SimpleDialogueManager.py

class SimpleDialogueManager(object):
    """
    This is a simple dialogue manager to test the telegram bot.
    The main part of our bot will be written here.
    """
    def __init__(self):
        # Instantiate all the models and TFIDF Objects.
        print("Loading resources...")
        # Instantiate a Chatterbot for Chitchat type questions
        from chatterbot import ChatBot
        from chatterbot.trainers import ChatterBotCorpusTrainer
        chatbot = ChatBot('MLWhizChatterbot')
        trainer = ChatterBotCorpusTrainer(chatbot)
        trainer.train('chatterbot.corpus.english')
        self.chitchat_bot = chatbot
        print("Loading Word2vec model...")
        # Instantiate the Google's pre-trained Word2Vec model.
        self.model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) 
        print("Loading Classifier objects...")
        # Load the intent classifier and tag classifier
        self.intent_recognizer =  pickle.load(open('resources/intent_clf.pkl', 'rb'))
        self.tag_classifier =  pickle.load(open('resources/tag_clf.pkl', 'rb'))
        # Load the TFIDF vectorizer object
        self.tfidf_vectorizer = pickle.load(open('resources/tfidf.pkl', 'rb'))
        print("Finished Loading Resources")

    def get_similar_question(self,question,tag):
        # get the path where all question embeddings are kept and load the post_ids and post_embeddings
        embeddings_path = 'resources/embeddings_folder/' + tag + ".pkl"
        post_ids, post_embeddings = pickle.load(open(embeddings_path, 'rb'))
        # Get the embeddings for the question
        question_vec = question_to_vec(question, self.model, 300)
        # find index of most similar post
        best_post_index = pairwise_distances_argmin(question_vec,
                                                    post_embeddings)
        # return best post id
        return post_ids[best_post_index]
    def generate_answer(self, question): 
        prepared_question = text_prepare(question)
        features = self.tfidf_vectorizer.transform([prepared_question])
        # find intent
        intent = self.intent_recognizer.predict(features)[0]
        # Chit-chat part:   
        if intent == 'dialogue':
            response = self.chitchat_bot.get_response(question)
        # Stack Overflow Question
        else:
            # find programming language
            tag = self.tag_classifier.predict(features)[0]
            # find most similar question post id
            post_id = self.get_similar_question(question,tag)[0]
            # respond with 
            response = 'I think its about %s\nThis thread might help you: https://stackoverflow.com/questions/%s' % (tag, post_id)
        return response