Delete app.py

app.py

@@ -1,150 +0,0 @@
-import gradio as gr
-import pickle
-import numpy as np
-import pandas as pd
-import json
-
-print("🚀 Cargando modelo UFC Predictor...")
-
-# Cargar modelo y preprocesadores
-try:
-    with open("ufc_best_model.pkl", "rb") as f:
-        model = pickle.load(f)
-    
-    with open("ufc_scaler.pkl", "rb") as f:
-        scaler = pickle.load(f)
-        
-    with open("ufc_imputer.pkl", "rb") as f:
-        imputer = pickle.load(f)
-        
-    with open("ufc_model_metadata.json", "r") as f:
-        metadata = json.load(f)
-        
-    with open("ufc_feature_ranges.json", "r") as f:
-        ranges = json.load(f)
-        
-    print("✅ Modelo y archivos cargados correctamente")
-    print(f"🏆 Modelo: {metadata['best_model_name']}")
-    print(f"📊 Accuracy: {metadata['best_accuracy']:.4f}")
-    
-except Exception as e:
-    print(f"❌ Error cargando archivos: {e}")
-    raise e
-
-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 basado en las estadísticas
-    """
-    try:
-        # 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)
-        fighter_2_accuracy = fighter_2_str / (fighter_2_str + 10)
-        
-        # Crear array de entrada en el orden CORRECTO
-        input_features = [
-            kd_diff, str_diff, td_diff, sub_diff,  # Diferencias
-            fighter_1_kd, fighter_2_kd,            # KDs individuales
-            fighter_1_str, fighter_2_str,          # Golpes
-            fighter_1_td, fighter_2_td,            # Takedowns
-            fighter_1_sub, fighter_2_sub,          # Submissions
-            fighter_1_accuracy, fighter_2_accuracy, # Precisiones
-            round_num,                             # Round
-            method_encoded                         # Método codificado
-        ]
-        
-        # Añadir one-hot encoding para weight_class
-        weight_classes = [
-            'weight_class_Bantamweight', 'weight_class_Catch Weight',
-            'weight_class_Featherweight', 'weight_class_Flyweight',
-            'weight_class_Heavyweight', 'weight_class_Light Heavyweight',
-            'weight_class_Lightweight', 'weight_class_Middleweight',
-            'weight_class_Welterweight'
-        ]
-        
-        for wc in weight_classes:
-            input_features.append(1 if wc == f"weight_class_{weight_class}" else 0)
-        
-        # 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]
-            explanation = f"Fighter 1 tiene {confidence:.1%} de probabilidad de ganar"
-        else:
-            winner = "Fighter 2" 
-            confidence = probability[0]
-            explanation = f"Fighter 2 tiene {confidence:.1%} de probabilidad de ganar"
-        
-        return {
-            "🏆 Ganador predicho": winner,
-            "📈 Confianza": f"{confidence:.1%}",
-            "🥊 Probabilidad Fighter 1": f"{probability[1]:.1%}",
-            "🥊 Probabilidad Fighter 2": f"{probability[0]:.1%}",
-            "💡 Análisis": explanation
-        }
-        
-    except Exception as e:
-        return {"❌ Error": f"Error en predicción: {str(e)}"}
-
-# Crear interfaz Gradio
-print("🎨 Creando interfaz Gradio...")
-
-# Definir inputs
-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 (encoded) - 0-10 scale")
-]
-
-# 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 basado en estadísticas de los peleadores. Modelo entrenado con accuracy del 99%",
-    examples=[
-        [2, 120, 3, 1, 0, 80, 1, 0, 3, "Lightweight", 5],  # Fighter 1 favorito
-        [0, 80, 1, 0, 3, 150, 4, 2, 2, "Welterweight", 5]   # Fighter 2 favorito
-    ]
-)
-
-print("✅ Interfaz creada. Iniciando...")
-
-if __name__ == "__main__":
-    demo.launch()