add app files

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+from gradio_imageslider import ImageSlider
+import numpy as np
+import torch
+
+import fastai
+from deoldify import device
+from deoldify.device_id import DeviceId
+from deoldify.visualize import *
+
+import warnings
+from collections.abc import Sized # Import Sized from collections.abc
+warnings.filterwarnings("ignore", category=UserWarning, message=".*?Your .*? set is empty.*?")
+
+# Set the device to use for computation
+# choices:  CPU, GPU0...GPU7
+if torch.cuda.is_available():
+    device.set(device=DeviceId.GPU0)
+else:
+    device.set(device=DeviceId.CPU)
+
+# Load the pre-trained model
+colorizer = get_image_colorizer(artistic=True)
+
+
+def colorizer_fn(input_img, render_factor):
+  """
+  Colorize grayscale images/photos
+  - @param input_img old (grayscale) image 
+  - @param render_factor render_factor
+  """
+  if input_img is not None and input_img !='':
+    output_img = colorizer.get_transformed_image(
+        path=input_img,
+        render_factor=int(render_factor),
+        watermarked=watermarked,
+        post_process=True,
+    )
+  else:
+    print('Provide an image and try again.')
+
+  return (input_img, output_img) # Return a tuple of old and color Image to be plotted with ImageSlider()
+
+title = "AI Image Colorizer"
+description = "Colorize old images with AI"
+examples = [["./demo.jpg"],]
+
+demo = gr.Interface(
+    fn=colorizer_fn,
+    inputs=[gr.Image(type="filepath" , label="Old image"), gr.Slider(0, 40, label="Render Factor", value=10)],
+    outputs=ImageSlider(type="pil", label="Old vs Colored image"),
+    examples=examples,
+    title=title,
+    description=description,
+)
+
+# Launch the demo
+demo.launch()

requirements.txt

@@ -0,0 +1,10 @@
+
+numpy
+torch
+torchvision
+fastai
+pillow
+scipy
+opencv-python
+deoldify
+