Update app.py

app.py

@@ -1126,52 +1126,8 @@ 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
-# import os
-
-# # Set PYTORCH_CUDA_ALLOC_CONF to handle memory fragmentation
-# os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
-
-# # Check if CUDA (GPU) is available, otherwise fallback to CPU
-# device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# # Function to initialize Flux bot model with GPU memory management
-# def initialize_flux_bot():
-#     try:
-#         torch.cuda.empty_cache()  # Clear GPU memory cache
-#         pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16)  # Use FP16
-#         pipe.to(device)  # Move the model to the correct device (GPU/CPU)
-#     except torch.cuda.OutOfMemoryError:
-#         print("CUDA out of memory, switching to CPU.")
-#         pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float32)  # Use FP32 for CPU
-#         pipe.to("cpu")
-#     return pipe
-
-# # Function to generate image using Flux bot on the specified device
-# def generate_image_flux(prompt):
-#     pipe = initialize_flux_bot()
-#     image = pipe(
-#         prompt,
-#         guidance_scale=0.0,
-#         num_inference_steps=2,  # Reduced steps to save memory
-#         max_sequence_length=128,  # Reduced sequence length to save memory
-#         generator=torch.Generator(device).manual_seed(0)
-#     ).images[0]
-#     return image
-
-# # Hardcoded prompts for the 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"
-
-# # Function to update images
-# def update_images():
-#     image_1 = generate_image_flux(hardcoded_prompt_1)
-#     image_2 = generate_image_flux(hardcoded_prompt_2)
-#     image_3 = generate_image_flux(hardcoded_prompt_3)
-#     return image_1, image_2, image_3
+#Flux Coding
+
 
 # Existing prompts for the Flux API
 hardcoded_prompt_1 = "A high quality cinematic image for Toyota Truck in Birmingham skyline shot in the style of Michael Mann"
@@ -1180,7 +1136,7 @@ hardcoded_prompt_3 = "A high quality cinematic image for Taylor Swift concert in
 
 # Function to call the Flux API and generate images
 def generate_image_flux(prompt):
-    # client = Client("black-forest-labs/FLUX.1-schnell")
+    # client = Client("black-forest-labs/FLUX.1-schnell",hf_token=hf_token)
     client = Client("Pijush2023/radar_flux")
     result = client.predict(
         prompt=prompt,
@@ -1507,13 +1463,7 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
             events_output = gr.HTML(value=fetch_local_events())
 
         with gr.Column():
-            # image_output_1 = gr.Image(value=generate_image_flux(hardcoded_prompt_1), width=400, height=400)
-            # image_output_2 = gr.Image(value=generate_image_flux(hardcoded_prompt_2), width=400, height=400)
-            # image_output_3 = gr.Image(value=generate_image_flux(hardcoded_prompt_3), width=400, height=400)
-    
-            # # Refresh button to update images
-            # refresh_button = gr.Button("Refresh Images")
-            # refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3])
+           
           
             # Call update_images during the initial load to display images when the interface appears
             initial_images = update_images()