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@@ -112,3 +112,4 @@ Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0102_testv2.exe filte
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0103_testv2.exe filter=lfs diff=lfs merge=lfs -text
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0105_testv2.exe filter=lfs diff=lfs merge=lfs -text
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0107_testv3.exe filter=lfs diff=lfs merge=lfs -text
+Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0108_testv3.exe filter=lfs diff=lfs merge=lfs -text

Danbooru Prompt Selector/TEST2024/NAIA_0108_testv3.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07fc66ac08ff36bda1bf9755af210e91454c219f8819ce5192c504e4b643bdf2
+size 837990050

Danbooru Prompt Selector/TEST2024/NAIA_search.py

@@ -147,6 +147,7 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
                 return None
 
     #OR 처리
+    ndf = None
     if curly_brace_group:
         for keyword in curly_brace_group:
             or_search_keyword = [item.strip() for item in keyword[1:-1].split('|')]
@@ -155,6 +156,7 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
                 keywords = [item.strip() for item in keyword.split(',')]
                 matched_rows = None
                 for keyword in keywords:
+                    ndfs = []
                     for column in ['copyright', 'character', 'artist', 'meta', 'general']:
                         request_regex = False
                         if any(char in keyword for char in special_chars): 
@@ -171,19 +173,20 @@ 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)]
                             print(keyword, len(matched_rows), len(ndf))
                             if not ndf.empty:
-                                matched_rows = ndf.copy()
-                        if keyword == keywords[0] and not matched_rows.empty:
-                            break
-                        else:
-                            if not matched_rows.empty and not ndf.empty:
+                                ndfs.append(ndf.copy())
+                                del(ndf)
                                 ndf = None
-                                break
-
+                    if ndfs:
+                        matched_rows = pd.concat(ndfs, ignore_index=True)
+                        matched_rows = matched_rows.drop_duplicates(subset=['general'])
+                        ndfs.clear()
+                    else:
+                        matched_rows.drop_duplicates(subset=['general'])
                 if not matched_rows.empty:
                     results = pd.concat([results, matched_rows])
                     print(results)
             del[[df]]
-            results = results.drop_duplicates()
+            results = results.drop_duplicates(subset=['general'])
             df = results.copy()
             del[[results]]
         if(len(df) == 0):