code: def predict(*args):
      df = pd.DataFrame([args], columns=X_train.columns)
      df = df.astype({col: "category" for col in categorical_columns})
      pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))
      return {
          ">50K": float(pos_pred[0]),
          "<=50K": 1 - float(pos_pred[0]),
      }
  
file_path: gradio/demo/xgboost-income-prediction-with-explainability/run.py
imports: import gradio
  import random
  import matplotlib.pyplot
  import pandas
  import shap
  import xgboost
  from datasets import load_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: predict() and returns: dict