Update app.py

app.py

@@ -4,7 +4,7 @@ from classifier import classify_toxic_comment
 
 # Clear function for resetting the UI
 def clear_inputs():
-    return "", 0, "", []
+    return "", 0, "", [], "", ""
 
 # Custom CSS for styling
 custom_css = """
@@ -60,6 +60,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
     with gr.Row():
         with gr.Column(scale=2):
             prediction_output = gr.Textbox(label="Prediction", placeholder="Prediction will appear here...")
+            toxicity_output = gr.Textbox(label="Toxicity Score", placeholder="Toxicity score will appear here...")
+            bias_output = gr.Textbox(label="Bias Score", placeholder="Bias score will appear here...")
         with gr.Column(scale=1):
             confidence_output = gr.Slider(
                 label="Confidence",
@@ -88,23 +90,31 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
     def handle_classification(comment, history):
         if history is None:
             history = []
-        prediction, confidence, color = classify_toxic_comment(comment)
-        history.append({"comment": comment, "prediction": prediction, "confidence": confidence})
+        prediction, confidence, color, toxicity_score, bias_score = classify_toxic_comment(comment)
+        history.append({
+            "comment": comment,
+            "prediction": prediction,
+            "confidence": confidence,
+            "toxicity_score": toxicity_score,
+            "bias_score": bias_score
+        })
         threshold_message = "High Confidence" if confidence >= 0.7 else "Low Confidence"
         threshold_color = "green" if confidence >= 0.7 else "orange"
-        return prediction, confidence, color, history, threshold_message, threshold_color
+        toxicity_display = f"{toxicity_score} (Scale: 0 to 1, lower is less toxic)" if toxicity_score is not None else "N/A"
+        bias_display = f"{bias_score} (Scale: 0 to 1, lower indicates less bias)" if bias_score is not None else "N/A"
+        return prediction, confidence, color, history, threshold_message, threshold_color, toxicity_display, bias_display
 
     def handle_feedback(feedback, comment):
         return f"Thank you for your feedback: {feedback}\nAdditional comment: {comment}"
 
     submit_btn.click(
-        fn=lambda: ("Classifying...", 0, "", None, "", ""),  # Show loading state
+        fn=lambda: ("Classifying...", 0, "", None, "", "", "Calculating...", "Calculating..."),  # Show loading state
         inputs=[],
-        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display]
+        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display, toxicity_output, bias_output]
     ).then(
         fn=handle_classification,
         inputs=[comment_input, history_output],
-        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display]
+        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display, toxicity_output, bias_output]
     ).then(
         fn=lambda prediction, confidence, color: f"<span style='color: {color}; font-size: 20px; font-weight: bold;'>{prediction}</span>",
         inputs=[prediction_output, confidence_output, label_display],
@@ -124,7 +134,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
     clear_btn.click(
         fn=clear_inputs,
         inputs=[],
-        outputs=[comment_input, confidence_output, label_display, history_output]
+        outputs=[comment_input, confidence_output, label_display, history_output, toxicity_output, bias_output]
     )
 
     gr.Markdown(