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@@ -113,3 +113,4 @@ Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0103_testv2.exe filte
 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[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0110_testv3.exe filter=lfs diff=lfs merge=lfs -text

Danbooru Prompt Selector/TEST2024/NAIA_0110_testv3.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2aace41512cef7fe9cb102e20225956722f56abb4079477639593ac7e5ec3303
+size 837993022

Danbooru Prompt Selector/TEST2024/NAIA_random_function_core.py

@@ -38,6 +38,11 @@ def RFP(popped_row, fix_prompt, after_prompt, auto_hide_prompt, rm_a, rm_s, rm_c
         for keyword in temp_general:
             general.remove(keyword)
     fix = [item.strip() for item in fix_prompt[:-1].split(',')]
+    if rm_a == 0:
+        if popped_row['artist']:
+            artists = [item.strip() for item in popped_row['artist'].split(',')]
+            artist = ["artist:" + _artist for _artist in artists]
+            fix = fix + artist
     if rm_s == 0:
         if popped_row['copyright']:
             series = [item.strip() for item in popped_row['copyright'].split(',')]
@@ -62,11 +67,6 @@ def RFP(popped_row, fix_prompt, after_prompt, auto_hide_prompt, rm_a, rm_s, rm_c
             character = [item.strip() for item in popped_row['character'].split(',')]
             processed[fix_index:fix_index] = character
             fix_index+=len(character)
-    if rm_a == 0:
-        if popped_row['artist']:
-            artists = [item.strip() for item in popped_row['artist'].split(',')]
-            artist = ["artist:" + _artist for _artist in artists]
-            processed[fix_index:fix_index] = artist
     if magic_word["random_artist"] == True:
         processed.insert(fix_index, magic_word["random_artist_name"])
         

Danbooru Prompt Selector/TEST2024/NAIA_search.py

@@ -154,19 +154,18 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
             results = pd.DataFrame()
             for keyword in or_search_keyword:
                 keywords = [item.strip() for item in keyword.split(',')]
-                matched_rows = None
+                matched_rows = pd.DataFrame()
                 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): 
+                    request_regex = False
+                    if any(char in keyword for char in special_chars): 
                             keyword = re.escape(keyword)
                             request_regex = True
-                        if keyword == keywords[0]:
-                            if request_regex: matched_rows = df[df[column].str.contains(keyword, na=False, regex=True)]
-                            else: matched_rows = df[df[column].str.contains(keyword, na=False)]
+                    for column in ['copyright', 'character', 'artist', 'meta', 'general']:
+                        if keyword == keywords[0] or keyword == re.escape(keywords[0]):
+                            if request_regex: matched_rows = pd.concat([matched_rows, df[df[column].str.contains(keyword, na=False, regex=True)]], ignore_index=True)
+                            else: matched_rows = pd.concat([matched_rows, df[df[column].str.contains(keyword, na=False)]], ignore_index=True)
                         else:
-                            print(keyword, len(matched_rows))
                             if request_regex:
                                 ndf = matched_rows[matched_rows[column].str.contains(keyword, na=False, regex=True)]
                             else: 
@@ -184,6 +183,7 @@ def search(df, search_request, exclude_request, E=None, N=None, S=None, G=None):
                         matched_rows.drop_duplicates(subset=['general'])
                 if not matched_rows.empty:
                     results = pd.concat([results, matched_rows])
+                    del[matched_rows]
                     print(results)
             del[[df]]
             results = results.drop_duplicates(subset=['general'])