Update app.py

app.py

@@ -1125,10 +1125,48 @@ 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)
@@ -1158,9 +1196,12 @@ 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)
+time.sleep(2)  # Wait for 2 seconds before generating the next image
 
+img3_path = generate_flux_image(hardcoded_prompt_3)
 
 
 
@@ -1462,10 +1503,13 @@ 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_2 = gr.Image(value=img2_path)
-            image_output_3 = gr.Image(value=img3_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")
+