raise FileNotFoundError("data.txt not found")
+
+    data = np.genfromtxt('data.txt', delimiter=',', dtype=int)
+    if data.shape[1] != 6:
+        raise ValueError("Each row in data.txt must have exactly 6 numbers (5 regular + 1 bonus)")
+
+    regular_numbers = data[:, :5]
+    bonus_numbers = data[:, 5]
+
+    train_size = int(0.8 * len(data))
+
+    x_train = regular_numbers[:train_size]
+    y_train_main = [regular_numbers[:train_size][:, i] for i in range(5)]
+    y_train_bonus = bonus_numbers[:train_size]
+
+    x_val = regular_numbers[train_size:]
+    y_val_main = [regular_numbers[train_size:][:, i] for i in range(5)]
+    y_val_bonus = bonus_numbers[train_size:]
+
+    return (x_train, y_train_main, y_train_bonus), (x_val, y_val_main, y_val_bonus)
+
+def create_model():
+    print_status("Building neural network model...")
+    input_layer = layers.Input(shape=(5,))
+    x = layers.Embedding(input_dim=44, output_dim=32)(input_layer)
+    x = layers.LSTM(64)(x)
+
+    output_main = [layers.Dense(44, activation='softmax', name=f'main_output_{i}')(x) for i in range(5)]
+    output_bonus = layers.Dense(44, activation='softmax', name='bonus_output')(x)
+
+    model = keras.Model(inputs=input_layer, outputs=output_main + [output_bonus])
+
+    loss_dict = {f'main_output_{i}': 'sparse_categorical_crossentropy' for i in range(5)}
+    loss_dict['bonus_output'] = 'sparse_categorical_crossentropy'
+
+    metrics_dict = {f'main_output_{i}': 'accuracy' for i in range(5)}
+    metrics_dict['bonus_output'] = 'accuracy'
+
+    model.compile(
+        optimizer='adam',
+        loss=loss_dict,
+        metrics=metrics_dict
+    )
+    return model
+
+def train_model(model, x_train, y_train_main, y_train_bonus, x_val, y_val_main, y_val_bonus):
+    print_status("Training model (100 epochs)...\n")
+    train_targets = {f'main_output_{i}': y_train_main[i] for i in range(5)}
+    train_targets['bonus_output'] = y_train_bonus
+
+    val_targets = {f'main_output_{i}': y_val_main[i] for i in range(5)}
+    val_targets['bonus_output'] = y_val_bonus
+
+    callback = CustomTrainingCallback()
+
+    return model.fit(
+        x_train, train_targets,
+        validation_data=(x_val, val_targets),
+        epochs=100,
+        verbose=0,  # Desactiva la salida estándar
+        callbacks=[callback]
+    )
+
+def predict_numbers(model, x_val):
+    predictions = model.predict(x_val)
+    pred_main = np.array([np.argmax(p, axis=1) for p in predictions[:-1]]).T
+    pred_bonus = np.argmax(predictions[-1], axis=1)
+    return pred_main, pred_bonus
+
+def print_results(pred_main, pred_bonus):
+    print_status("Generating predictions...")
+    print("\033[1;36m" + "=" * 60 + "\033[0m")
+    print("\033[1;32m" + "🎯 PREDICTED NUMBERS 🎯".center(60) + "\033[0m")
+    print("\033[1;36m" + "-" * 60 + "\033[0m")
+    if pred_main.shape[0] > 0:
+        main_nums = ', '.join(map(str, np.sort(pred_main[0])))
+        print(f"\033[1;33mMain Numbers:\033[0m \033[1;37m{main_nums}\033[0m")
+        print(f"\033[1;33mBonus Number:\033[0m \033[1;37m{pred_bonus[0]}\033[0m")
+    else:
+        print("\033[1;31mNo predictions available\033[0m")
+    print("\033[1;36m" + "=" * 60 + "\033[0m")
+
+# === Ejecución Principal ===
+def main():
+    print("\x1b[H\x1b[2J\x1b[3J")  # Limpia la consola
+    print_intro()
+    try:
+        (x_train, y_train_main, y_train_bonus), (x_val, y_val_main, y_val_bonus) = load_data()
+        model = create_model()
+        train_model(model, x_train, y_train_main, y_train_bonus, x_val, y_val_main, y_val_bonus)
+        pred_main, pred_bonus = predict_numbers(model, x_val)
+        print_results(pred_main, pred_bonus)
+    except Exception as e:
+        print("\033[1;31m[!] Error:\033[0m", str(e))
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

data.txt

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+10,12,24,32,37,12
+3,21,31,33,40,14
+5,12,17,19,22,16
+6,23,32,34,4,14
+1,15,2,23,31,1
+1,16,18,2,25,15
+4,5,19,28,43,5
+2,9,22,23,25,5
+11,26,31,35,43,6
+24,34,35,36,4,5
+5,11,18,31,32,9
+18,32,34,36,38,13

requirements.txt

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+tensorflow>=2.10
+numpy>=1.22
+art>=5.9