update conversation logging

src/app.py

@@ -140,6 +140,17 @@ with st.sidebar:
     st.sidebar.caption(f"LLM: {selected_model}")
     st.sidebar.caption(f"Client: {client_type.upper()}")
     st.sidebar.caption(f"Session ID: {st.session_state.get('client_key', 'None')[:20]}...")
+    
+    # Google Sheets status
+    if st.session_state.feedback_manager.is_connected():
+        st.sidebar.success("📊 Google Sheets: Connected")
+        # Check if conversation logging is available
+        if hasattr(st.session_state.feedback_manager, 'conversation_worksheet') and st.session_state.feedback_manager.conversation_worksheet:
+            st.sidebar.success("📝 Conversation Logging: Active")
+        else:
+            st.sidebar.warning("📝 Conversation Logging: Not Available")
+    else:
+        st.sidebar.error("📊 Google Sheets: Not Connected")
 
 # Show title and description
 st.title("💬 Depression Assistant Chatbot")
@@ -255,12 +266,6 @@ for idx, message in enumerate(st.session_state.messages):
                                 st.error("❌ Failed to save feedback. Please check your Google Sheets configuration.")
                         else:
                             st.warning("⚠️ Please select at least one rating before submitting feedback.")
-                    
-                    # Show Google Sheets connection status
-                    if st.session_state.feedback_manager.is_connected():
-                        pass
-                    else:
-                        st.caption("⚠️ Google Sheets not configured - feedback will not be saved")
             else:
                 st.success("✅ Feedback already submitted for this response")
 
@@ -315,7 +320,8 @@ if user_input := st.chat_input("Ask me questions about the CANMAT depression gui
             Rag.llm_client = original_client
         
         t1 = time.perf_counter()
-        print(f"[Time] Retriever + Generator takes: {t1- t0:.2f} seconds in total.")
+        response_time = t1 - t0
+        print(f"[Time] Retriever + Generator takes: {response_time:.2f} seconds in total.")
         print(f"============== Finish R-A-Generation for Current Query {user_input} ==============")
         
         # Save the response and sources
@@ -325,6 +331,25 @@ if user_input := st.chat_input("Ask me questions about the CANMAT depression gui
         message_idx = len(st.session_state.messages) - 1
         st.session_state.message_sources[message_idx] = results
         
+        # Log conversation to Google Sheets
+        try:
+            current_embedder = get_current_model_info()["embedder_name"] if get_current_model_info() else "Unknown"
+            session_id = st.session_state.get('client_key', 'Unknown')
+            
+            st.session_state.feedback_manager.log_conversation(
+                session_id=session_id,
+                user_query=user_input,
+                ai_response=collected,
+                embedder_model=current_embedder,
+                llm_model=selected_model,
+                temperature=temperature,
+                top_p=top_p,
+                max_length=max_length,
+                response_time=response_time
+            )
+        except Exception as log_error:
+            print(f"Warning: Failed to log conversation to Google Sheets: {log_error}")
+        
         st.rerun()
         
     except Exception as e:

src/feedback_utils.py

@@ -71,9 +71,30 @@ class FeedbackManager:
                 ]
                 self.worksheet.append_row(headers)
             
+            # Get or create the conversation log worksheet
+            try:
+                self.conversation_worksheet = self.sheet.worksheet("Conversations")
+            except gspread.WorksheetNotFound:
+                # Create the worksheet with headers
+                self.conversation_worksheet = self.sheet.add_worksheet(title="Conversations", rows="1000", cols="10")
+                headers = [
+                    "Timestamp",
+                    "Session ID",
+                    "User Query", 
+                    "AI Response",
+                    "Embedder Model",
+                    "LLM Model",
+                    "Temperature",
+                    "Top P",
+                    "Max Length",
+                    "Response Time (seconds)"
+                ]
+                self.conversation_worksheet.append_row(headers)
+            
         except Exception as e:
             st.error(f"❌ Failed to initialize Google Sheets: {str(e)}")
             self.client = None
+            self.conversation_worksheet = None
     
     def save_feedback(self, user_query, ai_response, source_rating, answer_rating, 
                      feedback_q1,
@@ -114,3 +135,37 @@ class FeedbackManager:
     def is_connected(self):
         """Check if Google Sheets connection is available"""
         return self.client is not None and self.worksheet is not None
+    
+    def log_conversation(self, session_id, user_query, ai_response, 
+                        embedder_model, llm_model, temperature, top_p, max_length, response_time):
+        """Log conversation data to Google Sheets"""
+        if not hasattr(self, 'conversation_worksheet') or not self.conversation_worksheet:
+            print("⚠️ Google Sheets conversation logging not initialized. Skipping log.")
+            return False
+        
+        try:
+            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            
+            # Truncate long responses to fit in Google Sheets cells
+            user_query_truncated = user_query[:500] + "..." if len(user_query) > 500 else user_query
+            ai_response_truncated = ai_response[:1000] + "..." if len(ai_response) > 1000 else ai_response
+            
+            row = [
+                timestamp,
+                session_id,
+                user_query_truncated,
+                ai_response_truncated,
+                embedder_model,
+                llm_model,
+                temperature,
+                top_p,
+                max_length,
+                round(response_time, 2)
+            ]
+            
+            self.conversation_worksheet.append_row(row)
+            return True
+            
+        except Exception as e:
+            print(f"❌ Failed to log conversation: {str(e)}")
+            return False