Update app.py

app.py

@@ -11,12 +11,15 @@ try:
     inpaint_model = DiffusionPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1").to(device)
 except Exception as e:
     print(f"Error initializing model: {e}")
+    inpaint_model = None
 
 def process_image(prompt, image, style, upscale_factor, inpaint):
     try:
         # Ensure the image is in the correct format
         if image is not None:
             image = image.convert("RGB")
+        else:
+            image = None
 
         # Log the input parameters
         print(f"Prompt: {prompt}")
@@ -26,11 +29,11 @@ def process_image(prompt, image, style, upscale_factor, inpaint):
 
         # Example placeholder logic for using the pipeline
         # Replace with actual pipeline logic based on selected style
-        if inpaint:
+        if inpaint and inpaint_model:
             result = inpaint_model(prompt=prompt, image=image, guidance_scale=7.5)
         else:
-            result = inpaint_model(prompt=prompt, guidance_scale=7.5)
-        
+            result = inpaint_model(prompt=prompt, guidance_scale=7.5) if inpaint_model else None
+
         # Check if the result is valid
         if result and hasattr(result, 'images') and len(result.images) > 0:
             return result.images[0]
@@ -38,7 +41,9 @@ def process_image(prompt, image, style, upscale_factor, inpaint):
             return "Error: No image returned from model."
 
     except Exception as e:
-        return f"Error in process_image function: {e}"
+        error_message = f"Error in process_image function: {e}"
+        print(error_message)
+        return error_message
 
 # Define the Gradio interface
 with gr.Blocks() as demo: