context = {}

ERROR_THRESHOLD = 0.25
def classify(sentence):
    results = model.predict([bow(sentence, words)])[0]
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], r[1]))
    return return_list

def response(sentence, userID='123', show_details=False):
    results = classify(sentence)
    if results:
        while results:
            for i in intents['intents']:
                if i['tag'] == results[0][0]:
                    if 'context_set' in i:
                        if show_details: print ('context:', i['context_set'])
                        context[userID] = i['context_set']


                    if not 'context_filter' in i or (userID in context and 'context_filter' in i and i['context_filter'] == context[userID]):
                        if show_details: print ('tag:', i['tag'])
                        # sorteia uma resposta da intenção
                        return print("ChatBot:",random.choice(i['responses']))

            results.pop(0)

#funcao para receber frases e dar respostas com detalhes
def mensagem(frase):
    response(frase, show_details=True)

#example
mensagem("oi tudo bem ?")