fix threshold slider and matplotlib warning

app.py

@@ -12,7 +12,7 @@ from torchvision.transforms import InterpolationMode
 import torchvision.transforms.functional as TF
 from huggingface_hub import hf_hub_download
 import numpy as np
-import matplotlib.cm as cm
+import matplotlib.colormaps as cm
 
 class Fit(torch.nn.Module):
     def __init__(
@@ -170,7 +170,7 @@ def run_classifier(image: Image.Image, threshold):
         tag_score[allowed_tags[indices[i]]] = values[i].item()
     sorted_tag_score = dict(sorted(tag_score.items(), key=lambda item: item[1], reverse=True))
 
-    return *create_tags(threshold, sorted_tag_score), img
+    return *create_tags(threshold, sorted_tag_score), img, sorted_tag_score
 
 def create_tags(threshold, sorted_tag_score: dict):
     filtered_tag_score = {key: value for key, value in sorted_tag_score.items() if value > threshold}
@@ -178,7 +178,7 @@ def create_tags(threshold, sorted_tag_score: dict):
     return text_no_impl, filtered_tag_score
 
 def clear_image():
-    return "", {}, None
+    return "", {}, None, {}
 
 def cam_inference(img, threshold, evt: gr.SelectData):
     target_tag = evt.value
@@ -274,6 +274,7 @@ with gr.Blocks(css=".output-class { display: none; }") as demo:
     Special thanks to Minotoro at frosting.ai for providing the compute power for this project.
     """)
     original_image_state = gr.State() # stash a copy of the input image
+    sorted_tag_score_state = gr.State(value={}) # stash a copy of the input image
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(label="Source", sources=['upload'], type='pil', height=512, show_label=False)
@@ -285,18 +286,18 @@ with gr.Blocks(css=".output-class { display: none; }") as demo:
     image_input.upload(
         fn=run_classifier,
         inputs=[image_input, threshold_slider],
-        outputs=[tag_string, label_box, original_image_state]
+        outputs=[tag_string, label_box, original_image_state, sorted_tag_score_state]
     )
 
     image_input.clear(
         fn=clear_image,
         inputs=[],
-        outputs=[tag_string, label_box, original_image_state]
+        outputs=[tag_string, label_box, original_image_state, sorted_tag_score_state]
     )
 
     threshold_slider.input(
         fn=create_tags,
-        inputs=[threshold_slider],
+        inputs=[threshold_slider, sorted_tag_score_state],
         outputs=[tag_string, label_box]
     )