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, "", [], "", "", "", "", 0, "", "", "", "", ""
 
 # Custom CSS for styling
 custom_css = """
@@ -75,6 +75,23 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
         label_display = gr.HTML()
         threshold_display = gr.HTML()
 
+    with gr.Accordion("Paraphrased Output (if Toxic)", open=False):
+        paraphrased_comment_output = gr.Textbox(label="Paraphrased Comment", placeholder="Paraphrased comment will appear here if the input is toxic...")
+        paraphrased_prediction_output = gr.Textbox(label="Paraphrased Prediction", placeholder="Prediction will appear here...")
+        paraphrased_toxicity_output = gr.Textbox(label="Paraphrased Toxicity Score", placeholder="Toxicity score will appear here...")
+        paraphrased_bias_output = gr.Textbox(label="Paraphrased Bias Score", placeholder="Bias score will appear here...")
+        paraphrased_confidence_output = gr.Slider(
+            label="Paraphrased Confidence",
+            minimum=0,
+            maximum=1,
+            value=0,
+            interactive=False
+        )
+        paraphrased_label_display = gr.HTML()
+        semantic_similarity_output = gr.Textbox(label="Semantic Similarity", placeholder="Semantic similarity score will appear here...")
+        emotion_shift_output = gr.Textbox(label="Emotion Shift", placeholder="Emotion shift will appear here...")
+        empathy_score_output = gr.Textbox(label="Empathy Score", placeholder="Empathy score will appear here...")
+
     with gr.Accordion("Prediction History", open=False):
         history_output = gr.JSON(label="Previous Predictions")
 
@@ -90,31 +107,75 @@ 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, toxicity_score, bias_score = classify_toxic_comment(comment)
+        (
+            prediction, confidence, color, toxicity_score, bias_score,
+            paraphrased_comment, paraphrased_prediction, paraphrased_confidence,
+            paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
+            semantic_similarity, emotion_shift, empathy_score
+        ) = classify_toxic_comment(comment)
+        
         history.append({
             "comment": comment,
             "prediction": prediction,
             "confidence": confidence,
             "toxicity_score": toxicity_score,
-            "bias_score": bias_score
+            "bias_score": bias_score,
+            "paraphrased_comment": paraphrased_comment,
+            "paraphrased_prediction": paraphrased_prediction,
+            "paraphrased_confidence": paraphrased_confidence,
+            "paraphrased_toxicity_score": paraphrased_toxicity_score,
+            "paraphrased_bias_score": paraphrased_bias_score,
+            "semantic_similarity": semantic_similarity,
+            "emotion_shift": emotion_shift,
+            "empathy_score": empathy_score
         })
+        
         threshold_message = "High Confidence" if confidence >= 0.7 else "Low Confidence"
         threshold_color = "green" if confidence >= 0.7 else "orange"
         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
+        
+        paraphrased_comment_display = paraphrased_comment if paraphrased_comment else "N/A (Comment was non-toxic)"
+        paraphrased_prediction_display = paraphrased_prediction if paraphrased_prediction else "N/A"
+        paraphrased_confidence_display = paraphrased_confidence if paraphrased_confidence else 0
+        paraphrased_toxicity_display = f"{paraphrased_toxicity_score} (Scale: 0 to 1, lower is less toxic)" if paraphrased_toxicity_score is not None else "N/A"
+        paraphrased_bias_display = f"{paraphrased_bias_score} (Scale: 0 to 1, lower indicates less bias)" if paraphrased_bias_score is not None else "N/A"
+        paraphrased_label_html = f"<span style='color: {paraphrased_color}; font-size: 20px; font-weight: bold;'>{paraphrased_prediction}</span>" if paraphrased_prediction else ""
+        semantic_similarity_display = f"{semantic_similarity} (Scale: 0 to 1, higher is better)" if semantic_similarity is not None else "N/A"
+        emotion_shift_display = emotion_shift if emotion_shift else "N/A"
+        empathy_score_display = f"{empathy_score} (Scale: 0 to 1, higher indicates more empathy)" if empathy_score is not None else "N/A"
+
+        return (
+            prediction, confidence, color, history, threshold_message, threshold_color,
+            toxicity_display, bias_display,
+            paraphrased_comment_display, paraphrased_prediction_display, paraphrased_confidence_display,
+            paraphrased_toxicity_display, paraphrased_bias_display, paraphrased_label_html,
+            semantic_similarity_display, emotion_shift_display, empathy_score_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, "", "", "Calculating...", "Calculating..."),  # Show loading state
+        fn=lambda: ("Classifying...", 0, "", None, "", "", "Calculating...", "Calculating...", "Paraphrasing...", "Calculating...", 0, "Calculating...", "Calculating...", "", "Calculating...", "Calculating...", "Calculating..."),  # Show loading state
         inputs=[],
-        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display, toxicity_output, bias_output]
+        outputs=[
+            prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display,
+            toxicity_output, bias_output,
+            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
+            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
+            semantic_similarity_output, emotion_shift_output, empathy_score_output
+        ]
     ).then(
         fn=handle_classification,
         inputs=[comment_input, history_output],
-        outputs=[prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display, toxicity_output, bias_output]
+        outputs=[
+            prediction_output, confidence_output, label_display, history_output, threshold_display, threshold_display,
+            toxicity_output, bias_output,
+            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
+            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
+            semantic_similarity_output, emotion_shift_output, empathy_score_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],
@@ -134,7 +195,12 @@ 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, toxicity_output, bias_output]
+        outputs=[
+            comment_input, confidence_output, label_display, history_output, toxicity_output, bias_output,
+            paraphrased_comment_output, paraphrased_prediction_output, paraphrased_confidence_output,
+            paraphrased_toxicity_output, paraphrased_bias_output, paraphrased_label_display,
+            semantic_similarity_output, emotion_shift_output, empathy_score_output
+        ]
     )
 
     gr.Markdown(