Update app.py

app.py

@@ -8,6 +8,9 @@ from PIL import Image
 from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
 import gc
 
+report_url = 'https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5'
+sketch_url = 'https://editor.p5js.org/kfahn/full/Ntzq9HWhx'
+
 def create_key(seed=0):
     return jax.random.PRNGKey(seed)
 
@@ -19,12 +22,6 @@ def wandb_report(url):
     iframe = f'<iframe src ={url} style="border:none;height:1024px;width:100%"/frame>'
     return gr.HTML(iframe)
 
-mytitle = 'Animal Pose Control Net'
-mydescription = 'This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.'
-
-report_url = 'https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5'
-sketch_url = 'https://editor.p5js.org/kfahn/full/Ntzq9HWhx'
-
 control_img = 'myimage.jpg'
 
 controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
@@ -81,7 +78,7 @@ with gr.Blocks(theme='kfahn/AnimalPose') as demo:
       """) 
   with gr.Row():
     with gr.Column():
-      prompts  = gr.Textbox(label="Prompt", placeholder="yellow dog standing on a lawn, best quality, highres")
+      prompts  = gr.Textbox(label="Prompt", placeholder="animal standing, best quality, highres")
       negative_prompts  = gr.Textbox(label="Negative Prompt", value="lowres, bad muzzle, bad anatomy, missing ears, missing paws")
       conditioning_image = gr.Image(label="Conditioning Image")
       run_btn = gr.Button("Run")
@@ -99,7 +96,6 @@ with gr.Blocks(theme='kfahn/AnimalPose') as demo:
         [Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5)
         """)     
 
-  #run_btn.click(fn=infer, title = mytitle, description = mydescription, inputs = [prompts, negative_prompts, conditioning_image], outputs = output)
   run_btn.click(fn=infer, inputs = [prompts, negative_prompts, conditioning_image], outputs = output)
     
 #gr.Interface(fn=infer, inputs = ["text", "text", "image"], outputs = output,