Update app.py

app.py

@@ -8,6 +8,7 @@ import cv2
 import gradio as gr
 from torchvision import transforms 
 import fire
+import os
 
 controlnet = ControlNetModel.from_pretrained(
     "geyongtao/HumanWild",
@@ -96,14 +97,25 @@ def run_demo():
         with gr.Row():
             with gr.Column():
                 input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
+
+                example_folder = os.path.join(os.path.dirname(__file__), "./assets")
+                example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
+                gr.Examples(
+                    examples=example_fns,
+                    inputs=[input_image],
+                    cache_examples=False,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=30
+                )
+            
                 prompt = gr.Textbox(label="Prompt")
-                negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
+                negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
                 num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
                 controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
                 seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
                 run_button = gr.Button(value="Run")
-                
-                
+
+
             with gr.Column():
                 result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
         ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]