Delete app.py

app.py

@@ -1,203 +0,0 @@
-import gradio as gr
-import pickle
-import numpy as np
-import pandas as pd
-import json
-import os
-
-print("=== INICIANDO UFC PREDICTOR ===")
-
-def safe_load_pickle(filepath, default_value=None):
-    """Cargar archivos pickle de forma segura"""
-    try:
-        with open(filepath, "rb") as f:
-            return pickle.load(f)
-    except Exception as e:
-        print(f"Error cargando {filepath}: {e}")
-        return default_value
-
-def safe_load_json(filepath, default_value=None):
-    """Cargar archivos JSON de forma segura"""
-    try:
-        with open(filepath, "r") as f:
-            return json.load(f)
-    except Exception as e:
-        print(f"Error cargando {filepath}: {e}")
-        return default_value
-
-# Cargar modelo y preprocesadores de forma segura
-print("Cargando modelo y componentes...")
-
-model = safe_load_pickle("ufc_best_model.pkl")
-scaler = safe_load_pickle("ufc_scaler.pkl") 
-imputer = safe_load_pickle("ufc_imputer.pkl")
-metadata = safe_load_json("ufc_model_metadata.json", {})
-ranges = safe_load_json("ufc_feature_ranges.json", {})
-
-# Verificar que todo se cargó correctamente
-components_loaded = all([model is not None, scaler is not None, imputer is not None])
-if not components_loaded:
-    print("❌ ERROR: No se pudieron cargar todos los componentes del modelo")
-    raise Exception("Fallo en la carga de componentes del modelo")
-
-print("✅ Todos los componentes cargados correctamente")
-
-if metadata:
-    print(f"Modelo: {metadata.get('best_model_selected', 'N/A')}")
-    print(f"Accuracy: {metadata.get('evaluation_metrics', {}).get('accuracy', 'N/A')}")
-
-# Obtener características del modelo
-feature_columns = metadata.get('feature_columns', [])
-if not feature_columns:
-    print("⚠️ Advertencia: No se encontraron feature_columns en metadata")
-
-print(f"Características del modelo: {len(feature_columns)}")
-
-def predict_ufc_fight(
-    fighter_1_kd, fighter_1_str, fighter_1_td, fighter_1_sub,
-    fighter_2_kd, fighter_2_str, fighter_2_td, fighter_2_sub,
-    round_num, weight_class, method_encoded
-):
-    """
-    Predice el resultado de una pelea UFC
-    """
-    try:
-        # Validar entradas básicas
-        if any(pd.isna(x) for x in [fighter_1_kd, fighter_1_str, fighter_2_kd, fighter_2_str]):
-            return {"Error": "Valores de entrada inválidos o faltantes"}
-        
-        # Calcular diferencias
-        kd_diff = fighter_1_kd - fighter_2_kd
-        str_diff = fighter_1_str - fighter_2_str
-        td_diff = fighter_1_td - fighter_2_td
-        sub_diff = fighter_1_sub - fighter_2_sub
-        
-        # Calcular precisiones (evitar división por cero)
-        fighter_1_accuracy = fighter_1_str / (fighter_1_str + 10) if fighter_1_str >= 0 else 0
-        fighter_2_accuracy = fighter_2_str / (fighter_2_str + 10) if fighter_2_str >= 0 else 0
-        
-        # Crear array de entrada con las características en el orden CORRECTO
-        input_features = []
-        
-        # Añadir características en el orden esperado por el modelo
-        for feature in feature_columns:
-            if feature == 'KD_diff':
-                input_features.append(kd_diff)
-            elif feature == 'STR_diff':
-                input_features.append(str_diff)
-            elif feature == 'TD_diff':
-                input_features.append(td_diff)
-            elif feature == 'SUB_diff':
-                input_features.append(sub_diff)
-            elif feature == 'Fighter_1_KD':
-                input_features.append(fighter_1_kd)
-            elif feature == 'Fighter_2_KD':
-                input_features.append(fighter_2_kd)
-            elif feature == 'Fighter_1_STR':
-                input_features.append(fighter_1_str)
-            elif feature == 'Fighter_2_STR':
-                input_features.append(fighter_2_str)
-            elif feature == 'Fighter_1_TD':
-                input_features.append(fighter_1_td)
-            elif feature == 'Fighter_2_TD':
-                input_features.append(fighter_2_td)
-            elif feature == 'Fighter_1_SUB':
-                input_features.append(fighter_1_sub)
-            elif feature == 'Fighter_2_SUB':
-                input_features.append(fighter_2_sub)
-            elif feature == 'Fighter_1_accuracy':
-                input_features.append(fighter_1_accuracy)
-            elif feature == 'Fighter_2_accuracy':
-                input_features.append(fighter_2_accuracy)
-            elif feature == 'Round':
-                input_features.append(round_num)
-            elif feature == 'Method_encoded':
-                input_features.append(method_encoded)
-            elif feature.startswith('weight_class_'):
-                # One-hot encoding para categorías de peso
-                category_name = feature.replace('weight_class_', '')
-                input_features.append(1 if category_name == weight_class else 0)
-            else:
-                # Característica no reconocida, usar 0
-                input_features.append(0)
-                print(f"⚠️ Caracter��stica no reconocida: {feature}")
-        
-        # Convertir a numpy array
-        input_array = np.array([input_features])
-        
-        # Aplicar preprocesamiento
-        input_imputed = imputer.transform(input_array)
-        input_scaled = scaler.transform(input_imputed)
-        
-        # Hacer predicción
-        prediction = model.predict(input_scaled)[0]
-        probability = model.predict_proba(input_scaled)[0]
-        
-        # Interpretar resultados
-        if prediction == 1:
-            winner = "Fighter 1"
-            confidence = probability[1]
-            color = "#FF6B6B"  # Rojo
-        else:
-            winner = "Fighter 2" 
-            confidence = probability[0]
-            color = "#4ECDC4"  # Verde
-        
-        return {
-            "Ganador predicho": winner,
-            "Confianza": f"{confidence:.1%}",
-            "Probabilidad Fighter 1": f"{probability[1]:.1%}",
-            "Probabilidad Fighter 2": f"{probability[0]:.1%}",
-            "Análisis": f"El modelo predice que {winner} ganará con {confidence:.1%} de confianza"
-        }
-        
-    except Exception as e:
-        error_msg = f"Error en predicción: {str(e)}"
-        print(error_msg)
-        return {"Error": error_msg}
-
-# Crear interfaz Gradio
-print("Configurando interfaz Gradio...")
-
-# Definir inputs con valores por defecto razonables
-inputs = [
-    gr.Number(label="Fighter 1 - Knockdowns", value=0, minimum=0, maximum=10, step=1),
-    gr.Number(label="Fighter 1 - Golpes significativos", value=50, minimum=0, maximum=500, step=1),
-    gr.Number(label="Fighter 1 - Takedowns", value=1, minimum=0, maximum=20, step=1),
-    gr.Number(label="Fighter 1 - Intentos de sumisión", value=0, minimum=0, maximum=10, step=1),
-    gr.Number(label="Fighter 2 - Knockdowns", value=0, minimum=0, maximum=10, step=1),
-    gr.Number(label="Fighter 2 - Golpes significativos", value=45, minimum=0, maximum=500, step=1),
-    gr.Number(label="Fighter 2 - Takedowns", value=2, minimum=0, maximum=20, step=1),
-    gr.Number(label="Fighter 2 - Intentos de sumisión", value=1, minimum=0, maximum=10, step=1),
-    gr.Slider(1, 5, value=3, step=1, label="Round"),
-    gr.Dropdown(
-        choices=[
-            "Bantamweight", "Catch Weight", "Featherweight", "Flyweight",
-            "Heavyweight", "Light Heavyweight", "Lightweight", "Middleweight", "Welterweight"
-        ],
-        value="Lightweight",
-        label="Categoría de Peso"
-    ),
-    gr.Slider(0, 10, value=5, step=1, label="Método (0-10)")
-]
-
-# Crear la aplicación
-demo = gr.Interface(
-    fn=predict_ufc_fight,
-    inputs=inputs,
-    outputs="json",
-    title="UFC Fight Predictor",
-    description="Predice el resultado de peleas UFC usando Machine Learning. Modelo entrenado con datos reales.",
-    examples=[
-        [2, 120, 3, 1, 0, 80, 1, 0, 3, "Lightweight", 5],
-        [0, 80, 1, 0, 3, 150, 4, 2, 2, "Welterweight", 5],
-        [1, 100, 2, 0, 1, 95, 1, 1, 4, "Middleweight", 6]
-    ],
-    theme="default"
-)
-
-print("✅ Interfaz configurada")
-
-if __name__ == "__main__":
-    print("🚀 Iniciando aplicación...")
-    demo.launch(server_name="0.0.0.0", server_port=7860)