Update app.py

update placeholder prompt

app.py

@@ -78,14 +78,13 @@ with gr.Blocks(css=".gradio-container {background-image: linear-gradient(to bott
       <h1 style="text-align: center; font-size: 32px; color: white;">
       🐕 Animal Pose Control Net 🐈
       </h1>
-      <h3 style="text-align: left; font-size: 20px; color: white;"> This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with a new type of conditioning.</h3>  
+      <h3 style="text-align: left; font-size: 20px; color: white;"> This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with a new type of conditioning. The dataset was built using the OpenPifPaf Animalpose plugin.</h3>  
       <h3 style="text-align: left; font-size: 20px; color: white;"> While this is definitely a work in progress, you can still try it out by using the p5 sketch to create a keypoint image and using it as the conditioning image.</h3>
-      <h3 style="text-align: left; font-size: 20px; color: white;"> The model was generated as part of the Hugging Face Jax Diffusers sprint.  Thank you to both Hugging Face and Google Cloud who provided the TPUs for training!
-      <h3 style="text-align: left; font-size: 20px; color: white;"> The dataset was built using the OpenPifPaf Animalpose plugin.</h3>
+      <h3 style="text-align: left; font-size: 20px; color: white;"> The model was generated as part of the Hugging Face Jax Diffusers sprint.  Thank you to both Hugging Face and Google Cloud who provided the TPUs for training!</h3>
       """) 
   with gr.Row():
     with gr.Column():
-      prompts  = gr.Textbox(label="Prompt", placeholder="animal standing, best quality, highres")
+      prompts  = gr.Textbox(label="Prompt", placeholder="dog standing in grass, best quality, highres")
       negative_prompts  = gr.Textbox(label="Negative Prompt", value="lowres, two heads, bad muzzle, bad anatomy, missing ears, missing paws")
       conditioning_image = gr.Image(label="Conditioning Image")
       #  conditioning_image = gr.Image(label="Conditioning Image", value=default_example[3])