shap

app.py

@@ -54,7 +54,7 @@ with gr.Blocks() as demo:
             output = gr.Text(label="Model prediction")
             plot = gr.Plot()
     with gr.Row():
-        with gr.Accordion("See model explanability"):
+        with gr.Accordion("See model explanability", open=False):
             waterfall_plot = gr.Plot()
 
     def submit_inputs(
@@ -111,8 +111,10 @@ with gr.Blocks() as demo:
 
         ## explainability plots
         rf_model = model.steps[-1][1]
+        print("model: ", rf_model)
         explainer = shap.Explainer(rf_model)
         shap_values = explainer(df)
+        print(shap_values.shape)  # should show (num_samples, 1)
         shap_waterfall_plot = shap.plots.waterfall(shap_values[0])
 
         ## save user's data in hopsworks