Create app.py

app.py

@@ -0,0 +1,34 @@
+import cv2
+import numpy
+import os
+import torch
+import random
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+
+import gradio as gr
+from skimage.restoration import inpaint as p
+import torchvision.transforms as transforms
+
+
+def restore_image(input_image):
+    img = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
+    threshold_value = 200 
+    _, mask = cv2.threshold(img, threshold_value, 255, cv2.THRESH_BINARY)
+    channels = cv2.split(img)
+    inpaint_channels = []
+    for channel in channels:
+        inpaint_result = p.inpaint_biharmonic(channel, mask)
+        inpaint_channels.append(inpaint_result)
+
+    result_img = cv2.merge(inpaint_channels)
+    filename = "output.jpg"
+    cv2.imwrite(filename, result_img)    
+    return filename
+
+
+# Define the Gradio app interface
+inputs = gr.Image(label="Upload Image")
+outputs = gr.Image(label="Restored_Image.")
+title = "Image Restoration Using Pix2Pix-GAN"
+description = "Restore the Quality of your Old damaged Images To New Looking Images Using Artificial Intelligence"