Update app.py

app.py

@@ -1,9 +1,10 @@
 import gradio as gr
-from inference import evo_chat_predict, get_model_config, get_gpt_response
+from inference import evo_chat_predict, get_model_config, get_gpt_response, load_model
 import pandas as pd
 import csv
 import os
 from datetime import datetime
+from retrain_from_feedback import train_evo
 
 feedback_log = []
 
@@ -52,27 +53,24 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
 
         # Log feedback
         if selected in ["Evo", "GPT"]:
-            feedback_log.append(new_row)
+            feedback_log.append(new_row + [selected, "yes" if selected == "Evo" else "no"])
 
         return evo_text, gpt_text, stats_text, updated_df
 
-    # 🔁 Retrain Evo using retrain.py
+    # 🔁 Retrain Evo
     def retrain_evo():
         if not feedback_log:
             return "⚠️ No feedback data to retrain from."
 
-        # Save log to feedback_log.csv in required format
         with open("feedback_log.csv", "w", newline="") as f:
             writer = csv.writer(f)
-            writer.writerow(["prompt", "context", "label"])
+            writer.writerow(["question", "option1", "option2", "answer", "confidence", "reasoning", "context", "user_preference", "evo_was_correct"])
             for row in feedback_log:
-                prompt = row[0]
-                context = row[6]  # context_used
-                label = 0 if row[3] == row[1] else 1  # 0 if answer == option1 else 1
-                writer.writerow([prompt, context, label])
+                writer.writerow(row)
 
-        os.system("python retrain.py")
-        return "✅ Evo retrained from feedback_log.csv."
+        train_evo()
+        load_model()
+        return f"✅ Evo retrained on {len(feedback_log)} entries and reloaded."
 
     # 🧹 Clear UI
     def clear_fields():
@@ -86,7 +84,7 @@ with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; co
             filepath = f"feedback_{timestamp}.csv"
             with open(filepath, "w", newline="") as f:
                 writer = csv.writer(f)
-                writer.writerow(["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"])
+                writer.writerow(["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context", "User Pref", "Evo Correct"])
                 writer.writerows(feedback_log)
             return f"✅ Feedback exported to {filepath}"
         else: