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JanetBackEnd
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gcube_func_added

This commit is contained in:
ahmed531998 2023-04-18 02:59:58 +02:00
parent e705b0498c
commit 3752b6c40c
3 changed files with 50 additions and 40 deletions
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4
Recommender.py
View File

@ -32,7 +32,7 @@ class Recommender:
for i, material in new_material.iterrows():
self._match_tags(username, material, interest)
def make_recommendation(self, username):
def make_recommendation(self, username, name):
if len(self.curr_recommendations[username]) == 0:
return ""
to_consider = [idx for idx, value in enumerate(self.recommended[username]) if value == False]
@ -42,7 +42,7 @@ class Recommender:
index = self.recommended[username][index]
#while self.recommended[index] == True:
# index = random.choice(list(range(0, len(self.curr_recommendations))))
recommendation = "Hey " + username + "! This " + self.curr_recommendations[username][index][
recommendation = "Hey " + name + "! This " + self.curr_recommendations[username][index][
'type'].lower() + " about " + ', '.join(
self.curr_recommendations[username][index]['tags']).lower() + " was posted recently by " + \
self.curr_recommendations[username][index][

6
ResponseGenerator.py
View File

@ -88,11 +88,11 @@ class ResponseGenerator:
return self.db[db_type].iloc[[I[0]][0]].reset_index(drop=True).loc[0]
def gen_response(self, action, utterance=None, username=None, vrename=None, state=None, consec_history=None, chitchat_history=None):
def gen_response(self, action, utterance=None, name=None, username=None, vrename=None, state=None, consec_history=None, chitchat_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 catalogue of the " + vrename +" 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!"
return "Hey " + name + "! it's Janet! I am here to help you make use of the datasets and papers in the catalogue of the " + vrename +" 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)
prompt = self.recommender.make_recommendation(username, name)
if prompt != "":
return prompt
else:

80
main.py
View File

@ -6,6 +6,7 @@ from flask import Flask, render_template, request, jsonify
from flask_cors import CORS, cross_origin
import psycopg2
import spacy
import requests
import spacy_transformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
@ -40,20 +41,20 @@ cur = conn.cursor()
users = {}
def vre_fetch():
def vre_fetch(token):
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())
users[token]['args']['vre'].get_vre_update()
users[token]['args']['vre'].index_periodic_update()
users[token]['args']['rg'].update_index(vre.get_index())
users[token]['args']['rg'].update_db(vre.get_db())
def user_interest_decay(user):
def user_interest_decay(token):
while True:
print("decaying interests after 3 minutes for " + user.username)
print("decaying interests after 3 minutes for " + users[token]['username'])
time.sleep(180)
user.decay_interests()
users[token]['user'].decay_interests()
def clear_inactive():
while True:
@ -69,34 +70,52 @@ def health():
@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}
token = request.get_json().get("token")
headers = {"gcube-token": token, "Accept": "application/json"}
if token not in users:
url = 'https://api.d4science.org/rest/2/people/profile'
response = requests.get(url, headers=headers)
username = response.json()['result']['username']
name = response.json()['result']['fullname']
vre = VRE("assistedlab", token, retriever)
vre.init()
index = vre.get_index()
db = vre.get_db()
rg = ResponseGenerator(index,db, rec, generators, retriever)
args = {'vre': vre, 'rg': rg}
users[token] = {'username': username, 'name': name, 'dm': DM(), 'activity': 0, 'user': User(username, token), 'args': args}
threading.Thread(target=vre_fetch, args=(token,), name='updatevre_'+users[token]['username']).start()
threading.Thread(target=user_interest_decay, args=(token,), name='decayinterest_'+users[token]['username']).start()
message = {"answer": "Hi " + name.split()[0] + '!', "assignedname": username}
else:
message = {"answer": "welcome back " + username, "assignedname": username}
message = {"answer": "welcome back " + users[token]['name'].split()[0] + '!', "assignedname": users[token]['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']
token = request.get_json().get("token")
dm = users[token]['dm']
user = users[token]['user']
rg = users[token]['args']['rg']
vre = users[token]['args']['vre']
message = {}
if text == "<HELP_ON_START>":
state = {'help': True, 'inactive': False, 'modified_query':"", 'intent':""}
dm.update(state)
action = dm.next_action()
response = rg.gen_response(action, vrename=vre.name)
response = rg.gen_response(action, vrename=vre.name, username=users[token]['username'], name=users[token]['name'].split()[0])
message = {"answer": response}
elif text == "<RECOMMEND_ON_IDLE>":
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)
response = rg.gen_response(action, username=users[token]['username'],name=users[token]['name'].split()[0], vrename=vre.name)
message = {"answer": response}
new_state = {'modified_query': response}
dm.update(new_state)
@ -114,7 +133,7 @@ def predict():
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)
rec.generate_recommendations(users[token]['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)
@ -124,9 +143,11 @@ def predict():
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
users[token]['dm'] = dm
users[token]['user'] = user
users[token]['activity'] = 0
users[token]['args']['vre'] = vre
users[token]['args']['rg'] = rg
return reply
@app.route('/api/feedback', methods = ['POST'])
@ -170,21 +191,10 @@ if __name__ == "__main__":
'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,'