Update app.py

app.py

@@ -4,6 +4,7 @@ import gradio as gr
 import pandas as pd
 
 from classifier import classify
+from statistics import mean
 
 
 HFTOKEN = os.environ["HF_TOKEN"]
@@ -14,7 +15,7 @@ def load_and_analyze_csv(file, text_field, event_model):
     if text_field not in df.columns:
         raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
 
-    floods, fires, nones = [], [], []
+    floods, fires, nones, scores = [], [], [], []
     for post in df[text_field].to_list():
         res = classify(post, event_model, HFTOKEN)
         if res["event"] == 'flood':
@@ -23,12 +24,14 @@ def load_and_analyze_csv(file, text_field, event_model):
             fires.append(post)
         else:
             nones.append(post)
-            
+        scores.append(res["score"])
+
+    model_confidence = round(mean(scores), 5)   
     fire_related = gr.CheckboxGroup(choices=fires)
     flood_related = gr.CheckboxGroup(choices=floods)
     not_related = gr.CheckboxGroup(choices=nones)
-    # time.sleep(5)
-    return flood_related, fire_related, not_related
+    
+    return flood_related, fire_related, not_related, model_confidence
 
 def analyze_selected_texts(selections):
     selected_texts = selections
@@ -64,9 +67,10 @@ with gr.Blocks() as demo:
             with gr.Column():
                 gr.Markdown("""### None""")
                 none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True) 
-                
-            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.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.Tab("Question Answering"):
         # XXX Add some button disabling here, if the classification process is not completed first XXX