import os import warnings import faiss import torch from flask import Flask, render_template, request, jsonify from flask_cors import CORS, cross_origin import psycopg2 import spacy import spacy_transformers import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline from User import User from VRE import VRE from NLU import NLU from DM import DM from Recommender import Recommender from ResponseGenerator import ResponseGenerator import pandas as pd import time import threading from sentence_transformers import SentenceTransformer app = Flask(__name__) url = os.getenv("FRONTEND_URL_WITH_PORT") cors = CORS(app, resources={r"/api/predict": {"origins": url}, r"/api/feedback": {"origins": url}, r"/api/dm": {"origins": url}, r"/health": {"origins": "*"} }) conn = psycopg2.connect( host="janet-pg", database=os.getenv("POSTGRES_DB"), user=os.getenv("POSTGRES_USER"), password=os.getenv("POSTGRES_PASSWORD")) cur = conn.cursor() users = {} def vre_fetch(): while True: time.sleep(1000) print('getting new material') vre.get_vre_update() vre.index_periodic_update() rg.update_index(vre.get_index()) rg.update_db(vre.get_db()) def user_interest_decay(user): while True: print("decaying interests after 3 minutes for " + user.username) time.sleep(180) user.decay_interests() def clear_inactive(): while True: time.sleep(1) for username in users: if users[username]['activity'] > 3600: del users[username] users[username]['activity'] += 1 @app.route("/health", methods=['GET']) def health(): return "Success", 200 @app.route("/api/dm", methods=['POST']) def init_dm(): username = request.get_json().get("username") token = '2c1e8f88-461c-42c0-8cc1-b7660771c9a3-843339462' if username not in users: users[username] = {'dm': DM(), 'activity': 0, 'user': User(username, token)} threading.Thread(target=user_interest_decay, args=(users[username]['user'],), name='decayinterest_'+username).start() message = {"answer": "your assigned name is " + username, "assignedname": username} else: message = {"answer": "welcome back " + username, "assignedname": username} return message @app.route("/api/predict", methods=['POST']) def predict(): text = request.get_json().get("message") username = request.get_json().get("username") dm = users[username]['dm'] user = users[username]['user'] message = {} if text == "": state = {'help': True, 'inactive': False, 'modified_query':"", 'intent':""} dm.update(state) action = dm.next_action() response = rg.gen_response(action, vrename=vre.name) message = {"answer": response} elif text == "": state = {'help': False, 'inactive': True, 'modified_query':"recommed: ", 'intent':""} dm.update(state) action = dm.next_action() response = rg.gen_response(action, username=user.username, vrename=vre.name) message = {"answer": response} new_state = {'modified_query': response} dm.update(new_state) else: state = nlu.process_utterance(text, dm.get_consec_history(), dm.get_sep_history()) state['help'] = False state['inactive'] = False old_user_interests = user.get_user_interests() old_vre_material = pd.concat([vre.db['paper_db'], vre.db['dataset_db']]).reset_index(drop=True) user_interests = [] for entity in state['entities']: if entity['entity'] == 'TOPIC': user_interests.append(entity['value']) user.update_interests(user_interests) new_user_interests = user.get_user_interests() new_vre_material = pd.concat([vre.db['paper_db'], vre.db['dataset_db']]).reset_index(drop=True) if (new_user_interests != old_user_interests or len(old_vre_material) != len(new_vre_material)): rec.generate_recommendations(username, new_user_interests, new_vre_material) dm.update(state) action = dm.next_action() response = rg.gen_response(action=action, utterance=state['modified_query'], state=dm.get_recent_state(), consec_history=dm.get_consec_history(), chitchat_history=dm.get_chitchat_history(), vrename=vre.name) message = {"answer": response, "query": text, "cand": "candidate", "history": dm.get_consec_history(), "modQuery": state['modified_query']} if state['intent'] == "QA": response = response.split("_______ \n The answer is: ")[1] new_state = {'modified_query': response, 'intent': state['intent']} dm.update(new_state) reply = jsonify(message) users[username]['dm'] = dm users[username]['user'] = user users[username]['activity'] = 0 return reply @app.route('/api/feedback', methods = ['POST']) def feedback(): data = request.get_json().get("feedback") print(data) cur.execute('INSERT INTO feedback_experimental (query, history, janet_modified_query, is_modified_query_correct, user_modified_query, evidence_useful, response, preferred_response, response_length_feedback, response_fluency_feedback, response_truth_feedback, response_useful_feedback, response_time_feedback, response_intent) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)', (data['query'], data['history'], data['modQuery'], data['queryModCorrect'], data['correctQuery'], data['evidence'], data['janetResponse'], data['preferredResponse'], data['length'], data['fluency'], data['truthfulness'], data['usefulness'], data['speed'], data['intent']) ) conn.commit() reply = jsonify({"status": "done"}) return reply if __name__ == "__main__": warnings.filterwarnings("ignore") device = "cuda" if torch.cuda.is_available() else "cpu" device_flag = torch.cuda.current_device() if torch.cuda.is_available() else -1 query_rewriter = pipeline("text2text-generation", model="castorini/t5-base-canard") intent_classifier = pipeline("sentiment-analysis", model='/models/intent_classifier', device=device_flag) entity_extractor = spacy.load("/models/entity_extractor") offensive_classifier = pipeline("sentiment-analysis", model='/models/offensive_classifier', device=device_flag) ambig_classifier = pipeline("sentiment-analysis", model='/models/ambig_classifier', device=device_flag) coref_resolver = spacy.load("en_coreference_web_trf") nlu = NLU(query_rewriter, coref_resolver, intent_classifier, offensive_classifier, entity_extractor, ambig_classifier) #load retriever and generator retriever = SentenceTransformer('/models/retriever/').to(device) qa_generator = pipeline("text2text-generation", model="/models/train_qa", device=device_flag) summ_generator = pipeline("text2text-generation", model="/models/train_summ", device=device_flag) chat_generator = pipeline("text2text-generation", model="/models/train_chat", device=device_flag) amb_generator = pipeline("text2text-generation", model="/models/train_amb_gen", device=device_flag) generators = {'qa': qa_generator, 'chat': chat_generator, 'amb': amb_generator, 'summ': summ_generator} #load vre token = '2c1e8f88-461c-42c0-8cc1-b7660771c9a3-843339462' vre = VRE("assistedlab", token, retriever) vre.init() index = vre.get_index() db = vre.get_db() threading.Thread(target=vre_fetch, name='updatevre').start() threading.Thread(target=clear_inactive, name='clear').start() rec = Recommender(retriever) rg = ResponseGenerator(index,db, rec, generators, retriever) cur.execute('CREATE TABLE IF NOT EXISTS feedback_experimental (id serial PRIMARY KEY,' 'query text NOT NULL,' 'history text NOT NULL,' 'janet_modified_query text NOT NULL,' 'is_modified_query_correct text NOT NULL,' 'user_modified_query text NOT NULL, evidence_useful text NOT NULL,' 'response text NOT NULL,' 'preferred_response text,' 'response_length_feedback text NOT NULL,' 'response_fluency_feedback text NOT NULL,' 'response_truth_feedback text NOT NULL,' 'response_useful_feedback text NOT NULL,' 'response_time_feedback text NOT NULL,' 'response_intent text NOT NULL);' ) conn.commit() app.run(host='0.0.0.0')