Upload app.py

app.py

@@ -24,6 +24,8 @@ import shap
 import gradio as gr
 import random
 import re
+import textwrap
+
 
 
 #Read data.
@@ -34,6 +36,12 @@ variables = ['Age', 'Sex', 'Ethnicity', 'Weight', 'Height', 'Systolic_Blood_Pres
 x1 = x1[variables]
 
 
+#Define feature names.
+f_names = x1.columns
+f_names = [f_names.replace('__', ' - ') for f in f_names]
+f_names = [f_names.replace('_', ' ') for f in f_names]
+
+
 #Assign unique values as answer options.
 unique_SEX = ['Male', 'Female', 'Unknown']
 unique_RACE = ['White', 'Black', 'Asian', 'American Indian', 'Pacific Islander', 'Other', 'Unknown']
@@ -171,12 +179,21 @@ def y1_predict_rf(*args):
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
 
+def wrap_labels(ax, width, break_long_words=False):
+    labels = []
+    for label in ax.get_yticklabels():
+        text = label.get_text()
+        labels.append(textwrap.fill(text, width=width, 
+                                    break_long_words=break_long_words))
+    ax.set_yticklabels(labels, rotation=0)
+    
+
 #Define interpret for y1 (mortality).
 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))
-    fig_y1 = shap.bar_plot(shap_values[0], max_display = 10, show = False, feature_names = x1.columns)
+    fig_y1 = shap.bar_plot(shap_values[0], max_display = 10, show = False, feature_names = f_names)
     fig_y1 = plt.gcf()
     ax_y1 = plt.gca()
     fig_y1.set_figheight(6)