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