Update app.py

app.py

@@ -19,7 +19,7 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=0.8,model="Realistic_V5.0", scheduler="DPMSolverMultistepScheduler-Karras",seed=42):
+def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=0.8,model="Realistic_V5.0", scheduler="DPMSolverMultistepScheduler-Karras"):
     
     pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE",vae=vae).to("cuda")
     
@@ -141,7 +141,6 @@ with image_blocks as demo:
                             steps = gr.Number(value=40, minimum=10, maximum=100, step=1, label="steps")
                             strength = gr.Number(value=0.8, minimum=0.01, maximum=1.0, step=0.01, label="strength")
                             negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
-                            seed = gr.Dropdown(label="Seed", choices=[0,21,42], value="42")
                         with gr.Row(equal_height=True):
                             models = ["Realistic_V5.0","Realistic_V5.1","Realistic_V6.0","Epic_Realism"]
                             model = gr.Dropdown(label="Models",choices=models,value="Realistic_V5.0")
@@ -157,8 +156,8 @@ with image_blocks as demo:
                         share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
             
 
-    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler,seed], outputs=[image_out,share_btn_container], api_name='run')
-    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler,seed], outputs=[image_out,share_btn_container])
+    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler], outputs=[image_out,share_btn_container], api_name='run')
+    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler], outputs=[image_out,share_btn_container])
     share_button.click(None, [], [], _js=share_js)