JanetBackEnd/ResponseGenerator.py

from sentence_transformers import models, SentenceTransformer
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import faiss
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
from datetime import datetime

class ResponseGenerator:
    def __init__(self, index, db,recommender,generators, retriever, num_retrieved=1):
        self.generators = generators
        self.retriever = retriever 
        self.recommender = recommender 
        self.db = db
        self.index = index
        self.num_retrieved = num_retrieved
        self.paper = {}
        self.dataset = {}

    def update_index(self, index):
        self.index = index
    def update_db(self, db):
        self.db = db
  
    def _get_resources_links(self, item):
        if len(item) == 0:
            return []
        links = []
        for rsrc in item['resources']:
            links.append(rsrc['url'])
        return links

    def _get_matching_titles(self, rsrc, title):
        cand = self.db[rsrc].loc[self.db[rsrc]['title'] == title.lower()].reset_index(drop=True)
        if not cand.empty:
            return cand.loc[0]
        else:
            return {}
  
    def _get_matching_authors(self, rsrc, author):
        cand = self.db[rsrc].loc[self.db[rsrc]['author'] == author.lower()].reset_index(drop=True)
        if not cand.empty:
            return cand.loc[0]
        else:
            return {}
  
    def _get_matching_topics(self, rsrc, topic):
        matches = []
        score = 0.7
        for i, cand in self.db[rsrc].iterrows():
            for tag in cand['tags']:
                sim = cosine_similarity(np.array(self.retriever.encode([tag])), np.array(self.retriever.encode([topic.lower()])))
                if sim > score:
                    if(len(matches)>0):
                        matches[0] = cand
                    else:
                        matches.append(cand)
                    score = sim
        if len(matches) > 0:
            return matches[0]
        else:
            return []
  
    def _search_index(self, index_type, db_type, query):
        xq = self.retriever.encode([query])
        D, I = self.index[index_type].search(xq, self.num_retrieved)
        return self.db[db_type].iloc[[I[0]][0]].reset_index(drop=True).loc[0]


    def gen_response(self, action, utterance=None, username=None, state=None, consec_history=None):
        if action == "Help":
            return "Hey it's Janet! I am here to help you make use of the datasets and papers in the VRE. I can answer questions whose answers may be inside the papers. I can summarize papers for you. I can also chat with you. So, whichever it is, I am ready to chat!"
        elif action == "Recommend":
            prompt = self.recommender.make_recommendation(username)
            if prompt != "":
                return prompt
            else:
                return "I can help you with exploiting the contents of the VRE, just let me know!"    
    
        elif action == "OffenseReject":
            return "I am sorry, I cannot answer to this kind of language"
    
        elif action == "ConvGen":
            gen_kwargs = {"length_penalty": 2.5, "num_beams":2, "max_length": 30}
            answer = self.generators['chat']('history: '+ consec_history + ' ' + utterance + ' persona: ' + 'I am Janet. My name is Janet. I am an AI developed by CNR to help VRE users.' , **gen_kwargs)[0]['generated_text']
            return answer
    
        elif action == "findPaper":
            for entity in state['entities']:
                if (entity['entity'] == 'TITLE'):
                    self.paper = self._get_matching_titles('paper_db', entity['value'])
                    links = self._get_resources_links(self.paper)
                    if len(self.paper) > 0 and len(links) > 0:
                        return str("Here is the paper you want: " + self.paper['title'] + '. ' + "It can be downloaded at " +  links[0])
                    else:
                        self.paper = self._search_index('paper_titles_index', 'paper_db', entity['value'])
                        links = self._get_resources_links(self.paper)
                        return str("This paper could be relevant: " + self.paper['title'] + '. ' + "It can be downloaded at " +  links[0])
                if(entity['entity'] == 'TOPIC'):
                    self.paper = self._get_matching_topics('paper_db', entity['value'])
                    links = self._get_resources_links(self.paper)
                    if len(self.paper) > 0 and len(links) > 0:
                        return str("This paper could be relevant: " + self.paper['title'] + '. ' + "It can be downloaded at " +  links[0])
            
                if(entity['entity'] == 'AUTHOR'):
                    self.paper = self._get_matching_authors('paper_db', entity['value'])
                    links = self._get_resources_links(self.paper)
                    if len(self.paper) > 0 and len(links) > 0:
                        return str("Here is the paper you want: " + self.paper['title'] + '. ' + "It can be downloaded at " +  links[0])
            
            self.paper = self._search_index('paper_desc_index', 'paper_db', utterance)
            links = self._get_resources_links(self.paper)
            return str("This paper could be relevant: " + self.paper['title'] + '. ' + "It can be downloaded at " +  links[0])
        
        elif action == "findDataset":
            for entity in state['entities']:
                if (entity['entity'] == 'TITLE'):
                    self.dataset = self._get_matching_titles('dataset_db', entity['value'])
                    links = self._get_resources_links(self.dataset)
                    if len(self.dataset) > 0 and len(links) > 0:
                        return str("Here is the dataset you wanted: " + self.dataset['title'] + '. ' + "It can be downloaded at " +  links[0])
                    else:
                        self.dataset = self._search_index('dataset_titles_index', 'dataset_db', entity['value'])
                        links = self._get_resources_links(self.dataset)
                        return str("This dataset could be relevant: " + self.dataset['title'] + '. ' + "It can be downloaded at " +  links[0])
                if(entity['entity'] == 'TOPIC'):
                    self.dataset = self._get_matching_topics('dataset_db', entity['value'])
                    links = self._get_resources_links(self.dataset)
                    if len(self.dataset) > 0 and len(links) > 0:
                        return str("This dataset could be relevant: " + self.dataset['title'] + '. ' + "It can be downloaded at " +  links[0])
        
                if(entity['entity'] == 'AUTHOR'):
                    self.dataset = self._get_matching_authors('dataset_db', entity['value'])
                    links = self._get_resources_links(self.dataset)
                    if len(self.dataset) > 0 and len(links) > 0:
                        return str("Here is the dataset you want: " + self.dataset['title'] + '. ' + "It can be downloaded at " +  links[0])
        
            self.dataset = self._search_index('dataset_desc_index', 'dataset_db', utterance)
            links = self._get_resources_links(self.dataset)
            return str("This dataset could be relevant: " + self.dataset['title'] + '. ' + "It can be downloaded at " +  links[0])
  

        elif action == "RetGen":
            #retrieve the most relevant paragraph
            content = str(self._search_index('content_index', 'content_db', utterance)['content'])
            #generate the answer
            gen_seq = 'question: '+utterance+" context: "+content
        
            #handle return random 2 answers
            gen_kwargs = {"length_penalty": 0.5, "num_beams":2, "max_length": 60}
            answer = self.generators['qa'](gen_seq, **gen_kwargs)[0]['generated_text']
            return str(answer)
      
        elif action == "sumPaper":
            if len(self.paper) == 0:
                for entity in state['entities']:
                    if (entity['entity'] == 'TITLE'):
                        self.paper = self._get_matching_titles('paper_db', entity['value'])
                        if (len(self.paper) > 0):
                            break
                if len(self.paper) == 0:
                    return "I cannot seem to find the requested paper. Try again by specifying the title of the paper."
            #implement that
            df = self.db['content_db'][self.db['content_db']['paperid'] == self.paper['id']]
            answer = ""
            for i, row in df.iterrows():
                gen_seq = 'summarize: '+row['content']
                gen_kwargs = {"length_penalty": 1.5, "num_beams":6, "max_length": 120}
                answer = self.generators['summ'](gen_seq, **gen_kwargs)[0]['generated_text'] + ' '
            return answer
        elif action == "Clarify":
            if state['intent'] in ['FINDPAPER', 'SUMMARIZEPAPER'] and len(state['entities']) == 0:
                if len(self.paper) == 0:
                    return 'Please specify the title, the topic of the paper of interest.'
            elif state['intent'] == 'FINDDATASET' and len(state['entities']) == 0:
                if len(self.dataset) == 0:
                    return 'Please specify the title, the topic of the dataset of interest.'
            else:
                gen_kwargs = {"length_penalty": 2.5, "num_beams":8, "max_length": 120}
                question = self.generators['amb']('question: '+ utterance + ' context: ' + consec_history , **gen_kwargs)[0]['generated_text']
                return question