Update app.py

app.py

@@ -253,6 +253,40 @@ def query_sql_agent(user_query, selected_model_name):
     except Exception as e:
         return f"Erro ao consultar o agente SQL: {e}"
 
+advanced_mode_enabled = False  # Novo estado global
+
+def toggle_advanced_mode(state):
+    global advanced_mode_enabled
+    advanced_mode_enabled = state
+    logging.info(f"[MODO AVANÇADO] {'Ativado' if state else 'Desativado'}")
+    return "Modo avançado ativado." if state else "Modo avançado desativado."
+
+def refine_response_with_llm(user_question, sql_response, chart_md=""):
+    prompt = (
+        f"Pergunta do usuário:\n{user_question}\n\n"
+        f"Resposta gerada pelo agente SQL:\n{sql_response}\n\n"
+        "Mantenha a resposta original e faça os ajustes e melhorias posteriormente.\n"
+        "Sua tarefa é refinar, complementar e melhorar a resposta.\n"
+        "Adicione interpretações estatísticas ou insights relevantes."
+    )
+
+    logging.info(f"[DEBUG] Prompt enviado ao modelo de refinamento:\n{prompt}\n")
+
+    try:
+        response = hf_client.chat.completions.create(
+            model=LLAMA_MODELS["LLaMA 70B"],
+            messages=[{"role": "system", "content": prompt}],
+            max_tokens=1200,
+            stream=False
+        )
+        improved_response = response["choices"][0]["message"]["content"]
+        logging.info(f"[DEBUG] Resposta do modelo de refinamento:\n{improved_response}\n")
+        return improved_response + ("\n\n" + chart_md if chart_md else "")
+
+    except Exception as e:
+        logging.error(f"[ERRO] Falha ao refinar resposta com LLM: {e}")
+        return sql_response + ("\n\n" + chart_md if chart_md else "")
+
 def chatbot_response(user_input, selected_model_name):
     start_time = time.time()
     response = query_sql_agent(user_input, selected_model_name)
@@ -260,6 +294,9 @@ def chatbot_response(user_input, selected_model_name):
 
     model_id = LLAMA_MODELS[selected_model_name]
 
+    if advanced_mode_enabled:
+        response = refine_response_with_llm(user_input, response)
+
     history_log.append({
         "Modelo LLM": model_id,
         "Pergunta": user_input,
@@ -281,6 +318,7 @@ def toggle_history():
     show_history_flag = not show_history_flag
     return history_log if show_history_flag else {}
 
+
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Column(scale=1):
@@ -288,6 +326,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             model_selector = gr.Dropdown(list(LLAMA_MODELS.keys()), value="LLaMA 70B", label="")
             csv_file = gr.File(file_types=[".csv"], label="")
             upload_feedback = gr.Markdown()
+            advanced_checkbox = gr.Checkbox(label="Refinar Resposta")
             reset_btn = gr.Button("Resetar")
 
         with gr.Column(scale=4):
@@ -317,6 +356,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             history_btn.click(toggle_history, outputs=history_output)
             csv_file.change(handle_csv_and_clear_chat, inputs=csv_file, outputs=[upload_feedback, chatbot])
             reset_btn.click(reset_all, outputs=[upload_feedback, chatbot, csv_file])
+            advanced_checkbox.change(toggle_advanced_mode, inputs=advanced_checkbox, outputs=[])
 
 if __name__ == "__main__":
     demo.launch(share=False)