Update app.py

app.py

@@ -1,11 +1,8 @@
 import gradio as gr
-from inference import evo_chat_predict, get_model_config, get_system_stats, get_gpt_response
+from inference import evo_chat_predict, get_model_config, get_system_stats, get_gpt_response, retrain_from_feedback
 import pandas as pd
 import csv
-import os
 from datetime import datetime
-from inference import retrain_from_feedback
-
 
 feedback_log = []
 
@@ -19,7 +16,7 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
 
     with gr.Row():
         with gr.Column():
-            query = gr.Textbox(label="🧠 Your Question", placeholder="e.g. What should you do if there’s a fire?", lines=1)
+            query = gr.Textbox(label="🧠 Your Question", placeholder="e.g. What should you do if there’s a fire?")
             option1 = gr.Textbox(label="❌ Option 1", placeholder="Enter the first option")
             option2 = gr.Textbox(label="❌ Option 2", placeholder="Enter the second option")
             feedback = gr.Radio(["Evo", "GPT"], label="🧠 Who was better?", info="Optional – fuels evolution", interactive=True)
@@ -34,7 +31,10 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
             gpt_box = gr.Textbox(label="🤖 GPT-3.5", interactive=False)
             status_box = gr.Textbox(label="🔵 Status", interactive=False)
 
-        convo = gr.Dataframe(headers=["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"], interactive=False, wrap=True, label="📜 Conversation History")
+        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):
@@ -43,19 +43,18 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
         gpt_text = get_gpt_response(q)
         stats = get_model_config()
         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
+    # 🔁 Actual retraining
     def retrain_evo():
-    status = retrain_from_feedback(feedback_log)
-    return status
-
+        status = retrain_from_feedback(feedback_log)
+        return status
 
     # 🧹 Clear UI
     def clear_fields():
+        feedback_log.clear()
         return "", "", "", "", "", pd.DataFrame(columns=["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"])
 
     # 📤 Export feedback
@@ -72,15 +71,10 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
             return "⚠️ No feedback to export."
 
     # 🔘 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])
+    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, inputs=[], outputs=[status_box])
+    clear_btn.click(fn=clear_fields, inputs=[], outputs=[query, option1, option2, evo_box, gpt_box, convo])
+    export_btn.click(fn=log_feedback_to_csv, inputs=[], outputs=[status_box])
 
 if __name__ == "__main__":
     demo.launch()