ValueError: Dimension mismatch

Another solution will be using vector.vocabulary

# after trainning the data
vector = CountVectorizer()
vector.fit(self.x_data)
training_data = vector.transform(self.x_data)
bayes = MultinomialNB()
bayes.fit(training_data, y_data)

# use vector.vocabulary for predict
vector = CountVectorizer(vocabulary=vector.vocabulary_) #vocabulary is a parameter, it should be vocabulary_ as it is an attribute.
text_vector = vector.transform(text)
trained_model.predict_prob(text_vector)

Sounds to me, like you just need to use vectorizer.transform for the test dataset, since the training dataset fixes the vocabulary (you cannot know the full vocabulary including the training set afterall). Just to be clear, thats vectorizer.transform instead of vectorizer.fit_transform.