Update app.py

app.py

@@ -54,10 +54,6 @@ st.markdown("""
 A machine learning pipeline for predicting disease-causing genes post-genome-wide association study in blood pressure.
 """)
 
-#gene_input = st.text_input('Input Single HGNC Gene:') 
-#df = df_total[df_total.index == gene_input] 
-#st.dataframe(df)
-
 
 collect_genes = lambda x : [str(i) for i in re.split(",|, ", x) if i != ""]
 
@@ -68,6 +64,7 @@ explainer = shap.TreeExplainer(xgb)
 if len(gene_list) > 1:
     df = df_total[df_total.index.isin(gene_list)]
     st.dataframe(df)
+    df.drop(columns='XGB_Score', inplace=True)
     shap_values = explainer.shap_values(df)
     summary_plot = shap.summary_plot(shap_values, df)
     st.caption("SHAP Summary Plot of All Input Genes")
@@ -80,6 +77,7 @@ else:
 input_gene = st.text_input("Input individual HGNC gene:")
 df2 = df_total[df_total.index == input_gene]
 st.dataframe(df2)
+df2.drop(columns='XGB_Score', inplace=True)
 if len(input_gene) == 1:
     shap_values = explainer.shap_values(df2)
     shap.initjs()