Update app.py

app.py

@@ -4,6 +4,7 @@ import numpy as np
 from PIL import Image
 import json
 from huggingface_hub import hf_hub_download
+from functools import partial # Import partial
 
 # --- Constants ---
 MODEL_REPO = "AngelBottomless/camie-tagger-onnxruntime"
@@ -72,7 +73,7 @@ def format_prompt_style_output(results_by_cat: dict, all_artist_tags_probs: list
 
     artist_prompt_tags = [tag.replace("_", " ").replace("(", r"\\(").replace(")", r"\\)") for tag, prob in artist_tags_with_probs]
     character_prompt_tags = [tag.replace("_", " ").replace("(", r"\\(").replace(")", r"\\)") for tag, prob in character_tags_with_probs]
-    general_prompt_tags = [tag.replace("_", " ").replace("(", r"\\(").replace(")", r"\\)") for tag, prob in general_tags_with_probs]
+    general_prompt_tags = [tag.replace("_", " ").replace("(", r"\\(").replace(")", r"\\)") for tag, prob in general_tags_probs]
 
     prompt_tags = artist_prompt_tags + character_prompt_tags + general_prompt_tags
 
@@ -142,11 +143,13 @@ def create_gradio_interface(session: ort.InferenceSession, metadata: dict) -> gr
             with gr.Column():
                 output_box = gr.Markdown("")
 
+        # Create a partial function with session and metadata pre-filled
+        tag_image_with_model = partial(tag_image, session=session, metadata=metadata)
+
         tag_button.click(
-            fn=tag_image,
+            fn=tag_image_with_model, # Use the partially applied function
             inputs=[image_in, format_choice],
             outputs=output_box,
-            extra_args=[session, metadata] # Pass session and metadata as extra arguments
         )
 
         gr.Markdown("----\n**Model:** [Camie Tagger ONNX](https://huggingface.co/AngelBottomless/camie-tagger-onnxruntime)   •   **Base Model:** Camais03/camie-tagger (61% F1 on 70k tags)   •   **ONNX Runtime:** for efficient CPU inference   •   *Demo built with Gradio Blocks.*")