Update app.py

app.py

@@ -5,14 +5,14 @@ import torch
 from diffusers import DiffusionPipeline
 import gradio as gr
 
-# 2. Retrieve token from space secrets for the gated model access
+# 2. Retrieve token from space secrets
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 # 3. Optimize the pipeline instantiation for RAM limits
 model_id = "black-forest-labs/FLUX.1-dev"
 pipe = DiffusionPipeline.from_pretrained(
     model_id, 
-    torch_dtype=torch.bfloat16,     # Half-precision vectors save footprint
+    torch_dtype=torch.bfloat16,     # Use half-precision for memory savings
     low_cpu_mem_usage=True,         # Prevent loading full weights into CPU system RAM at once
     token=HF_TOKEN
 )
@@ -29,24 +29,25 @@ def generate_icon(prompt, num_inference_steps=28, guidance_scale=3.5):
     
     full_prompt = f"Icon Kit, {prompt}, minimalist UI UX design element, flat line icon, uniform stroke, solid background"
     
-    # Run prediction execution loop
+    # Run prediction
     output = pipe(
         prompt=full_prompt,
         num_inference_steps=int(num_inference_steps),
         guidance_scale=float(guidance_scale)
     )
     
+    # Extract the exact first PIL Image item from the output list safely
     image = output.images[0]
     
     # CRUCIAL: Immediately dump weights back to CPU to cleanly yield the Zero GPU slot
     pipe.to("cpu")
     return image
 
-# 6. Expose API Endpoints using a standard structured Gradio Interface
+# 6. Expose API Endpoints using Gradio Interface
 demo = gr.Interface(
     fn=generate_icon,
     inputs=[
-        gr.Textbox(label="Icon Subject", placeholder="Enter icon concept...", value="a settings gear wheel"),
+        gr.Textbox(label="Icon Subject (e.g., 'a settings gear wheel')", placeholder="Enter icon concept..."),
         gr.Slider(minimum=15, maximum=40, value=28, step=1, label="Inference Steps"),
         gr.Slider(minimum=1.0, maximum=10.0, value=3.5, step=0.5, label="Guidance Scale")
     ],