Update app.py

app.py

@@ -51,14 +51,14 @@ 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
-            - 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 classification' buttton.\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
         Evaluation:\n
-            - 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.
+        \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.
         """)
         with gr.Row(equal_height=True):
             with gr.Column(scale=4):
@@ -86,15 +86,21 @@ with gr.Blocks() as demo:
         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])
 
-        gr.Markdown(r"""
-        Accuracy: is the model's ability to make correct predicitons.
-        It is the fraction of correct prediction out of the total predictions.
-        $
-        \text{Accuracy} = \frac{\text{Correct predictions}}{\text{All predictions}} * 100
-        $
-        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 }])
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=6):
+                gr.Markdown(r"""
+                Accuracy: is the model's ability to make correct predicitons.
+                It is the fraction of correct prediction out of the total predictions.
+                $
+                \text{Accuracy} = \frac{\text{Correct predictions}}{\text{All predictions}} * 100
+                $
+                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 }])
+            with gr.Column(scale=4):
+                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)
                 
     with gr.Tab("Question Answering"):
         # XXX Add some button disabling here, if the classification process is not completed first XXX