Upload app.py

app.py

@@ -0,0 +1,137 @@
+from asyncore import write
+from pickletools import stringnl
+import streamlit as st
+import pandas as pd
+
+# 모델 준비하기
+from transformers import RobertaForSequenceClassification, AutoTokenizer
+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 = AutoTokenizer.from_pretrained('klue/roberta-base')
+    model = RobertaForSequenceClassification.from_pretrained('klue/roberta-base', num_labels=495)
+
+    model_checkpoint = 'upsampling_20.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 = 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(',', '')
+
+# md_input: 모델에 입력할 input 값 정의
+md_input = ', '.join([business, business_work, work_department, work_position, what_do_i])
+
+## 임시 확인
+# st.write(md_input)
+
+# 버튼
+if st.button('확인'):
+    ## 버튼 클릭 시 수행사항
+    ### 모델 실행
+    query_tokens = md_input.split(',')
+
+    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
+
+    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 = 5
+    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)