Upload app.py

app.py

@@ -163,15 +163,16 @@ def y1_interpret_xgb(*args):
     df = pd.DataFrame([args], columns=x1.columns)
     df = df.astype({col: "category" for col in categorical_columns1})
     shap_values = y1_explainer_xgb.shap_values(xgb.DMatrix(df, enable_categorical=True))
-    scores_desc = list(zip(shap_values[0], x1.columns))
-    scores_desc = sorted(scores_desc)
-    fig_m = plt.figure(facecolor='white')
-    fig_m.set_size_inches(14, 24)
-    plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
-    plt.title("Feature Shap Values", fontsize = 24, pad = 20, fontweight = 'bold')
-    plt.yticks(fontsize=12)
-    plt.xlabel("Shap Value", fontsize = 16, labelpad=8, fontweight = 'bold')
-    plt.ylabel("Feature", fontsize = 16, labelpad=14, fontweight = 'bold')
+    shap.plots.bar(shap_values[0])
+    fig_m = plt.gcf()
+    ax = plt.gca()
+    fig_m.set_figheight(12)
+    fig_m.set_figwidth(5)
+    plt.xlabel("Mean |SHAP Value|", fontsize =12, fontweight = 'heavy', labelpad = 8)
+    plt.tick_params(axis="y",direction="out", labelsize = 12)
+    plt.tick_params(axis="x",direction="out", labelsize = 12)
+    wrap_labels(ax, 30)
+    ax.figure
     return fig_m
 
 def y1_interpret_lgb(*args):
@@ -427,11 +428,11 @@ with gr.Blocks(title = "NTDB-Epidural") as demo:
                 with gr.Row():
             
                     gr.Markdown(
-                    """
                     <br/>
+                    """
                     ## Mortality
-                    <br/>
                     """
+                    <br/>
                     )
                 
                 with gr.Row():