Update app.py

app.py

@@ -10,6 +10,9 @@ GIT_REPO = "https://github.com/luiz-ouroboros/payment_api.git"
 PROJECT_DIR = "payment_api"
 STORAGE_DIR = "storage"
 
+# Configurar a chave da API da OpenAI
+os.environ["OPENAI_API_KEY"] = "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"
+
 # Baixar o repositório do GitHub (se não existir)
 if not os.path.exists(PROJECT_DIR):
     print("Clonando repositório...")
@@ -28,21 +31,30 @@ documents = SimpleDirectoryReader(PROJECT_DIR).load_data()
 
 # Verificar se o diretório de persistência existe
 if not os.path.exists(STORAGE_DIR):
-    # Criar o índice e persistir os dados
-    print("Criando índice e persistindo dados...")
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=STORAGE_DIR)
+    try:
+        print("Criando índice e persistindo dados...")
+        index = VectorStoreIndex.from_documents(documents)
+        index.storage_context.persist(persist_dir=STORAGE_DIR)
+    except Exception as e:
+        print(f"Erro ao criar o índice: {e}")
+        raise
 else:
-    # Carregar o índice existente
-    print("Carregando índice existente...")
-    storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
-    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+    try:
+        print("Carregando índice existente...")
+        storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
+        index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+    except Exception as e:
+        print(f"Erro ao carregar o índice: {e}")
+        raise
 
 # Função para responder perguntas sobre o projeto
 def respond(message, history):
-    query_engine = index.as_query_engine()
-    response = query_engine.query(message)
-    return response.response
+    try:
+        query_engine = index.as_query_engine()
+        response = query_engine.query(message)
+        return response.response
+    except Exception as e:
+        return f"Erro ao processar a pergunta: {e}"
 
 # Interface do chatbot no Gradio
 demo = gr.ChatInterface(respond, title="IA Especializada no Meu Projeto")