Update app.py

app.py

@@ -35,12 +35,6 @@ def inference(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device=device).manual_seed(seed)
-
-    progress(0, "Starting image generation...")
-
-    for i in range(1, num_inference_steps + 1):
-        if i % (num_inference_steps // 10) == 0:
-            progress(i / num_inference_steps * 100, f"Processing step {i} of {num_inference_steps}...")
     
     image = pipeline(
         prompt=prompt,
@@ -52,8 +46,6 @@ def inference(
         joint_attention_kwargs={"scale": lora_scale},
     ).images[0]
 
-    progress(100, "Completed!")
-
     return image, seed
 
 
@@ -142,9 +134,15 @@ with gr.Blocks(css=css) as demo:
                     value=1.0,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt], outputs=[Image.open("./example.jpg")])
+        gr.Examples(
+            examples=examples,
+            fn=inference,
+            inputs=[prompt],
+            outputs=[Image.open("./image.jpg"), seed],
+            cache_examples="lazy"
+        )
 
-        gr.Markdown("Free of use, but both the dataset that FLUX has been fine-tuned on, as well as the FLUX.1-dev model are licensed under a non-commercial license.")
+        gr.Markdown("### Disclaimer\nFree of use, but both the dataset that FLUX has been fine-tuned on, as well as the FLUX.1-dev model are licensed under a non-commercial license.")
 
     gr.on(
         triggers=[run_button.click, prompt.submit],