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README.md

@@ -4,7 +4,7 @@ emoji: 👀📦
 colorFrom: red
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.36.1
+sdk_version: 4.37.2
 app_file: app.py
 pinned: false
 license: openrail

app.py

@@ -102,35 +102,35 @@ def main():
             input_ban_tags,
         ]
 
-        translate_t2t_text_button.click(translate_prompt, inputs=[t2t_text], outputs=[t2t_text])
+        translate_t2t_text_button.click(translate_prompt, [t2t_text], [t2t_text])
 
         generate_from_text_btn.click(
             predict_text_to_tags,
-            inputs=[t2t_text, t2t_max_tokens, t2t_temperature, t2t_top_k, t2t_top_p, t2t_repeat_penalty],
-            outputs=[
+            [t2t_text, t2t_max_tokens, t2t_temperature, t2t_top_k, t2t_top_p, t2t_repeat_penalty],
+            [
                 input_general,
             ],
         )
 
-        copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy]).then(
-            gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS,
+        copy_input_btn.click(compose_prompt_to_copy, [input_character, input_copyright, input_general], [input_tags_to_copy]).success(
+            gradio_copy_text, [input_tags_to_copy], js=COPY_ACTION_JS,
         )
 
         generate_btn.click(
             parse_upsampling_output(v2.on_generate),
-            inputs=[
+            [
                 *v2.input_components,
             ],
-            outputs=[output_text, elapsed_time_md, copy_btn, copy_btn_pony],
-        ).then(
-            convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony],
-        ).then(
-            insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np],
-        ).then(
-            insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np],
+            [output_text, elapsed_time_md, copy_btn, copy_btn_pony],
+        ).success(
+            convert_danbooru_to_e621_prompt, [output_text, tag_type], [output_text_pony],
+        ).success(
+            insert_recom_prompt, [output_text, dummy_np, recom_animagine], [output_text, dummy_np],
+        ).success(
+            insert_recom_prompt, [output_text_pony, dummy_np, recom_pony], [output_text_pony, dummy_np],
         )
-        copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS)
-        copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS)
+        copy_btn.click(gradio_copy_text, [output_text], js=COPY_ACTION_JS)
+        copy_btn_pony.click(gradio_copy_text, [output_text_pony], js=COPY_ACTION_JS)
 
     ui.launch()
 

tagger.py

@@ -81,6 +81,14 @@ def character_list_to_series_list(character_list):
     return output_series_tag
 
 
+def select_random_character(series: str, character: str):
+    from random import randrange
+    character_list = list(anime_series_dict.keys())
+    character = character_list[randrange(len(character_list) - 1)]
+    series = anime_series_dict.get(character.split(",")[0].strip(), "")
+    return series, character
+
+
 def danbooru_to_e621(dtag, e621_dict):
     def d_to_e(match, e621_dict):
         dtag = match.group(0)
@@ -415,28 +423,24 @@ def predict_tags(image: Image.Image, general_threshold: float = 0.3, character_t
     results = {
         wd_model.config.id2label[i]: float(logit.float()) for i, logit in enumerate(logits)
     }
-
     # rating, character, general
     rating, character, general = postprocess_results(
         results, general_threshold, character_threshold
     )
-
     prompt = gen_prompt(
         list(rating.keys()), list(character.keys()), list(general.keys())
     )
-
     output_series_tag = ""
     output_series_list = character_list_to_series_list(character.keys())
     if output_series_list:
         output_series_tag = output_series_list[0]
     else:
         output_series_tag = ""
-
     return output_series_tag, ", ".join(character.keys()), prompt, gr.update(interactive=True),
 
 
 def predict_tags_wd(image: Image.Image, input_tags: str, algo: list[str], general_threshold: float = 0.3, character_threshold: float = 0.8):
-    if algo and not "Use WD Tagger" in algo:
+    if not "Use WD Tagger" in algo and len(algo) != 0:
         return "", "", input_tags, gr.update(interactive=True),
     return predict_tags(image, general_threshold, character_threshold)