JanetBackEnd/ResponseGenerator.py

181 lines
9.3 KiB
Python
Raw Normal View History

2023-03-30 15:17:54 +02:00
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
2023-04-04 05:34:47 +02:00
from datetime import datetime
2023-03-30 15:17:54 +02:00
class ResponseGenerator:
2023-04-04 05:34:47 +02:00
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 = {}
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
def update_index(self, index):
self.index = index
def update_db(self, db):
self.db = db
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
def _get_resources_links(self, item):
if len(item) == 0:
return []
links = []
for rsrc in item['resources']:
links.append(rsrc['url'])
return links
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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 {}
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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 []
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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]
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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!"
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
elif action == "OffenseReject":
return "I am sorry, I cannot answer to this kind of language"
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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)
2023-03-30 15:17:54 +02:00
links = self._get_resources_links(self.paper)
2023-04-04 05:34:47 +02:00
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])
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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)
2023-03-30 15:17:54 +02:00
links = self._get_resources_links(self.dataset)
2023-04-04 05:34:47 +02:00
return str("This dataset could be relevant: " + self.dataset['title'] + '. ' + "It can be downloaded at " + links[0])
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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)
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
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
2023-03-30 15:17:54 +02:00
2023-04-04 05:34:47 +02:00
elif action == "ClarifyResource":
if state['intent'] in ['FINDPAPER', 'SUMMARIZEPAPER']:
return 'Please specify the title, the topic or the paper of interest.'
else:
return 'Please specify the title, the topic or the dataset of interest.'
elif action == "GenClarify":
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