app.py

import gradio as gr
import numpy as np
from PIL import Image
from skimage.util import random_noise
from skimage.restoration import denoise_nl_means, estimate_sigma

def add_noise(image, mean, var):
    sigma = var ** 0.5
    noisy_image = random_noise(np.array(image), mode='gaussian', mean=mean, var=var)
    noisy_image = (255 * noisy_image).astype(np.uint8)  # Scale back to 0-255
    return Image.fromarray(noisy_image)

def remove_noise(image):
    image = image.convert('L')
    image_np = np.array(image)
    sigma_est = np.mean(estimate_sigma(image_np, multichannel=False))
    denoised_image = denoise_nl_means(image_np, h=1.15 * sigma_est, fast_mode=True,
                                      patch_size=5, patch_distance=6, multichannel=False)
    denoised_image = (255 * denoised_image).astype(np.uint8)  # Scale back to 0-255
    return Image.fromarray(denoised_image)

# Define Gradio interface
interface = gr.Interface(
    fn=add_noise,
    inputs=[
        gr.Image(label="Original Image"),
        gr.Slider(0, 0.05, step=0.001, value=0, label="Mean of Gaussian Noise"),
        gr.Slider(0, 0.01, step=0.0001, value=0.001, label="Variance of Gaussian Noise")
    ],
    outputs=gr.Image(label="Processed Image"),
    title="Image Noise Addition and Removal",
    description="Add Gaussian noise to an image and attempt to remove it. Adjust the mean and variance for different effects."
)

# Launch the app
interface.launch()