Update app.py

app.py

@@ -83,8 +83,8 @@ with gr.Blocks() as demo:
                 none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True) 
 
         # model_confidence = gr.Number(label="Model Confidence")
-        predict_button.click(load_and_analyze_csv, inputs=[file_input, text_field, event_model], 
-                             outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence])
+        # predict_button.click(load_and_analyze_csv, inputs=[file_input, text_field, event_model], 
+        #                      outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence])
 
         with gr.Row(equal_height=True):
             with gr.Column(scale=5):
@@ -97,11 +97,14 @@ with gr.Blocks() as demo:
                 Model Confidence: is the mean probabilty of each case 
                 belonging to their assigned classes. A value of 1 is best.
                 """, latex_delimiters=[{ "left": "$", "right": "$", "display": True }])
-                model_confidence = gr.Number(label="Model Confidence", value=0)
+                model_confidence = gr.Number(label="Model Confidence")
             with gr.Column(scale=5):
                 correct = gr.Number(label="Number of correct classifications", value=0)
                 incorrect = gr.Number(label="Number of incorrect classifications", value=0)
                 accuracy = gr.Number(label="Model Accuracy", value=0)
+
+            predict_button.click(load_and_analyze_csv, inputs=[file_input, text_field, event_model], 
+                             outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence])
                 
     with gr.Tab("Question Answering"):
         # XXX Add some button disabling here, if the classification process is not completed first XXX