def __init__(self):
#userdictionary
super().__init__()
self.dialogue_hist = defaultdict(list)
# Set random seed
np.random.seed(args.randseed)
torch.manual_seed(args.randseed)
if args.gpu:
torch.cuda.manual_seed(args.randseed)
if os.path.isfile(args.stoi):
self.stoi = np.load(args.stoi, allow_pickle=True).item()
self.itos = {v: k for k, v in self.stoi.items()}
# Get data
# chat_data = DialogBatcher(gpu=args.gpu)
self.model = KGSentient(hidden_size=args.hidden_size,
max_r=args.resp_len,
gpu=args.gpu,
n_words=len(self.stoi) + 1,
emb_dim=args.words_dim,
kb_max_size=200,
b_size=args.batch_size,
lr=args.lr,
dropout=args.rnn_dropout,
emb_drop=args.emb_drop,
teacher_forcing_ratio=args.teacher_forcing,
sos_tok=self.stoi['<sos>'],
eos_tok=self.stoi['<eos>'],
itos=self.itos,
first_kg_token=self.stoi['o0'])
if args.gpu:
self.model = self.model.cuda()
self.model_name = 'Sentient_model2'
self.model = load_model(self.model, self.model_name, gpu=args.gpu)
self.bot = BotAgent(self.model, stoi=self.stoi, itos=self.itos)
class SoccerBot(ChatBot):
def __init__(self):
#userdictionary
super().__init__()
self.dialogue_hist = defaultdict(list)
# Set random seed
np.random.seed(args.randseed)
torch.manual_seed(args.randseed)
if args.gpu:
torch.cuda.manual_seed(args.randseed)
if os.path.isfile(args.stoi):
self.stoi = np.load(args.stoi, allow_pickle=True).item()
self.itos = {v: k for k, v in self.stoi.items()}
# Get data
# chat_data = DialogBatcher(gpu=args.gpu)
self.model = KGSentient(hidden_size=args.hidden_size,
max_r=args.resp_len,
gpu=args.gpu,
n_words=len(self.stoi) + 1,
emb_dim=args.words_dim,
kb_max_size=200,
b_size=args.batch_size,
lr=args.lr,
dropout=args.rnn_dropout,
emb_drop=args.emb_drop,
teacher_forcing_ratio=args.teacher_forcing,
sos_tok=self.stoi['<sos>'],
eos_tok=self.stoi['<eos>'],
itos=self.itos,
first_kg_token=self.stoi['o0'])
if args.gpu:
self.model = self.model.cuda()
self.model_name = 'Sentient_model2'
self.model = load_model(self.model, self.model_name, gpu=args.gpu)
self.bot = BotAgent(self.model, stoi=self.stoi, itos=self.itos)
def generate_answer(self, utterance, userid, nlu_result=None):
team = 'Argentina_kg'
try:
if not self.dialogue_hist[userid]:
#query = request.form['text']
#rint (query)
self.dialogue_hist[userid].append(utterance)
resp = self.bot.response(utterance, team)
#resp = jsonify({"status": "success","response":resp[0]})
#if not request.cookies.get('query'):
#resp.set_cookie('query', request.cookies.get('query') + ' '+query)
#else:
# resp.set_cookie('query', request.cookies.get('query') + ' ' + query)
#print (request.cookies.get('query'))
self.dialogue_hist[userid].append(resp[0])
return resp[0], 0.5, "Acceptable"
else:
self.dialogue_hist[userid].append(utterance)
query = ' '.join(q for q in self.dialogue_hist[userid])
resp = self.bot.response(query, team)
self.dialogue_hist[userid].append(resp[0])
print(resp[0])
return resp[0], 0.5, "Acceptable"
#print (self.dialogue_hist[userid])
except Exception as e:
#print (e)
traceback.print_exc(file=sys.stdout)
return "", 0, "Acceptable"
def delete_histories(self, userids, delete_all=False):
for userid in userids:
if userid in self.dialogue_hist:
self.dialogue_hist.pop(userid)
else:
print("Soccerbot warning: Userid not found in dict.")
if delete_all:
self.dialogue_hist = defaultdict(list)
def __repr__(self):
return ("soccerbot")
np.random.seed(args.randseed)
torch.manual_seed(args.randseed)
if args.gpu:
torch.cuda.manual_seed(args.randseed)
#args.data_dir = 'preproc_files/'
# Team data
team_kg = np.load(args.data_dir+'team_kg.npy',allow_pickle=True).item()
# Get data
chat_data = DialogBatcher(gpu=args.gpu, max_kb_len=35, max_sent_len=20)
# Get model
model = KGSentient(hidden_size=args.hidden_size, max_r=args.resp_len, gpu=args.gpu, n_words=chat_data.n_words,
emb_dim=args.words_dim, kb_max_size=chat_data.max_kb_len, b_size=args.batch_size, lr=args.lr,
dropout=args.rnn_dropout, emb_drop=args.emb_drop, teacher_forcing_ratio=args.teacher_forcing,
pretrained_emb=chat_data.vectors, sos_tok=chat_data.stoi['<sos>'], eos_tok=chat_data.stoi['<eos>'],
itos=chat_data.geti2w, first_kg_token=631)
metrics = EmbeddingMetrics(embeddig_dict=chat_data.vocab_glove)
model_name = 'Sentient_model2'
test_results = 'test_predicted_kg_attn2.csv'
test_out = pd.DataFrame()
w2i = np.load("vocab/w2i_incar.npy",allow_pickle=True).item()
i2w = {v:k for k,v in w2i.items()}
ent_file = open("data/kvr_entities_incar.txt","r",encoding="utf-8")
global_entity_list=set()