Upload app (1).py

app (1).py

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+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%}",
+            "Analisis": 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],
+        [0, 80, 1, 0, 3, 150, 4, 2, 2, "Welterweight", 5]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()