Update app.py

app.py

@@ -34,26 +34,27 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
 
         convo = gr.Dataframe(headers=["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"], interactive=False, wrap=True, label="📜 Conversation History")
 
+    # 🔍 Ask Evo
     def ask_evo(q, opt1, opt2, hist):
         result = evo_chat_predict(hist, q, [opt1, opt2])
         evo_text = f"Answer: {result['answer']} (Confidence: {result['confidence']})\n\nReasoning: {result['reasoning']}"
-        evo_box.update(value=evo_text)
-        gpt_box.update(value=get_gpt_response(q))
+        gpt_text = get_gpt_response(q)
         stats = get_model_config()
-        evo_stats.update(value=f"Layers: {stats['num_layers']} | Heads: {stats['num_heads']} | FFN: {stats['ffn_dim']} | Memory: {stats['memory_enabled']} | Phase: V2.2")
-        return evo_text, result['context_used'], result['answer'], result['confidence'], result['reasoning'], result['context_used']
+        stats_text = f"Layers: {stats['num_layers']} | Heads: {stats['num_heads']} | FFN: {stats['ffn_dim']} | Memory: {stats['memory_enabled']} | Phase: {stats['phase']} | Accuracy: {stats['accuracy']}"
+        # Update conversation history
+        new_row = [q, opt1, opt2, result["answer"], result["confidence"], result["reasoning"], result["context_used"]]
+        updated_df = pd.concat([hist, pd.DataFrame([new_row], columns=hist.columns)], ignore_index=True)
+        return evo_text, gpt_text, stats_text, updated_df
 
+    # 🧠 Simulated retraining
     def retrain_evo():
-        status_box.update(value="Retraining Evo... (simulated)")
+        return "Retraining Evo... (simulated)"
 
+    # 🧹 Clear UI
     def clear_fields():
-        query.update(value="")
-        option1.update(value="")
-        option2.update(value="")
-        evo_box.update(value="")
-        gpt_box.update(value="")
-        status_box.update(value="")
+        return "", "", "", "", "", pd.DataFrame(columns=["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"])
 
+    # 📤 Export feedback
     def log_feedback_to_csv():
         if feedback_log:
             timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -62,14 +63,20 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
                 writer = csv.writer(f)
                 writer.writerow(["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"])
                 writer.writerows(feedback_log)
-            status_box.update(value=f"✅ Feedback exported to {filepath}")
+            return f"✅ Feedback exported to {filepath}"
         else:
-            status_box.update(value="⚠️ No feedback to export.")
+            return "⚠️ No feedback to export."
 
-    evo_btn.click(fn=ask_evo, inputs=[query, option1, option2, convo], outputs=[evo_box, gpt_box, convo])
-    retrain_btn.click(fn=retrain_evo, inputs=[], outputs=[status_box])
-    clear_btn.click(fn=clear_fields, inputs=[], outputs=[])
-    export_btn.click(fn=log_feedback_to_csv, inputs=[], outputs=[status_box])
+    # 🔘 Event bindings
+    evo_btn.click(
+        fn=ask_evo,
+        inputs=[query, option1, option2, convo],
+        outputs=[evo_box, gpt_box, evo_stats, convo]
+    )
+
+    retrain_btn.click(fn=retrain_evo, outputs=[status_box])
+    clear_btn.click(fn=clear_fields, outputs=[query, option1, option2, evo_box, gpt_box, convo])
+    export_btn.click(fn=log_feedback_to_csv, outputs=[status_box])
 
 if __name__ == "__main__":
     demo.launch()