Delete app_2020.py

app_2020.py

@@ -1,140 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-# 모델 준비하기
-from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer
-import numpy as np
-import pandas as pd
-import torch
-import os
-
-# 제목 입력
-st.header('한국표준산업분류 자동코딩 서비스')
-
-# 재로드 안하도록
-@st.experimental_memo(max_entries=20)
-def md_loading():
-    ## cpu
-    # device = torch.device('cpu')
-
-    tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
-    model = XLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-large', num_labels=493)
-
-    model_checkpoint = 'base1_43_11.bin'
-    project_path = './'
-    output_model_file = os.path.join(project_path, model_checkpoint)
-
-    model.load_state_dict(torch.load(output_model_file, map_location=torch.device('cpu')))
-
-################################## label tbl 수정
-    label_tbl = np.load('./label_table.npy')
-    loc_tbl = pd.read_csv('./kisc_table.csv', encoding='utf-8')
-
-    print('ready')
-
-    return tokenizer, model, label_tbl, loc_tbl
-
-# 모델 로드
-tokenizer, model, label_tbl, loc_tbl = md_loading()
-
-
-# 텍스트 input 박스
-# business = st.text_input('사업체명', '충청지방통계청').replace(',', '')
-# business_work = st.text_input('사업체 하는일', '통계서비스 제공 및 지역통계 허브').replace(',', '')
-# work_department = st.text_input('근무부서', '지역통계과').replace(',', '')
-# work_position = st.text_input('직책', '주무관').replace(',', '')
-# what_do_i = st.text_input('내가 하는 일', '통계데이터센터 운영').replace(',', '')
-
-input_box = st.text_input()
-
-# md_input: 모델에 입력할 input 값 정의
-md_input = input_box
-
-## 임시 확인
-# st.write(md_input)
-
-# 버튼
-if st.button('확인'):
-    ## 버튼 클릭 시 수행사항
-    ### 모델 실행
-    query_tokens = md_input
-
-    input_ids = np.zeros(shape=[1, 64])
-    attention_mask = np.zeros(shape=[1, 64])
-
-    # seq = '[CLS] '
-    # try:
-    #     for i in range(5):
-    #         seq += query_tokens[i] + ' '
-    # except:
-    #     None
-
-    seq = query_tokens
-
-    tokens = tokenizer.tokenize(seq)
-    ids = tokenizer.convert_tokens_to_ids(tokens)
-
-    length = len(ids)
-    if length > 64:
-        length = 64
-
-    for i in range(length):
-        input_ids[0, i] = ids[i]
-        attention_mask[0, i] = 1
-
-    input_ids = torch.from_numpy(input_ids).type(torch.long)
-    attention_mask = torch.from_numpy(attention_mask).type(torch.long)
-
-    outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=None)
-    logits = outputs.logits
-
-    # # 단독 예측 시
-    # arg_idx = torch.argmax(logits, dim=1)
-    # print('arg_idx:', arg_idx)
-
-    # num_ans = label_tbl[arg_idx]
-    # str_ans = loc_tbl['항목명'][loc_tbl['코드'] == num_ans].values
-
-    # 상위 k번째까지 예측 시
-    k = 10
-    topk_idx = torch.topk(logits.flatten(), k).indices    
-
-    num_ans_topk = label_tbl[topk_idx]
-    str_ans_topk = [loc_tbl['항목명'][loc_tbl['코드'] == k] for k in num_ans_topk]
-
-    # print(num_ans, str_ans)
-    # print(num_ans_topk)
-
-    # print('사업체명:', query_tokens[0])
-    # print('사업체 하는일:', query_tokens[1])
-    # print('근무부서:', query_tokens[2])
-    # print('직책:', query_tokens[3])
-    # print('내가 하는일:', query_tokens[4])
-    # print('산업코드 및 분류:', num_ans, str_ans)
-
-    # ans = ''
-    # ans1, ans2, ans3 = '', '', ''
-
-    ## 모델 결과값 출력
-    # st.write("산업코드 및 분류:", num_ans, str_ans[0])
-    # st.write("세분류 코드")
-    # for i in range(k):
-    #     st.write(str(i+1) + '순위:', num_ans_topk[i], str_ans_topk[i].iloc[0])
-
-    # print(num_ans)
-    # print(str_ans, type(str_ans))
-
-    str_ans_topk_list = []
-    for i in range(k):
-        str_ans_topk_list.append(str_ans_topk[i].iloc[0])
-
-    # print(str_ans_topk_list)
-
-    ans_topk_df = pd.DataFrame({
-        'NO': range(1, k+1),
-        '세분류 코드': num_ans_topk,
-        '세분류 명칭': str_ans_topk_list
-    })
-    ans_topk_df = ans_topk_df.set_index('NO')
-
-    st.dataframe(ans_topk_df)