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_test.py
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#%%
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from utils.load_model import load_ner
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from utils.input_process import make_ner_input
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from utils.ner_utils import make_name_list, show_name_list, combine_similar_names
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import torch
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from utils.train_model import KCSN
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from utils.arguments import get_train_args
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args = get_train_args()
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path ='model/model.ckpt'
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model = KCSN(args)
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checkpoint = torch.load(path)
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model.load_state_dict(checkpoint['model'])
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# model = checkpoint['model']
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# %%
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with open('test/test.txt', "r", encoding="utf-8") as f:
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file_content = f.read()
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content = make_ner_input(file_content)
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name_list, time, place = make_name_list(content, checkpoint)
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name_dic = show_name_list(name_list)
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similar_name = combine_similar_names(name_dic)
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for i in similar_name:
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print(i)
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# %% CSN ๋ชจ๋ธ
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import torch
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from utils.fs_utils import get_alias2id, find_speak
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from utils.ner_utils import make_name_list
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from utils.input_process import make_ner_input, make_instance_list, input_data_loader
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checkpoint = torch.load('./model/final.pth')
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model = checkpoint['model']
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model.to('cpu')
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tokenizer = checkpoint['tokenizer']
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check_name = './data/name.txt'
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alias2id = get_alias2id(check_name)
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with open('test/KoCSN_test.txt', "r", encoding="utf-8") as f:
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file_content = f.read()
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instances, instance_num = make_instance_list(file_content)
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inputs = input_data_loader(instances, alias2id)
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output = find_speak(model, inputs, tokenizer, alias2id)
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def make_script(texts, instance_num, output):
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script = []
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for idx, text in enumerate(texts):
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if idx in instance_num
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#%%
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n = int(input())
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num = list(map(int, input().split()))
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ans = []
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for i, j in enumerate(num):
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print(i, j)
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if len(ans) == 0:
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ans.append(i+1)
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else:
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ans.insert(len(ans)-j, i+1)
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print(ans)
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# %%
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main.py
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"""
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This is main.py
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"""
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from fastapi import FastAPI, File, UploadFile, Form, Request
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import HTMLResponse, RedirectResponse, JSONResponse
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from fastapi.templating import Jinja2Templates
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from pydantic import BaseModel
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from typing import List
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class AppData:
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def __init__(self):
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self.file_content = ""
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self.name_list = []
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self.place = []
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self.times = []
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self.name_dic = {}
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self.end_output = []
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class ItemListRequest(BaseModel):
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nameList: List[str]
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app_data = AppData()
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# ์ค์
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app = FastAPI()
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app.mount("/static", StaticFiles(directory="static"), name="static")
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templates = Jinja2Templates(directory="templates")
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@app.get("/", response_class=HTMLResponse)
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async def page_home(request: Request):
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"""INDEX.HTML ํ๋ฉด"""
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return templates.TemplateResponse("index.html", {"request": request})
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@app.get("/put.html", response_class=HTMLResponse)
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async def page_put(request: Request):
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"""PUT.HTML ํ๋ฉด"""
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return templates.TemplateResponse("put.html", {"request": request})
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@app.get("/confirm.html", response_class=HTMLResponse)
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async def page_confirm(request: Request):
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"""confirm.HTML ํ๋ฉด"""
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return templates.TemplateResponse("confirm.html",{
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"request": request, "file_content": app_data.file_content})
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@app.get("/result.html", response_class=HTMLResponse)
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async def page_result(request: Request):
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"""result.HTML ํ๋ฉด"""
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return templates.TemplateResponse("result.html", {"request": request})
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@app.get("/user.html", response_class=HTMLResponse)
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async def page_user(request: Request):
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"""user.HTML ํ๋ฉด"""
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return templates.TemplateResponse("user.html", {"request": request})
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@app.get("/final.html", response_class=HTMLResponse)
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async def page_final(request: Request):
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"""final.HTML ํ๋ฉด"""
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return templates.TemplateResponse("final.html", {"request": request,
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"output": app_data.end_output,
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"place": app_data.place,
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"time": app_data.times})
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@app.post("/upload", response_class=HTMLResponse)
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async def upload_file(file: UploadFile = File(...)):
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"""ํ์ผ ์
๋ก๋ ๋ฐ ์ ์ฅ"""
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with open("uploads/" + file.filename, "wb") as f:
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f.write(file.file.read())
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with open("uploads/" + file.filename, "r", encoding="utf-8") as f:
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app_data.file_content = f.read()
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return RedirectResponse(url="/put.html")
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@app.post("/ners", response_class=JSONResponse)
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async def ner_file():
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"""์ ์ฅ๋ ํ์ผ์ NER ์์
์ ํด์ ํ์๋ ์ฅ์๋ฅผ ๊ตฌ๋ถ"""
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from utils.load_model import load_ner
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from utils.input_process import make_ner_input
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from utils.ner_utils import make_name_list, show_name_list, combine_similar_names
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content = app_data.file_content
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_, ner_checkpoint = load_ner()
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contents = make_ner_input(content)
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name_list, times, places = make_name_list(contents, ner_checkpoint)
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name_dic = show_name_list(name_list)
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similar_name = combine_similar_names(name_dic)
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result_list = [', '.join(names) for names, _ in similar_name.items()]
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app_data.place = ' '.join(places)
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app_data.times = ' '.join(times)
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# JSONResponse๋ก ์๋ต
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return JSONResponse(content={"itemList": result_list})
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@app.post("/kcsn", response_class=JSONResponse)
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async def kcsn_file(request_data: ItemListRequest):
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"""์ฌ์ฉ์๊ฐ ์ฌ๋ ค์ค ํ์ผ์ ๋ํด์ KCSN ๋ชจ๋ธ ๋์"""
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import torch
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from utils.fs_utils import get_alias2id, find_speak, making_script
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from utils.input_process import make_instance_list, input_data_loader
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from utils.train_model import KCSN
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from utils.ner_utils import convert_name2codename, convert_codename2name
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content = app_data.file_content
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name_list = request_data.nameList
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name_dic = {}
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for idx, name in enumerate(name_list):
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name_dic[f'&C{idx:02d}&'] = name.split(', ')
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content_re = convert_name2codename(name_dic, content)
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# checkpoint = torch.load('./model/final.pth')
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# model = checkpoint['model']
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# model.to('cpu')
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# tokenizer = checkpoint['tokenizer']
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from utils.arguments import get_train_args
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from transformers import AutoTokenizer
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args = get_train_args()
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path ='model/model.ckpt'
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model = KCSN(args)
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model.to('cpu')
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checkpoint = torch.load(path)
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tokenizer = AutoTokenizer.from_pretrained(args.bert_pretrained_dir)
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model.load_state_dict(checkpoint['model'])
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check_name = 'data/name.txt'
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alias2id = get_alias2id(check_name)
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instances, instance_num = make_instance_list(content_re)
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inputs = input_data_loader(instances, alias2id)
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output = find_speak(model, inputs, tokenizer, alias2id)
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outputs = convert_codename2name(name_dic, output)
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app_data.end_output = making_script(content, outputs, instance_num)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="127.0.0.1", port=8000)
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