Delete app.py

app.py

@@ -1,194 +0,0 @@
-import gradio as gr
-import pickle
-import numpy as np
-import pandas as pd
-import json
-import os
-import sys
-
-print("=== INICIANDO UFC PREDICTOR ===")
-
-# Configurar para evitar warnings
-import warnings
-warnings.filterwarnings('ignore')
-
-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")
-    # Crear un modelo dummy para evitar crash
-    from sklearn.linear_model import LogisticRegression
-    model = LogisticRegression()
-    # Entrenar con datos dummy
-    import numpy as np
-    X_dummy = np.random.rand(10, len(metadata.get('feature_columns', 10)))
-    y_dummy = np.random.randint(0, 2, 10)
-    model.fit(X_dummy, y_dummy)
-    print("⚠️ Usando modelo dummy por fallo en carga")
-
-print("✅ Componentes cargados")
-
-if metadata:
-    model_name = metadata.get('best_model_selected', 'Logistic Regression')
-    accuracy = metadata.get('evaluation_metrics', {}).get('accuracy', 0.9892)
-    print(f"Modelo: {model_name}")
-    print(f"Accuracy: {accuracy:.4f}")
-
-feature_columns = metadata.get('feature_columns', [])
-if not feature_columns:
-    print("⚠️ Advertencia: Usando características por defecto")
-    feature_columns = [
-        'KD_diff', 'STR_diff', 'TD_diff', 'SUB_diff',
-        'Fighter_1_KD', 'Fighter_2_KD', 'Fighter_1_STR', 'Fighter_2_STR',
-        'Fighter_1_TD', 'Fighter_2_TD', 'Fighter_1_SUB', 'Fighter_2_SUB',
-        'Fighter_1_accuracy', 'Fighter_2_accuracy', 'Round', 'Method_encoded'
-    ]
-
-print(f"Características: {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
-        inputs = [fighter_1_kd, fighter_1_str, fighter_2_kd, fighter_2_str]
-        if any(x is None or pd.isna(x) for x in inputs):
-            return {"Error": "Valores de entrada inválidos"}
-        
-        # 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
-        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
-        
-        # Mapeo de características
-        feature_mapping = {
-            'KD_diff': kd_diff,
-            'STR_diff': str_diff,
-            'TD_diff': td_diff,
-            'SUB_diff': sub_diff,
-            'Fighter_1_KD': fighter_1_kd,
-            'Fighter_2_KD': fighter_2_kd,
-            'Fighter_1_STR': fighter_1_str,
-            'Fighter_2_STR': fighter_2_str,
-            'Fighter_1_TD': fighter_1_td,
-            'Fighter_2_TD': fighter_2_td,
-            'Fighter_1_SUB': fighter_1_sub,
-            'Fighter_2_SUB': fighter_2_sub,
-            'Fighter_1_accuracy': fighter_1_accuracy,
-            'Fighter_2_accuracy': fighter_2_accuracy,
-            'Round': round_num,
-            'Method_encoded': method_encoded
-        }
-        
-        # Construir array de entrada
-        input_features = []
-        for feature in feature_columns:
-            if feature in feature_mapping:
-                input_features.append(feature_mapping[feature])
-            elif feature.startswith('weight_class_'):
-                category_name = feature.replace('weight_class_', '')
-                input_features.append(1 if category_name == weight_class else 0)
-            else:
-                input_features.append(0)  # Valor por defecto
-        
-        # Convertir y preprocesar
-        input_array = np.array([input_features])
-        input_imputed = imputer.transform(input_array)
-        input_scaled = scaler.transform(input_imputed)
-        
-        # Predecir
-        prediction = model.predict(input_scaled)[0]
-        probability = model.predict_proba(input_scaled)[0]
-        
-        # Resultados
-        if prediction == 1:
-            winner = "Fighter 1"
-            confidence = probability[1]
-        else:
-            winner = "Fighter 2" 
-            confidence = probability[0]
-        
-        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:
-        return {"Error": f"Error en predicción: {str(e)}"}
-
-# Interfaz Gradio
-print("Configurando interfaz...")
-
-inputs = [
-    gr.Number(label="Fighter 1 - Knockdowns", value=0, minimum=0, maximum=10),
-    gr.Number(label="Fighter 1 - Golpes significativos", value=50, minimum=0, maximum=500),
-    gr.Number(label="Fighter 1 - Takedowns", value=1, minimum=0, maximum=20),
-    gr.Number(label="Fighter 1 - Intentos de sumisión", value=0, minimum=0, maximum=10),
-    gr.Number(label="Fighter 2 - Knockdowns", value=0, minimum=0, maximum=10),
-    gr.Number(label="Fighter 2 - Golpes significativos", value=45, minimum=0, maximum=500),
-    gr.Number(label="Fighter 2 - Takedowns", value=2, minimum=0, maximum=20),
-    gr.Number(label="Fighter 2 - Intentos de sumisión", value=1, minimum=0, maximum=10),
-    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)")
-]
-
-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 con 98.9% de accuracy.",
-    examples=[
-        [2, 120, 3, 1, 0, 80, 1, 0, 3, "Lightweight", 5],
-        [0, 80, 1, 0, 3, 150, 4, 2, 2, "Welterweight", 5]
-    ]
-)
-
-print("✅ Interfaz lista")
-
-if __name__ == "__main__":
-    demo.launch()