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import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
from model import Generator # Assuming you are using Hammad712's model structure
# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Generator().to(device)
model.load_state_dict(torch.load('generator.pth', map_location=device))
model.eval()
# Define preprocessing and postprocessing
preprocess = transforms.Compose([
transforms.Resize((256, 256)),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor()
])
postprocess = transforms.ToPILImage()
def colorize_image(input_image):
input_tensor = preprocess(input_image).unsqueeze(0).to(device)
with torch.no_grad():
output_tensor = model(input_tensor)
output_image = postprocess(output_tensor.squeeze(0).cpu().clamp(0, 1))
return output_image
def reset():
return None, None
with gr.Blocks() as demo:
gr.Markdown("# 🎨 Image Colorization App")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Upload your grayscale image", type="pil")
clear_button = gr.Button("🔄 Reset / Clear")
download_button = gr.File(label="Download Colorized Image")
with gr.Column():
output_image = gr.Image(label="Colorized Image")
colorize_btn = gr.Button("✨ Colorize Image")
colorize_btn.click(
colorize_image,
inputs=input_image,
outputs=output_image
)
clear_button.click(
reset,
inputs=[],
outputs=[input_image, output_image]
)
# Allow download after processing
def prepare_download(image):
if image:
path = "colorized_output.png"
image.save(path)
return path
else:
return None
output_image.change(
prepare_download,
inputs=output_image,
outputs=download_button
)
demo.launch()
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