Update app.py

app.py

@@ -6,6 +6,18 @@ import sklearn
 import xgboost
 import shap
 st.set_option('deprecation.showPyplotGlobalUse', False)
+
+# CSS to inject contained in a string
+hide_table_row_index = """
+            <style>
+            thead tr th:first-child {display:none}
+            tbody th {display:none}
+            </style>
+            """
+
+# Inject CSS with Markdown
+st.markdown(hide_table_row_index, unsafe_allow_html=True)
+
 seed=42
 
 annotations = pd.read_csv("annotations_dataset.csv")
@@ -33,7 +45,6 @@ xgb = xgboost.XGBRegressor(
 
 
 xgb.fit(X, Y)
-
 prediction_list = list(xgb.predict(annotations))
 predictions = [round(prediction, 2) for prediction in prediction_list]
 
@@ -98,16 +109,18 @@ else:
 
 input_gene = st.text_input("Input individual HGNC gene:")
 df2 = df_total[df_total.index == input_gene]
+df2_shap = df_total[df_total.index == input_gene]
+df2['Gene'] = df2.index
 st.dataframe(df2)
 df2.drop(columns='XGB_Score', inplace=True)
 
 if input_gene:
-    shap_values = explainer.shap_values(df2)
+    shap_values = explainer.shap_values(df2_shap)
     shap.getjs()
     force_plot = shap.force_plot(
     explainer.expected_value,
     shap_values,
-    df2, 
+    df2_shap , 
     matplotlib = True,show=False)
     st.pyplot(fig=force_plot)
 else:
@@ -117,4 +130,5 @@ st.markdown("""
 Total Gene Prioritisation Results:
 """)
 
+df_total['Gene'] = df_total.index
 st.dataframe(df_total)