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import requests
import streamlit as st
from streamlit_lottie import st_lottie
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
# Page Config
st.set_page_config(
page_title="๋
ธ๋ ๊ฐ์ฌ nํ์",
page_icon="๐",
layout="wide"
)
### Model
tokenizer = AutoTokenizer.from_pretrained("wumusill/final_project_kogpt2")
@st.cache(show_spinner=False)
def load_model():
model = AutoModelForCausalLM.from_pretrained("wumusill/final_project_kogpt2")
return model
model = load_model()
# Class : Dict ์ค๋ณต ํค ์ถ๋ ฅ
class poem(object):
def __init__(self,letter):
self.letter = letter
def __str__(self):
return self.letter
def __repr__(self):
return "'"+self.letter+"'"
def n_line_poem(input_letter):
# ๋์ ๋ฒ์น ์ฌ์
dooeum = {"๋ผ":"๋", "๋ฝ":"๋", "๋":"๋", "๋":"๋ ", "๋":"๋จ", "๋":"๋ฉ", "๋":"๋ญ",
"๋":"๋ด", "๋ญ":"๋", "๋":"์ฝ", "๋ต":"์ฝ", "๋ฅ":"์", "๋":"์", "๋
":"์ฌ",
"๋ ค":"์ฌ", "๋
":"์ญ", "๋ ฅ":"์ญ", "๋
":"์ฐ", "๋ จ":"์ฐ", "๋
":"์ด", "๋ ฌ":"์ด",
"๋
":"์ผ", "๋ ด":"์ผ", "๋ ต":"์ฝ", "๋
":"์", "๋ น":"์", "๋
":"์", "๋ก":"์",
"๋ก":"๋
ธ", "๋ก":"๋
น", "๋ก ":"๋
ผ", "๋กฑ":"๋", "๋ขฐ":"๋", "๋จ":"์", "๋ฃ":"์",
"๋ฃก":"์ฉ", "๋ฃจ":"๋", "๋ด":"์ ", "๋ฅ":"์ ", "๋ต":"์ก", "๋ฅ":"์ก", "๋ฅ":"์ค",
"๋ฅ ":"์จ", "๋ฅญ":"์ต", "๋ฅต":"๋", "๋ฆ":"๋ ", "๋ฆ":"๋ฅ", "๋":"์ด", "๋ฆฌ":"์ด",
"๋ฆฐ":'์ธ', '๋ฆผ':'์', '๋ฆฝ':'์
'}
# ๊ฒฐ๊ณผ๋ฌผ์ ๋ด์ list
res_l = []
# ํ ๊ธ์์ฉ ์ธ๋ฑ์ค์ ํจ๊ป ๊ฐ์ ธ์ด
for idx, val in enumerate(input_letter):
# ๋์ ๋ฒ์น ์ ์ฉ
if val in dooeum.keys():
val = dooeum[val]
while True:
# ๋ง์ฝ idx ๊ฐ 0 ์ด๋ผ๋ฉด == ์ฒซ ๊ธ์
if idx == 0:
# ์ฒซ ๊ธ์ ์ธ์ฝ๋ฉ
input_ids = tokenizer.encode(
val, add_special_tokens=False, return_tensors="pt")
# print(f"{idx}๋ฒ ์ธ์ฝ๋ฉ : {input_ids}\n") # 2์ฐจ์ ํ
์
# ์ฒซ ๊ธ์ ์ธ์ฝ๋ฉ ๊ฐ์ผ๋ก ๋ฌธ์ฅ ์์ฑ
output_sequence = model.generate(
input_ids=input_ids,
do_sample=True, max_length=42,
min_length=5, temperature=0.9, repetition_penalty=1.5,
no_repeat_ngram_size=2)[0]
# print("์ฒซ ๊ธ์ ์ธ์ฝ๋ฉ ํ generate ๊ฒฐ๊ณผ:", output_sequence, "\n") # tensor
# ์ฒซ ๊ธ์๊ฐ ์๋๋ผ๋ฉด
else:
# ํ ์์
input_ids = tokenizer.encode(
val, add_special_tokens=False, return_tensors="pt")
# print(f"{idx}๋ฒ ์งธ ๊ธ์ ์ธ์ฝ๋ฉ : {input_ids} \n")
# ์ข๋ ๋งค๋๋ฌ์ด ์ผํ์๋ฅผ ์ํด ์ด์ ์ธ์ฝ๋ฉ๊ณผ ์ง๊ธ ์ธ์ฝ๋ฉ ์ฐ๊ฒฐ
link_with_pre_sentence = torch.cat((generated_sequence, input_ids[0]), 0)
link_with_pre_sentence = torch.reshape(link_with_pre_sentence, (1, len(link_with_pre_sentence)))
# print(f"์ด์ ํ
์์ ์ฐ๊ฒฐ๋ ํ
์ {link_with_pre_sentence} \n")
# ์ธ์ฝ๋ฉ ๊ฐ์ผ๋ก ๋ฌธ์ฅ ์์ฑ
output_sequence = model.generate(
input_ids=link_with_pre_sentence,
do_sample=True, max_length=42,
min_length=5, temperature=0.9, repetition_penalty=1.5,
no_repeat_ngram_size=2)[0]
# print(f"{idx}๋ฒ ์ธ์ฝ๋ฉ ํ generate : {output_sequence}")
# ์์ฑ๋ ๋ฌธ์ฅ ๋ฆฌ์คํธ๋ก ๋ณํ (์ธ์ฝ๋ฉ ๋์ด์๊ณ , ์์ฑ๋ ๋ฌธ์ฅ ๋ค๋ก padding ์ด ์๋ ์ํ)
generated_sequence = output_sequence.tolist()
# print(f"{idx}๋ฒ ์ธ์ฝ๋ฉ ๋ฆฌ์คํธ : {generated_sequence} \n")
# padding index ์๊น์ง slicing ํจ์ผ๋ก์จ padding ์ ๊ฑฐ, padding์ด ์์ ์๋ ์๊ธฐ ๋๋ฌธ์ ์กฐ๊ฑด๋ฌธ ํ์ธ ํ ์ ๊ฑฐ
if tokenizer.pad_token_id in generated_sequence:
generated_sequence = generated_sequence[:generated_sequence.index(tokenizer.pad_token_id)]
generated_sequence = torch.tensor(generated_sequence)
# print(f"{idx}๋ฒ ์ธ์ฝ๋ฉ ๋ฆฌ์คํธ ํจ๋ฉ ์ ๊ฑฐ ํ ๋ค์ ํ
์ : {generated_sequence} \n")
# ์ฒซ ๊ธ์๊ฐ ์๋๋ผ๋ฉด, generate ๋ ์์ ๋ง ๊ฒฐ๊ณผ๋ฌผ list์ ๋ค์ด๊ฐ ์ ์๊ฒ ์ ๋ฌธ์ฅ์ ๋ํ ์ธ์ฝ๋ฉ ๊ฐ ์ ๊ฑฐ
# print(generated_sequence)
if idx != 0:
# ์ด์ ๋ฌธ์ฅ์ ๊ธธ์ด ์ดํ๋ก ์ฌ๋ผ์ด์ฑํด์ ์ ๋ฌธ์ฅ ์ ๊ฑฐ
generated_sequence = generated_sequence[len_sequence:]
len_sequence = len(generated_sequence)
# print("len_seq", len_sequence)
# ์์ ๊ทธ๋๋ก ๋ฑ์ผ๋ฉด ๋ค์ ํด์, ์๋๋ฉด while๋ฌธ ํ์ถ
if len_sequence > 1:
break
# ๊ฒฐ๊ณผ๋ฌผ ๋ฆฌ์คํธ์ ๋ด๊ธฐ
res_l.append(generated_sequence)
poem_dict = {}
for letter, res in zip(input_letter, res_l):
decode_res = tokenizer.decode(res, clean_up_tokenization_spaces=True, skip_special_tokens=True)
poem_dict[poem(letter)] = decode_res
return poem_dict
###
# Image(.gif)
@st.cache(show_spinner=False)
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_url = "https://assets7.lottiefiles.com/private_files/lf30_fjln45y5.json"
lottie_json = load_lottieurl(lottie_url)
st_lottie(lottie_json, speed=1, height=200, key="initial")
# Title
row0_spacer1, row0_1, row0_spacer2, row0_2, row0_spacer3 = st.columns(
(0.01, 2, 0.05, 0.5, 0.01)
)
with row0_1:
st.markdown("# ํ๊ธ ๋
ธ๋ ๊ฐ์ฌ nํ์โ")
st.markdown("### ๐ฆ๋ฉ์์ด์ฌ์์ฒ๋ผ AIS7๐ฆ - ํ์ด๋ ํ๋ก์ ํธ")
with row0_2:
st.write("")
st.write("")
st.write("")
st.subheader("1์กฐ - ํดํ๋ฆฌ")
st.write("์ด์งํ, ์ต์ง์, ๊ถ์ํฌ, ๋ฌธ์ข
ํ, ๊ตฌ์ํ, ๊น์์ค")
st.write('---')
# Explanation
row1_spacer1, row1_1, row1_spacer2 = st.columns((0.01, 0.01, 0.01))
with row1_1:
st.markdown("### nํ์ ๊ฐ์ด๋๋ผ์ธ")
st.markdown("1. ํ๋จ์ ์๋ ํ
์คํธ๋ฐ์ 5์ ์ดํ ํ๊ธ ๋จ์ด๋ฅผ ๋ฃ์ด์ฃผ์ธ์")
st.markdown("2. 'nํ์ ์ ์ํ๊ธฐ' ๋ฒํผ์ ํด๋ฆญํด์ฃผ์ธ์")
st.write('---')
# Model & Input
row2_spacer1, row2_1, row2_spacer2= st.columns((0.01, 0.01, 0.01))
# Word Input
with row2_1:
word_input = st.text_input(
"nํ์์ ์ฌ์ฉํ ํ๊ธ ๋จ์ด๋ฅผ ์ ๊ณ ๋ฒํผ์ ๋๋ฌ์ฃผ์ธ์.(์ต๋ 5์) ๐",
placeholder='ํ๊ธ ๋จ์ด๋ฅผ ์
๋ ฅํด์ฃผ์ธ์',
max_chars=5
)
word_input = re.sub("[^๊ฐ-ํฃ]", "", word_input)
if st.button('nํ์ ์ ์ํ๊ธฐ'):
if word_input == "":
st.error("์จ์ ํ ํ๊ธ ๋จ์ด๋ฅผ ์ฌ์ฉํด์ฃผ์ธ์!")
else:
st.write("nํ์ ๋จ์ด : ", word_input)
with st.spinner('์ ์ ๊ธฐ๋ค๋ ค์ฃผ์ธ์...'):
result = n_line_poem(word_input)
st.success('์๋ฃ๋์ต๋๋ค!')
for r in result:
st.write(f'{r} : {result[r]}')
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