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@@ -135,3 +135,4 @@ Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0222_testv2.exe filte
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0222_testv3.exe filter=lfs diff=lfs merge=lfs -text
 NAIA_0224_testv1.exe filter=lfs diff=lfs merge=lfs -text
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0226_testv1.exe filter=lfs diff=lfs merge=lfs -text
+Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0228_testv2.exe filter=lfs diff=lfs merge=lfs -text

Danbooru Prompt Selector/TEST2024/NAIA_0228_testv2.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcf08a1a57c417ff09cec03b41545f9200ef30c3ede7f440be8222ce1da09cf1
+size 854809343

Danbooru Prompt Selector/TEST2024/NAIA_search.py

@@ -1,6 +1,8 @@
 import pandas as pd
 import re
 
+pd.options.mode.chained_assignment = None
+
 # 모든 키워드를 포함하는 행만 필터링하는 함수
 def filter_rows_containing_all_keywords(df, keywords):
     special_chars = r".^$*+?{}[]\|()"
@@ -170,7 +172,7 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
                                 ndf = matched_rows[matched_rows[column].str.contains(keyword, na=False, regex=True)]
                             else: 
                                 ndf = matched_rows[matched_rows[column].str.contains(keyword, na=False)]
-                            print(keyword, len(matched_rows), len(ndf))
+                            #print(keyword, len(matched_rows), len(ndf))
                             if not ndf.empty:
                                 ndfs.append(ndf.copy())
                                 del(ndf)
@@ -184,7 +186,7 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
                 if not matched_rows.empty:
                     results = pd.concat([results, matched_rows])
                     del[matched_rows]
-                    print(results)
+                    #print(results)
             del[[df]]
             results = results.drop_duplicates(subset=['general'])
             df = results.copy()