Update app.py

app.py

@@ -649,27 +649,27 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             
             # --- NEW: Handler for Employer Branding Agent Chat ---
             eb_agent_instance_dict = {"agent": None} # To store agent instance across calls if needed, or re-init
-
+            
             async def handle_eb_agent_chat(user_message: str, chat_history_list: list, current_token_state: dict):
                 # Expected outputs: [eb_agent_chatbot_ui, eb_agent_chat_history_st, eb_agent_chat_input_ui, eb_agent_status_md, eb_agent_schema_display_md]
                 # (5 components)
-
+            
                 if not EB_AGENT_AVAILABLE or not os.getenv('GEMINI_API_KEY'):
                     no_key_msg = "L'Agente AI non è disponibile. Assicurati che GEMINI_API_KEY sia configurata."
                     chat_history_list.append([user_message, no_key_msg])
                     # Yield updates for all 5 components
                     yield chat_history_list, chat_history_list, gr.update(value=""), gr.update(value=no_key_msg), gr.update(value="Nessuno schema disponibile.")
                     return
-
+            
                 if not user_message.strip():
                     # Yield updates for all 5 components
                     yield chat_history_list, chat_history_list, gr.update(value=""), gr.update(value="Stato Agente: Per favore, inserisci una domanda."), gr.update() # No change to schema display
                     return
-
+            
                 status_update_msg = "Stato Agente: Elaborazione della tua richiesta..."
                 # Show user message immediately, update status
                 yield chat_history_list + [[user_message, None]], chat_history_list + [[user_message, None]], gr.update(value=""), gr.update(value=status_update_msg), gr.update()
-
+            
                 # Prepare DataFrames for the agent
                 df_follower_stats = current_token_state.get("bubble_follower_stats_df", pd.DataFrame())
                 df_posts = current_token_state.get("bubble_posts_df", pd.DataFrame()) 
@@ -699,46 +699,45 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                     embedding_model_name=EB_AGENT_EMBEDDING_MODEL
                 )
                 
+                # Initialize the agent's chat history with previous conversation
                 agent_internal_history = []
-                for user_q, ai_r in chat_history_list: # Use the passed chat_history_list
+                for user_q, ai_r in chat_history_list:
                     if user_q: agent_internal_history.append({"role": "user", "content": user_q})
-                    if ai_r: agent_internal_history.append({"role": "assistant", "content": ai_r})
+                    if ai_r: agent_internal_history.append({"role": "model", "content": ai_r})
+                
+                # ADD THE CURRENT USER MESSAGE TO THE AGENT'S HISTORY - THIS WAS MISSING!
+                agent_internal_history.append({"role": "user", "content": user_message})
                 current_agent.chat_history = agent_internal_history
-
+            
                 try:
+                    # Initialize the agent first
+                    init_success = await current_agent.initialize()
+                    if not init_success:
+                        error_msg = "Errore: Impossibile inizializzare l'agente AI."
+                        updated_history = chat_history_list + [[user_message, error_msg]]
+                        yield updated_history, updated_history, gr.update(value=""), gr.update(value="Stato Agente: Errore di inizializzazione"), gr.update(value=schemas_text_for_display)
+                        return
+            
                     logging.info(f"Sending to EB Agent. User: '{user_message}'. DF Keys: {list(dataframes_for_agent.keys())}")
                     ai_response = await current_agent.process_query(user_query=user_message)
                     
-                    updated_gradio_history = []
-                    temp_hist = current_agent.chat_history 
-                    for i in range(0, len(temp_hist), 2):
-                        u_msg = temp_hist[i]['content']
-                        a_msg = temp_hist[i+1]['content'] if i+1 < len(temp_hist) else "Thinking..." 
-                        updated_gradio_history.append([u_msg, a_msg])
+                    # Update the chat history with the AI response
+                    updated_history = chat_history_list + [[user_message, ai_response]]
                     
                     status_update_msg = "Stato Agente: Risposta ricevuta."
                     # Yield final updates for all 5 components
-                    yield updated_gradio_history, updated_gradio_history, gr.update(value=""), gr.update(value=status_update_msg), gr.update(value=schemas_text_for_display)
-
+                    yield updated_history, updated_history, gr.update(value=""), gr.update(value=status_update_msg), gr.update(value=schemas_text_for_display)
+            
                 except Exception as e:
                     logging.error(f"Error during EB Agent processing: {e}", exc_info=True)
                     error_msg = f"# Errore dell'Agente AI:\n{type(e).__name__}: {str(e)}"
                     
-                    # Ensure the current turn in chat_history_list reflects the error
-                    # The last item in chat_history_list is [user_message, None] from the first yield
-                    current_turn_history = chat_history_list + [[user_message, error_msg]] # This might duplicate user message if not careful
-                    
-                    # Let's reconstruct history carefully to avoid duplicates
-                    final_error_history = chat_history_list # This already has [user_message, None] as last item if first yield happened
-                    if final_error_history and final_error_history[-1][0] == user_message and final_error_history[-1][1] is None:
-                        final_error_history[-1][1] = error_msg # Update the assistant part of the last entry
-                    else: # Should not happen if first yield was correct
-                        final_error_history.append([user_message, error_msg])
-
-
+                    # Update history with error message
+                    updated_history = chat_history_list + [[user_message, error_msg]]
+            
                     status_update_msg = f"Stato Agente: Errore - {type(e).__name__}"
                     # Yield error updates for all 5 components
-                    yield final_error_history, final_error_history, gr.update(value=""), gr.update(value=status_update_msg), gr.update(value=schemas_text_for_display)
+                    yield updated_history, updated_history, gr.update(value=""), gr.update(value=status_update_msg), gr.update(value=schemas_text_for_display)
 
 
             def clear_eb_agent_chat_history():