Update app.py

app.py

@@ -2,16 +2,26 @@ import gradio as gr
 from diffusers import StableDiffusionImg2ImgPipeline
 import torch
 from PIL import Image
+import numpy as np
 
 # Load the model (this runs when the Space starts)
 model_id = "nitrosocke/Ghibli-Diffusion"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)  # Use float32 for CPU
-#pipe = pipe.to("cuda")  # Use GPU if available in the Space
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
 
 # Define the inference function
 def ghibli_transform(input_image, prompt="ghibli style", strength=0.75, guidance_scale=7.5):
-    # Convert Gradio image (numpy array) to PIL Image and resize
-    init_image = Image.fromarray(input_image).convert("RGB").resize((768, 768))
+    # Check if input_image is valid
+    if input_image is None:
+        raise gr.Error("Please upload an image before clicking Transform!")
+    
+    # Ensure input_image is a NumPy array and convert to PIL
+    if not isinstance(input_image, np.ndarray):
+        raise gr.Error("Input image format is invalid. Expected a NumPy array.")
+    
+    try:
+        init_image = Image.fromarray(input_image).convert("RGB").resize((768, 768))
+    except Exception as e:
+        raise gr.Error(f"Failed to process image: {str(e)}")
     
     # Generate the Ghibli-style image
     output = pipe(