Update app.py

app.py

@@ -1,11 +1,7 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
 import dask
-from dask import delayed, compute
-from concurrent.futures import ThreadPoolExecutor
-import os
-os.environ['HF_HOME'] = '/blabla/cache/'
-
+from dask import delayed
 
 # Load model
 pipe = DiffusionPipeline.from_pretrained("prompthero/openjourney-v4")
@@ -19,27 +15,23 @@ def generate_image(prompt, num_inference_steps=50):
     image = pipe(prompt, num_inference_steps=num_inference_steps).images[0]
     return image
 
+# Dask-delayed function to utilize multi-core CPU processing
 @delayed
 def dask_generate(prompt):
     return generate_image(prompt)
 
 def parallel_generate(prompt):
-    # Use multithreading to speed up the computation by processing multiple images simultaneously
-    with ThreadPoolExecutor(max_workers=4) as executor:
-        futures = [executor.submit(dask_generate, prompt) for _ in range(4)]  # Example with 4 threads
-        results = [future.result() for future in futures]
-
     # Execute the generation using Dask to potentially improve processing speed
-    images = compute(*results)
-    return images[0]  # Return the first image generated for simplicity
+    image = dask.compute(dask_generate(prompt))[0]
+    return image
 
 # Gradio interface
 iface = gr.Interface(
     fn=parallel_generate,
     inputs=gr.Textbox(label="Prompt", placeholder="Enter your prompt here"),
     outputs=gr.Image(type="pil"),
-    title="Multithreaded CPU Optimized Image Generation",
-    description="Enter a prompt to generate an image efficiently using CPU optimization and multithreading."
+    title="CPU Optimized Image Generation",
+    description="Enter a prompt to generate an image efficiently using CPU optimization."
 )
 
 # Launch the Gradio app