Update app.py

app.py

@@ -1130,7 +1130,6 @@ import torch
 from diffusers import FluxPipeline
 from PIL import Image
 
-# Flux Image Generation
 # Load the Flux pipeline
 flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
 flux_pipe.enable_model_cpu_offload()  # Save some VRAM by offloading to CPU if needed
@@ -1157,12 +1156,10 @@ hardcoded_prompt_1 = "A high quality cinematic image for Toyota Truck in Birming
 hardcoded_prompt_2 = "A high quality cinematic image for Alabama Quarterback close up emotional shot in the style of Michael Mann"
 hardcoded_prompt_3 = "A high quality cinematic image for Taylor Swift concert in Birmingham skyline style of Michael Mann"
 
-# Generate the images automatically upon launching the interface
-def show_images_on_startup():
-    img1 = generate_flux_image(hardcoded_prompt_1)
-    img2 = generate_flux_image(hardcoded_prompt_2)
-    img3 = generate_flux_image(hardcoded_prompt_3)
-    return img1, img2, img3
+# Generate the images immediately
+img1_path = generate_flux_image(hardcoded_prompt_1)
+img2_path = generate_flux_image(hardcoded_prompt_2)
+img3_path = generate_flux_image(hardcoded_prompt_3)
 
 
 
@@ -1464,11 +1461,10 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
             events_output = gr.HTML(value=fetch_local_events())
 
         with gr.Column():
-            gr.Markdown("<h1 style='color: blue;'>Cinematic Images Generated with Flux</h1>")
-            image_output_1 = gr.Image()
-            image_output_2 = gr.Image()
-            image_output_3 = gr.Image()
-            demo.load(fn=show_images_on_startup, outputs=[image_output_1, image_output_2, image_output_3])
+            # Display the pre-generated images directly
+            image_output_1 = gr.Image(value=img1_path)
+            image_output_2 = gr.Image(value=img2_path)
+            image_output_3 = gr.Image(value=img3_path)