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Update app.py
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app.py
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import gradio as gr
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from
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import
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import numpy as np
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from PIL import Image
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import io
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#
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def deblur_image(input_image):
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byte_arr = io.BytesIO()
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output_image.save(byte_arr, format='PNG')
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byte_arr.seek(0)
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return byte_arr
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#
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iface = gr.Interface(
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# Launch the Gradio
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iface.launch()
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import gradio as gr
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from transformers import pipeline
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import torch
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from PIL import Image
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import io
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# Check for CUDA availability and set device
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if torch.cuda.is_available():
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device = torch.device("cuda")
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print(f"Using CUDA device: {torch.cuda.get_device_name(0)}")
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else:
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device = torch.device("cpu")
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print("Using CPU. CUDA is not available.")
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try:
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# Initialize the deblurring pipeline with the specified model and device
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deblurrer = pipeline("image-to-image", model="google/maxim-s3-deblurring-reds", device=device)
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except Exception as e:
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print(f"Error loading the model: {e}")
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exit() # Exit if model loading fails
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def deblur_image(input_image):
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try:
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output = deblurrer(input_image)
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deblurred_image = output[0]
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# Convert PIL Image to Bytes for download
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img_byte_arr = io.BytesIO()
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deblurred_image.save(img_byte_arr, format='PNG') # or JPEG, etc.
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img_byte_arr = img_byte_arr.getvalue()
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return deblurred_image, img_byte_arr # Return both image and bytes
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except Exception as e:
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print(f"Error during deblurring: {e}")
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return None, None
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# Create the Gradio interface
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iface = gr.Interface(
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fn=deblur_image,
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inputs=gr.Image(type="pil", label="Upload Blurred Image"),
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outputs=[
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gr.Image(type="pil", label="Deblurred Image"),
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gr.File(label="Download Deblurred Image", file_types=[".png", ".jpg", ".jpeg"]) # Added File output
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],
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title="Deblurring App",
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description="Deblur your images using the google/maxim-s3-deblurring-reds model.",
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examples=[["blurred_image.jpg"]],
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)
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# Launch the Gradio app
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iface.launch()
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