Update app.py

app.py

@@ -41,7 +41,13 @@ def analyze_selected_texts(selections):
     result_df = pd.DataFrame({"Selected Text": selected_texts, "Analysis": analysis_results})
     return result_df
 
+def calculate_accuracy(flood_selections, fire_selections, none_selections)
+    incorrect = len(flood_selections) + len(fire_selections) + len(none_selections)
 
+    return incorrect
+    
+
+            
 with gr.Blocks() as demo:
     event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier"]
 
@@ -51,14 +57,14 @@ with gr.Blocks() as demo:
         # T4.5 Relevance Classifier Demo 
         This is a demo created to explore floods and wildfire classification in social media posts.\n
         Usage:\n
-        \tUpload .tsv data file (must contain a text column with social media posts).\n
-        \tNext, type the name of the text column.\n
-        \tThen, choose a BERT classifier model from the drop down.\n
-        \tFinally, click the 'start classification' buttton.\n
+            -Upload .tsv data file (must contain a text column with social media posts).\n
+            -Next, type the name of the text column.\n
+            -Then, choose a BERT classifier model from the drop down.\n
+            -Finally, click the 'start prediction' buttton.\n
         Evaluation:\n
-        \tTo evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.\n
-        \tThen, click on the 'Calculate Accuracy' button.\n
-        \tThen, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
+            -To evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.\n
+            -Then, click on the 'Calculate Accuracy' button.\n
+            -Then, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
         """)
         with gr.Row(equal_height=True):
             with gr.Column(scale=4):
@@ -82,10 +88,6 @@ with gr.Blocks() as demo:
                 gr.Markdown("""### None""")
                 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])
-
         with gr.Row(equal_height=True):
             with gr.Column(scale=5):
                 gr.Markdown(r"""
@@ -106,8 +108,11 @@ with gr.Blocks() as demo:
                 incorrect = gr.Number(label="Number of incorrect classifications", value=0)
                 accuracy = gr.Number(label="Model Accuracy", value=0)
 
+            accuracy_button = gr.Button("Calculate Accuracy")
             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])
+            accuracy_button.click(calculate_accuracy, inputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output], 
+                             outputs=incorrect)
                 
     with gr.Tab("Question Answering"):
         # XXX Add some button disabling here, if the classification process is not completed first XXX