app.py

import gradio as gr
import torch
from diffusers import DiffusionPipeline
from PIL import Image
import numpy as np
from torchvision import transforms

device = "cuda" if torch.cuda.is_available() else "cpu"

# Initialize the inpainting model
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

# Load a test image
def load_test_image():
    try:
        # Provide the absolute path to a test image
        return Image.open("/absolute/path/to/your/test_image.png")
    except Exception as e:
        print(f"Error loading test image: {e}")
        return None

def process_image(prompt, image, style, upscale_factor, inpaint):
    if image is None:
        image = load_test_image()
        if image is None:
            return None, "No image received and failed to load test image."

    try:
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)
        elif isinstance(image, torch.Tensor):
            image = transforms.ToPILImage()(image)
        elif not isinstance(image, Image.Image):
            return None, f"Unsupported image format: {type(image)}."
        
        print(f"Received image: {image.size}")
        # Placeholder for processing logic
        return image, None
    except Exception as e:
        error_message = f"Error in process_image function: {e}"
        print(error_message)
        return None, error_message

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Enter your prompt")
            image_input = gr.Image(label="Image (for inpainting)", type="pil")
            style_input = gr.Dropdown(choices=["Fooocus Style", "SAI Anime"], label="Select Style")
            upscale_input = gr.Slider(minimum=1, maximum=4, step=1, label="Upscale Factor")
            inpaint_input = gr.Checkbox(label="Enable Inpainting")
            
            output_image = gr.Image(label="Generated Image", type="pil")
            error_output = gr.Textbox(label="Error Details", lines=4, placeholder="Error details will appear here")

            generate_button = gr.Button("Generate Image")
            generate_button.click(
                process_image,
                inputs=[prompt_input, image_input, style_input, upscale_input, inpaint_input],
                outputs=[output_image, error_output]
            )

demo.launch()