test gradio

app.py

@@ -7,13 +7,6 @@ import os
 import spaces,tempfile
 import torch
 
-from diffusers import AnimateDiffSparseControlNetPipeline
-from diffusers.models import AutoencoderKL, MotionAdapter, SparseControlNetModel
-from diffusers.schedulers import DPMSolverMultistepScheduler
-from diffusers.utils import export_to_gif, load_image
-from diffusers import AutoPipelineForText2Image
-import openai,json
-
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -25,17 +18,15 @@ lora_path = "Jl-wei/ui-diffuser-v2"
 pipe.load_lora_weights(lora_path)
 pipe.to("cuda")
 
-
+@spaces.GPU
 def gui_generation(text, num_imgs):
     prompt = f"Mobile app: {text}"
     images = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, height=512, width=288, num_images_per_prompt=num_imgs).images
     yield images
 
-with gr.Blocks() as demo:
-    gallery = gr.Gallery(columns=[3], rows=[1], object_fit="contain", height="auto")
-    number_slider = gr.Slider(1, 30, value=2, step=1, label="Batch size")
-    prompt_box = gr.Textbox(label="Prompt", placeholder="Health monittoring report")
-    gr.Interface(gui_generation, inputs=[prompt_box, number_slider], outputs=gallery)
+gallery = gr.Gallery(columns=[3], rows=[1], object_fit="contain", height="auto")
+number_slider = gr.Slider(1, 30, value=2, step=1, label="Batch size")
+prompt_box = gr.Textbox(label="Prompt", placeholder="Health monittoring report")
+interface = gr.Interface(gui_generation, inputs=[prompt_box, number_slider], outputs=gallery)
 
-if __name__ == "__main__":
-    demo.launch()
+interface.launch()