Update app.py

app.py

@@ -1125,48 +1125,10 @@ def handle_model_choice_change(selected_model):
         # Default case: allow interaction
         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
 
-# import gradio as gr
-# import torch
-# from diffusers import FluxPipeline
-# from PIL import Image
-
-# # 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
-
-# # Function to generate image using Flux
-# def generate_flux_image(prompt: str):
-#     generator = torch.Generator("cpu").manual_seed(0)  # For reproducibility
-#     image = flux_pipe(
-#         prompt,
-#         guidance_scale=0.0,
-#         num_inference_steps=4,
-#         max_sequence_length=256,
-#         generator=generator
-#     ).images[0]
-    
-#     # Save image temporarily and return for display
-#     temp_image_path = f"temp_flux_image_{hash(prompt)}.png"
-#     image.save(temp_image_path)
-    
-#     return temp_image_path
-
-# # Hardcoded prompts for generating images
-# hardcoded_prompt_1 = "A high quality cinematic image for Toyota Truck in Birmingham skyline shot in the style of Michael Mann"
-# 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 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)
-
-
 import gradio as gr
 import torch
 from diffusers import FluxPipeline
 from PIL import Image
-import time
 
 # Load the Flux pipeline
 flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
@@ -1196,12 +1158,10 @@ hardcoded_prompt_3 = "A high quality cinematic image for Taylor Swift concert in
 
 # Generate the images immediately
 img1_path = generate_flux_image(hardcoded_prompt_1)
-time.sleep(2)  # Wait for 2 seconds before generating the next image
+# img2_path = generate_flux_image(hardcoded_prompt_2)
+# img3_path = generate_flux_image(hardcoded_prompt_3)
 
-img2_path = generate_flux_image(hardcoded_prompt_2)
-time.sleep(2)  # Wait for 2 seconds before generating the next image
 
-img3_path = generate_flux_image(hardcoded_prompt_3)
 
 
 
@@ -1503,12 +1463,10 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
 
         with gr.Column():
             # Display the pre-generated images directly
-            # image_output_1 = gr.Image(value=img1_path)
+            image_output_1 = gr.Image(value=img1_path)
             # image_output_2 = gr.Image(value=img2_path)
             # image_output_3 = gr.Image(value=img3_path)
-            image_output_1 = gr.Image(value=img1_path, label="Image 1")
-            image_output_2 = gr.Image(value=img2_path, label="Image 2")
-            image_output_3 = gr.Image(value=img3_path, label="Image 3")
+