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Author: Vishwa22

dialogue_manager.py

@@ -0,0 +1,93 @@
+import os
+from sklearn.metrics.pairwise import pairwise_distances_argmin
+
+from chatterbot import ChatBot
+from utils import *
+paths = RESOURCE_PATH
+
+class ThreadRanker(object):
+    def __init__(self, paths):
+        self.word_embeddings, self.embeddings_dim = load_embeddings(paths['WORD_EMBEDDINGS'])
+        self.thread_embeddings_folder = paths['THREAD_EMBEDDINGS_FOLDER']
+
+    def __load_embeddings_by_tag(self, tag_name):
+        embeddings_path = os.path.join(self.thread_embeddings_folder, tag_name + ".pkl")
+        thread_ids, thread_embeddings = unpickle_file(embeddings_path)
+        return thread_ids, thread_embeddings
+
+    def get_best_thread(self, question, tag_name):
+        """ Returns id of the most similar thread for the question.
+            The search is performed across the threads with a given tag.
+        """
+        thread_ids, thread_embeddings = self.__load_embeddings_by_tag(tag_name)
+
+        # HINT: you have already implemented a similar routine in the 3rd assignment.
+        
+        question_vec = question_to_vec(question, self.word_embeddings, self.embeddings_dim)#### YOUR CODE HERE ####
+        from sklearn.metrics.pairwise import cosine_similarity
+        best_thread = np.argmax(cosine_similarity([question_vec],thread_embeddings)[0])#### YOUR CODE HERE ####
+        
+        return thread_ids[best_thread]
+
+
+class DialogueManager(object):
+    def __init__(self, paths):
+        print("Loading resources...")
+
+        # Intent recognition:
+        self.intent_recognizer = unpickle_file(paths['INTENT_RECOGNIZER'])
+        self.tfidf_vectorizer = unpickle_file(paths['TFIDF_VECTORIZER'])
+
+        self.ANSWER_TEMPLATE = 'I think its about %s\nThis thread might help you: https://stackoverflow.com/questions/%s'
+
+        # Goal-oriented part:
+        self.tag_classifier = unpickle_file(paths['TAG_CLASSIFIER'])
+        self.thread_ranker = ThreadRanker(paths)
+
+    def create_chitchat_bot(self):
+        """Initializes self.chitchat_bot with some conversational model."""
+
+        # Hint: you might want to create and train chatterbot.ChatBot here.
+        # It could be done by creating ChatBot with the *trainer* parameter equals 
+        # "chatterbot.trainers.ChatterBotCorpusTrainer"
+        # and then calling *train* function with "chatterbot.corpus.english" param
+        
+        ########################
+        #### YOUR CODE HERE ####
+        from chatterbot.trainers import ChatterBotCorpusTrainer
+
+        mybot = ChatBot("Training Example")
+        mybot.set_trainer(ChatterBotCorpusTrainer)
+
+        mybot.train("chatterbot.corpus.english")
+        
+        return mybot
+       
+        ########################
+       
+    def generate_answer(self, question):
+        """Combines stackoverflow and chitchat parts using intent recognition."""
+
+        # Recognize intent of the question using `intent_recognizer`.
+        # Don't forget to prepare question and calculate features for the question.
+        
+        prepared_question = text_prepare(question)#### YOUR CODE HERE ####
+        features = self.tfidf_vectorizer.transform([prepared_question])#### YOUR CODE HERE ####
+        intent = self.intent_recognizer.predict(features)#### YOUR CODE HERE ####
+        
+        # Chit-chat part:   
+        if intent == 'dialogue':
+            # Pass question to chitchat_bot to generate a response.
+            mybot=self.create_chitchat_bot()
+            response = mybot.get_response(question)#### YOUR CODE HERE ####
+            return response
+        
+        # Goal-oriented part:
+        else:        
+            # Pass features to tag_classifier to get predictions.
+            tag = self.tag_classifier.predict(features)#### YOUR CODE HERE ####
+            
+            # Pass prepared_question to thread_ranker to get predictions.
+            thread_id = self.thread_ranker.get_best_thread(prepared_question,tag[0])#### YOUR CODE HERE ####
+           
+            return self.ANSWER_TEMPLATE % (tag, thread_id)