app.py

import os
import warnings
from pathlib import Path

import gradio as gr
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import numpy as np

# Check if CUDA is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")

# Model definition
class DeblurNet(nn.Module):
    def __init__(self):
        super(DeblurNet, self).__init__()
        self.enc_conv1 = self.conv_block(3, 64)
        self.enc_conv2 = self.conv_block(64, 128)
        self.enc_conv3 = self.conv_block(128, 256)
        self.bottleneck = self.conv_block(256, 512)
        self.dec_conv1 = self.conv_block(512 + 256, 256)
        self.dec_conv2 = self.conv_block(256 + 128, 128)
        self.dec_conv3 = self.conv_block(128 + 64, 64)
        self.final_conv = nn.Conv2d(64, 3, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

    def conv_block(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x1 = self.enc_conv1(x)
        x2 = self.pool(x1)
        x2 = self.enc_conv2(x2)
        x3 = self.pool(x2)
        x3 = self.enc_conv3(x3)
        x4 = self.pool(x3)
        x4 = self.bottleneck(x4)
        x = self.upsample(x4)
        x = torch.cat([x, x3], dim=1)
        x = self.dec_conv1(x)
        x = self.upsample(x)
        x = torch.cat([x, x2], dim=1)
        x = self.dec_conv2(x)
        x = self.upsample(x)
        x = torch.cat([x, x1], dim=1)
        x = self.dec_conv3(x)
        x = self.final_conv(x)
        return torch.tanh(x)

# Load model
model = DeblurNet().to(device)
model_path = os.path.join('model', 'best_deblur_model.pth')

# Ensure model path exists before loading
if os.path.exists(model_path):
    model.load_state_dict(torch.load(model_path, map_location=device))
    model.eval()
    print("Model loaded successfully.")
else:
    print(f"Model file not found at {model_path}. Please check the path.")

# Image processing functions
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

def postprocess_image(tensor):
    """Post-process the output tensor into a displayable image."""
    tensor = tensor * 0.5 + 0.5
    tensor = torch.clamp(tensor, 0, 1)
    image = tensor.cpu().detach().numpy()
    image = np.transpose(image, (1, 2, 0))
    return (image * 255).astype(np.uint8)

def deblur_image(filepath):
    """Deblurs the uploaded image."""
    if not filepath:
        return None

    try:
        # Load image from filepath
        input_image = Image.open(filepath).convert("RGB")

        # Save original size
        original_size = input_image.size

        # Preprocess
        input_tensor = transform(input_image).unsqueeze(0).to(device)

        # Inference
        with torch.no_grad():
            output_tensor = model(input_tensor)

        # Post-process
        output_image = postprocess_image(output_tensor[0])

        # Resize back to original size
        output_image = Image.fromarray(output_image).resize(original_size)

        return np.array(output_image)

    except Exception as e:
        print(f"Error processing image: {e}")
        return None

# ✅ Your original CSS with fullscreen button removed
custom_css = """
/* Completely hide fullscreen and share buttons */
button[title="Fullscreen"], 
button[title="Share"],
.gr-button[title="Fullscreen"],
.gr-button[title="Share"] {
    display: none !important;          /* Remove from the layout */
    opacity: 0 !important;             /* Make it invisible */
    visibility: hidden !important;     /* Ensure it's hidden */
    width: 0 !important;               /* Collapse the button size */
    height: 0 !important;              /* Collapse the button size */
    overflow: hidden !important;       /* Prevent any content visibility */
    pointer-events: none !important;   /* Disable all interactions */
}
/* Hide Gradio's footer and header */
footer, header, .gradio-footer, .gradio-header {
    display: none !important;
}
/* Non-draggable images */
img {
    pointer-events: none !important;
    -webkit-user-drag: none !important;
    user-select: none !important;
}
/* Styling adjustments */
body, .gradio-container {
    background-color: #000000 !important;
    color: white !important;
}
.gr-button {
    background: #1e90ff !important;
    color: white !important;
    border: none !important;
    padding: 10px 20px !important;
    font-size: 14px !important;
    cursor: pointer;
}
.gr-button:hover {
    background: #0056b3 !important;
}
.gr-box, .gr-input, .gr-output {
    background-color: #1c1c1c !important;
    color: white !important;
    border: 1px solid #333333 !important;
}
"""

# ✅ Gradio interface
demo = gr.Interface(
    fn=deblur_image,
    inputs=gr.File(label="Input", type="filepath"),
    outputs=gr.Image(type="numpy", label="Deblurred Result"),
    title="Image Deblurring",
    description="Upload a blurry image.",
    css=custom_css
)

# ✅ Launch Gradio app
if __name__ == "__main__":
    demo.launch()