refactor examples and processing pipeline to simplify input parameters

app.py

@@ -140,12 +140,11 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
         print(f"Error in infer: {str(e)}")  
         return None, f"Error generating image: {str(e)}"
  
-# Update examples to be a nested list with all required inputs
+# Update examples to be a list of prompts only, not including other parameters
 examples = [
-    # Format: [prompt, seed, randomize_seed, width, height, num_inference_steps]
-    ["a backpack for kids, flower style", 42, True, 512, 512, 6],
-    ["medieval flip flops", 123, True, 512, 512, 6],
-    ["cat shaped cake mold", 456, True, 512, 512, 6],
+    "a backpack for kids, flower style",
+    "medieval flip flops",
+    "cat shaped cake mold",
 ]
 
 css="""
@@ -187,8 +186,9 @@ def handle_example_click(example_prompt):
     return example_prompt, "Example selected - click 'Refine prompt with Mistral' to process"
 
 # Create a combined function that handles the whole pipeline from example to image
+# This version gets the parameters from the UI components
 @spaces.GPU()
-def process_example_pipeline(example_prompt, seed, randomize_seed, width, height, num_inference_steps, progress=gr.Progress()):
+def process_example_pipeline(example_prompt, progress=gr.Progress()):
     # Step 1: Update status
     progress(0, desc="Starting example processing")
     progress_status = "Selected example: " + example_prompt
@@ -200,16 +200,8 @@ def process_example_pipeline(example_prompt, seed, randomize_seed, width, height
     if not refined:
         return example_prompt, "", None, "Failed to refine prompt: " + status
     
-    progress(0.5, desc="Prompt refined, generating image")
-    progress_status = status
-    
-    # Step 3: Generate the image
-    image, image_status = infer(refined, seed, randomize_seed, width, height, num_inference_steps, progress)
-    
-    progress(1.0, desc="Process complete")
-    final_status = f"{progress_status} → {image_status}"
-    
-    return example_prompt, refined, image, final_status
+    # Return only the refined prompt and status - don't generate image
+    return example_prompt, refined, "Prompt refined successfully!"
 
 def create_interface():
     # Preload models if needed
@@ -281,25 +273,13 @@ def create_interface():
                         value=6,
                     )
 
-            # Examples section - use the pipeline function for examples
+            # Examples section - simplified version that only updates the prompt fields
             gr.Examples(
-                examples=examples,  # Now properly formatted as nested list
+                examples=examples,  # Now just a list of prompts
                 fn=process_example_pipeline,
-                inputs=[
-                    prompt,
-                    seed,
-                    randomize_seed, 
-                    width, 
-                    height,
-                    num_inference_steps
-                ],
-                outputs=[
-                    prompt,
-                    refined_prompt,
-                    generated_image, 
-                    error_box
-                ],
-                cache_examples=True,  # Can be cached now
+                inputs=[prompt],  # Only input is the prompt
+                outputs=[prompt, refined_prompt, error_box],  # Don't output image
+                cache_examples=True,
             )
 
         # Event handlers