Update app.py

app.py

@@ -9,48 +9,58 @@ from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score
 
+# Cargar los datos
 data = pd.read_csv('https://raw.githubusercontent.com/gradio-app/titanic/master/train.csv')
 data.head()
 
-def encode_ages(df): # Binning ages 
-    df.Age = df.Age.fillna(-0.5)
+# Función para binning de edades
+def encode_ages(df): 
+    df.loc[:, 'Age'] = df['Age'].fillna(-0.5)
     bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
-    categories = pd.cut(df.Age, bins, labels=False)
-    df.Age = categories
+    categories = pd.cut(df['Age'], bins, labels=False)
+    df.loc[:, 'Age'] = categories
     return df
 
-def encode_fares(df): # Binning fares
-    df.Fare = df.Fare.fillna(-0.5)
+# Función para binning de tarifas
+def encode_fares(df): 
+    df.loc[:, 'Fare'] = df['Fare'].fillna(-0.5)
     bins = (-1, 0, 8, 15, 31, 1000)
-    categories = pd.cut(df.Fare, bins, labels=False)
-    df.Fare = categories
+    categories = pd.cut(df['Fare'], bins, labels=False)
+    df.loc[:, 'Fare'] = categories
     return df
 
+# Función para codificar el sexo
 def encode_sex(df):
     mapping = {"male": 0, "female": 1}
-    return df.replace({'Sex': mapping})
+    df.loc[:, 'Sex'] = df['Sex'].map(mapping)
+    return df
 
+# Función para transformar todas las características
 def transform_features(df):
     df = encode_ages(df)
     df = encode_fares(df)
     df = encode_sex(df)
     return df
 
+# Selección de columnas y transformación de datos
 train = data[['PassengerId', 'Fare', 'Age', 'Sex', 'Survived']]
 train = transform_features(train)
 train.head()
 
-
+# Separación en características (X) y etiqueta (y)
 X_all = train.drop(['Survived', 'PassengerId'], axis=1)
 y_all = train['Survived']
 
+# División en conjunto de entrenamiento y prueba
 num_test = 0.20
 X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)
 
+# Entrenamiento del modelo
 clf = RandomForestClassifier()
 clf.fit(X_train, y_train)
 predictions = clf.predict(X_test)
 
+# Función de predicción para Gradio
 def predict_survival(sex, age, fare):
     df = pd.DataFrame.from_dict({'Sex': [sex], 'Age': [age], 'Fare': [fare]})
     df = encode_sex(df)
@@ -59,9 +69,10 @@ def predict_survival(sex, age, fare):
     pred = clf.predict_proba(df)[0]
     return {'Perishes': float(pred[0]), 'Survives': float(pred[1])}
     
+# Definir la interfaz de Gradio
 sex = gr.Radio(['female', 'male'], label="Sex", value="male")
-age = gr.Slider(minimum=0, maximum=100, default=22, label="Age")
-fare = gr.Slider(minimum=0, maximum=200, default=100, label="Fare (british pounds)")
+age = gr.Slider(minimum=0, maximum=100, value=22, label="Age")
+fare = gr.Slider(minimum=0, maximum=200, value=100, label="Fare (british pounds)")
 
 gr.Interface(predict_survival, [sex, age, fare], "label", live=True, thumbnail="https://raw.githubusercontent.com/gradio-app/hub-titanic/master/thumbnail.png", analytics_enabled=False,
-    theme="soft", title="Demo Titanic", description="Cual es la probabilidad que un pasajero sobreviva.").launch();
+    theme="soft", title="Demo Titanic", description="¿Cuál es la probabilidad de que un pasajero sobreviva?").launch();