Update app.py

app.py

@@ -6,6 +6,7 @@ import sklearn
 import xgboost
 import shap
 from shap_plots import shap_summary_plot
+from dynamic_shap_plot import matplotlib_to_plotly, summary_plot_plotly_fig
 import plotly.tools as tls
 import dash_core_components as dcc
 import matplotlib
@@ -111,8 +112,9 @@ if len(gene_list) > 1:
     print(df_shap.columns)
     shap_values = explainer.shap_values(df_shap)
     #summary_plot = shap.summary_plot(shap_values, df_shap)
-    summary_plot = shap_summary_plot(shap_values, features=df_shap, feature_names=df_shap.columns, max_display=8)
-    st.pyplot(fig=summary_plot)
+    #summary_plot = shap_summary_plot(shap_values, features=df_shap, feature_names=df_shap.columns, max_display=8)
+    dynamic_plot = summary_plot_plotly_fig(shap_values, df_shap=df_shap, feature_names=df_shap.columns) 
+    st.pyplot(fig=dynamic_plot)
     st.caption("SHAP Summary Plot of All Input Genes")
     feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
     feature_order = feature_order[-min(8, len(feature_order)):]