Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import torch
 from diffusers import DiffusionPipeline
+from PIL import Image
 
 # Set up device
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -13,18 +14,29 @@ except Exception as e:
 
 def process_image(prompt, image, style, upscale_factor, inpaint):
     try:
-        # Convert the image to the appropriate format if needed
+        # Ensure the image is in the correct format
         if image is not None:
             image = image.convert("RGB")
 
+        # Log the input parameters
+        print(f"Prompt: {prompt}")
+        print(f"Style: {style}")
+        print(f"Upscale Factor: {upscale_factor}")
+        print(f"Inpaint: {inpaint}")
+
         # Example placeholder logic for using the pipeline
-        # Here we assume the pipeline can handle both image and prompt; adjust as needed
+        # Replace with actual pipeline logic based on selected style
         if inpaint:
             result = inpaint_model(prompt=prompt, image=image, guidance_scale=7.5)
         else:
             result = inpaint_model(prompt=prompt, guidance_scale=7.5)
         
-        return result.images[0]  # Return the first image from the result
+        # Check if the result is valid
+        if result and hasattr(result, 'images') and len(result.images) > 0:
+            return result.images[0]
+        else:
+            return "Error: No image returned from model."
+
     except Exception as e:
         return f"Error in process_image function: {e}"