app.py

import gradio as gr
from transformers import pipeline
import torch
from PIL import Image
import io

# Check for CUDA availability and set device
if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f"Using CUDA device: {torch.cuda.get_device_name(0)}")
else:
    device = torch.device("cpu")
    print("Using CPU. CUDA is not available.")

try:
    # Initialize the deblurring pipeline with the specified model and device
    deblurrer = pipeline("image-to-image", model="google/maxim-s3-deblurring-reds", device=device)
except Exception as e:
    print(f"Error loading the model: {e}")
    exit()  # Exit if model loading fails

def deblur_image(input_image):
    try:
        output = deblurrer(input_image)
        deblurred_image = output[0]

        # Convert PIL Image to Bytes for download
        img_byte_arr = io.BytesIO()
        deblurred_image.save(img_byte_arr, format='PNG')  # or JPEG, etc.
        img_byte_arr = img_byte_arr.getvalue()

        return deblurred_image, img_byte_arr  # Return both image and bytes
    except Exception as e:
        print(f"Error during deblurring: {e}")
        return None, None

# Create the Gradio interface
iface = gr.Interface(
    fn=deblur_image,
    inputs=gr.Image(type="pil", label="Upload Blurred Image"),
    outputs=[
        gr.Image(type="pil", label="Deblurred Image"),
        gr.File(label="Download Deblurred Image", file_types=[".png", ".jpg", ".jpeg"]) # Added File output
    ],
    title="Deblurring App",
    description="Deblur your images using the google/maxim-s3-deblurring-reds model.",
    examples=[["blurred_image.jpg"]],
)

# Launch the Gradio app
iface.launch()