Update app.py

app.py

@@ -107,11 +107,13 @@ def demo():
             chunk_size = gr.Slider(100, 1000, value=500, label="Tamanho dos Chunks")
             chunk_overlap = gr.Slider(0, 200, value=50, label="Sobreposição")
             process_btn = gr.Button("Processar PDFs")
+            process_status = gr.Textbox(label="Status do Processamento", interactive=False)
             
         with gr.Tab("🧠 Modelo"):
             model_selector = gr.Dropdown(list_llm_simple, label="Selecione o Modelo", value=list_llm_simple[0])
             temperature = gr.Slider(0, 1, value=0.7, label="Criatividade")
             load_model_btn = gr.Button("Carregar Modelo")
+            model_status = gr.Textbox(label="Status do Modelo", interactive=False)
             
         with gr.Tab("💬 Chat"):
             chatbot = gr.Chatbot(height=400)
@@ -120,25 +122,28 @@ def demo():
             
         # Eventos
         process_btn.click(
-            lambda files, cs, co: create_db(load_doc([f.name for f in files], cs, co), "docs"),
+            lambda files, cs, co: (create_db(load_doc([f.name for f in files], cs, co), "docs"), "Documentos processados!"),
             inputs=[pdf_input, chunk_size, chunk_overlap],
-            outputs=vector_db
+            outputs=[vector_db, process_status]
         )
         
         load_model_btn.click(
-            lambda model, temp: initialize_llmchain(list_llm[list_llm_simple.index(model)], temp, 512, 3, vector_db.value),
+            lambda model, temp: (initialize_llmchain(list_llm[list_llm_simple.index(model)], temp, 512, 3, vector_db.value), "Modelo carregado!"),
             inputs=[model_selector, temperature],
-            outputs=qa_chain
+            outputs=[qa_chain, model_status]
         )
         
         def respond(message, chat_history):
+            if qa_chain.value is None:
+                return "Por favor, carregue um modelo primeiro.", chat_history
             result = qa_chain.value({"question": message, "chat_history": chat_history})
             response = result["answer"]
             sources = "\n".join([f"📄 Página {doc.metadata['page']+1}: {doc.page_content[:50]}..." 
                                for doc in result["source_documents"][:2]])
-            return f"{response}\n\n🔍 Fontes:\n{sources}"
+            chat_history.append((message, f"{response}\n\n🔍 Fontes:\n{sources}"))
+            return "", chat_history
         
-        msg.submit(respond, [msg, chatbot], chatbot)
+        msg.submit(respond, [msg, chatbot], [msg, chatbot])
         
     demo.launch()