JanetBackEnd/main.py

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"/predict": {"origins": url}, r"/feedback": {"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"))
"""
conn = psycopg2.connect(host="https://janet-app-db.d4science.org",
        database="janet",
        user="janet_user",
        password="2fb5e81fec5a2d906a04")
"""
cur = conn.cursor()


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():
    while True:
        print("decaying interests after 3 minutes")
        time.sleep(180)
        user.decay_interests()

@app.route("/health", methods=['GET'])
def check_health():
    return "Success", 200

@app.route("/predict", methods=['POST'])
def predict():
    text = request.get_json().get("message")
    message = {}
    if text == "<HELP_ON_START>":
        state = {'help': True, 'inactive': False, 'modified_query':""}
        dm.update(state)
        action = dm.next_action()
        response = rg.gen_response(action)
        message = {"answer": response}
    elif text == "<RECOMMEND_ON_IDLE>":
        state = {'help': False, 'inactive': True, 'modified_query':"recommed: "}
        dm.update(state)
        action = dm.next_action()
        response = rg.gen_response(action, username=user.username)
        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']]).drop_duplicates(keep=False).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']]).drop_duplicates(keep=False).reset_index(drop=True)
        if (new_user_interests() != old_user_interests or old_vre_material != new_vre_material):
            rec.generate_recommendations(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())
        message = {"answer": response, "query": text, "cand": "candidate", "history": dm.get_consec_history(), "modQuery": state['modified_query']}
        new_state = {'modified_query': response}
        dm.update(new_state)
    reply = jsonify(message)
    return reply

@app.route('/feedback', methods = ['POST'])
def feedback():
    data = request.get_json().get("feedback")
    print(data)
    
    cur.execute('INSERT INTO feedback_trial (query, history, janet_modified_query, is_modified_query_correct, user_modified_query, 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)',
            (data['query'], data['history'], data['modQuery'],
             data['queryModCorrect'], data['correctQuery'],
             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/BigRetriever/').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()
    user = User("ahmed", token)

    threading.Thread(target=vre_fetch, name='updatevre').start()
    
    threading.Thread(target=user_interest_decay, name='decayinterest').start()

    rec = Recommender(retriever)
    
    dm = DM()

    rg = ResponseGenerator(index,db, rec, generators, retriever)

    
    cur.execute('CREATE TABLE IF NOT EXISTS feedback_trial (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,'
                                 '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', port=4000)