Update app.py

app.py

@@ -1,4 +1,4 @@
-'''
+
 import argparse
 import random
 import numpy as np
@@ -14,13 +14,6 @@ from transformers.models.bert.tokenization_bert import BertTokenizer
 import sentencepiece as spm
 
 
-logging.getLogger("lightning").setLevel(logging.ERROR)
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
 
 
     
@@ -33,7 +26,7 @@ add_args(args)
  
 
     
-#model = KobeModel(args)
+model = KobeModel(args)
 
 
 
@@ -54,26 +47,14 @@ cond_tokenizer.Load(args.cond_vocab_file)
 
 
 
-
-
-
 #model = model.load_from_checkpoint("/root/kobe-v2/1ja19m5t/checkpoints/epoch=19-step=66080.ckpt", args=args)
 #model = model.load_from_checkpoint("/root/kobe-v2/37ht1cvz/checkpoints/epoch=11-step=396384.ckpt", args=args)
 
-trainer = pl.Trainer(accelerator='gpu', devices=1, max_epochs=-1)
-'''
-
-
-import streamlit as st
-
-st.write("Most appearing words including stopwords")
+trainer = pl.Trainer()
 
 
-choice = st.selectbox(
 
-    'Select the items you want?',
-
-    ('Pen','Pencil','Eraser','Sharpener','Notebook'))
+import streamlit as st
 
 
 input1 = st.selectbox(
@@ -179,7 +160,7 @@ cond = aspect+" "+cond
 #print(fact)
 #print(cond)
 
-'''
+
 tokenizer = text_tokenizer
 title_token_ids=tokenizer.encode(title, add_special_tokens=False)
 condition_token_ids=cond_tokenizer.EncodeAsIds(cond)
@@ -199,4 +180,3 @@ dm = KobeDataModule(
 for d in dm.test_dataloader():
     st.write("result:")
     st.write(''.join(model.test_step(d ,1)).replace(" ",""))
-'''