code: def interpret(*args):
      df = pd.DataFrame([args], columns=X_train.columns)
      df = df.astype({col: "category" for col in categorical_columns})
      shap_values = explainer.shap_values(
          xgb.DMatrix(df, enable_categorical=True)
      )
      scores_desc = list(zip(shap_values[0], X_train.columns))
      scores_desc = sorted(scores_desc)
      fig_m = plt.figure(tight_layout=True)
      plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
      plt.title("Feature Shap Values")
      plt.ylabel("Shap Value")
      plt.xlabel("Feature")
      plt.tight_layout()
      return fig_m
  
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 2 for calling a global function with example usage: interpret() and returns: fig_m