From 64df82ac7c92cd7dc7ed25669cf702c3bf045794 Mon Sep 17 00:00:00 2001
From: bigbell999 <bigbell999@knu.ac.kr>
Date: Mon, 9 Dec 2024 17:11:11 +0900
Subject: [PATCH] =?UTF-8?q?PATCH:=20=EB=AA=85=EC=82=AC=ED=99=94=20?=
 =?UTF-8?q?=ED=95=A8=EC=88=98=20=EC=88=98=EC=A0=95=20=EB=A1=A4=EB=B0=B1?=
MIME-Version: 1.0
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---
 ai_server_code/ai_modules.py | 84 ------------------------------------
 1 file changed, 84 deletions(-)

diff --git a/ai_server_code/ai_modules.py b/ai_server_code/ai_modules.py
index a7d5f77..9a92c0d 100644
--- a/ai_server_code/ai_modules.py
+++ b/ai_server_code/ai_modules.py
@@ -64,7 +64,6 @@ def get_korean_time():
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # 단어 명사화 함수.
-'''
 def transformed_query(content):
     # 중복된 단어를 제거한 명사를 담을 리스트
     query_nouns = []
@@ -266,89 +265,6 @@ def transformed_query(content):
 
     query_nouns = list(set(query_nouns))
     return query_nouns
-'''
-
-def transformed_query(content):
-    query_nouns = []
-    okt = Okt()
-
-    # 1. 패턴 매칭 및 사전 정의 키워드
-    patterns = {
-        r'\d+(?:학년도|년|학년|월|일|학기|시|분|초|기|개|차)?': lambda x: query_nouns.extend(x),
-        r'[a-zA-Z]+': lambda x: query_nouns.extend(word.upper() for word in x),
-    }
-
-    # 정규식 패턴 처리
-    for pattern, action in patterns.items():
-        matches = re.findall(pattern, content)
-        action(matches)
-        for match in matches:
-            content = content.replace(match, '')
-
-    # 2. 키워드 매핑 처리
-    keyword_map = {
-        '시간표': [],
-        'EXIT': ['출구'],
-        '벤처아카데미': ['벤처아카데미'],
-        '군': ['군', '군휴학', '군입대'],
-        '인컴': ['인공지능컴퓨팅'],
-        '학부생': ['학부생'],
-        '공대': ['E'],
-        '설명회': ['설명회'],
-        '컴학': ['컴퓨터학부'],
-        '컴퓨터': ['컴퓨터학부', '컴퓨터비전'],
-        '종프': ['종합설계프로젝트'],
-        '대회': ['경진대회'],
-        '튜터': ['TUTOR'],
-        '탑싯': ['TOPCIT'],
-        '시험': ['시험'],
-        '하계': ['여름', '하계'],
-        '동계': ['겨울', '동계'],
-        '겨울': ['겨울', '동계'],
-        '여름': ['여름', '하계'],
-        '성인지': ['성인지'],
-        '첨성인': ['첨성인'],
-        '글솦': ['글솝'],
-        '수꾸': ['수강꾸러미'],
-        '장학금': ['장학생', '장학'],
-        '장학생': ['장학금', '장학'],
-        '에이빅': ['에이빅', 'ABEEK'],
-        '선이수': ['선이수'],
-        '학자금': ['학자금'],
-        '오픈 소스': ['오픈소스'],
-        '오픈소스': ['오픈소스'],
-        '재이수': ['재이수'],
-        '재 수강': ['재이수'],
-        '과목': ['강의', '강좌'],
-        '강의': ['과목', '강좌'],
-        '강좌': ['강좌'],
-        '외국어': ['외국어'],
-        '부전공': ['부전공'],
-        '계절학기': ['수업'],
-        '채용': ['모집', '공고'],
-        '공지': ['공지사항'],
-        '사항': ['공지사항'],
-        '공지사항': ['공지사항'],
-        '사원': ['신입'],
-        '신입사원': ['신입'],
-        '카테캠': ['카카오', '테크', '캠퍼스']
-    }
-
-    # 키워드 매핑 적용
-    for keyword, additions in keyword_map.items():
-        if keyword in content:
-            query_nouns.extend(additions)
-            content = content.replace(keyword, '')
-
-    # 3. Okt 명사 추출
-    additional_nouns = [noun for noun in okt.nouns(content) if len(noun) > 1]
-    query_nouns.extend(additional_nouns)
-
-    # 4. 중복 제거 및 반환
-    query_nouns = list(dict.fromkeys(query_nouns))
-    return query_nouns
-
-
 ###################################################################################################